diff --git a/apps/open-swe/src/__tests__/data/summarize-history-input.json b/apps/open-swe/src/__tests__/data/summarize-history-input.json new file mode 100644 index 00000000..da76d4f1 --- /dev/null +++ b/apps/open-swe/src/__tests__/data/summarize-history-input.json @@ -0,0 +1,5269 @@ +{ + "internalMessages": [ + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessage"], + "kwargs": { + "id": "do-not-render-bfab355e-40ec-451c-a0a4-fed481a66134", + "content": "Initialize sandbox", + "additional_kwargs": { + "hidden": true, + "customNodeEvents": [ + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "skipped" + }, + "action": "Resuming sandbox", + "nodeId": "initialize", + "actionId": "dde4114d-d6f2-4288-ac82-cc7104245372", + "createdAt": "2025-07-28T20:43:20.626Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "skipped" + }, + "action": "Pulling latest changes", + "nodeId": "initialize", + "actionId": "01643fbc-147f-42b6-bb53-41ee21940572", + "createdAt": "2025-07-28T20:43:20.626Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "pending", + "sandboxSessionId": null + }, + "action": "Creating sandbox", + "nodeId": "initialize", + "actionId": "f1718063-0317-426e-bf71-6f2f8ebc25f0", + "createdAt": "2025-07-28T20:43:20.626Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "success", + "sandboxSessionId": null + }, + "action": "Creating sandbox", + "nodeId": "initialize", + "actionId": "f1718063-0317-426e-bf71-6f2f8ebc25f0", + "createdAt": "2025-07-28T20:43:22.492Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "pending", + "sandboxSessionId": "ae96d762-14bd-4178-87f4-139e5b0f6383" + }, + "action": "Cloning repository", + "nodeId": "initialize", + "actionId": "2f22e7f1-ad30-4503-914b-8b4eb8e192ce", + "createdAt": "2025-07-28T20:43:22.492Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "success", + "sandboxSessionId": "ae96d762-14bd-4178-87f4-139e5b0f6383" + }, + "action": "Cloning repository", + "nodeId": "initialize", + "actionId": "2f22e7f1-ad30-4503-914b-8b4eb8e192ce", + "createdAt": "2025-07-28T20:43:48.057Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "success", + "sandboxSessionId": "ae96d762-14bd-4178-87f4-139e5b0f6383" + }, + "action": "Checking out branch", + "nodeId": "initialize", + "actionId": "891a61c0-4d01-4019-9c0a-2812564ac38a", + "createdAt": "2025-07-28T20:43:48.057Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "pending", + "sandboxSessionId": "ae96d762-14bd-4178-87f4-139e5b0f6383" + }, + "action": "Generating codebase tree", + "nodeId": "initialize", + "actionId": "0be20b8f-253e-4e30-9e3e-9b34eb8f6eaf", + "createdAt": "2025-07-28T20:43:48.057Z" + }, + { + "data": { + "repo": "langchain-ai/langgraph", + "runId": "1f06bf37-f203-6ffd-b66f-f90871bc17ef", + "branch": "open-swe/f801d704-025e-4441-950c-12ffb2484849", + "status": "success", + "sandboxSessionId": "ae96d762-14bd-4178-87f4-139e5b0f6383" + }, + "action": "Generating codebase tree", + "nodeId": "initialize", + "actionId": "0be20b8f-253e-4e30-9e3e-9b34eb8f6eaf", + "createdAt": "2025-07-28T20:43:48.619Z" + } + ] + }, + "response_metadata": {}, + "invalid_tool_calls": [], + "tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_model_and_tools` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for static model initialization, dynamic model setup, and the model resolution functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01THiKkTNARV2PXD99rGaqNP", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _setup_model_and_tools(self) -> None:\\n \\\"\\\"\\\"Handle model resolution and tool binding.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _setup_model_and_tools(self) -> None:\\n \\\"\\\"\\\"Handle model resolution and tool binding.\\\"\\\"\\\"\\n # Handle static model initialization\\n if not self.is_dynamic_model:\\n model = self.model\\n \\n # String to BaseChatModel conversion using init_chat_model\\n if isinstance(model, str):\\n try:\\n from langchain.chat_models import ( # type: ignore[import-not-found]\\n init_chat_model,\\n )\\n except ImportError:\\n raise ImportError(\\n \\\"Please install langchain (`pip install langchain`) to \\\"\\n \\\"use ':' string syntax for `model` parameter.\\\"\\n )\\n\\n model = cast(BaseChatModel, init_chat_model(model))\\n\\n # Tool binding with _should_bind_tools check\\n if (\\n _should_bind_tools(model, self.tool_classes, num_builtin=len(self.llm_builtin_tools)) # type: ignore[arg-type]\\n and len(self.tool_classes + self.llm_builtin_tools) > 0\\n ):\\n model = cast(BaseChatModel, model).bind_tools(\\n self.tool_classes + self.llm_builtin_tools # type: ignore[operator]\\n )\\n\\n # Prompt runnable creation\\n self.static_model: Optional[Runnable] = _get_prompt_runnable(self.prompt) | model # type: ignore[operator]\\n else:\\n # Dynamic model setup - runnable created at runtime\\n self.static_model = None\\n\\n # Create _resolve_model/_aresolve_model functions for runtime model resolution\\n def _resolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if self.is_dynamic_model:\\n return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\\n else:\\n return self.static_model\\n\\n async def _aresolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Async resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if self.is_async_dynamic_model:\\n resolved_model = await self.model(state, runtime) # type: ignore[misc,operator]\\n return _get_prompt_runnable(self.prompt) | resolved_model\\n elif self.is_dynamic_model:\\n return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\\n else:\\n return self.static_model\\n\\n # Store the resolver functions as instance methods\\n self._resolve_model = _resolve_model\\n self._aresolve_model = _aresolve_model\"}" + } + ], + "additional_kwargs": { + "id": "msg_01B1VdJHxDiTkHBwhqmSoqyi", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 521, + "cache_read_input_tokens": 57704, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01THiKkTNARV2PXD99rGaqNP", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _setup_model_and_tools(self) -> None:\\n \\\"\\\"\\\"Handle model resolution and tool binding.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _setup_model_and_tools(self) -> None:\\n \\\"\\\"\\\"Handle model resolution and tool binding.\\\"\\\"\\\"\\n # Handle static model initialization\\n if not self.is_dynamic_model:\\n model = self.model\\n \\n # String to BaseChatModel conversion using init_chat_model\\n if isinstance(model, str):\\n try:\\n from langchain.chat_models import ( # type: ignore[import-not-found]\\n init_chat_model,\\n )\\n except ImportError:\\n raise ImportError(\\n \\\"Please install langchain (`pip install langchain`) to \\\"\\n \\\"use ':' string syntax for `model` parameter.\\\"\\n )\\n\\n model = cast(BaseChatModel, init_chat_model(model))\\n\\n # Tool binding with _should_bind_tools check\\n if (\\n _should_bind_tools(model, self.tool_classes, num_builtin=len(self.llm_builtin_tools)) # type: ignore[arg-type]\\n and len(self.tool_classes + self.llm_builtin_tools) > 0\\n ):\\n model = cast(BaseChatModel, model).bind_tools(\\n self.tool_classes + self.llm_builtin_tools # type: ignore[operator]\\n )\\n\\n # Prompt runnable creation\\n self.static_model: Optional[Runnable] = _get_prompt_runnable(self.prompt) | model # type: ignore[operator]\\n else:\\n # Dynamic model setup - runnable created at runtime\\n self.static_model = None\\n\\n # Create _resolve_model/_aresolve_model functions for runtime model resolution\\n def _resolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if self.is_dynamic_model:\\n return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\\n else:\\n return self.static_model\\n\\n async def _aresolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Async resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if self.is_async_dynamic_model:\\n resolved_model = await self.model(state, runtime) # type: ignore[misc,operator]\\n return _get_prompt_runnable(self.prompt) | resolved_model\\n elif self.is_dynamic_model:\\n return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\\n else:\\n return self.static_model\\n\\n # Store the resolver functions as instance methods\\n self._resolve_model = _resolve_model\\n self._aresolve_model = _aresolve_model\"}" + } + ], + "id": "msg_01B1VdJHxDiTkHBwhqmSoqyi", + "usage_metadata": { + "input_tokens": 1182, + "output_tokens": 965, + "total_tokens": 2147, + "input_token_details": { + "cache_read": 57704, + "cache_creation": 521 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _setup_model_and_tools(self) -> None:\n \"\"\"Handle model resolution and tool binding.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _setup_model_and_tools(self) -> None:\n \"\"\"Handle model resolution and tool binding.\"\"\"\n # Handle static model initialization\n if not self.is_dynamic_model:\n model = self.model\n \n # String to BaseChatModel conversion using init_chat_model\n if isinstance(model, str):\n try:\n from langchain.chat_models import ( # type: ignore[import-not-found]\n init_chat_model,\n )\n except ImportError:\n raise ImportError(\n \"Please install langchain (`pip install langchain`) to \"\n \"use ':' string syntax for `model` parameter.\"\n )\n\n model = cast(BaseChatModel, init_chat_model(model))\n\n # Tool binding with _should_bind_tools check\n if (\n _should_bind_tools(model, self.tool_classes, num_builtin=len(self.llm_builtin_tools)) # type: ignore[arg-type]\n and len(self.tool_classes + self.llm_builtin_tools) > 0\n ):\n model = cast(BaseChatModel, model).bind_tools(\n self.tool_classes + self.llm_builtin_tools # type: ignore[operator]\n )\n\n # Prompt runnable creation\n self.static_model: Optional[Runnable] = _get_prompt_runnable(self.prompt) | model # type: ignore[operator]\n else:\n # Dynamic model setup - runnable created at runtime\n self.static_model = None\n\n # Create _resolve_model/_aresolve_model functions for runtime model resolution\n def _resolve_model(\n state: StateSchema, runtime: Runtime[ContextT]\n ) -> LanguageModelLike:\n \"\"\"Resolve the model to use, handling both static and dynamic models.\"\"\"\n if self.is_dynamic_model:\n return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\n else:\n return self.static_model\n\n async def _aresolve_model(\n state: StateSchema, runtime: Runtime[ContextT]\n ) -> LanguageModelLike:\n \"\"\"Async resolve the model to use, handling both static and dynamic models.\"\"\"\n if self.is_async_dynamic_model:\n resolved_model = await self.model(state, runtime) # type: ignore[misc,operator]\n return _get_prompt_runnable(self.prompt) | resolved_model\n elif self.is_dynamic_model:\n return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\n else:\n return self.static_model\n\n # Store the resolver functions as instance methods\n self._resolve_model = _resolve_model\n self._aresolve_model = _aresolve_model" + }, + "id": "toolu_01THiKkTNARV2PXD99rGaqNP", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "6419b628-0f4d-44d3-8dab-6bac8811a216", + "tool_call_id": "toolu_01THiKkTNARV2PXD99rGaqNP", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 2) - implementing the `_AgentBuilder._setup_model_and_tools` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01LdWNCAqXUzwwnnxDivW599", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [367,420]}" + } + ], + "additional_kwargs": { + "id": "msg_015KMTvu8SQLThCYvRz4n5KX", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1016, + "cache_read_input_tokens": 58225, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01LdWNCAqXUzwwnnxDivW599", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [367,420]}" + } + ], + "id": "msg_015KMTvu8SQLThCYvRz4n5KX", + "usage_metadata": { + "input_tokens": 1181, + "output_tokens": 183, + "total_tokens": 1364, + "input_token_details": { + "cache_read": 58225, + "cache_creation": 1016 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [367, 420] + }, + "id": "toolu_01LdWNCAqXUzwwnnxDivW599", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "75acbd43-a9dd-4367-9a3f-b35f9e6696dd", + "tool_call_id": "toolu_01LdWNCAqXUzwwnnxDivW599", + "content": "367: def _setup_model_and_tools(self) -> None:\n368: \"\"\"Handle model resolution and tool binding.\"\"\"\n369: # Handle static model initialization\n370: if not self.is_dynamic_model:\n371: model = self.model\n372: \n373: # String to BaseChatModel conversion using init_chat_model\n374: if isinstance(model, str):\n375: try:\n376: from langchain.chat_models import ( # type: ignore[import-not-found]\n377: init_chat_model,\n378: )\n379: except ImportError:\n380: raise ImportError(\n381: \"Please install langchain (`pip install langchain`) to \"\n382: \"use ':' string syntax for `model` parameter.\"\n383: )\n384: \n385: model = cast(BaseChatModel, init_chat_model(model))\n386: \n387: # Tool binding with _should_bind_tools check\n388: if (\n389: _should_bind_tools(model, self.tool_classes, num_builtin=len(self.llm_builtin_tools)) # type: ignore[arg-type]\n390: and len(self.tool_classes + self.llm_builtin_tools) > 0\n391: ):\n392: model = cast(BaseChatModel, model).bind_tools(\n393: self.tool_classes + self.llm_builtin_tools # type: ignore[operator]\n394: )\n395: \n396: # Prompt runnable creation\n397: self.static_model: Optional[Runnable] = _get_prompt_runnable(self.prompt) | model # type: ignore[operator]\n398: else:\n399: # Dynamic model setup - runnable created at runtime\n400: self.static_model = None\n401: \n402: # Create _resolve_model/_aresolve_model functions for runtime model resolution\n403: def _resolve_model(\n404: state: StateSchema, runtime: Runtime[ContextT]\n405: ) -> LanguageModelLike:\n406: \"\"\"Resolve the model to use, handling both static and dynamic models.\"\"\"\n407: if self.is_dynamic_model:\n408: return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\n409: else:\n410: return self.static_model\n411: \n412: async def _aresolve_model(\n413: state: StateSchema, runtime: Runtime[ContextT]\n414: ) -> LanguageModelLike:\n415: \"\"\"Async resolve the model to use, handling both static and dynamic models.\"\"\"\n416: if self.is_async_dynamic_model:\n417: resolved_model = await self.model(state, runtime) # type: ignore[misc,operator]\n418: return _get_prompt_runnable(self.prompt) | resolved_model\n419: elif self.is_dynamic_model:\n420: return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I can see that I've successfully completed the current task (index 2) - implementing the `_AgentBuilder._setup_model_and_tools` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_011VKuxUfbXxGzAjniEiM8Kx", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [420,430]}" + } + ], + "additional_kwargs": { + "id": "msg_01PjwWgrEdZd4HUJ7UYXNqE9", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 999, + "cache_read_input_tokens": 59241, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_011VKuxUfbXxGzAjniEiM8Kx", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [420,430]}" + } + ], + "id": "msg_01PjwWgrEdZd4HUJ7UYXNqE9", + "usage_metadata": { + "input_tokens": 1181, + "output_tokens": 182, + "total_tokens": 1363, + "input_token_details": { + "cache_read": 59241, + "cache_creation": 999 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [420, 430] + }, + "id": "toolu_011VKuxUfbXxGzAjniEiM8Kx", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "a4634243-2757-4a56-9dfb-d0e6f14e9763", + "tool_call_id": "toolu_011VKuxUfbXxGzAjniEiM8Kx", + "content": "420: return _get_prompt_runnable(self.prompt) | self.model(state, runtime) # type: ignore[operator]\n421: else:\n422: return self.static_model\n423: \n424: # Store the resolver functions as instance methods\n425: self._resolve_model = _resolve_model\n426: self._aresolve_model = _aresolve_model\n427: \n428: def _create_model_node(self) -> RunnableCallable:\n429: \"\"\"Create the core LLM interaction node.\"\"\"\n430: # Implementation will be added in next task", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 2) - implementing the `_AgentBuilder._setup_model_and_tools` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **Static model initialization** - Done:\n- String to BaseChatModel conversion using init_chat_model\n- Tool binding with _should_bind_tools check \n- Prompt runnable creation\n\n✅ **Dynamic model setup** - Done:\n- Sets static_model to None for dynamic models\n\n✅ **Create _resolve_model/_aresolve_model functions** - Done:\n- Both sync and async model resolution functions\n- Stored as instance methods for runtime model resolution\n\nThe implementation correctly extracts and implements all the model and tool setup logic from the original `create_react_agent` function, including:\n\n1. **Static model handling** - Converts string models to BaseChatModel, binds tools when needed, creates prompt runnable\n2. **Dynamic model handling** - Defers model creation to runtime\n3. **Model resolution functions** - Both sync and async versions that handle static vs dynamic models appropriately" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01JcqEraTHD7NvDDrU8cJo1f", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._setup_model_and_tools` method with all required functionality. The method handles static model initialization by converting string models to BaseChatModel using init_chat_model (with proper ImportError handling), performs tool binding using _should_bind_tools check when needed, and creates prompt runnables by combining the prompt with the model. For dynamic models, it properly sets static_model to None to defer model creation to runtime. The method also creates both sync (_resolve_model) and async (_aresolve_model) model resolution functions that handle the differences between static and dynamic models, storing them as instance methods for use during graph execution. The implementation faithfully extracts and replicates all the model and tool setup logic from the original create_react_agent function.\"}" + } + ], + "additional_kwargs": { + "id": "msg_01AraCEqig8UWjRTvAKCdk3X", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 342, + "cache_read_input_tokens": 60240, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01JcqEraTHD7NvDDrU8cJo1f", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._setup_model_and_tools` method with all required functionality. The method handles static model initialization by converting string models to BaseChatModel using init_chat_model (with proper ImportError handling), performs tool binding using _should_bind_tools check when needed, and creates prompt runnables by combining the prompt with the model. For dynamic models, it properly sets static_model to None to defer model creation to runtime. The method also creates both sync (_resolve_model) and async (_aresolve_model) model resolution functions that handle the differences between static and dynamic models, storing them as instance methods for use during graph execution. The implementation faithfully extracts and replicates all the model and tool setup logic from the original create_react_agent function.\"}" + } + ], + "id": "msg_01AraCEqig8UWjRTvAKCdk3X", + "usage_metadata": { + "input_tokens": 1181, + "output_tokens": 485, + "total_tokens": 1666, + "input_token_details": { + "cache_read": 60240, + "cache_creation": 342 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder._setup_model_and_tools` method with all required functionality. The method handles static model initialization by converting string models to BaseChatModel using init_chat_model (with proper ImportError handling), performs tool binding using _should_bind_tools check when needed, and creates prompt runnables by combining the prompt with the model. For dynamic models, it properly sets static_model to None to defer model creation to runtime. The method also creates both sync (_resolve_model) and async (_aresolve_model) model resolution functions that handle the differences between static and dynamic models, storing them as instance methods for use during graph execution. The implementation faithfully extracts and replicates all the model and tool setup logic from the original create_react_agent function." + }, + "id": "toolu_01JcqEraTHD7NvDDrU8cJo1f", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "0bbeb7c9-8be1-4467-92b5-552004a46f4d", + "tool_call_id": "toolu_01JcqEraTHD7NvDDrU8cJo1f", + "content": "Saved task status as completed for task Implement _AgentBuilder._setup_model_and_tools method to handle static model initialization (string to BaseChatModel conversion using init_chat_model, tool binding with _should_bind_tools check, prompt runnable creation), dynamic model setup, and create _resolve_model/_aresolve_model functions for runtime model resolution", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_node` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the call_model and acall_model functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_018aZK6DidBQiDNfUdBEpSee", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [720,780]}" + } + ], + "additional_kwargs": { + "id": "msg_01P5oNyTU3HFDkSugZCpq78r", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 576, + "cache_read_input_tokens": 60582, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_018aZK6DidBQiDNfUdBEpSee", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [720,780]}" + } + ], + "id": "msg_01P5oNyTU3HFDkSugZCpq78r", + "usage_metadata": { + "input_tokens": 1181, + "output_tokens": 183, + "total_tokens": 1364, + "input_token_details": { + "cache_read": 60582, + "cache_creation": 576 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [720, 780] + }, + "id": "toolu_018aZK6DidBQiDNfUdBEpSee", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "15a486e7-b5b9-4ae4-94f2-38bdc8886d91", + "tool_call_id": "toolu_018aZK6DidBQiDNfUdBEpSee", + "content": "720: \"Please install langchain (`pip install langchain`) to \"\n721: \"use ':' string syntax for `model` parameter.\"\n722: )\n723: \n724: model = cast(BaseChatModel, init_chat_model(model))\n725: \n726: if (\n727: _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools)) # type: ignore[arg-type]\n728: and len(tool_classes + llm_builtin_tools) > 0\n729: ):\n730: model = cast(BaseChatModel, model).bind_tools(\n731: tool_classes + llm_builtin_tools # type: ignore[operator]\n732: )\n733: \n734: static_model: Optional[Runnable] = _get_prompt_runnable(prompt) | model # type: ignore[operator]\n735: else:\n736: # For dynamic models, we'll create the runnable at runtime\n737: static_model = None\n738: \n739: # If any of the tools are configured to return_directly after running,\n740: # our graph needs to check if these were called\n741: should_return_direct = {t.name for t in tool_classes if t.return_direct}\n742: \n743: def _resolve_model(\n744: state: StateSchema, runtime: Runtime[ContextT]\n745: ) -> LanguageModelLike:\n746: \"\"\"Resolve the model to use, handling both static and dynamic models.\"\"\"\n747: if is_dynamic_model:\n748: return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\n749: else:\n750: return static_model\n751: \n752: async def _aresolve_model(\n753: state: StateSchema, runtime: Runtime[ContextT]\n754: ) -> LanguageModelLike:\n755: \"\"\"Async resolve the model to use, handling both static and dynamic models.\"\"\"\n756: if is_async_dynamic_model:\n757: resolved_model = await model(state, runtime) # type: ignore[misc,operator]\n758: return _get_prompt_runnable(prompt) | resolved_model\n759: elif is_dynamic_model:\n760: return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\n761: else:\n762: return static_model\n763: \n764: def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\n765: has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\n766: all_tools_return_direct = (\n767: all(call[\"name\"] in should_return_direct for call in response.tool_calls)\n768: if isinstance(response, AIMessage)\n769: else False\n770: )\n771: remaining_steps = _get_state_value(state, \"remaining_steps\", None)\n772: is_last_step = _get_state_value(state, \"is_last_step\", False)\n773: return (\n774: (remaining_steps is None and is_last_step and has_tool_calls)\n775: or (\n776: remaining_steps is not None\n777: and remaining_steps < 1\n778: and all_tools_return_direct\n779: )\n780: or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_node` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the call_model and acall_model functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01FMCZ8jDMYYQVcLD8yxdvx5", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [780,850]}" + } + ], + "additional_kwargs": { + "id": "msg_01S1bTbUmmZXUEqMRR8U2knd", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1091, + "cache_read_input_tokens": 61158, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01FMCZ8jDMYYQVcLD8yxdvx5", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [780,850]}" + } + ], + "id": "msg_01S1bTbUmmZXUEqMRR8U2knd", + "usage_metadata": { + "input_tokens": 1180, + "output_tokens": 183, + "total_tokens": 1363, + "input_token_details": { + "cache_read": 61158, + "cache_creation": 1091 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [780, 850] + }, + "id": "toolu_01FMCZ8jDMYYQVcLD8yxdvx5", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "d3b5015b-e527-4e1f-89bf-1cf9b0cd76d4", + "tool_call_id": "toolu_01FMCZ8jDMYYQVcLD8yxdvx5", + "content": "780: or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\n781: )\n782: \n783: def _get_model_input_state(state: StateSchema) -> StateSchema:\n784: if pre_model_hook is not None:\n785: messages = (\n786: _get_state_value(state, \"llm_input_messages\")\n787: ) or _get_state_value(state, \"messages\")\n788: error_msg = f\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\"\n789: else:\n790: messages = _get_state_value(state, \"messages\")\n791: error_msg = (\n792: f\"Expected input to call_model to have 'messages' key, but got {state}\"\n793: )\n794: \n795: if messages is None:\n796: raise ValueError(error_msg)\n797: \n798: _validate_chat_history(messages)\n799: # we're passing messages under `messages` key, as this is expected by the prompt\n800: if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\n801: state.messages = messages # type: ignore\n802: else:\n803: state[\"messages\"] = messages # type: ignore\n804: \n805: return state\n806: \n807: # Define the function that calls the model\n808: def call_model(\n809: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n810: ) -> StateSchema:\n811: if is_async_dynamic_model:\n812: msg = (\n813: \"Async model callable provided but agent invoked synchronously. \"\n814: \"Use agent.ainvoke() or agent.astream(), or \"\n815: \"provide a sync model callable.\"\n816: )\n817: raise RuntimeError(msg)\n818: \n819: model_input = _get_model_input_state(state)\n820: \n821: if is_dynamic_model:\n822: # Resolve dynamic model at runtime and apply prompt\n823: dynamic_model = _resolve_model(state, runtime)\n824: response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\n825: else:\n826: response = cast(AIMessage, static_model.invoke(model_input, config)) # type: ignore[union-attr]\n827: \n828: # add agent name to the AIMessage\n829: response.name = name\n830: \n831: if _are_more_steps_needed(state, response):\n832: return {\n833: \"messages\": [\n834: AIMessage(\n835: id=response.id,\n836: content=\"Sorry, need more steps to process this request.\",\n837: )\n838: ]\n839: }\n840: # We return a list, because this will get added to the existing list\n841: return {\"messages\": [response]}\n842: \n843: async def acall_model(\n844: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n845: ) -> StateSchema:\n846: model_input = _get_model_input_state(state)\n847: \n848: if is_dynamic_model:\n849: # Resolve dynamic model at runtime and apply prompt\n850: # (supports both sync and async)", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_node` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the call_model and acall_model functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01SduPyx4cbaUu54tygWkbXU", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [850,900]}" + } + ], + "additional_kwargs": { + "id": "msg_01KsJtFQESQJvKcEe4hZUtQE", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1085, + "cache_read_input_tokens": 62249, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01SduPyx4cbaUu54tygWkbXU", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [850,900]}" + } + ], + "id": "msg_01KsJtFQESQJvKcEe4hZUtQE", + "usage_metadata": { + "input_tokens": 1179, + "output_tokens": 183, + "total_tokens": 1362, + "input_token_details": { + "cache_read": 62249, + "cache_creation": 1085 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [850, 900] + }, + "id": "toolu_01SduPyx4cbaUu54tygWkbXU", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "63834fdc-dd3b-4e48-94f4-caadc47463db", + "tool_call_id": "toolu_01SduPyx4cbaUu54tygWkbXU", + "content": "850: # (supports both sync and async)\n851: dynamic_model = await _aresolve_model(state, runtime)\n852: response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\n853: else:\n854: response = cast(AIMessage, await static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\n855: \n856: # add agent name to the AIMessage\n857: response.name = name\n858: if _are_more_steps_needed(state, response):\n859: return {\n860: \"messages\": [\n861: AIMessage(\n862: id=response.id,\n863: content=\"Sorry, need more steps to process this request.\",\n864: )\n865: ]\n866: }\n867: # We return a list, because this will get added to the existing list\n868: return {\"messages\": [response]}\n869: \n870: input_schema: StateSchemaType\n871: if pre_model_hook is not None:\n872: # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\n873: if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\n874: # For Pydantic schemas\n875: from pydantic import create_model\n876: \n877: input_schema = create_model(\n878: \"CallModelInputSchema\",\n879: llm_input_messages=(list[AnyMessage], ...),\n880: __base__=state_schema,\n881: )\n882: else:\n883: # For TypedDict schemas\n884: class CallModelInputSchema(state_schema): # type: ignore\n885: llm_input_messages: list[AnyMessage]\n886: \n887: input_schema = CallModelInputSchema\n888: else:\n889: input_schema = state_schema\n890: \n891: def generate_structured_response(\n892: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n893: ) -> StateSchema:\n894: if is_async_dynamic_model:\n895: msg = (\n896: \"Async model callable provided but agent invoked synchronously. \"\n897: \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\n898: )\n899: raise RuntimeError(msg)\n900: ", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_node` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the call_model and acall_model functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_016XjhMqN5E6KUSWUuptLRUm", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_model_node(self) -> RunnableCallable:\\n \\\"\\\"\\\"Create the core LLM interaction node.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_model_node(self) -> RunnableCallable:\\n \\\"\\\"\\\"Create the core LLM interaction node.\\\"\\\"\\\"\\n def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\\n has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\\n all_tools_return_direct = (\\n all(call[\\\"name\\\"] in self.should_return_direct for call in response.tool_calls)\\n if isinstance(response, AIMessage)\\n else False\\n )\\n remaining_steps = _get_state_value(state, \\\"remaining_steps\\\", None)\\n is_last_step = _get_state_value(state, \\\"is_last_step\\\", False)\\n return (\\n (remaining_steps is None and is_last_step and has_tool_calls)\\n or (\\n remaining_steps is not None\\n and remaining_steps < 1\\n and all_tools_return_direct\\n )\\n or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\\n )\\n\\n def _get_model_input_state(state: StateSchema) -> StateSchema:\\n if self.pre_model_hook is not None:\\n messages = (\\n _get_state_value(state, \\\"llm_input_messages\\\")\\n ) or _get_state_value(state, \\\"messages\\\")\\n error_msg = f\\\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\\\"\\n else:\\n messages = _get_state_value(state, \\\"messages\\\")\\n error_msg = (\\n f\\\"Expected input to call_model to have 'messages' key, but got {state}\\\"\\n )\\n\\n if messages is None:\\n raise ValueError(error_msg)\\n\\n # Message validation with _validate_chat_history\\n _validate_chat_history(messages)\\n \\n # we're passing messages under `messages` key, as this is expected by the prompt\\n if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\\n state.messages = messages # type: ignore\\n else:\\n state[\\\"messages\\\"] = messages # type: ignore\\n\\n return state\\n\\n # Define the function that calls the model\\n def call_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if self.is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or \\\"\\n \\\"provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n model_input = _get_model_input_state(state)\\n\\n # Model resolution\\n if self.is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n dynamic_model = self._resolve_model(state, runtime)\\n response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, self.static_model.invoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = self.name\\n\\n # Remaining steps management\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n async def acall_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n model_input = _get_model_input_state(state)\\n\\n # Model resolution\\n if self.is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n # (supports both sync and async)\\n dynamic_model = await self._aresolve_model(state, runtime)\\n response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, await self.static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = self.name\\n \\n # Remaining steps management\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n # Proper input schema handling\\n input_schema: StateSchemaType\\n if self.pre_model_hook is not None:\\n # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\\n if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\\n # For Pydantic schemas\\n from pydantic import create_model\\n\\n input_schema = create_model(\\n \\\"CallModelInputSchema\\\",\\n llm_input_messages=(list[AnyMessage], ...),\\n __base__=self.state_schema,\\n )\\n else:\\n # For TypedDict schemas\\n class CallModelInputSchema(self.state_schema): # type: ignore\\n llm_input_messages: list[AnyMessage]\\n\\n input_schema = CallModelInputSchema\\n else:\\n input_schema = self.state_schema\\n\\n return RunnableCallable(call_model, acall_model, input_schema=input_schema)\"}" + } + ], + "additional_kwargs": { + "id": "msg_018HgvoCPyXAed6obNsL5Qn4", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 832, + "cache_read_input_tokens": 63334, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_016XjhMqN5E6KUSWUuptLRUm", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_model_node(self) -> RunnableCallable:\\n \\\"\\\"\\\"Create the core LLM interaction node.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_model_node(self) -> RunnableCallable:\\n \\\"\\\"\\\"Create the core LLM interaction node.\\\"\\\"\\\"\\n def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\\n has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\\n all_tools_return_direct = (\\n all(call[\\\"name\\\"] in self.should_return_direct for call in response.tool_calls)\\n if isinstance(response, AIMessage)\\n else False\\n )\\n remaining_steps = _get_state_value(state, \\\"remaining_steps\\\", None)\\n is_last_step = _get_state_value(state, \\\"is_last_step\\\", False)\\n return (\\n (remaining_steps is None and is_last_step and has_tool_calls)\\n or (\\n remaining_steps is not None\\n and remaining_steps < 1\\n and all_tools_return_direct\\n )\\n or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\\n )\\n\\n def _get_model_input_state(state: StateSchema) -> StateSchema:\\n if self.pre_model_hook is not None:\\n messages = (\\n _get_state_value(state, \\\"llm_input_messages\\\")\\n ) or _get_state_value(state, \\\"messages\\\")\\n error_msg = f\\\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\\\"\\n else:\\n messages = _get_state_value(state, \\\"messages\\\")\\n error_msg = (\\n f\\\"Expected input to call_model to have 'messages' key, but got {state}\\\"\\n )\\n\\n if messages is None:\\n raise ValueError(error_msg)\\n\\n # Message validation with _validate_chat_history\\n _validate_chat_history(messages)\\n \\n # we're passing messages under `messages` key, as this is expected by the prompt\\n if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\\n state.messages = messages # type: ignore\\n else:\\n state[\\\"messages\\\"] = messages # type: ignore\\n\\n return state\\n\\n # Define the function that calls the model\\n def call_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if self.is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or \\\"\\n \\\"provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n model_input = _get_model_input_state(state)\\n\\n # Model resolution\\n if self.is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n dynamic_model = self._resolve_model(state, runtime)\\n response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, self.static_model.invoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = self.name\\n\\n # Remaining steps management\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n async def acall_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n model_input = _get_model_input_state(state)\\n\\n # Model resolution\\n if self.is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n # (supports both sync and async)\\n dynamic_model = await self._aresolve_model(state, runtime)\\n response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, await self.static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = self.name\\n \\n # Remaining steps management\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n # Proper input schema handling\\n input_schema: StateSchemaType\\n if self.pre_model_hook is not None:\\n # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\\n if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\\n # For Pydantic schemas\\n from pydantic import create_model\\n\\n input_schema = create_model(\\n \\\"CallModelInputSchema\\\",\\n llm_input_messages=(list[AnyMessage], ...),\\n __base__=self.state_schema,\\n )\\n else:\\n # For TypedDict schemas\\n class CallModelInputSchema(self.state_schema): # type: ignore\\n llm_input_messages: list[AnyMessage]\\n\\n input_schema = CallModelInputSchema\\n else:\\n input_schema = self.state_schema\\n\\n return RunnableCallable(call_model, acall_model, input_schema=input_schema)\"}" + } + ], + "id": "msg_018HgvoCPyXAed6obNsL5Qn4", + "usage_metadata": { + "input_tokens": 1178, + "output_tokens": 1726, + "total_tokens": 2904, + "input_token_details": { + "cache_read": 63334, + "cache_creation": 832 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _create_model_node(self) -> RunnableCallable:\n \"\"\"Create the core LLM interaction node.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _create_model_node(self) -> RunnableCallable:\n \"\"\"Create the core LLM interaction node.\"\"\"\n def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\n has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\n all_tools_return_direct = (\n all(call[\"name\"] in self.should_return_direct for call in response.tool_calls)\n if isinstance(response, AIMessage)\n else False\n )\n remaining_steps = _get_state_value(state, \"remaining_steps\", None)\n is_last_step = _get_state_value(state, \"is_last_step\", False)\n return (\n (remaining_steps is None and is_last_step and has_tool_calls)\n or (\n remaining_steps is not None\n and remaining_steps < 1\n and all_tools_return_direct\n )\n or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\n )\n\n def _get_model_input_state(state: StateSchema) -> StateSchema:\n if self.pre_model_hook is not None:\n messages = (\n _get_state_value(state, \"llm_input_messages\")\n ) or _get_state_value(state, \"messages\")\n error_msg = f\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\"\n else:\n messages = _get_state_value(state, \"messages\")\n error_msg = (\n f\"Expected input to call_model to have 'messages' key, but got {state}\"\n )\n\n if messages is None:\n raise ValueError(error_msg)\n\n # Message validation with _validate_chat_history\n _validate_chat_history(messages)\n \n # we're passing messages under `messages` key, as this is expected by the prompt\n if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\n state.messages = messages # type: ignore\n else:\n state[\"messages\"] = messages # type: ignore\n\n return state\n\n # Define the function that calls the model\n def call_model(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n if self.is_async_dynamic_model:\n msg = (\n \"Async model callable provided but agent invoked synchronously. \"\n \"Use agent.ainvoke() or agent.astream(), or \"\n \"provide a sync model callable.\"\n )\n raise RuntimeError(msg)\n\n model_input = _get_model_input_state(state)\n\n # Model resolution\n if self.is_dynamic_model:\n # Resolve dynamic model at runtime and apply prompt\n dynamic_model = self._resolve_model(state, runtime)\n response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\n else:\n response = cast(AIMessage, self.static_model.invoke(model_input, config)) # type: ignore[union-attr]\n\n # add agent name to the AIMessage\n response.name = self.name\n\n # Remaining steps management\n if _are_more_steps_needed(state, response):\n return {\n \"messages\": [\n AIMessage(\n id=response.id,\n content=\"Sorry, need more steps to process this request.\",\n )\n ]\n }\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}\n\n async def acall_model(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n model_input = _get_model_input_state(state)\n\n # Model resolution\n if self.is_dynamic_model:\n # Resolve dynamic model at runtime and apply prompt\n # (supports both sync and async)\n dynamic_model = await self._aresolve_model(state, runtime)\n response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\n else:\n response = cast(AIMessage, await self.static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\n\n # add agent name to the AIMessage\n response.name = self.name\n \n # Remaining steps management\n if _are_more_steps_needed(state, response):\n return {\n \"messages\": [\n AIMessage(\n id=response.id,\n content=\"Sorry, need more steps to process this request.\",\n )\n ]\n }\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}\n\n # Proper input schema handling\n input_schema: StateSchemaType\n if self.pre_model_hook is not None:\n # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\n if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\n # For Pydantic schemas\n from pydantic import create_model\n\n input_schema = create_model(\n \"CallModelInputSchema\",\n llm_input_messages=(list[AnyMessage], ...),\n __base__=self.state_schema,\n )\n else:\n # For TypedDict schemas\n class CallModelInputSchema(self.state_schema): # type: ignore\n llm_input_messages: list[AnyMessage]\n\n input_schema = CallModelInputSchema\n else:\n input_schema = self.state_schema\n\n return RunnableCallable(call_model, acall_model, input_schema=input_schema)" + }, + "id": "toolu_016XjhMqN5E6KUSWUuptLRUm", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "d630be3f-570d-4937-a5c1-5f7a34c57fcc", + "tool_call_id": "toolu_016XjhMqN5E6KUSWUuptLRUm", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 3) - implementing the `_AgentBuilder._create_model_node` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01KtfM8M1ddVerxKDYFQcQbC", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [428,450]}" + } + ], + "additional_kwargs": { + "id": "msg_01GejT8PXbGoSaRwAuRLCfz4", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1777, + "cache_read_input_tokens": 64166, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01KtfM8M1ddVerxKDYFQcQbC", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [428,450]}" + } + ], + "id": "msg_01GejT8PXbGoSaRwAuRLCfz4", + "usage_metadata": { + "input_tokens": 1177, + "output_tokens": 181, + "total_tokens": 1358, + "input_token_details": { + "cache_read": 64166, + "cache_creation": 1777 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [428, 450] + }, + "id": "toolu_01KtfM8M1ddVerxKDYFQcQbC", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "2865f3c3-dac2-4f77-a704-e21f198a4c55", + "tool_call_id": "toolu_01KtfM8M1ddVerxKDYFQcQbC", + "content": "428: def _create_model_node(self) -> RunnableCallable:\n429: \"\"\"Create the core LLM interaction node.\"\"\"\n430: def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\n431: has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\n432: all_tools_return_direct = (\n433: all(call[\"name\"] in self.should_return_direct for call in response.tool_calls)\n434: if isinstance(response, AIMessage)\n435: else False\n436: )\n437: remaining_steps = _get_state_value(state, \"remaining_steps\", None)\n438: is_last_step = _get_state_value(state, \"is_last_step\", False)\n439: return (\n440: (remaining_steps is None and is_last_step and has_tool_calls)\n441: or (\n442: remaining_steps is not None\n443: and remaining_steps < 1\n444: and all_tools_return_direct\n445: )\n446: or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\n447: )\n448: \n449: def _get_model_input_state(state: StateSchema) -> StateSchema:\n450: if self.pre_model_hook is not None:", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I can see that I've successfully completed the current task (index 3) - implementing the `_AgentBuilder._create_model_node` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **Create call_model and acall_model functions** - Done:\n- Both sync and async versions implemented\n\n✅ **Message validation with _validate_chat_history** - Done:\n- Called in `_get_model_input_state` function\n\n✅ **Model resolution** - Done:\n- Uses `self._resolve_model` for sync and `self._aresolve_model` for async\n- Handles both static and dynamic models\n\n✅ **Remaining steps management** - Done:\n- `_are_more_steps_needed` function checks remaining steps and tool calls\n- Returns appropriate message when more steps are needed\n\n✅ **Message processing with proper input schema handling** - Done:\n- `_get_model_input_state` handles both regular messages and llm_input_messages\n- Proper schema handling for both Pydantic and TypedDict schemas\n- Dynamic input schema creation when pre_model_hook is present\n\n✅ **Both sync and async operations** - Done:\n- Separate call_model and acall_model functions\n- Proper error handling for async model with sync invocation\n\nThe implementation correctly extracts and implements all the model node creation logic from the original `create_react_agent` function, including proper error handling, message validation, model resolution, and schema management." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01C37xj1RVqCpsgmUiSpwkce", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_model_node` method with all required functionality. The method creates both sync (call_model) and async (acall_model) functions that handle message validation using _validate_chat_history, perform model resolution using the instance's _resolve_model and _aresolve_model methods, manage remaining steps with proper tool call and return_direct logic, and process messages with appropriate input schema handling. The implementation includes proper error handling for async models called synchronously, dynamic input schema creation for pre_model_hook scenarios (supporting both Pydantic and TypedDict schemas), and returns a RunnableCallable with the correct input schema. The method faithfully extracts and replicates all the model node creation logic from the original create_react_agent function.\"}" + } + ], + "additional_kwargs": { + "id": "msg_011ovCaSD7L1Sfuz3CM3BWQ3", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 549, + "cache_read_input_tokens": 65943, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01C37xj1RVqCpsgmUiSpwkce", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_model_node` method with all required functionality. The method creates both sync (call_model) and async (acall_model) functions that handle message validation using _validate_chat_history, perform model resolution using the instance's _resolve_model and _aresolve_model methods, manage remaining steps with proper tool call and return_direct logic, and process messages with appropriate input schema handling. The implementation includes proper error handling for async models called synchronously, dynamic input schema creation for pre_model_hook scenarios (supporting both Pydantic and TypedDict schemas), and returns a RunnableCallable with the correct input schema. The method faithfully extracts and replicates all the model node creation logic from the original create_react_agent function.\"}" + } + ], + "id": "msg_011ovCaSD7L1Sfuz3CM3BWQ3", + "usage_metadata": { + "input_tokens": 1176, + "output_tokens": 574, + "total_tokens": 1750, + "input_token_details": { + "cache_read": 65943, + "cache_creation": 549 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder._create_model_node` method with all required functionality. The method creates both sync (call_model) and async (acall_model) functions that handle message validation using _validate_chat_history, perform model resolution using the instance's _resolve_model and _aresolve_model methods, manage remaining steps with proper tool call and return_direct logic, and process messages with appropriate input schema handling. The implementation includes proper error handling for async models called synchronously, dynamic input schema creation for pre_model_hook scenarios (supporting both Pydantic and TypedDict schemas), and returns a RunnableCallable with the correct input schema. The method faithfully extracts and replicates all the model node creation logic from the original create_react_agent function." + }, + "id": "toolu_01C37xj1RVqCpsgmUiSpwkce", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "556dd498-3fae-4e78-af63-c2a661ecc38a", + "tool_call_id": "toolu_01C37xj1RVqCpsgmUiSpwkce", + "content": "Saved task status as completed for task Implement _AgentBuilder._create_model_node method to create call_model and acall_model functions that handle message validation with _validate_chat_history, model resolution, remaining steps management, and message processing with proper input schema handling for both sync and async operations", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessage"], + "kwargs": { + "id": "6e0a1326-b14e-439b-b9b3-dd1df797789a", + "content": "Looks like I'm running out of tokens. I'm going to summarize the conversation history to free up space.", + "tool_calls": [ + { + "id": "847acf8d-deeb-489f-b146-e3835aeadbd2", + "name": "summarize_conversation_history", + "args": { + "reasoning": "I'm running out of tokens. I'm going to summarize all of the messages since my last summary message to free up space." + } + } + ], + "additional_kwargs": { + "summary_message": true + }, + "invalid_tool_calls": [], + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "465097e3-3c65-4af1-beb5-c3d9444219fd", + "tool_call_id": "847acf8d-deeb-489f-b146-e3835aeadbd2", + "content": "- The user requests a refactor of the `create_react_agent` function in `libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py` to use an internal helper class `_AgentBuilder` for maintainability and readability without unnecessary abstractions.\n\n- `_AgentBuilder` is an internal scaffolding utility that constructs a configurable ReAct-style agent graph using LangGraph and LangChain components. It encapsulates configuration logic, validation, and dynamic construction of nodes and edges based on user input.\n\n- Core responsibilities of `_AgentBuilder`:\n 1. Model & Tool Integration: Accepts a language model (`BaseChatModel`, `Runnable`, or string reference) and optionally binds it to tool definitions (`BaseTool`, `ToolNode`, callable, or dict schema). Determines if tool binding is needed and wraps model invocation with a prompt handling mechanism.\n 2. State Schema Management: Dynamically sets the graph's state schema, defaulting to built-in `AgentState` variants. Validates custom state schemas to ensure required fields (`messages`, `remaining_steps`, `structured_response` if needed) are present.\n 3. Prompt Handling: Supports various prompt formats (`str`, `SystemMessage`, `Callable`, or `Runnable`). Wraps them as a `RunnableCallable` for use in the graph.\n 4. Model Node Construction: Creates the `agent` node that calls the LLM, validates chat history, invokes the model, and decides whether to continue (based on remaining steps, tool calls, etc.).\n 5. Optional Structured Output: If `response_format` is provided, adds a final node to generate a structured output using `.with_structured_output()` on the model.\n 6. Hooks: Supports `pre_model_hook` (for message pruning/summarization before LLM call) and `post_model_hook` (for human-in-the-loop, validation, or guardrails after LLM response). Each hook becomes a node in the workflow graph if defined.\n 7. Routing Logic: Defines conditional logic to route execution to tools (if tool calls exist), structured output generation (if enabled), or termination (if no further action is needed). Supports both v1 (single batched tool call) and v2 (distributed tool call routing via `Send` API).\n 8. Graph Assembly: Constructs a `StateGraph` with all configured nodes and conditional edges. Supports entry/exit customization and debugging features.\n\n- Key methods to implement in `_AgentBuilder`:\n - `__init__`: Store all parameters as instance variables, validate `version` parameter, handle deprecated `config_schema` parameter with warning, validate `state_schema` requirements, set default `state_schema` based on `response_format`, process tools (handle `ToolNode` vs sequence), determine model characteristics (`is_dynamic_model`, `is_async_dynamic_model`), and identify tools with `return_direct` behavior.\n - `_setup_model_and_tools`: Handle static model initialization (string to `BaseChatModel` conversion using `init_chat_model`), tool binding with `_should_bind_tools` check, prompt runnable creation, dynamic model setup, and create `_resolve_model`/`_aresolve_model` functions for runtime model resolution.\n - `_create_model_node`: Create `call_model` and `acall_model` functions that handle message validation with `_validate_chat_history`, model resolution, remaining steps management, and message processing with proper input schema handling for both sync and async operations.\n - `_create_structured_response_node`: Create `generate_structured_response` and `agenerate_structured_response` functions that handle system prompt injection for tuple `response_format`, model resolution, structured output generation using `.with_structured_output()`, and return `structured_response` in state.\n - `_create_model_router`: Define `should_continue` function to route based on last message type: if no tool calls route to `post_model_hook`/`generate_structured_response`/`END`, if tool calls present route to `tools` (v1) or `Send` list for parallel execution (v2), with proper `post_model_hook` integration.\n - `_create_tools_router`: Define `route_tool_responses` function to check for `return_direct` tools in reversed message order, handle parallel tool call scenarios with `return_direct`, and route to `END` or entrypoint accordingly.\n - `_setup_hooks`: Add `pre_model_hook` node with edge to `agent` if provided, add `post_model_hook` node with conditional routing (`post_model_hook_router` function) that handles pending tool calls, structured response generation, and `END` routing based on state.\n - `build`: Create `StateGraph` with proper schema, add `agent` node with `_create_model_node`, handle tool-calling vs non-tool-calling workflows, add structured response node if needed, set up all conditional edges with proper path mappings, and compile with all provided options (`checkpointer`, `store`, `interrupt_before`, `interrupt_after`, `debug`, `name`).\n\n- Edge case handling:\n - Validates message-tool call consistency to prevent misaligned tool invocations.\n - Handles tool `return_direct` behavior carefully to skip further LLM calls if needed.\n - Automatically derives tool call context in v2 via `ToolCallWithContext` (from `libs/prebuilt/langgraph/prebuilt/_internal.py`).\n\n- The public API `create_react_agent` function (lines ~251-941 in `libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py`) is large (~700 lines) and handles:\n - Parameter validation including deprecated `config_schema`.\n - State schema validation and defaulting.\n - Tool processing (handling `ToolNode` and sequences).\n - Model characteristics detection (static vs dynamic, sync vs async).\n - Model initialization (string to `BaseChatModel` via `init_chat_model`).\n - Tool binding with `_should_bind_tools`.\n - Prompt handling with `_get_prompt_runnable`.\n - Creation of sync and async model call functions (`call_model`, `acall_model`) with message validation (`_validate_chat_history`).\n - Optional structured response generation node.\n - Routing logic for continuation or termination based on last message and tool calls.\n - Conditional edges for `post_model_hook`, `tools`, and structured response nodes.\n - Compilation of the `StateGraph` into a `CompiledStateGraph`.\n\n- Helper functions used:\n - `_get_state_value`: Extracts values from state dict or object.\n - `_get_prompt_runnable`: Converts various prompt formats to `Runnable`.\n - `_should_bind_tools`: Determines if tools need to be bound to model.\n - `_get_model`: Extracts `BaseChatModel` from `RunnableBinding`.\n - `_validate_chat_history`: Validates tool call/message consistency.\n\n- State schemas defined in the file:\n - `AgentState` (TypedDict) with keys: `messages`, `is_last_step`, `remaining_steps`.\n - `AgentStateWithStructuredResponse` extends `AgentState` with `structured_response`.\n - Pydantic variants also exist.\n\n- Tests in `libs/prebuilt/tests/test_react_agent.py` and `test_react_agent_graph.py` cover:\n - Various prompt types and usage.\n - Pre-model and post-model hooks.\n - Tool calling and return_direct behavior.\n - Structured response generation.\n - Version differences (v1 vs v2).\n\n- The refactor plan:\n 1. Create `_AgentBuilder` class in `libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py` with constructor and all required methods.\n 2. Implement `_AgentBuilder.__init__` to handle parameter storage, validation, tool processing, and model characteristic detection.\n 3. Implement `_AgentBuilder._setup_model_and_tools` for model initialization and resolution functions.\n 4. Implement `_AgentBuilder._create_model_node` for core LLM interaction.\n 5. Implement `_AgentBuilder._create_structured_response_node` for structured output.\n 6. Implement `_AgentBuilder._create_model_router` for routing after model call.\n 7. Implement `_AgentBuilder._create_tools_router` for routing after tool calls.\n 8. Implement `_AgentBuilder._setup_hooks` for pre/post model hooks.\n 9. Implement `_AgentBuilder.build` to assemble the full graph and compile it.\n 10. Refactor `create_react_agent` to instantiate `_AgentBuilder` and return `build()`, preserving signature and docstring.\n 11. Run `make format`, `make lint`, and `make test` in `libs/prebuilt` to ensure code quality and correctness.\n\n- The `_AgentBuilder` class has been created with constructor and method stubs, positioned before `create_react_agent` in the file.\n\n- The `_AgentBuilder.__init__` method has been fully implemented, including:\n - Handling deprecated `config_schema` with warning.\n - Validating `version`.\n - Validating `state_schema` keys.\n - Setting default `state_schema`.\n - Processing tools (handling `ToolNode` and sequences).\n - Detecting dynamic and async dynamic models.\n - Identifying tools with `return_direct`.\n - Setting `tool_calling_enabled` flag.\n - Initializing `static_model` to None.\n\n- The `_AgentBuilder._setup_model_and_tools` method will:\n - For static models:\n - Convert string model to `BaseChatModel` using `init_chat_model`.\n - Bind tools if needed using `_should_bind_tools`.\n - Create `static_model` as prompt runnable piped to model.\n - For dynamic models:\n - Set `static_model` to None.\n - Create `_resolve_model` and `_aresolve_model` functions for runtime model resolution, handling sync and async dynamic models.\n - Store these functions as instance methods.\n\n- The `_AgentBuilder._create_model_node` method will create sync and async callables that:\n - Validate chat history with `_validate_chat_history`.\n - Resolve model using `_resolve_model` or `_aresolve_model`.\n - Manage remaining steps and tool call logic.\n - Return a `RunnableCallable` with proper input schema.\n\n- The `_AgentBuilder._create_structured_response_node` method will create sync and async functions that:\n - Inject system prompt if `response_format` is a tuple.\n - Resolve model.\n - Use `.with_structured_output()` to generate structured response.\n - Return updated state with `structured_response`.\n\n- The `_AgentBuilder._create_model_router` method will define routing logic based on last message:\n - If no tool calls, route to `post_model_hook`, `generate_structured_response`, or `END`.\n - If tool calls present, route to `tools` (v1) or list of `Send` for parallel execution (v2).\n - Integrate `post_model_hook` if defined.\n\n- The `_AgentBuilder._create_tools_router` method will:\n - Check reversed message order for `return_direct` tools.\n - Handle parallel tool call scenarios with `return_direct`.\n - Route to `END` or entrypoint accordingly.\n\n- The `_AgentBuilder._setup_hooks` method will:\n - Add `pre_model_hook` node with edge to `agent` if provided.\n - Add `post_model_hook` node with conditional routing (`post_model_hook_router`) handling pending tool calls, structured response generation, and `END` routing.\n\n- The `_AgentBuilder.build` method will:\n - Create `StateGraph` with proper schema.\n - Add `agent` node with `_create_model_node`.\n - Handle tool-calling vs non-tool-calling workflows.\n - Add structured response node if needed.\n - Set up all conditional edges with proper path mappings.\n - Compile the graph with provided options (`checkpointer`, `store`, `interrupt_before`, `interrupt_after`, `debug`, `name`).\n\n- The public API `create_react_agent` will be refactored to:\n - Validate deprecated kwargs.\n - Instantiate `_AgentBuilder` with all parameters.\n - Return `builder.build()`.\n - Preserve exact function signature and docstring.\n\n- After refactor, run formatting, linting, and tests to ensure no regressions.\n\n- Relevant file paths:\n - `libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py` (main implementation)\n - `libs/prebuilt/langgraph/prebuilt/_internal.py` (defines `ToolCallWithContext`)\n - `libs/prebuilt/tests/test_react_agent.py` and `test_react_agent_graph.py` (tests)\n\n- Key imports used:\n - LangChain core: `BaseChatModel`, `Runnable`, `RunnableBinding`, `RunnableSequence`, `BaseTool`, `SystemMessage`, `AIMessage`, `ToolMessage`\n - LangGraph internal: `RunnableCallable`, `StateGraph`, `END`, `ToolNode`, `Runtime`, `Checkpointer`, `Send`\n - Pydantic for state schemas\n - Python standard: `inspect`, `warnings`, `typing` (e.g., `Union`, `Callable`, `Optional`, `Sequence`, `TypeVar`, `cast`, `get_type_hints`)\n\nThis context captures the essential information, design, and implementation details needed to proceed with the refactor of `create_react_agent` using the `_AgentBuilder` helper class.", + "additional_kwargs": { + "summary_message": true + }, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_structured_response_node` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the structured response functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01GRreZtpDwuGwNJJrW44ppS", + "name": "grep", + "input": "{\"query\": \"generate_structured_response\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 10}" + } + ], + "additional_kwargs": { + "id": "msg_01Ux6UVJbMee5w4HcjffbfhN", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 12542, + "cache_read_input_tokens": 18075, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01GRreZtpDwuGwNJJrW44ppS", + "index": 1, + "name": "grep", + "args": "{\"query\": \"generate_structured_response\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 10}" + } + ], + "id": "msg_01Ux6UVJbMee5w4HcjffbfhN", + "usage_metadata": { + "input_tokens": 1186, + "output_tokens": 170, + "total_tokens": 1356, + "input_token_details": { + "cache_read": 18075, + "cache_creation": 12542 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "generate_structured_response", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 10 + }, + "id": "toolu_01GRreZtpDwuGwNJJrW44ppS", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "30c307db-554e-4adf-b133-d8d8cd146ba6", + "tool_call_id": "toolu_01GRreZtpDwuGwNJJrW44ppS", + "content": "The following output was truncated due to its length exceeding the maximum allowed characters. Received 9061 characters, but only 5000 were allowed.\n\nlibs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n1015- )\r\n1016- else:\r\n1017- # For TypedDict schemas\r\n1018- class CallModelInputSchema(state_schema): # type: ignore\r\n1019- llm_input_messages: list[AnyMessage]\r\n1020-\r\n1021- input_schema = CallModelInputSchema\r\n1022- else:\r\n1023- input_schema = state_schema\r\n1024-\r\n1025: def generate_structured_response(\r\n1026- state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\r\n1027- ) -> StateSchema:\r\n1028- if is_async_dynamic_model:\r\n1029- msg = (\r\n1030- \"Async model callable provided but agent invoked synchronously. \"\r\n1031- \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\r\n1032- )\r\n1033- raise RuntimeError(msg)\r\n1034-\r\n1035- messages = _get_state_value(state, \"messages\")\r\n--\r\n1040-\r\n1041- resolved_model = _resolve_model(state, runtime)\r\n1042- model_with_structured_output = _get_model(\r\n1043- resolved_model\r\n1044- ).with_structured_output(\r\n1045- cast(StructuredResponseSchema, structured_response_schema)\r\n1046- )\r\n1047- response = model_with_structured_output.invoke(messages, config)\r\n1048- return {\"structured_response\": response}\r\n1049-\r\n1050: async def agenerate_structured_response(\r\n1051- state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\r\n1052- ) -> StateSchema:\r\n1053- messages = _get_state_value(state, \"messages\")\r\n1054- structured_response_schema = response_format\r\n1055- if isinstance(response_format, tuple):\r\n1056- system_prompt, structured_response_schema = response_format\r\n1057- messages = [SystemMessage(content=system_prompt)] + list(messages)\r\n1058-\r\n1059- resolved_model = await _aresolve_model(state, runtime)\r\n1060- model_with_structured_output = _get_model(\r\n--\r\n1081- entrypoint = \"agent\"\r\n1082-\r\n1083- workflow.set_entry_point(entrypoint)\r\n1084-\r\n1085- if post_model_hook is not None:\r\n1086- workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]\r\n1087- workflow.add_edge(\"agent\", \"post_model_hook\")\r\n1088-\r\n1089- if response_format is not None:\r\n1090- workflow.add_node(\r\n1091: \"generate_structured_response\",\r\n1092- RunnableC\n\n... [content truncated] ...\n\n elif isinstance(messages[-1], ToolMessage):\r\n1233- return entrypoint\r\n1234- elif response_format is not None:\r\n1235: return \"generate_structured_response\"\r\n1236- else:\r\n1237- return END\r\n1238-\r\n1239- workflow.add_conditional_edges(\r\n1240- \"post_model_hook\",\r\n1241- post_model_hook_router, # type: ignore[arg-type]\r\n1242- path_map=post_model_hook_paths,\r\n1243- )\r\n1244-\r\n1245- workflow.add_conditional_edges(\r\n\r\nlibs/prebuilt/tests/test_react_agent.py\r\n1833-\r\n1834- agent = create_react_agent(\r\n1835- model,\r\n1836- [get_weather],\r\n1837- response_format=WeatherResponse,\r\n1838- post_model_hook=post_model_hook,\r\n1839- state_schema=State,\r\n1840- )\r\n1841-\r\n1842- assert \"post_model_hook\" in agent.nodes\r\n1843: assert \"generate_structured_response\" in agent.nodes\r\n1844-\r\n1845- response = agent.invoke(\r\n1846- {\"messages\": [HumanMessage(\"What's the weather?\")], \"flag\": False}\r\n1847- )\r\n1848- assert response[\"flag\"] is True\r\n1849- assert response[\"structured_response\"] == expected_structured_response\r\n1850-\r\n1851- events = list(\r\n1852- agent.stream({\"messages\": [HumanMessage(\"What's the weather?\")], \"flag\": False})\r\n1853- )\r\n1854: assert \"generate_structured_response\" in events[-1]\r\n1855- assert events == [\r\n1856- {\r\n1857- \"agent\": {\r\n1858- \"messages\": [\r\n1859- AIMessage(\r\n1860- content=\"What's the weather?\",\r\n1861- additional_kwargs={},\r\n1862- response_metadata={},\r\n1863- id=\"2\",\r\n1864- tool_calls=[\r\n--\r\n1900- \"id\": \"1\",\r\n1901- \"type\": \"tool_call\",\r\n1902- }\r\n1903- ],\r\n1904- )\r\n1905- ]\r\n1906- }\r\n1907- },\r\n1908- {\"post_model_hook\": {\"flag\": True}},\r\n1909- {\r\n1910: \"generate_structured_response\": {\r\n1911- \"structured_response\": WeatherResponse(temperature=75.0)\r\n1912- }\r\n1913- },\r\n1914- ]\r\n1915-\r\n1916-\r\n1917-@pytest.mark.parametrize(\r\n1918- \"state_schema\", [AgentStateExtraKey, AgentStateExtraKeyPydantic]\r\n1919-)\r\n1920-def test_create_react_agent_inject_vars_with_post_model_hook(\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_structured_response_node` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the structured response functions." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_019yCUQXhSeA4STxMpsPq96e", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1025,1070]}" + } + ], + "additional_kwargs": { + "id": "msg_01N9nMnUcs96UBZUoCAuS1Uh", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1711, + "cache_read_input_tokens": 30617, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_019yCUQXhSeA4STxMpsPq96e", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1025,1070]}" + } + ], + "id": "msg_01N9nMnUcs96UBZUoCAuS1Uh", + "usage_metadata": { + "input_tokens": 1186, + "output_tokens": 181, + "total_tokens": 1367, + "input_token_details": { + "cache_read": 30617, + "cache_creation": 1711 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1025, 1070] + }, + "id": "toolu_019yCUQXhSeA4STxMpsPq96e", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "23a05462-acf3-44e4-a661-95c2b7590a50", + "tool_call_id": "toolu_019yCUQXhSeA4STxMpsPq96e", + "content": "1025: def generate_structured_response(\n1026: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n1027: ) -> StateSchema:\n1028: if is_async_dynamic_model:\n1029: msg = (\n1030: \"Async model callable provided but agent invoked synchronously. \"\n1031: \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\n1032: )\n1033: raise RuntimeError(msg)\n1034: \n1035: messages = _get_state_value(state, \"messages\")\n1036: structured_response_schema = response_format\n1037: if isinstance(response_format, tuple):\n1038: system_prompt, structured_response_schema = response_format\n1039: messages = [SystemMessage(content=system_prompt)] + list(messages)\n1040: \n1041: resolved_model = _resolve_model(state, runtime)\n1042: model_with_structured_output = _get_model(\n1043: resolved_model\n1044: ).with_structured_output(\n1045: cast(StructuredResponseSchema, structured_response_schema)\n1046: )\n1047: response = model_with_structured_output.invoke(messages, config)\n1048: return {\"structured_response\": response}\n1049: \n1050: async def agenerate_structured_response(\n1051: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n1052: ) -> StateSchema:\n1053: messages = _get_state_value(state, \"messages\")\n1054: structured_response_schema = response_format\n1055: if isinstance(response_format, tuple):\n1056: system_prompt, structured_response_schema = response_format\n1057: messages = [SystemMessage(content=system_prompt)] + list(messages)\n1058: \n1059: resolved_model = await _aresolve_model(state, runtime)\n1060: model_with_structured_output = _get_model(\n1061: resolved_model\n1062: ).with_structured_output(\n1063: cast(StructuredResponseSchema, structured_response_schema)\n1064: )\n1065: response = await model_with_structured_output.ainvoke(messages, config)\n1066: return {\"structured_response\": response}\n1067: \n1068: if not tool_calling_enabled:\n1069: # Define a new graph\n1070: workflow = StateGraph(state_schema=state_schema, context_schema=context_schema)", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_structured_response_node` method as outlined in the current task. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01XUmiZNpoPkpy8bxfCKtekT", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [540,550]}" + } + ], + "additional_kwargs": { + "id": "msg_01DtTS2fS82eBHF92qUz4Qfa", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 855, + "cache_read_input_tokens": 32328, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01XUmiZNpoPkpy8bxfCKtekT", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [540,550]}" + } + ], + "id": "msg_01DtTS2fS82eBHF92qUz4Qfa", + "usage_metadata": { + "input_tokens": 1185, + "output_tokens": 172, + "total_tokens": 1357, + "input_token_details": { + "cache_read": 32328, + "cache_creation": 855 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [540, 550] + }, + "id": "toolu_01XUmiZNpoPkpy8bxfCKtekT", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "3dc0800c-a6d4-40f8-a914-b9959c83e87f", + "tool_call_id": "toolu_01XUmiZNpoPkpy8bxfCKtekT", + "content": "540: # We return a list, because this will get added to the existing list\n541: return {\"messages\": [response]}\n542: \n543: # Proper input schema handling\n544: input_schema: StateSchemaType\n545: if self.pre_model_hook is not None:\n546: # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\n547: if isinstance(self.state_schema, type) and issubclass(self.state_schema, BaseModel):\n548: # For Pydantic schemas\n549: from pydantic import create_model\n550: ", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_structured_response_node` method as outlined in the current task. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_0196UMgCTCB3J1hDmohVmEJk", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [560,570]}" + } + ], + "additional_kwargs": { + "id": "msg_01Uv7oiSg5GZVjM2BjnKVXTU", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 340, + "cache_read_input_tokens": 33183, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_0196UMgCTCB3J1hDmohVmEJk", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [560,570]}" + } + ], + "id": "msg_01Uv7oiSg5GZVjM2BjnKVXTU", + "usage_metadata": { + "input_tokens": 1184, + "output_tokens": 172, + "total_tokens": 1356, + "input_token_details": { + "cache_read": 33183, + "cache_creation": 340 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [560, 570] + }, + "id": "toolu_0196UMgCTCB3J1hDmohVmEJk", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "525aea62-715c-4ac9-84a9-5d2401c131d4", + "tool_call_id": "toolu_0196UMgCTCB3J1hDmohVmEJk", + "content": "560: \n561: input_schema = CallModelInputSchema\n562: else:\n563: input_schema = self.state_schema\n564: \n565: return RunnableCallable(call_model, acall_model, input_schema=input_schema)\n566: \n567: def _create_structured_response_node(self) -> Optional[RunnableCallable]:\n568: \"\"\"Create structured output generation node if needed.\"\"\"\n569: # Implementation will be added in next task\n570: pass", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_structured_response_node` method as outlined in the current task. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_014WqinUokFwUmBEeJrmtgmE", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_structured_response_node(self) -> Optional[RunnableCallable]:\\n \\\"\\\"\\\"Create structured output generation node if needed.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_structured_response_node(self) -> Optional[RunnableCallable]:\\n \\\"\\\"\\\"Create structured output generation node if needed.\\\"\\\"\\\"\\n if self.response_format is None:\\n return None\\n\\n def generate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if self.is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = self.response_format\\n \\n # System prompt injection for tuple response_format\\n if isinstance(self.response_format, tuple):\\n system_prompt, structured_response_schema = self.response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n # Model resolution\\n resolved_model = self._resolve_model(state, runtime)\\n \\n # Structured output generation using with_structured_output\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = model_with_structured_output.invoke(messages, config)\\n \\n # Return structured_response in state\\n return {\\\"structured_response\\\": response}\\n\\n async def agenerate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = self.response_format\\n \\n # System prompt injection for tuple response_format\\n if isinstance(self.response_format, tuple):\\n system_prompt, structured_response_schema = self.response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n # Model resolution\\n resolved_model = await self._aresolve_model(state, runtime)\\n \\n # Structured output generation using with_structured_output\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = await model_with_structured_output.ainvoke(messages, config)\\n \\n # Return structured_response in state\\n return {\\\"structured_response\\\": response}\\n\\n return RunnableCallable(\\n generate_structured_response, \\n agenerate_structured_response, \\n input_schema=self.state_schema\\n )\"}" + } + ], + "additional_kwargs": { + "id": "msg_01KLEXNDQxoxwyC7NLHVBu3d", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 308, + "cache_read_input_tokens": 33523, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_014WqinUokFwUmBEeJrmtgmE", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_structured_response_node(self) -> Optional[RunnableCallable]:\\n \\\"\\\"\\\"Create structured output generation node if needed.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_structured_response_node(self) -> Optional[RunnableCallable]:\\n \\\"\\\"\\\"Create structured output generation node if needed.\\\"\\\"\\\"\\n if self.response_format is None:\\n return None\\n\\n def generate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if self.is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = self.response_format\\n \\n # System prompt injection for tuple response_format\\n if isinstance(self.response_format, tuple):\\n system_prompt, structured_response_schema = self.response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n # Model resolution\\n resolved_model = self._resolve_model(state, runtime)\\n \\n # Structured output generation using with_structured_output\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = model_with_structured_output.invoke(messages, config)\\n \\n # Return structured_response in state\\n return {\\\"structured_response\\\": response}\\n\\n async def agenerate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = self.response_format\\n \\n # System prompt injection for tuple response_format\\n if isinstance(self.response_format, tuple):\\n system_prompt, structured_response_schema = self.response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n # Model resolution\\n resolved_model = await self._aresolve_model(state, runtime)\\n \\n # Structured output generation using with_structured_output\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = await model_with_structured_output.ainvoke(messages, config)\\n \\n # Return structured_response in state\\n return {\\\"structured_response\\\": response}\\n\\n return RunnableCallable(\\n generate_structured_response, \\n agenerate_structured_response, \\n input_schema=self.state_schema\\n )\"}" + } + ], + "id": "msg_01KLEXNDQxoxwyC7NLHVBu3d", + "usage_metadata": { + "input_tokens": 1184, + "output_tokens": 904, + "total_tokens": 2088, + "input_token_details": { + "cache_read": 33523, + "cache_creation": 308 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _create_structured_response_node(self) -> Optional[RunnableCallable]:\n \"\"\"Create structured output generation node if needed.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _create_structured_response_node(self) -> Optional[RunnableCallable]:\n \"\"\"Create structured output generation node if needed.\"\"\"\n if self.response_format is None:\n return None\n\n def generate_structured_response(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n if self.is_async_dynamic_model:\n msg = (\n \"Async model callable provided but agent invoked synchronously. \"\n \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\n )\n raise RuntimeError(msg)\n\n messages = _get_state_value(state, \"messages\")\n structured_response_schema = self.response_format\n \n # System prompt injection for tuple response_format\n if isinstance(self.response_format, tuple):\n system_prompt, structured_response_schema = self.response_format\n messages = [SystemMessage(content=system_prompt)] + list(messages)\n\n # Model resolution\n resolved_model = self._resolve_model(state, runtime)\n \n # Structured output generation using with_structured_output\n model_with_structured_output = _get_model(\n resolved_model\n ).with_structured_output(\n cast(StructuredResponseSchema, structured_response_schema)\n )\n response = model_with_structured_output.invoke(messages, config)\n \n # Return structured_response in state\n return {\"structured_response\": response}\n\n async def agenerate_structured_response(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n messages = _get_state_value(state, \"messages\")\n structured_response_schema = self.response_format\n \n # System prompt injection for tuple response_format\n if isinstance(self.response_format, tuple):\n system_prompt, structured_response_schema = self.response_format\n messages = [SystemMessage(content=system_prompt)] + list(messages)\n\n # Model resolution\n resolved_model = await self._aresolve_model(state, runtime)\n \n # Structured output generation using with_structured_output\n model_with_structured_output = _get_model(\n resolved_model\n ).with_structured_output(\n cast(StructuredResponseSchema, structured_response_schema)\n )\n response = await model_with_structured_output.ainvoke(messages, config)\n \n # Return structured_response in state\n return {\"structured_response\": response}\n\n return RunnableCallable(\n generate_structured_response, \n agenerate_structured_response, \n input_schema=self.state_schema\n )" + }, + "id": "toolu_014WqinUokFwUmBEeJrmtgmE", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "ac504d63-17e4-4a86-acea-c188ea0a7465", + "tool_call_id": "toolu_014WqinUokFwUmBEeJrmtgmE", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 4) - implementing the `_AgentBuilder._create_structured_response_node` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01BJe4cVfrbCcV6ft3ypmRQQ", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [567,590]}" + } + ], + "additional_kwargs": { + "id": "msg_01SZiAXZms2T6uqYjP6Lcjbh", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 955, + "cache_read_input_tokens": 33831, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01BJe4cVfrbCcV6ft3ypmRQQ", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [567,590]}" + } + ], + "id": "msg_01SZiAXZms2T6uqYjP6Lcjbh", + "usage_metadata": { + "input_tokens": 1183, + "output_tokens": 183, + "total_tokens": 1366, + "input_token_details": { + "cache_read": 33831, + "cache_creation": 955 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [567, 590] + }, + "id": "toolu_01BJe4cVfrbCcV6ft3ypmRQQ", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "15d8cd0a-1ca6-4d64-a628-73f83c2e986e", + "tool_call_id": "toolu_01BJe4cVfrbCcV6ft3ypmRQQ", + "content": "567: def _create_structured_response_node(self) -> Optional[RunnableCallable]:\n568: \"\"\"Create structured output generation node if needed.\"\"\"\n569: if self.response_format is None:\n570: return None\n571: \n572: def generate_structured_response(\n573: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n574: ) -> StateSchema:\n575: if self.is_async_dynamic_model:\n576: msg = (\n577: \"Async model callable provided but agent invoked synchronously. \"\n578: \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\n579: )\n580: raise RuntimeError(msg)\n581: \n582: messages = _get_state_value(state, \"messages\")\n583: structured_response_schema = self.response_format\n584: \n585: # System prompt injection for tuple response_format\n586: if isinstance(self.response_format, tuple):\n587: system_prompt, structured_response_schema = self.response_format\n588: messages = [SystemMessage(content=system_prompt)] + list(messages)\n589: \n590: # Model resolution", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 4) - implementing the `_AgentBuilder._create_structured_response_node` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01X9xRLDHH22vCGweiZAKY1b", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [590,620]}" + } + ], + "additional_kwargs": { + "id": "msg_013n8K5vZtSV5U2ijq8CCT3e", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 504, + "cache_read_input_tokens": 34786, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01X9xRLDHH22vCGweiZAKY1b", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [590,620]}" + } + ], + "id": "msg_013n8K5vZtSV5U2ijq8CCT3e", + "usage_metadata": { + "input_tokens": 1183, + "output_tokens": 183, + "total_tokens": 1366, + "input_token_details": { + "cache_read": 34786, + "cache_creation": 504 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [590, 620] + }, + "id": "toolu_01X9xRLDHH22vCGweiZAKY1b", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "4062a4a3-7e6f-408a-9b3e-02784ce8a902", + "tool_call_id": "toolu_01X9xRLDHH22vCGweiZAKY1b", + "content": "590: # Model resolution\n591: resolved_model = self._resolve_model(state, runtime)\n592: \n593: # Structured output generation using with_structured_output\n594: model_with_structured_output = _get_model(\n595: resolved_model\n596: ).with_structured_output(\n597: cast(StructuredResponseSchema, structured_response_schema)\n598: )\n599: response = model_with_structured_output.invoke(messages, config)\n600: \n601: # Return structured_response in state\n602: return {\"structured_response\": response}\n603: \n604: async def agenerate_structured_response(\n605: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n606: ) -> StateSchema:\n607: messages = _get_state_value(state, \"messages\")\n608: structured_response_schema = self.response_format\n609: \n610: # System prompt injection for tuple response_format\n611: if isinstance(self.response_format, tuple):\n612: system_prompt, structured_response_schema = self.response_format\n613: messages = [SystemMessage(content=system_prompt)] + list(messages)\n614: \n615: # Model resolution\n616: resolved_model = await self._aresolve_model(state, runtime)\n617: \n618: # Structured output generation using with_structured_output\n619: model_with_structured_output = _get_model(\n620: resolved_model", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 4) - implementing the `_AgentBuilder._create_structured_response_node` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01QbhZN6GbgKBbVAjZRYQ9Ev", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [620,635]}" + } + ], + "additional_kwargs": { + "id": "msg_01SfTJYUYiqHbnYxJBn1VfUy", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 577, + "cache_read_input_tokens": 35290, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01QbhZN6GbgKBbVAjZRYQ9Ev", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [620,635]}" + } + ], + "id": "msg_01SfTJYUYiqHbnYxJBn1VfUy", + "usage_metadata": { + "input_tokens": 1183, + "output_tokens": 183, + "total_tokens": 1366, + "input_token_details": { + "cache_read": 35290, + "cache_creation": 577 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [620, 635] + }, + "id": "toolu_01QbhZN6GbgKBbVAjZRYQ9Ev", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "88f0f48a-61b0-4601-b7ad-0d5d2b79882a", + "tool_call_id": "toolu_01QbhZN6GbgKBbVAjZRYQ9Ev", + "content": "620: resolved_model\n621: ).with_structured_output(\n622: cast(StructuredResponseSchema, structured_response_schema)\n623: )\n624: response = await model_with_structured_output.ainvoke(messages, config)\n625: \n626: # Return structured_response in state\n627: return {\"structured_response\": response}\n628: \n629: return RunnableCallable(\n630: generate_structured_response, \n631: agenerate_structured_response, \n632: input_schema=self.state_schema\n633: )\n634: \n635: def _create_model_router(self) -> Callable:", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 4) - implementing the `_AgentBuilder._create_structured_response_node` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **Create generate_structured_response and agenerate_structured_response functions** - Done:\n- Both sync and async versions implemented\n\n✅ **System prompt injection for tuple response_format** - Done:\n- Checks if `response_format` is a tuple and extracts system prompt and schema\n- Prepends SystemMessage with system prompt to messages list\n\n✅ **Model resolution** - Done:\n- Uses `self._resolve_model` for sync and `self._aresolve_model` for async\n- Handles both static and dynamic models\n\n✅ **Structured output generation using with_structured_output** - Done:\n- Uses `_get_model()` to extract the base model from the resolved model\n- Calls `.with_structured_output()` with the structured response schema\n- Proper casting to `StructuredResponseSchema`\n\n✅ **Return structured_response in state** - Done:\n- Both functions return `{\"structured_response\": response}`\n\n✅ **Proper error handling** - Done:\n- Checks for async model with sync invocation and raises appropriate RuntimeError\n\n✅ **Returns Optional[RunnableCallable]** - Done:\n- Returns `None` if `response_format` is None\n- Returns `RunnableCallable` with both sync and async functions and proper input schema\n\nThe implementation correctly extracts and implements all the structured response node creation logic from the original `create_react_agent` function, including proper system prompt injection, model resolution, and structured output generation." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01FP7jqKxQKB9E2vD2Y9orWc", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_structured_response_node` method with all required functionality. The method returns None if no response_format is provided, otherwise creates both sync (generate_structured_response) and async (agenerate_structured_response) functions that handle system prompt injection for tuple response_format by extracting the system prompt and prepending it as a SystemMessage, perform model resolution using the instance's _resolve_model and _aresolve_model methods, generate structured output using with_structured_output on the extracted base model, and return the structured_response in the state. The implementation includes proper error handling for async models called synchronously and returns a RunnableCallable with the correct input schema. The method faithfully extracts and replicates all the structured response node creation logic from the original create_react_agent function.\"}" + } + ], + "additional_kwargs": { + "id": "msg_01QoPukSq6BtkVx99GVsvL5p", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 365, + "cache_read_input_tokens": 35867, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01FP7jqKxQKB9E2vD2Y9orWc", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_structured_response_node` method with all required functionality. The method returns None if no response_format is provided, otherwise creates both sync (generate_structured_response) and async (agenerate_structured_response) functions that handle system prompt injection for tuple response_format by extracting the system prompt and prepending it as a SystemMessage, perform model resolution using the instance's _resolve_model and _aresolve_model methods, generate structured output using with_structured_output on the extracted base model, and return the structured_response in the state. The implementation includes proper error handling for async models called synchronously and returns a RunnableCallable with the correct input schema. The method faithfully extracts and replicates all the structured response node creation logic from the original create_react_agent function.\"}" + } + ], + "id": "msg_01QoPukSq6BtkVx99GVsvL5p", + "usage_metadata": { + "input_tokens": 1182, + "output_tokens": 634, + "total_tokens": 1816, + "input_token_details": { + "cache_read": 35867, + "cache_creation": 365 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder._create_structured_response_node` method with all required functionality. The method returns None if no response_format is provided, otherwise creates both sync (generate_structured_response) and async (agenerate_structured_response) functions that handle system prompt injection for tuple response_format by extracting the system prompt and prepending it as a SystemMessage, perform model resolution using the instance's _resolve_model and _aresolve_model methods, generate structured output using with_structured_output on the extracted base model, and return the structured_response in the state. The implementation includes proper error handling for async models called synchronously and returns a RunnableCallable with the correct input schema. The method faithfully extracts and replicates all the structured response node creation logic from the original create_react_agent function." + }, + "id": "toolu_01FP7jqKxQKB9E2vD2Y9orWc", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "b766e8fb-6d2f-40c7-9b66-52e267dc91a5", + "tool_call_id": "toolu_01FP7jqKxQKB9E2vD2Y9orWc", + "content": "Saved task status as completed for task Implement _AgentBuilder._create_structured_response_node method to create generate_structured_response and agenerate_structured_response functions that handle system prompt injection for tuple response_format, model resolution, structured output generation using with_structured_output, and return structured_response in state", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_router` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the routing function." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01NsHnrRGZE9RKiPP41BQbZc", + "name": "grep", + "input": "{\"query\": \"should_continue\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 15}" + } + ], + "additional_kwargs": { + "id": "msg_01PMN3kB7ESpJ1djdMEjafDs", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 715, + "cache_read_input_tokens": 36232, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01NsHnrRGZE9RKiPP41BQbZc", + "index": 1, + "name": "grep", + "args": "{\"query\": \"should_continue\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 15}" + } + ], + "id": "msg_01PMN3kB7ESpJ1djdMEjafDs", + "usage_metadata": { + "input_tokens": 1182, + "output_tokens": 165, + "total_tokens": 1347, + "input_token_details": { + "cache_read": 36232, + "cache_creation": 715 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "should_continue", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 15 + }, + "id": "toolu_01NsHnrRGZE9RKiPP41BQbZc", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "a52f404a-2f5d-44d9-824c-71e65a7f96c5", + "tool_call_id": "toolu_01NsHnrRGZE9RKiPP41BQbZc", + "content": "The following output was truncated due to its length exceeding the maximum allowed characters. Received 42175 characters, but only 5000 were allowed.\n\nexamples/chatbot-simulation-evaluation/simulation_utils.py\r\n74- \"\"\"\r\n75-\r\n76- messages: Annotated[List[AnyMessage], add_messages]\r\n77- inputs: Optional[dict[str, Any]]\r\n78-\r\n79-\r\n80-def create_chat_simulator(\r\n81- assistant: (\r\n82- Callable[[List[AnyMessage]], str | AIMessage]\r\n83- | Runnable[List[AnyMessage], str | AIMessage]\r\n84- ),\r\n85- simulated_user: Runnable[Dict, AIMessage],\r\n86- *,\r\n87- input_key: str,\r\n88- max_turns: int = 6,\r\n89: should_continue: Optional[Callable[[SimulationState], str]] = None,\r\n90-):\r\n91- \"\"\"Creates a chat simulator for evaluating a chatbot.\r\n92-\r\n93- Args:\r\n94- assistant: The chatbot assistant function or runnable object.\r\n95- simulated_user: The simulated user object.\r\n96- input_key: The key for the input to the chat simulation.\r\n97- max_turns: The maximum number of turns in the chat simulation. Default is 6.\r\n98: should_continue: Optional function to determine if the simulation should continue.\r\n99- If not provided, a default function will be used.\r\n100-\r\n101- Returns:\r\n102- The compiled chat simulation graph.\r\n103-\r\n104- \"\"\"\r\n105- graph_builder = StateGraph(SimulationState)\r\n106- graph_builder.add_node(\r\n107- \"user\",\r\n108- _create_simulated_user_node(simulated_user),\r\n109- )\r\n110- graph_builder.add_node(\r\n111- \"assistant\", _fetch_messages | assistant | _coerce_to_message\r\n112- )\r\n113- graph_builder.add_edge(\"assistant\", \"user\")\r\n114- graph_builder.add_conditional_edges(\r\n115- \"user\",\r\n116: should_continue or functools.partial(_should_continue, max_turns=max_turns),\r\n117- )\r\n118- # If your dataset has a 'leading question/input', then we route first to the assistant, otherwise, we let the user take the lead.\r\n119- graph_builder.add_edge(START, \"assistant\" if input_key is not None else \"user\")\r\n120-\r\n121- return (\r\n122- RunnableLambda(_prepare_example).bind(input_key=input_key)\r\n123- | graph_builder.compile()\r\n124- )\r\n125-\r\n126-\r\n127-## Private methods\r\n128-\r\n129-\r\n130-def _prepare_example(inputs: dict[str, Any], input_key: Optional[str] = None):\r\n131- if input_key is not None:\r\n--\r\n180- \"\"\"Simulated user accepts a {\"messages\": [...]} argument and returns a single message.\"\"\"\r\n181- return (\r\n182- _swap_roles\r\n183- | RunnableLambda(_invoke_simulated_user).bind(simulated_user=simulated_user)\r\n184- | _convert\n\n... [content truncated] ...\n\nmod/agent.py\r\n14-model_anth = ChatAnthropic(temperature=0, model_name=\"claude-3-sonnet-20240229\")\r\n15-model_oai = ChatOpenAI(temperature=0)\r\n16-\r\n17-model_anth = model_anth.bind_tools(tools)\r\n18-model_oai = model_oai.bind_tools(tools)\r\n19-\r\n20-prompt = open(Path(__file__).parent.parent / \"prompt.txt\").read()\r\n21-subprompt = open(Path(__file__).parent / \"subprompt.txt\").read()\r\n22-\r\n23-\r\n24-class AgentState(TypedDict):\r\n25- messages: Annotated[Sequence[BaseMessage], add_messages]\r\n26-\r\n27-\r\n28-# Define the function that determines whether to continue or not\r\n29:def should_continue(state):\r\n30- messages = state[\"messages\"]\r\n31- last_message = messages[-1]\r\n32- # If there are no tool calls, then we finish\r\n33- if not last_message.tool_calls:\r\n34- return \"end\"\r\n35- # Otherwise if there is, we continue\r\n36- else:\r\n37- return \"continue\"\r\n38-\r\n39-\r\n40-# Define the function that calls the model\r\n41-def call_model(state, config):\r\n42- if config[\"configurable\"].get(\"model\", \"anthropic\") == \"anthropic\":\r\n43- model = model_anth\r\n44- else:\r\n--\r\n58-\r\n59-# Define the two nodes we will cycle between\r\n60-workflow.add_node(\"agent\", call_model)\r\n61-workflow.add_node(\"action\", tool_node)\r\n62-\r\n63-# Set the entrypoint as `agent`\r\n64-# This means that this node is the first one called\r\n65-workflow.set_entry_point(\"agent\")\r\n66-\r\n67-# We now add a conditional edge\r\n68-workflow.add_conditional_edges(\r\n69- # First, we define the start node. We use `agent`.\r\n70- # This means these are the edges taken after the `agent` node is called.\r\n71- \"agent\",\r\n72- # Next, we pass in the function that will determine which node is called next.\r\n73: should_continue,\r\n74- # Finally we pass in a mapping.\r\n75- # The keys are strings, and the values are other nodes.\r\n76- # END is a special node marking that the graph should finish.\r\n77: # What will happen is we will call `should_continue`, and then the output of that\r\n78- # will be matched against the keys in this mapping.\r\n79- # Based on which one it matches, that node will then be called.\r\n80- {\r\n81- # If `tools`, then we call the tool node.\r\n82- \"continue\": \"action\",\r\n83- # Otherwise we finish.\r\n84- \"end\": END,\r\n85- },\r\n86-)\r\n87-\r\n88-# We now add a normal edge from `tools` to `agent`.\r\n89-# This means that after `tools` is called, `agent` node is called next.\r\n90-workflow.add_edge(\"action\", \"agent\")\r\n91-\r\n92-# Finally, we compile it!\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_router` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the routing function." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01Agg5464FJEs1Fii3gebei8", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1150,1200]}" + } + ], + "additional_kwargs": { + "id": "msg_01D6eBdCLZXsiHkDCxuDVvkU", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1775, + "cache_read_input_tokens": 36947, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01Agg5464FJEs1Fii3gebei8", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1150,1200]}" + } + ], + "id": "msg_01D6eBdCLZXsiHkDCxuDVvkU", + "usage_metadata": { + "input_tokens": 1182, + "output_tokens": 178, + "total_tokens": 1360, + "input_token_details": { + "cache_read": 36947, + "cache_creation": 1775 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1150, 1200] + }, + "id": "toolu_01Agg5464FJEs1Fii3gebei8", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "8364daae-e4b9-4111-8ca8-d2ad74b172b4", + "tool_call_id": "toolu_01Agg5464FJEs1Fii3gebei8", + "content": "1150: workflow.add_edge(\"agent\", \"post_model_hook\")\n1151: \n1152: if response_format is not None:\n1153: workflow.add_node(\n1154: \"generate_structured_response\",\n1155: RunnableCallable(\n1156: generate_structured_response,\n1157: agenerate_structured_response,\n1158: ),\n1159: )\n1160: if post_model_hook is not None:\n1161: workflow.add_edge(\"post_model_hook\", \"generate_structured_response\")\n1162: else:\n1163: workflow.add_edge(\"agent\", \"generate_structured_response\")\n1164: \n1165: return workflow.compile(\n1166: checkpointer=checkpointer,\n1167: store=store,\n1168: interrupt_before=interrupt_before,\n1169: interrupt_after=interrupt_after,\n1170: debug=debug,\n1171: name=name,\n1172: )\n1173: \n1174: # Define the function that determines whether to continue or not\n1175: def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n1176: messages = _get_state_value(state, \"messages\")\n1177: last_message = messages[-1]\n1178: # If there is no function call, then we finish\n1179: if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n1180: if post_model_hook is not None:\n1181: return \"post_model_hook\"\n1182: elif response_format is not None:\n1183: return \"generate_structured_response\"\n1184: else:\n1185: return END\n1186: # Otherwise if there is, we continue\n1187: else:\n1188: if version == \"v1\":\n1189: return \"tools\"\n1190: elif version == \"v2\":\n1191: if post_model_hook is not None:\n1192: return \"post_model_hook\"\n1193: return [\n1194: Send(\n1195: \"tools\",\n1196: ToolCallWithContext(\n1197: __type=\"tool_call_with_context\",\n1198: tool_call=tool_call,\n1199: state=state,\n1200: ),", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_router` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the routing function." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01UVawfkkj64Yfyu7qiZGGjA", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1200,1220]}" + } + ], + "additional_kwargs": { + "id": "msg_01PvUs6FZr79GMnRfZJj21Ht", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 815, + "cache_read_input_tokens": 38722, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01UVawfkkj64Yfyu7qiZGGjA", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1200,1220]}" + } + ], + "id": "msg_01PvUs6FZr79GMnRfZJj21Ht", + "usage_metadata": { + "input_tokens": 1182, + "output_tokens": 178, + "total_tokens": 1360, + "input_token_details": { + "cache_read": 38722, + "cache_creation": 815 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1200, 1220] + }, + "id": "toolu_01UVawfkkj64Yfyu7qiZGGjA", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "e491af45-8548-4fc5-b199-51aa4b578fcc", + "tool_call_id": "toolu_01UVawfkkj64Yfyu7qiZGGjA", + "content": "1200: ),\n1201: )\n1202: for tool_call in last_message.tool_calls\n1203: ]\n1204: \n1205: # Define a new graph\n1206: workflow = StateGraph(\n1207: state_schema=state_schema or AgentState, context_schema=context_schema\n1208: )\n1209: \n1210: # Define the two nodes we will cycle between\n1211: workflow.add_node(\n1212: \"agent\",\n1213: RunnableCallable(call_model, acall_model),\n1214: input_schema=input_schema,\n1215: )\n1216: workflow.add_node(\"tools\", tool_node)\n1217: \n1218: # Optionally add a pre-model hook node that will be called\n1219: # every time before the \"agent\" (LLM-calling node)\n1220: if pre_model_hook is not None:", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_router` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the routing function." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01DfeWS3YeJZqee7Q6qhu53L", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create routing logic for after model call.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create routing logic for after model call.\\\"\\\"\\\"\\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\\n messages = _get_state_value(state, \\\"messages\\\")\\n last_message = messages[-1]\\n \\n # If there is no function call, then we finish\\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\\n # Route to post_model_hook/generate_structured_response/END\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n elif self.response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n # Otherwise if there is, we continue\\n else:\\n if self.version == \\\"v1\\\":\\n # Route to tools (v1)\\n return \\\"tools\\\"\\n elif self.version == \\\"v2\\\":\\n # Proper post_model_hook integration for v2\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n # Send list for parallel execution (v2)\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in last_message.tool_calls\\n ]\\n \\n return should_continue\"}" + } + ], + "additional_kwargs": { + "id": "msg_01NMqDR2NAocJBBmbtPzE928", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 436, + "cache_read_input_tokens": 39537, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01DfeWS3YeJZqee7Q6qhu53L", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create routing logic for after model call.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create routing logic for after model call.\\\"\\\"\\\"\\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\\n messages = _get_state_value(state, \\\"messages\\\")\\n last_message = messages[-1]\\n \\n # If there is no function call, then we finish\\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\\n # Route to post_model_hook/generate_structured_response/END\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n elif self.response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n # Otherwise if there is, we continue\\n else:\\n if self.version == \\\"v1\\\":\\n # Route to tools (v1)\\n return \\\"tools\\\"\\n elif self.version == \\\"v2\\\":\\n # Proper post_model_hook integration for v2\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n # Send list for parallel execution (v2)\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in last_message.tool_calls\\n ]\\n \\n return should_continue\"}" + } + ], + "id": "msg_01NMqDR2NAocJBBmbtPzE928", + "usage_metadata": { + "input_tokens": 1181, + "output_tokens": 616, + "total_tokens": 1797, + "input_token_details": { + "cache_read": 39537, + "cache_creation": 436 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _create_model_router(self) -> Callable:\n \"\"\"Create routing logic for after model call.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _create_model_router(self) -> Callable:\n \"\"\"Create routing logic for after model call.\"\"\"\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n messages = _get_state_value(state, \"messages\")\n last_message = messages[-1]\n \n # If there is no function call, then we finish\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n # Route to post_model_hook/generate_structured_response/END\n if self.post_model_hook is not None:\n return \"post_model_hook\"\n elif self.response_format is not None:\n return \"generate_structured_response\"\n else:\n return END\n # Otherwise if there is, we continue\n else:\n if self.version == \"v1\":\n # Route to tools (v1)\n return \"tools\"\n elif self.version == \"v2\":\n # Proper post_model_hook integration for v2\n if self.post_model_hook is not None:\n return \"post_model_hook\"\n # Send list for parallel execution (v2)\n return [\n Send(\n \"tools\",\n ToolCallWithContext(\n __type=\"tool_call_with_context\",\n tool_call=tool_call,\n state=state,\n ),\n )\n for tool_call in last_message.tool_calls\n ]\n \n return should_continue" + }, + "id": "toolu_01DfeWS3YeJZqee7Q6qhu53L", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "6af026db-313d-4dac-aac8-daf34402123d", + "tool_call_id": "toolu_01DfeWS3YeJZqee7Q6qhu53L", + "content": "Error: No match found for replacement text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py. Please check your text and try again.", + "name": "str_replace_based_edit_tool", + "status": "error", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_router` method as outlined in the current task. Let me first find where this method stub is located." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_011pAd5fw2iXwpCTChghNAc3", + "name": "grep", + "input": "{\"query\": \"_create_model_router\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 3}" + } + ], + "additional_kwargs": { + "id": "msg_01FiXk4EvX5rU1BpeVzRUzSp", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 676, + "cache_read_input_tokens": 39973, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_011pAd5fw2iXwpCTChghNAc3", + "index": 1, + "name": "grep", + "args": "{\"query\": \"_create_model_router\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 3}" + } + ], + "id": "msg_01FiXk4EvX5rU1BpeVzRUzSp", + "usage_metadata": { + "input_tokens": 1180, + "output_tokens": 162, + "total_tokens": 1342, + "input_token_details": { + "cache_read": 39973, + "cache_creation": 676 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "_create_model_router", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 3 + }, + "id": "toolu_011pAd5fw2iXwpCTChghNAc3", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "50019133-08fd-4ee1-939a-7e41dc716b8c", + "tool_call_id": "toolu_011pAd5fw2iXwpCTChghNAc3", + "content": "libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n632- input_schema=self.state_schema\r\n633- )\r\n634- \r\n635: def _create_model_router(self) -> Callable:\r\n636- \"\"\"Create execution flow routing after model call.\"\"\"\r\n637- # Implementation will be added in next task\r\n638- pass\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_model_router` method as outlined in the current task. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01RpwdUm6s27vH8d21bRwrt5", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create execution flow routing after model call.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create execution flow routing after model call.\\\"\\\"\\\"\\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\\n messages = _get_state_value(state, \\\"messages\\\")\\n last_message = messages[-1]\\n \\n # If there is no function call, then we finish\\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\\n # Route to post_model_hook/generate_structured_response/END\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n elif self.response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n # Otherwise if there is, we continue\\n else:\\n if self.version == \\\"v1\\\":\\n # Route to tools (v1)\\n return \\\"tools\\\"\\n elif self.version == \\\"v2\\\":\\n # Proper post_model_hook integration for v2\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n # Send list for parallel execution (v2)\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in last_message.tool_calls\\n ]\\n \\n return should_continue\"}" + } + ], + "additional_kwargs": { + "id": "msg_01TkR94nC8no1W4q3XhXeq9f", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 264, + "cache_read_input_tokens": 40649, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01RpwdUm6s27vH8d21bRwrt5", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create execution flow routing after model call.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_model_router(self) -> Callable:\\n \\\"\\\"\\\"Create execution flow routing after model call.\\\"\\\"\\\"\\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\\n messages = _get_state_value(state, \\\"messages\\\")\\n last_message = messages[-1]\\n \\n # If there is no function call, then we finish\\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\\n # Route to post_model_hook/generate_structured_response/END\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n elif self.response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n # Otherwise if there is, we continue\\n else:\\n if self.version == \\\"v1\\\":\\n # Route to tools (v1)\\n return \\\"tools\\\"\\n elif self.version == \\\"v2\\\":\\n # Proper post_model_hook integration for v2\\n if self.post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n # Send list for parallel execution (v2)\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in last_message.tool_calls\\n ]\\n \\n return should_continue\"}" + } + ], + "id": "msg_01TkR94nC8no1W4q3XhXeq9f", + "usage_metadata": { + "input_tokens": 1180, + "output_tokens": 610, + "total_tokens": 1790, + "input_token_details": { + "cache_read": 40649, + "cache_creation": 264 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _create_model_router(self) -> Callable:\n \"\"\"Create execution flow routing after model call.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _create_model_router(self) -> Callable:\n \"\"\"Create execution flow routing after model call.\"\"\"\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n messages = _get_state_value(state, \"messages\")\n last_message = messages[-1]\n \n # If there is no function call, then we finish\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n # Route to post_model_hook/generate_structured_response/END\n if self.post_model_hook is not None:\n return \"post_model_hook\"\n elif self.response_format is not None:\n return \"generate_structured_response\"\n else:\n return END\n # Otherwise if there is, we continue\n else:\n if self.version == \"v1\":\n # Route to tools (v1)\n return \"tools\"\n elif self.version == \"v2\":\n # Proper post_model_hook integration for v2\n if self.post_model_hook is not None:\n return \"post_model_hook\"\n # Send list for parallel execution (v2)\n return [\n Send(\n \"tools\",\n ToolCallWithContext(\n __type=\"tool_call_with_context\",\n tool_call=tool_call,\n state=state,\n ),\n )\n for tool_call in last_message.tool_calls\n ]\n \n return should_continue" + }, + "id": "toolu_01RpwdUm6s27vH8d21bRwrt5", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "05f788f7-d358-4163-aa6d-710af50f0fa9", + "tool_call_id": "toolu_01RpwdUm6s27vH8d21bRwrt5", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 5) - implementing the `_AgentBuilder._create_model_router` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01Snfzv8W6NMxV35BY3sA5bn", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [635,670]}" + } + ], + "additional_kwargs": { + "id": "msg_01Febkpa8UtT2qQZuTmzDFpy", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 661, + "cache_read_input_tokens": 40913, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01Snfzv8W6NMxV35BY3sA5bn", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [635,670]}" + } + ], + "id": "msg_01Febkpa8UtT2qQZuTmzDFpy", + "usage_metadata": { + "input_tokens": 1179, + "output_tokens": 181, + "total_tokens": 1360, + "input_token_details": { + "cache_read": 40913, + "cache_creation": 661 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [635, 670] + }, + "id": "toolu_01Snfzv8W6NMxV35BY3sA5bn", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "778e5a92-f7c2-452e-b849-e124bbef81ee", + "tool_call_id": "toolu_01Snfzv8W6NMxV35BY3sA5bn", + "content": "635: def _create_model_router(self) -> Callable:\n636: \"\"\"Create execution flow routing after model call.\"\"\"\n637: def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n638: messages = _get_state_value(state, \"messages\")\n639: last_message = messages[-1]\n640: \n641: # If there is no function call, then we finish\n642: if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n643: # Route to post_model_hook/generate_structured_response/END\n644: if self.post_model_hook is not None:\n645: return \"post_model_hook\"\n646: elif self.response_format is not None:\n647: return \"generate_structured_response\"\n648: else:\n649: return END\n650: # Otherwise if there is, we continue\n651: else:\n652: if self.version == \"v1\":\n653: # Route to tools (v1)\n654: return \"tools\"\n655: elif self.version == \"v2\":\n656: # Proper post_model_hook integration for v2\n657: if self.post_model_hook is not None:\n658: return \"post_model_hook\"\n659: # Send list for parallel execution (v2)\n660: return [\n661: Send(\n662: \"tools\",\n663: ToolCallWithContext(\n664: __type=\"tool_call_with_context\",\n665: tool_call=tool_call,\n666: state=state,\n667: ),\n668: )\n669: for tool_call in last_message.tool_calls\n670: ]", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 5) - implementing the `_AgentBuilder._create_model_router` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01TcHNDCAvUfBvWdZcZLRfXG", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [670,675]}" + } + ], + "additional_kwargs": { + "id": "msg_017zatSwEG6dYgpfZoJoiEMb", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 652, + "cache_read_input_tokens": 41574, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01TcHNDCAvUfBvWdZcZLRfXG", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [670,675]}" + } + ], + "id": "msg_017zatSwEG6dYgpfZoJoiEMb", + "usage_metadata": { + "input_tokens": 1179, + "output_tokens": 181, + "total_tokens": 1360, + "input_token_details": { + "cache_read": 41574, + "cache_creation": 652 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [670, 675] + }, + "id": "toolu_01TcHNDCAvUfBvWdZcZLRfXG", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "9ae0333a-9def-499e-8971-2b119efee91e", + "tool_call_id": "toolu_01TcHNDCAvUfBvWdZcZLRfXG", + "content": "670: ]\n671: \n672: return should_continue\n673: \n674: def _create_tools_router(self) -> Optional[Callable]:\n675: \"\"\"Create post-tool-call routing based on return_direct.\"\"\"", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 5) - implementing the `_AgentBuilder._create_model_router` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **should_continue function** - Done:\n- Function created that routes based on last message type\n\n✅ **If no tool calls route to post_model_hook/generate_structured_response/END** - Done:\n- Checks if last message is not AIMessage or has no tool_calls\n- Routes to post_model_hook if present, then generate_structured_response if present, otherwise END\n\n✅ **If tool calls present route to tools (v1) or Send list for parallel execution (v2)** - Done:\n- For v1: returns \"tools\" string\n- For v2: returns list of Send objects for parallel execution using ToolCallWithContext\n\n✅ **Proper post_model_hook integration** - Done:\n- For v2 with tool calls, checks if post_model_hook is present and routes there first\n- Maintains proper routing hierarchy\n\nThe implementation correctly extracts and implements all the model routing logic from the original `create_react_agent` function, including proper version handling (v1 vs v2), post_model_hook integration, and parallel tool execution for v2." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01Rhg4rqMGFyaGE1EfsV2nSM", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_model_router` method with all required functionality. The method creates a should_continue function that routes based on the last message type: if there are no tool calls, it routes to post_model_hook (if present), then generate_structured_response (if present), otherwise END; if tool calls are present, it routes to \\\"tools\\\" for v1 or returns a list of Send objects for parallel execution in v2 using ToolCallWithContext. The implementation includes proper post_model_hook integration for v2, ensuring that when tool calls are present in v2, it first checks for and routes to post_model_hook if available. The method faithfully extracts and replicates all the model routing logic from the original create_react_agent function, handling both version differences and the complex routing hierarchy correctly.\"}" + } + ], + "additional_kwargs": { + "id": "msg_01S4Q71LddhAkiNci4QD5S3E", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 251, + "cache_read_input_tokens": 42226, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01Rhg4rqMGFyaGE1EfsV2nSM", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_model_router` method with all required functionality. The method creates a should_continue function that routes based on the last message type: if there are no tool calls, it routes to post_model_hook (if present), then generate_structured_response (if present), otherwise END; if tool calls are present, it routes to \\\"tools\\\" for v1 or returns a list of Send objects for parallel execution in v2 using ToolCallWithContext. The implementation includes proper post_model_hook integration for v2, ensuring that when tool calls are present in v2, it first checks for and routes to post_model_hook if available. The method faithfully extracts and replicates all the model routing logic from the original create_react_agent function, handling both version differences and the complex routing hierarchy correctly.\"}" + } + ], + "id": "msg_01S4Q71LddhAkiNci4QD5S3E", + "usage_metadata": { + "input_tokens": 1179, + "output_tokens": 542, + "total_tokens": 1721, + "input_token_details": { + "cache_read": 42226, + "cache_creation": 251 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder._create_model_router` method with all required functionality. The method creates a should_continue function that routes based on the last message type: if there are no tool calls, it routes to post_model_hook (if present), then generate_structured_response (if present), otherwise END; if tool calls are present, it routes to \"tools\" for v1 or returns a list of Send objects for parallel execution in v2 using ToolCallWithContext. The implementation includes proper post_model_hook integration for v2, ensuring that when tool calls are present in v2, it first checks for and routes to post_model_hook if available. The method faithfully extracts and replicates all the model routing logic from the original create_react_agent function, handling both version differences and the complex routing hierarchy correctly." + }, + "id": "toolu_01Rhg4rqMGFyaGE1EfsV2nSM", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "147f1afa-a467-41c6-b31c-98ec6e781369", + "tool_call_id": "toolu_01Rhg4rqMGFyaGE1EfsV2nSM", + "content": "Saved task status as completed for task Implement _AgentBuilder._create_model_router method (should_continue function) to route based on last message type: if no tool calls route to post_model_hook/generate_structured_response/END, if tool calls present route to tools (v1) or Send list for parallel execution (v2), with proper post_model_hook integration", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_tools_router` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the tools routing function." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01CEaWv5bcPyp7u7ZQNdoWH2", + "name": "grep", + "input": "{\"query\": \"route_tool_responses\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 15}" + } + ], + "additional_kwargs": { + "id": "msg_017z6AvF8Ju7Nr4mHfXi7BDD", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 637, + "cache_read_input_tokens": 42477, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01CEaWv5bcPyp7u7ZQNdoWH2", + "index": 1, + "name": "grep", + "args": "{\"query\": \"route_tool_responses\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 15}" + } + ], + "id": "msg_017z6AvF8Ju7Nr4mHfXi7BDD", + "usage_metadata": { + "input_tokens": 1179, + "output_tokens": 168, + "total_tokens": 1347, + "input_token_details": { + "cache_read": 42477, + "cache_creation": 637 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "route_tool_responses", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 15 + }, + "id": "toolu_01CEaWv5bcPyp7u7ZQNdoWH2", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "921dc12a-4f58-4c01-af58-ca28329c7535", + "tool_call_id": "toolu_01CEaWv5bcPyp7u7ZQNdoWH2", + "content": "libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n1333- else:\r\n1334- return END\r\n1335-\r\n1336- workflow.add_conditional_edges(\r\n1337- \"post_model_hook\",\r\n1338- post_model_hook_router, # type: ignore[arg-type]\r\n1339- path_map=post_model_hook_paths,\r\n1340- )\r\n1341-\r\n1342- workflow.add_conditional_edges(\r\n1343- \"agent\",\r\n1344- should_continue, # type: ignore[arg-type]\r\n1345- path_map=agent_paths,\r\n1346- )\r\n1347-\r\n1348: def route_tool_responses(state: StateSchema) -> str:\r\n1349- for m in reversed(_get_state_value(state, \"messages\")):\r\n1350- if not isinstance(m, ToolMessage):\r\n1351- break\r\n1352- if m.name in should_return_direct:\r\n1353- return END\r\n1354-\r\n1355- # handle a case of parallel tool calls where\r\n1356- # the tool w/ `return_direct` was executed in a different `Send`\r\n1357- if isinstance(m, AIMessage) and m.tool_calls:\r\n1358- if any(call[\"name\"] in should_return_direct for call in m.tool_calls):\r\n1359- return END\r\n1360-\r\n1361- return entrypoint\r\n1362-\r\n1363- if should_return_direct:\r\n1364- workflow.add_conditional_edges(\r\n1365: \"tools\", route_tool_responses, path_map=[entrypoint, END]\r\n1366- )\r\n1367- else:\r\n1368- workflow.add_edge(\"tools\", entrypoint)\r\n1369-\r\n1370- # Finally, we compile it!\r\n1371- # This compiles it into a LangChain Runnable,\r\n1372- # meaning you can use it as you would any other runnable\r\n1373- return workflow.compile(\r\n1374- checkpointer=checkpointer,\r\n1375- store=store,\r\n1376- interrupt_before=interrupt_before,\r\n1377- interrupt_after=interrupt_after,\r\n1378- debug=debug,\r\n1379- name=name,\r\n1380- )\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_tools_router` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for creating the tools routing function." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01TsFzieV5KQfdoiAuYKJFjL", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1348,1370]}" + } + ], + "additional_kwargs": { + "id": "msg_013TCP821BHNTggL7wXd97sE", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 769, + "cache_read_input_tokens": 43114, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01TsFzieV5KQfdoiAuYKJFjL", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1348,1370]}" + } + ], + "id": "msg_013TCP821BHNTggL7wXd97sE", + "usage_metadata": { + "input_tokens": 1179, + "output_tokens": 179, + "total_tokens": 1358, + "input_token_details": { + "cache_read": 43114, + "cache_creation": 769 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1348, 1370] + }, + "id": "toolu_01TsFzieV5KQfdoiAuYKJFjL", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "148acb2e-3f5e-4d16-bfed-2780f66852db", + "tool_call_id": "toolu_01TsFzieV5KQfdoiAuYKJFjL", + "content": "1348: def route_tool_responses(state: StateSchema) -> str:\n1349: for m in reversed(_get_state_value(state, \"messages\")):\n1350: if not isinstance(m, ToolMessage):\n1351: break\n1352: if m.name in should_return_direct:\n1353: return END\n1354: \n1355: # handle a case of parallel tool calls where\n1356: # the tool w/ `return_direct` was executed in a different `Send`\n1357: if isinstance(m, AIMessage) and m.tool_calls:\n1358: if any(call[\"name\"] in should_return_direct for call in m.tool_calls):\n1359: return END\n1360: \n1361: return entrypoint\n1362: \n1363: if should_return_direct:\n1364: workflow.add_conditional_edges(\n1365: \"tools\", route_tool_responses, path_map=[entrypoint, END]\n1366: )\n1367: else:\n1368: workflow.add_edge(\"tools\", entrypoint)\n1369: \n1370: # Finally, we compile it!", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._create_tools_router` method as outlined in the current task. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01J3tzGnjGEWhThzP2b1c6Js", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_tools_router(self) -> Optional[Callable]:\\n \\\"\\\"\\\"Create post-tool-call routing based on return_direct.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_tools_router(self) -> Optional[Callable]:\\n \\\"\\\"\\\"Create post-tool-call routing based on return_direct.\\\"\\\"\\\"\\n if not self.should_return_direct:\\n # No return_direct tools, so no routing needed\\n return None\\n\\n def route_tool_responses(state: StateSchema) -> str:\\n # Check for return_direct tools in reversed message order\\n for m in reversed(_get_state_value(state, \\\"messages\\\")):\\n if not isinstance(m, ToolMessage):\\n break\\n if m.name in self.should_return_direct:\\n return END\\n\\n # Handle parallel tool call scenarios with return_direct\\n # the tool w/ `return_direct` was executed in a different `Send`\\n if isinstance(m, AIMessage) and m.tool_calls:\\n if any(call[\\\"name\\\"] in self.should_return_direct for call in m.tool_calls):\\n return END\\n\\n # Route to entrypoint accordingly\\n return self.entrypoint\\n\\n return route_tool_responses\"}" + } + ], + "additional_kwargs": { + "id": "msg_01Gxw4dawbRWAVF4gEYM4SF1", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 488, + "cache_read_input_tokens": 43883, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01J3tzGnjGEWhThzP2b1c6Js", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _create_tools_router(self) -> Optional[Callable]:\\n \\\"\\\"\\\"Create post-tool-call routing based on return_direct.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _create_tools_router(self) -> Optional[Callable]:\\n \\\"\\\"\\\"Create post-tool-call routing based on return_direct.\\\"\\\"\\\"\\n if not self.should_return_direct:\\n # No return_direct tools, so no routing needed\\n return None\\n\\n def route_tool_responses(state: StateSchema) -> str:\\n # Check for return_direct tools in reversed message order\\n for m in reversed(_get_state_value(state, \\\"messages\\\")):\\n if not isinstance(m, ToolMessage):\\n break\\n if m.name in self.should_return_direct:\\n return END\\n\\n # Handle parallel tool call scenarios with return_direct\\n # the tool w/ `return_direct` was executed in a different `Send`\\n if isinstance(m, AIMessage) and m.tool_calls:\\n if any(call[\\\"name\\\"] in self.should_return_direct for call in m.tool_calls):\\n return END\\n\\n # Route to entrypoint accordingly\\n return self.entrypoint\\n\\n return route_tool_responses\"}" + } + ], + "id": "msg_01Gxw4dawbRWAVF4gEYM4SF1", + "usage_metadata": { + "input_tokens": 1178, + "output_tokens": 482, + "total_tokens": 1660, + "input_token_details": { + "cache_read": 43883, + "cache_creation": 488 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _create_tools_router(self) -> Optional[Callable]:\n \"\"\"Create post-tool-call routing based on return_direct.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _create_tools_router(self) -> Optional[Callable]:\n \"\"\"Create post-tool-call routing based on return_direct.\"\"\"\n if not self.should_return_direct:\n # No return_direct tools, so no routing needed\n return None\n\n def route_tool_responses(state: StateSchema) -> str:\n # Check for return_direct tools in reversed message order\n for m in reversed(_get_state_value(state, \"messages\")):\n if not isinstance(m, ToolMessage):\n break\n if m.name in self.should_return_direct:\n return END\n\n # Handle parallel tool call scenarios with return_direct\n # the tool w/ `return_direct` was executed in a different `Send`\n if isinstance(m, AIMessage) and m.tool_calls:\n if any(call[\"name\"] in self.should_return_direct for call in m.tool_calls):\n return END\n\n # Route to entrypoint accordingly\n return self.entrypoint\n\n return route_tool_responses" + }, + "id": "toolu_01J3tzGnjGEWhThzP2b1c6Js", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "07099f84-147d-4e3a-97dd-c8574062883d", + "tool_call_id": "toolu_01J3tzGnjGEWhThzP2b1c6Js", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 6) - implementing the `_AgentBuilder._create_tools_router` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01PSywtc3KAo5PkLVP44mp4v", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [674,700]}" + } + ], + "additional_kwargs": { + "id": "msg_015YH8T1HTfsZi73bKK6wVAs", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 533, + "cache_read_input_tokens": 44371, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01PSywtc3KAo5PkLVP44mp4v", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [674,700]}" + } + ], + "id": "msg_015YH8T1HTfsZi73bKK6wVAs", + "usage_metadata": { + "input_tokens": 1177, + "output_tokens": 181, + "total_tokens": 1358, + "input_token_details": { + "cache_read": 44371, + "cache_creation": 533 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [674, 700] + }, + "id": "toolu_01PSywtc3KAo5PkLVP44mp4v", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "c1ddb92b-1e52-49c5-9faf-566e2716ebb5", + "tool_call_id": "toolu_01PSywtc3KAo5PkLVP44mp4v", + "content": "674: def _create_tools_router(self) -> Optional[Callable]:\n675: \"\"\"Create post-tool-call routing based on return_direct.\"\"\"\n676: if not self.should_return_direct:\n677: # No return_direct tools, so no routing needed\n678: return None\n679: \n680: def route_tool_responses(state: StateSchema) -> str:\n681: # Check for return_direct tools in reversed message order\n682: for m in reversed(_get_state_value(state, \"messages\")):\n683: if not isinstance(m, ToolMessage):\n684: break\n685: if m.name in self.should_return_direct:\n686: return END\n687: \n688: # Handle parallel tool call scenarios with return_direct\n689: # the tool w/ `return_direct` was executed in a different `Send`\n690: if isinstance(m, AIMessage) and m.tool_calls:\n691: if any(call[\"name\"] in self.should_return_direct for call in m.tool_calls):\n692: return END\n693: \n694: # Route to entrypoint accordingly\n695: return self.entrypoint\n696: \n697: return route_tool_responses\n698: \n699: def _setup_hooks(self, workflow: StateGraph) -> str:\n700: \"\"\"Add pre/post model hook nodes and return entrypoint.\"\"\"", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 6) - implementing the `_AgentBuilder._create_tools_router` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **route_tool_responses function** - Done:\n- Function created that checks for return_direct tools\n\n✅ **Check for return_direct tools in reversed message order** - Done:\n- Iterates through messages in reverse order\n- Breaks when encountering non-ToolMessage\n- Checks if tool name is in should_return_direct set\n\n✅ **Handle parallel tool call scenarios with return_direct** - Done:\n- After the loop, checks if the last non-ToolMessage is an AIMessage with tool_calls\n- Uses `any()` to check if any tool call name is in should_return_direct\n- Handles the case where return_direct tool was executed in a different Send\n\n✅ **Route to END or entrypoint accordingly** - Done:\n- Returns END if return_direct tool found\n- Returns self.entrypoint otherwise\n\n✅ **Returns Optional[Callable]** - Done:\n- Returns None if no return_direct tools exist (no routing needed)\n- Returns the route_tool_responses function if return_direct tools exist\n\nThe implementation correctly extracts and implements all the tools routing logic from the original `create_react_agent` function, including proper handling of parallel tool execution scenarios and return_direct behavior." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_012Wr7YPbvsFS3dHhdys6Q43", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_tools_router` method with all required functionality. The method returns None if no return_direct tools exist (no routing needed), otherwise creates a route_tool_responses function that checks for return_direct tools in reversed message order by iterating through messages in reverse and breaking at non-ToolMessage types. It handles parallel tool call scenarios with return_direct by checking if the last non-ToolMessage is an AIMessage with tool_calls and using any() to verify if any tool call name is in the should_return_direct set. The function routes to END if a return_direct tool is found, otherwise routes to self.entrypoint. The method faithfully extracts and replicates all the tools routing logic from the original create_react_agent function, properly handling both sequential and parallel tool execution scenarios with return_direct behavior.\"}" + } + ], + "additional_kwargs": { + "id": "msg_01WAWE6AQGBxqsNFY5XNaHv8", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 545, + "cache_read_input_tokens": 44904, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_012Wr7YPbvsFS3dHhdys6Q43", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._create_tools_router` method with all required functionality. The method returns None if no return_direct tools exist (no routing needed), otherwise creates a route_tool_responses function that checks for return_direct tools in reversed message order by iterating through messages in reverse and breaking at non-ToolMessage types. It handles parallel tool call scenarios with return_direct by checking if the last non-ToolMessage is an AIMessage with tool_calls and using any() to verify if any tool call name is in the should_return_direct set. The function routes to END if a return_direct tool is found, otherwise routes to self.entrypoint. The method faithfully extracts and replicates all the tools routing logic from the original create_react_agent function, properly handling both sequential and parallel tool execution scenarios with return_direct behavior.\"}" + } + ], + "id": "msg_01WAWE6AQGBxqsNFY5XNaHv8", + "usage_metadata": { + "input_tokens": 1177, + "output_tokens": 576, + "total_tokens": 1753, + "input_token_details": { + "cache_read": 44904, + "cache_creation": 545 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder._create_tools_router` method with all required functionality. The method returns None if no return_direct tools exist (no routing needed), otherwise creates a route_tool_responses function that checks for return_direct tools in reversed message order by iterating through messages in reverse and breaking at non-ToolMessage types. It handles parallel tool call scenarios with return_direct by checking if the last non-ToolMessage is an AIMessage with tool_calls and using any() to verify if any tool call name is in the should_return_direct set. The function routes to END if a return_direct tool is found, otherwise routes to self.entrypoint. The method faithfully extracts and replicates all the tools routing logic from the original create_react_agent function, properly handling both sequential and parallel tool execution scenarios with return_direct behavior." + }, + "id": "toolu_012Wr7YPbvsFS3dHhdys6Q43", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "1490e90a-58f4-4e0b-96b9-4e8952aa5d86", + "tool_call_id": "toolu_012Wr7YPbvsFS3dHhdys6Q43", + "content": "Saved task status as completed for task Implement _AgentBuilder._create_tools_router method (route_tool_responses function) to check for return_direct tools in reversed message order, handle parallel tool call scenarios with return_direct, and route to END or entrypoint accordingly", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_hooks` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for setting up pre and post model hooks." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_011KN9z7H2nWy1hwTC5Vi9bx", + "name": "grep", + "input": "{\"query\": \"post_model_hook_router\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 15}" + } + ], + "additional_kwargs": { + "id": "msg_011icBF7X3hu7NDvnubEZSfz", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 647, + "cache_read_input_tokens": 45449, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_011KN9z7H2nWy1hwTC5Vi9bx", + "index": 1, + "name": "grep", + "args": "{\"query\": \"post_model_hook_router\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 15}" + } + ], + "id": "msg_011icBF7X3hu7NDvnubEZSfz", + "usage_metadata": { + "input_tokens": 1177, + "output_tokens": 170, + "total_tokens": 1347, + "input_token_details": { + "cache_read": 45449, + "cache_creation": 647 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "post_model_hook_router", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 15 + }, + "id": "toolu_011KN9z7H2nWy1hwTC5Vi9bx", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "acc810d2-cc67-48f7-b6ea-70291943a15f", + "tool_call_id": "toolu_011KN9z7H2nWy1hwTC5Vi9bx", + "content": "libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n1302- agenerate_structured_response,\r\n1303- ),\r\n1304- )\r\n1305- if post_model_hook is not None:\r\n1306- post_model_hook_paths.append(\"generate_structured_response\")\r\n1307- else:\r\n1308- agent_paths.append(\"generate_structured_response\")\r\n1309- else:\r\n1310- if post_model_hook is not None:\r\n1311- post_model_hook_paths.append(END)\r\n1312- else:\r\n1313- agent_paths.append(END)\r\n1314-\r\n1315- if post_model_hook is not None:\r\n1316-\r\n1317: def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\r\n1318- \"\"\"Route to the next node after post_model_hook.\r\n1319-\r\n1320- Routes to one of:\r\n1321- * \"tools\": if there are pending tool calls without a corresponding message.\r\n1322- * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\r\n1323- * END: if no pending tool calls exist and no response_format is specified.\r\n1324- \"\"\"\r\n1325-\r\n1326- messages = _get_state_value(state, \"messages\")\r\n1327- tool_messages = [\r\n1328- m.tool_call_id for m in messages if isinstance(m, ToolMessage)\r\n1329- ]\r\n1330- last_ai_message = next(\r\n1331- m for m in reversed(messages) if isinstance(m, AIMessage)\r\n1332- )\r\n--\r\n1343- tool_call=tool_call,\r\n1344- state=state,\r\n1345- ),\r\n1346- )\r\n1347- for tool_call in pending_tool_calls\r\n1348- ]\r\n1349- elif isinstance(messages[-1], ToolMessage):\r\n1350- return entrypoint\r\n1351- elif response_format is not None:\r\n1352- return \"generate_structured_response\"\r\n1353- else:\r\n1354- return END\r\n1355-\r\n1356- workflow.add_conditional_edges(\r\n1357- \"post_model_hook\",\r\n1358: post_model_hook_router, # type: ignore[arg-type]\r\n1359- path_map=post_model_hook_paths,\r\n1360- )\r\n1361-\r\n1362- workflow.add_conditional_edges(\r\n1363- \"agent\",\r\n1364- should_continue, # type: ignore[arg-type]\r\n1365- path_map=agent_paths,\r\n1366- )\r\n1367-\r\n1368- def route_tool_responses(state: StateSchema) -> str:\r\n1369- for m in reversed(_get_state_value(state, \"messages\")):\r\n1370- if not isinstance(m, ToolMessage):\r\n1371- break\r\n1372- if m.name in should_return_direct:\r\n1373- return END\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_hooks` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for setting up pre and post model hooks." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01Vcsioo2UQF58SckFWWhgGc", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1315,1360]}" + } + ], + "additional_kwargs": { + "id": "msg_01GW2six7SCzA1Wkr7tyaiGo", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 963, + "cache_read_input_tokens": 46096, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01Vcsioo2UQF58SckFWWhgGc", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1315,1360]}" + } + ], + "id": "msg_01GW2six7SCzA1Wkr7tyaiGo", + "usage_metadata": { + "input_tokens": 1177, + "output_tokens": 179, + "total_tokens": 1356, + "input_token_details": { + "cache_read": 46096, + "cache_creation": 963 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1315, 1360] + }, + "id": "toolu_01Vcsioo2UQF58SckFWWhgGc", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "89b44ec5-8fdd-4277-89bf-5148e13b2a12", + "tool_call_id": "toolu_01Vcsioo2UQF58SckFWWhgGc", + "content": "1315: if post_model_hook is not None:\n1316: \n1317: def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n1318: \"\"\"Route to the next node after post_model_hook.\n1319: \n1320: Routes to one of:\n1321: * \"tools\": if there are pending tool calls without a corresponding message.\n1322: * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\n1323: * END: if no pending tool calls exist and no response_format is specified.\n1324: \"\"\"\n1325: \n1326: messages = _get_state_value(state, \"messages\")\n1327: tool_messages = [\n1328: m.tool_call_id for m in messages if isinstance(m, ToolMessage)\n1329: ]\n1330: last_ai_message = next(\n1331: m for m in reversed(messages) if isinstance(m, AIMessage)\n1332: )\n1333: pending_tool_calls = [\n1334: c for c in last_ai_message.tool_calls if c[\"id\"] not in tool_messages\n1335: ]\n1336: \n1337: if pending_tool_calls:\n1338: return [\n1339: Send(\n1340: \"tools\",\n1341: ToolCallWithContext(\n1342: __type=\"tool_call_with_context\",\n1343: tool_call=tool_call,\n1344: state=state,\n1345: ),\n1346: )\n1347: for tool_call in pending_tool_calls\n1348: ]\n1349: elif isinstance(messages[-1], ToolMessage):\n1350: return entrypoint\n1351: elif response_format is not None:\n1352: return \"generate_structured_response\"\n1353: else:\n1354: return END\n1355: \n1356: workflow.add_conditional_edges(\n1357: \"post_model_hook\",\n1358: post_model_hook_router, # type: ignore[arg-type]\n1359: path_map=post_model_hook_paths,\n1360: )", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_hooks` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for setting up pre and post model hooks." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_016uRGbrFipjrppA3Yft2DAd", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1280,1320]}" + } + ], + "additional_kwargs": { + "id": "msg_01Nh8gg4NV4nEk9gU4c6zQzc", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 774, + "cache_read_input_tokens": 47059, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_016uRGbrFipjrppA3Yft2DAd", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1280,1320]}" + } + ], + "id": "msg_01Nh8gg4NV4nEk9gU4c6zQzc", + "usage_metadata": { + "input_tokens": 1177, + "output_tokens": 179, + "total_tokens": 1356, + "input_token_details": { + "cache_read": 47059, + "cache_creation": 774 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1280, 1320] + }, + "id": "toolu_016uRGbrFipjrppA3Yft2DAd", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "cf2dea44-e2aa-4c45-a43e-65db0993b8a3", + "tool_call_id": "toolu_016uRGbrFipjrppA3Yft2DAd", + "content": "1280: \n1281: # Set the entrypoint as `agent`\n1282: # This means that this node is the first one called\n1283: workflow.set_entry_point(entrypoint)\n1284: \n1285: agent_paths = []\n1286: post_model_hook_paths = [entrypoint, \"tools\"]\n1287: \n1288: # Add a post model hook node if post_model_hook is provided\n1289: if post_model_hook is not None:\n1290: workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]\n1291: agent_paths.append(\"post_model_hook\")\n1292: workflow.add_edge(\"agent\", \"post_model_hook\")\n1293: else:\n1294: agent_paths.append(\"tools\")\n1295: \n1296: # Add a structured output node if response_format is provided\n1297: if response_format is not None:\n1298: workflow.add_node(\n1299: \"generate_structured_response\",\n1300: RunnableCallable(\n1301: generate_structured_response,\n1302: agenerate_structured_response,\n1303: ),\n1304: )\n1305: if post_model_hook is not None:\n1306: post_model_hook_paths.append(\"generate_structured_response\")\n1307: else:\n1308: agent_paths.append(\"generate_structured_response\")\n1309: else:\n1310: if post_model_hook is not None:\n1311: post_model_hook_paths.append(END)\n1312: else:\n1313: agent_paths.append(END)\n1314: \n1315: if post_model_hook is not None:\n1316: \n1317: def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n1318: \"\"\"Route to the next node after post_model_hook.\n1319: \n1320: Routes to one of:", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_hooks` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for setting up pre and post model hooks." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01K9obTNLDDsc5BFbXHcxeCz", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1220,1250]}" + } + ], + "additional_kwargs": { + "id": "msg_01CTA2k8aySyJXcGYf1UCdC7", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 720, + "cache_read_input_tokens": 47833, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01K9obTNLDDsc5BFbXHcxeCz", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1220,1250]}" + } + ], + "id": "msg_01CTA2k8aySyJXcGYf1UCdC7", + "usage_metadata": { + "input_tokens": 1176, + "output_tokens": 179, + "total_tokens": 1355, + "input_token_details": { + "cache_read": 47833, + "cache_creation": 720 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1220, 1250] + }, + "id": "toolu_01K9obTNLDDsc5BFbXHcxeCz", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "bccf5b3e-230e-4796-93bb-e57787aac56d", + "tool_call_id": "toolu_01K9obTNLDDsc5BFbXHcxeCz", + "content": "1220: checkpointer=checkpointer,\n1221: store=store,\n1222: interrupt_before=interrupt_before,\n1223: interrupt_after=interrupt_after,\n1224: debug=debug,\n1225: name=name,\n1226: )\n1227: \n1228: # Define the function that determines whether to continue or not\n1229: def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n1230: messages = _get_state_value(state, \"messages\")\n1231: last_message = messages[-1]\n1232: # If there is no function call, then we finish\n1233: if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n1234: if post_model_hook is not None:\n1235: return \"post_model_hook\"\n1236: elif response_format is not None:\n1237: return \"generate_structured_response\"\n1238: else:\n1239: return END\n1240: # Otherwise if there is, we continue\n1241: else:\n1242: if version == \"v1\":\n1243: return \"tools\"\n1244: elif version == \"v2\":\n1245: if post_model_hook is not None:\n1246: return \"post_model_hook\"\n1247: return [\n1248: Send(\n1249: \"tools\",\n1250: ToolCallWithContext(", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_hooks` method as outlined in the current task. Let me examine the original implementation to extract the relevant logic for setting up pre and post model hooks." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01GynJ3NoDoseR7frcLRfGKr", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1270,1290]}" + } + ], + "additional_kwargs": { + "id": "msg_014GoS31EJ1B4KczHQxABVgE", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 572, + "cache_read_input_tokens": 48553, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01GynJ3NoDoseR7frcLRfGKr", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1270,1290]}" + } + ], + "id": "msg_014GoS31EJ1B4KczHQxABVgE", + "usage_metadata": { + "input_tokens": 1176, + "output_tokens": 179, + "total_tokens": 1355, + "input_token_details": { + "cache_read": 48553, + "cache_creation": 572 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1270, 1290] + }, + "id": "toolu_01GynJ3NoDoseR7frcLRfGKr", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "7eb10c70-9275-489b-87c8-deb28da0b7cc", + "tool_call_id": "toolu_01GynJ3NoDoseR7frcLRfGKr", + "content": "1270: workflow.add_node(\"tools\", tool_node)\n1271: \n1272: # Optionally add a pre-model hook node that will be called\n1273: # every time before the \"agent\" (LLM-calling node)\n1274: if pre_model_hook is not None:\n1275: workflow.add_node(\"pre_model_hook\", pre_model_hook) # type: ignore[arg-type]\n1276: workflow.add_edge(\"pre_model_hook\", \"agent\")\n1277: entrypoint = \"pre_model_hook\"\n1278: else:\n1279: entrypoint = \"agent\"\n1280: \n1281: # Set the entrypoint as `agent`\n1282: # This means that this node is the first one called\n1283: workflow.set_entry_point(entrypoint)\n1284: \n1285: agent_paths = []\n1286: post_model_hook_paths = [entrypoint, \"tools\"]\n1287: \n1288: # Add a post model hook node if post_model_hook is provided\n1289: if post_model_hook is not None:\n1290: workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder._setup_hooks` method as outlined in the current task. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_012V6ewLfvVQfR1DZ1aaKVjw", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _setup_hooks(self, workflow: StateGraph) -> str:\\n \\\"\\\"\\\"Add pre/post model hook nodes and return entrypoint.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _setup_hooks(self, workflow: StateGraph) -> str:\\n \\\"\\\"\\\"Add pre/post model hook nodes and return entrypoint.\\\"\\\"\\\"\\n # Add pre_model_hook node with edge to agent if provided\\n if self.pre_model_hook is not None:\\n workflow.add_node(\\\"pre_model_hook\\\", self.pre_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"pre_model_hook\\\", \\\"agent\\\")\\n entrypoint = \\\"pre_model_hook\\\"\\n else:\\n entrypoint = \\\"agent\\\"\\n\\n # Add post_model_hook node with conditional routing if provided\\n if self.post_model_hook is not None:\\n workflow.add_node(\\\"post_model_hook\\\", self.post_model_hook) # type: ignore[arg-type]\\n\\n def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\\n \\\"\\\"\\\"Route to the next node after post_model_hook.\\n\\n Routes to one of:\\n * \\\"tools\\\": if there are pending tool calls without a corresponding message.\\n * \\\"generate_structured_response\\\": if no pending tool calls exist and response_format is specified.\\n * END: if no pending tool calls exist and no response_format is specified.\\n \\\"\\\"\\\"\\n messages = _get_state_value(state, \\\"messages\\\")\\n tool_messages = [\\n m.tool_call_id for m in messages if isinstance(m, ToolMessage)\\n ]\\n last_ai_message = next(\\n m for m in reversed(messages) if isinstance(m, AIMessage)\\n )\\n pending_tool_calls = [\\n c for c in last_ai_message.tool_calls if c[\\\"id\\\"] not in tool_messages\\n ]\\n\\n # Handle pending tool calls\\n if pending_tool_calls:\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in pending_tool_calls\\n ]\\n # Structured response generation\\n elif isinstance(messages[-1], ToolMessage):\\n return entrypoint\\n elif self.response_format is not None:\\n return \\\"generate_structured_response\\\"\\n # END routing based on state\\n else:\\n return END\\n\\n # Store the router function for use in build method\\n self.post_model_hook_router = post_model_hook_router\\n\\n return entrypoint\"}" + } + ], + "additional_kwargs": { + "id": "msg_01VmqPrK9527gqtVNPc3m7JB", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 516, + "cache_read_input_tokens": 49125, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_012V6ewLfvVQfR1DZ1aaKVjw", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def _setup_hooks(self, workflow: StateGraph) -> str:\\n \\\"\\\"\\\"Add pre/post model hook nodes and return entrypoint.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def _setup_hooks(self, workflow: StateGraph) -> str:\\n \\\"\\\"\\\"Add pre/post model hook nodes and return entrypoint.\\\"\\\"\\\"\\n # Add pre_model_hook node with edge to agent if provided\\n if self.pre_model_hook is not None:\\n workflow.add_node(\\\"pre_model_hook\\\", self.pre_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"pre_model_hook\\\", \\\"agent\\\")\\n entrypoint = \\\"pre_model_hook\\\"\\n else:\\n entrypoint = \\\"agent\\\"\\n\\n # Add post_model_hook node with conditional routing if provided\\n if self.post_model_hook is not None:\\n workflow.add_node(\\\"post_model_hook\\\", self.post_model_hook) # type: ignore[arg-type]\\n\\n def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\\n \\\"\\\"\\\"Route to the next node after post_model_hook.\\n\\n Routes to one of:\\n * \\\"tools\\\": if there are pending tool calls without a corresponding message.\\n * \\\"generate_structured_response\\\": if no pending tool calls exist and response_format is specified.\\n * END: if no pending tool calls exist and no response_format is specified.\\n \\\"\\\"\\\"\\n messages = _get_state_value(state, \\\"messages\\\")\\n tool_messages = [\\n m.tool_call_id for m in messages if isinstance(m, ToolMessage)\\n ]\\n last_ai_message = next(\\n m for m in reversed(messages) if isinstance(m, AIMessage)\\n )\\n pending_tool_calls = [\\n c for c in last_ai_message.tool_calls if c[\\\"id\\\"] not in tool_messages\\n ]\\n\\n # Handle pending tool calls\\n if pending_tool_calls:\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in pending_tool_calls\\n ]\\n # Structured response generation\\n elif isinstance(messages[-1], ToolMessage):\\n return entrypoint\\n elif self.response_format is not None:\\n return \\\"generate_structured_response\\\"\\n # END routing based on state\\n else:\\n return END\\n\\n # Store the router function for use in build method\\n self.post_model_hook_router = post_model_hook_router\\n\\n return entrypoint\"}" + } + ], + "id": "msg_01VmqPrK9527gqtVNPc3m7JB", + "usage_metadata": { + "input_tokens": 1176, + "output_tokens": 858, + "total_tokens": 2034, + "input_token_details": { + "cache_read": 49125, + "cache_creation": 516 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def _setup_hooks(self, workflow: StateGraph) -> str:\n \"\"\"Add pre/post model hook nodes and return entrypoint.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def _setup_hooks(self, workflow: StateGraph) -> str:\n \"\"\"Add pre/post model hook nodes and return entrypoint.\"\"\"\n # Add pre_model_hook node with edge to agent if provided\n if self.pre_model_hook is not None:\n workflow.add_node(\"pre_model_hook\", self.pre_model_hook) # type: ignore[arg-type]\n workflow.add_edge(\"pre_model_hook\", \"agent\")\n entrypoint = \"pre_model_hook\"\n else:\n entrypoint = \"agent\"\n\n # Add post_model_hook node with conditional routing if provided\n if self.post_model_hook is not None:\n workflow.add_node(\"post_model_hook\", self.post_model_hook) # type: ignore[arg-type]\n\n def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n \"\"\"Route to the next node after post_model_hook.\n\n Routes to one of:\n * \"tools\": if there are pending tool calls without a corresponding message.\n * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\n * END: if no pending tool calls exist and no response_format is specified.\n \"\"\"\n messages = _get_state_value(state, \"messages\")\n tool_messages = [\n m.tool_call_id for m in messages if isinstance(m, ToolMessage)\n ]\n last_ai_message = next(\n m for m in reversed(messages) if isinstance(m, AIMessage)\n )\n pending_tool_calls = [\n c for c in last_ai_message.tool_calls if c[\"id\"] not in tool_messages\n ]\n\n # Handle pending tool calls\n if pending_tool_calls:\n return [\n Send(\n \"tools\",\n ToolCallWithContext(\n __type=\"tool_call_with_context\",\n tool_call=tool_call,\n state=state,\n ),\n )\n for tool_call in pending_tool_calls\n ]\n # Structured response generation\n elif isinstance(messages[-1], ToolMessage):\n return entrypoint\n elif self.response_format is not None:\n return \"generate_structured_response\"\n # END routing based on state\n else:\n return END\n\n # Store the router function for use in build method\n self.post_model_hook_router = post_model_hook_router\n\n return entrypoint" + }, + "id": "toolu_012V6ewLfvVQfR1DZ1aaKVjw", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "89847d03-80d7-491f-a3ad-0a684f8cf06a", + "tool_call_id": "toolu_012V6ewLfvVQfR1DZ1aaKVjw", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 7) - implementing the `_AgentBuilder._setup_hooks` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_013aTeUh9UQMKbW8vtSn9uuP", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [699,730]}" + } + ], + "additional_kwargs": { + "id": "msg_013TSUvR9hDWRnNoL8Ky3jGc", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 909, + "cache_read_input_tokens": 49641, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_013aTeUh9UQMKbW8vtSn9uuP", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [699,730]}" + } + ], + "id": "msg_013TSUvR9hDWRnNoL8Ky3jGc", + "usage_metadata": { + "input_tokens": 1175, + "output_tokens": 179, + "total_tokens": 1354, + "input_token_details": { + "cache_read": 49641, + "cache_creation": 909 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [699, 730] + }, + "id": "toolu_013aTeUh9UQMKbW8vtSn9uuP", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "4bcdc4a7-d16a-4727-a32d-7ba4b2f2e55a", + "tool_call_id": "toolu_013aTeUh9UQMKbW8vtSn9uuP", + "content": "699: def _setup_hooks(self, workflow: StateGraph) -> str:\n700: \"\"\"Add pre/post model hook nodes and return entrypoint.\"\"\"\n701: # Add pre_model_hook node with edge to agent if provided\n702: if self.pre_model_hook is not None:\n703: workflow.add_node(\"pre_model_hook\", self.pre_model_hook) # type: ignore[arg-type]\n704: workflow.add_edge(\"pre_model_hook\", \"agent\")\n705: entrypoint = \"pre_model_hook\"\n706: else:\n707: entrypoint = \"agent\"\n708: \n709: # Add post_model_hook node with conditional routing if provided\n710: if self.post_model_hook is not None:\n711: workflow.add_node(\"post_model_hook\", self.post_model_hook) # type: ignore[arg-type]\n712: \n713: def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n714: \"\"\"Route to the next node after post_model_hook.\n715: \n716: Routes to one of:\n717: * \"tools\": if there are pending tool calls without a corresponding message.\n718: * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\n719: * END: if no pending tool calls exist and no response_format is specified.\n720: \"\"\"\n721: messages = _get_state_value(state, \"messages\")\n722: tool_messages = [\n723: m.tool_call_id for m in messages if isinstance(m, ToolMessage)\n724: ]\n725: last_ai_message = next(\n726: m for m in reversed(messages) if isinstance(m, AIMessage)\n727: )\n728: pending_tool_calls = [\n729: c for c in last_ai_message.tool_calls if c[\"id\"] not in tool_messages\n730: ]", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 7) - implementing the `_AgentBuilder._setup_hooks` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01Q9YkqQ7BG6q267w8Deg8m1", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [730,760]}" + } + ], + "additional_kwargs": { + "id": "msg_012Gdf5msESizgrstimJhnzm", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 701, + "cache_read_input_tokens": 50550, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01Q9YkqQ7BG6q267w8Deg8m1", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [730,760]}" + } + ], + "id": "msg_012Gdf5msESizgrstimJhnzm", + "usage_metadata": { + "input_tokens": 1175, + "output_tokens": 179, + "total_tokens": 1354, + "input_token_details": { + "cache_read": 50550, + "cache_creation": 701 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [730, 760] + }, + "id": "toolu_01Q9YkqQ7BG6q267w8Deg8m1", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "2dac27f8-e50d-4838-954b-6107449c8ee8", + "tool_call_id": "toolu_01Q9YkqQ7BG6q267w8Deg8m1", + "content": "730: ]\n731: \n732: # Handle pending tool calls\n733: if pending_tool_calls:\n734: return [\n735: Send(\n736: \"tools\",\n737: ToolCallWithContext(\n738: __type=\"tool_call_with_context\",\n739: tool_call=tool_call,\n740: state=state,\n741: ),\n742: )\n743: for tool_call in pending_tool_calls\n744: ]\n745: # Structured response generation\n746: elif isinstance(messages[-1], ToolMessage):\n747: return entrypoint\n748: elif self.response_format is not None:\n749: return \"generate_structured_response\"\n750: # END routing based on state\n751: else:\n752: return END\n753: \n754: # Store the router function for use in build method\n755: self.post_model_hook_router = post_model_hook_router\n756: \n757: return entrypoint\n758: \n759: def build(self) -> CompiledStateGraph:\n760: \"\"\"Assemble the complete graph based on all configuration options.\"\"\"", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 7) - implementing the `_AgentBuilder._setup_hooks` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **Add pre_model_hook node with edge to agent if provided** - Done:\n- Checks if `self.pre_model_hook` is not None\n- Adds \"pre_model_hook\" node to workflow\n- Adds edge from \"pre_model_hook\" to \"agent\"\n- Sets entrypoint to \"pre_model_hook\" if provided, otherwise \"agent\"\n\n✅ **Add post_model_hook node with conditional routing** - Done:\n- Checks if `self.post_model_hook` is not None\n- Adds \"post_model_hook\" node to workflow\n- Creates `post_model_hook_router` function for conditional routing\n\n✅ **post_model_hook_router function handles pending tool calls** - Done:\n- Extracts messages and finds tool_messages by tool_call_id\n- Finds last AI message and identifies pending tool calls\n- Returns list of Send objects for pending tool calls using ToolCallWithContext\n\n✅ **Structured response generation** - Done:\n- Routes to entrypoint if last message is ToolMessage\n- Routes to \"generate_structured_response\" if response_format is not None\n\n✅ **END routing based on state** - Done:\n- Routes to END if no pending tool calls and no response_format\n\n✅ **Store router function and return entrypoint** - Done:\n- Stores `post_model_hook_router` as instance variable for use in build method\n- Returns the determined entrypoint\n\nThe implementation correctly extracts and implements all the hook setup logic from the original `create_react_agent` function, including proper conditional routing for post_model_hook that handles all the complex scenarios." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01QHCjuUdHypxLbT9VHh4fXf", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._setup_hooks` method with all required functionality. The method adds a pre_model_hook node with edge to agent if provided, setting the entrypoint appropriately. It also adds a post_model_hook node with conditional routing via a post_model_hook_router function that handles pending tool calls by identifying tool calls without corresponding ToolMessages and returning Send objects for parallel execution, manages structured response generation by routing to generate_structured_response when response_format is present, and provides END routing based on state when no pending tool calls or structured response is needed. The router function is stored as an instance variable for use in the build method. The method faithfully extracts and replicates all the hook setup logic from the original create_react_agent function, including the complex conditional routing scenarios for post_model_hook.\"}" + } + ], + "additional_kwargs": { + "id": "msg_017QgxsxV1JT4eoCj4NRhf8P", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 504, + "cache_read_input_tokens": 51251, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01QHCjuUdHypxLbT9VHh4fXf", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder._setup_hooks` method with all required functionality. The method adds a pre_model_hook node with edge to agent if provided, setting the entrypoint appropriately. It also adds a post_model_hook node with conditional routing via a post_model_hook_router function that handles pending tool calls by identifying tool calls without corresponding ToolMessages and returning Send objects for parallel execution, manages structured response generation by routing to generate_structured_response when response_format is present, and provides END routing based on state when no pending tool calls or structured response is needed. The router function is stored as an instance variable for use in the build method. The method faithfully extracts and replicates all the hook setup logic from the original create_react_agent function, including the complex conditional routing scenarios for post_model_hook.\"}" + } + ], + "id": "msg_017QgxsxV1JT4eoCj4NRhf8P", + "usage_metadata": { + "input_tokens": 1175, + "output_tokens": 671, + "total_tokens": 1846, + "input_token_details": { + "cache_read": 51251, + "cache_creation": 504 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder._setup_hooks` method with all required functionality. The method adds a pre_model_hook node with edge to agent if provided, setting the entrypoint appropriately. It also adds a post_model_hook node with conditional routing via a post_model_hook_router function that handles pending tool calls by identifying tool calls without corresponding ToolMessages and returning Send objects for parallel execution, manages structured response generation by routing to generate_structured_response when response_format is present, and provides END routing based on state when no pending tool calls or structured response is needed. The router function is stored as an instance variable for use in the build method. The method faithfully extracts and replicates all the hook setup logic from the original create_react_agent function, including the complex conditional routing scenarios for post_model_hook." + }, + "id": "toolu_01QHCjuUdHypxLbT9VHh4fXf", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "b26aebc9-dbf7-4eb8-ac79-fb7df34e448c", + "tool_call_id": "toolu_01QHCjuUdHypxLbT9VHh4fXf", + "content": "Saved task status as completed for task Implement _AgentBuilder._setup_hooks method to add pre_model_hook node with edge to agent if provided, add post_model_hook node with conditional routing (post_model_hook_router function) that handles pending tool calls, structured response generation, and END routing based on state", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder.build` method as outlined in the current task. This is the final method that assembles the complete graph based on all configuration options. Let me examine the original implementation to extract the relevant logic for creating the StateGraph and compiling it." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_017iCr6LQEyx2jQxR9KRAhV5", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [759,770]}" + } + ], + "additional_kwargs": { + "id": "msg_01NwciWWcg78DMKZx1MkUXau", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 752, + "cache_read_input_tokens": 51755, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_017iCr6LQEyx2jQxR9KRAhV5", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [759,770]}" + } + ], + "id": "msg_01NwciWWcg78DMKZx1MkUXau", + "usage_metadata": { + "input_tokens": 1175, + "output_tokens": 193, + "total_tokens": 1368, + "input_token_details": { + "cache_read": 51755, + "cache_creation": 752 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [759, 770] + }, + "id": "toolu_017iCr6LQEyx2jQxR9KRAhV5", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "a5274f34-8e23-4141-b8e8-a6220f51f12a", + "tool_call_id": "toolu_017iCr6LQEyx2jQxR9KRAhV5", + "content": "759: def build(self) -> CompiledStateGraph:\n760: \"\"\"Assemble the complete graph based on all configuration options.\"\"\"\n761: # Implementation will be added in next task\n762: pass\n763: \n764: \n765: def create_react_agent(\n766: model: Union[\n767: str,\n768: LanguageModelLike,\n769: Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],\n770: Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder.build` method as outlined in the current task. This is the final method that assembles the complete graph based on all configuration options. Let me examine the original implementation to extract the relevant logic for creating the StateGraph and compiling it." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01P82vzom5CC4owGEv7SvMnc", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1068,1120]}" + } + ], + "additional_kwargs": { + "id": "msg_01JMR2cECF25kZvfXQYexpkC", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 342, + "cache_read_input_tokens": 52507, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01P82vzom5CC4owGEv7SvMnc", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1068,1120]}" + } + ], + "id": "msg_01JMR2cECF25kZvfXQYexpkC", + "usage_metadata": { + "input_tokens": 1175, + "output_tokens": 195, + "total_tokens": 1370, + "input_token_details": { + "cache_read": 52507, + "cache_creation": 342 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1068, 1120] + }, + "id": "toolu_01P82vzom5CC4owGEv7SvMnc", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "6b0b2be6-c339-4eb4-b4a3-4c45a09de0b6", + "tool_call_id": "toolu_01P82vzom5CC4owGEv7SvMnc", + "content": "1068: return static_model\n1069: \n1070: def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\n1071: has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\n1072: all_tools_return_direct = (\n1073: all(call[\"name\"] in should_return_direct for call in response.tool_calls)\n1074: if isinstance(response, AIMessage)\n1075: else False\n1076: )\n1077: remaining_steps = _get_state_value(state, \"remaining_steps\", None)\n1078: is_last_step = _get_state_value(state, \"is_last_step\", False)\n1079: return (\n1080: (remaining_steps is None and is_last_step and has_tool_calls)\n1081: or (\n1082: remaining_steps is not None\n1083: and remaining_steps < 1\n1084: and all_tools_return_direct\n1085: )\n1086: or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\n1087: )\n1088: \n1089: def _get_model_input_state(state: StateSchema) -> StateSchema:\n1090: if pre_model_hook is not None:\n1091: messages = (\n1092: _get_state_value(state, \"llm_input_messages\")\n1093: ) or _get_state_value(state, \"messages\")\n1094: error_msg = f\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\"\n1095: else:\n1096: messages = _get_state_value(state, \"messages\")\n1097: error_msg = (\n1098: f\"Expected input to call_model to have 'messages' key, but got {state}\"\n1099: )\n1100: \n1101: if messages is None:\n1102: raise ValueError(error_msg)\n1103: \n1104: _validate_chat_history(messages)\n1105: # we're passing messages under `messages` key, as this is expected by the prompt\n1106: if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\n1107: state.messages = messages # type: ignore\n1108: else:\n1109: state[\"messages\"] = messages # type: ignore\n1110: \n1111: return state\n1112: \n1113: # Define the function that calls the model\n1114: def call_model(\n1115: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n1116: ) -> StateSchema:\n1117: if is_async_dynamic_model:\n1118: msg = (\n1119: \"Async model callable provided but agent invoked synchronously. \"\n1120: \"Use agent.ainvoke() or agent.astream(), or \"", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder.build` method as outlined in the current task. This is the final method that assembles the complete graph based on all configuration options. Let me examine the original implementation to extract the relevant logic for creating the StateGraph and compiling it." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_018F2inrZdzbV8nUkvz77gV5", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1200,1280]}" + } + ], + "additional_kwargs": { + "id": "msg_01RsSEU3ixfNWmZPKRHC6Roo", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 981, + "cache_read_input_tokens": 52849, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_018F2inrZdzbV8nUkvz77gV5", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1200,1280]}" + } + ], + "id": "msg_01RsSEU3ixfNWmZPKRHC6Roo", + "usage_metadata": { + "input_tokens": 1174, + "output_tokens": 195, + "total_tokens": 1369, + "input_token_details": { + "cache_read": 52849, + "cache_creation": 981 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1200, 1280] + }, + "id": "toolu_018F2inrZdzbV8nUkvz77gV5", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "9eb56a65-8247-42c8-8448-4e98bdb9148c", + "tool_call_id": "toolu_018F2inrZdzbV8nUkvz77gV5", + "content": "1200: if is_async_dynamic_model:\n1201: msg = (\n1202: \"Async model callable provided but agent invoked synchronously. \"\n1203: \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\n1204: )\n1205: raise RuntimeError(msg)\n1206: \n1207: messages = _get_state_value(state, \"messages\")\n1208: structured_response_schema = response_format\n1209: if isinstance(response_format, tuple):\n1210: system_prompt, structured_response_schema = response_format\n1211: messages = [SystemMessage(content=system_prompt)] + list(messages)\n1212: \n1213: resolved_model = _resolve_model(state, runtime)\n1214: model_with_structured_output = _get_model(\n1215: resolved_model\n1216: ).with_structured_output(\n1217: cast(StructuredResponseSchema, structured_response_schema)\n1218: )\n1219: response = model_with_structured_output.invoke(messages, config)\n1220: return {\"structured_response\": response}\n1221: \n1222: async def agenerate_structured_response(\n1223: state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n1224: ) -> StateSchema:\n1225: messages = _get_state_value(state, \"messages\")\n1226: structured_response_schema = response_format\n1227: if isinstance(response_format, tuple):\n1228: system_prompt, structured_response_schema = response_format\n1229: messages = [SystemMessage(content=system_prompt)] + list(messages)\n1230: \n1231: resolved_model = await _aresolve_model(state, runtime)\n1232: model_with_structured_output = _get_model(\n1233: resolved_model\n1234: ).with_structured_output(\n1235: cast(StructuredResponseSchema, structured_response_schema)\n1236: )\n1237: response = await model_with_structured_output.ainvoke(messages, config)\n1238: return {\"structured_response\": response}\n1239: \n1240: if not tool_calling_enabled:\n1241: # Define a new graph\n1242: workflow = StateGraph(state_schema=state_schema, context_schema=context_schema)\n1243: workflow.add_node(\n1244: \"agent\",\n1245: RunnableCallable(call_model, acall_model),\n1246: input_schema=input_schema,\n1247: )\n1248: if pre_model_hook is not None:\n1249: workflow.add_node(\"pre_model_hook\", pre_model_hook) # type: ignore[arg-type]\n1250: workflow.add_edge(\"pre_model_hook\", \"agent\")\n1251: entrypoint = \"pre_model_hook\"\n1252: else:\n1253: entrypoint = \"agent\"\n1254: \n1255: workflow.set_entry_point(entrypoint)\n1256: \n1257: if post_model_hook is not None:\n1258: workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]\n1259: workflow.add_edge(\"agent\", \"post_model_hook\")\n1260: \n1261: if response_format is not None:\n1262: workflow.add_node(\n1263: \"generate_structured_response\",\n1264: RunnableCallable(\n1265: generate_structured_response,\n1266: agenerate_structured_response,\n1267: ),\n1268: )\n1269: if post_model_hook is not None:\n1270: workflow.add_edge(\"post_model_hook\", \"generate_structured_response\")\n1271: else:\n1272: workflow.add_edge(\"agent\", \"generate_structured_response\")\n1273: \n1274: return workflow.compile(\n1275: checkpointer=checkpointer,\n1276: store=store,\n1277: interrupt_before=interrupt_before,\n1278: interrupt_after=interrupt_after,\n1279: debug=debug,\n1280: name=name,", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder.build` method as outlined in the current task. This is the final method that assembles the complete graph based on all configuration options. Let me examine the original implementation to extract the relevant logic for creating the StateGraph and compiling it." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01US9nFuJ3WMbDtRFF5YkDjS", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1280,1380]}" + } + ], + "additional_kwargs": { + "id": "msg_01UxyDBjM1rbm8cnpqgoDGPi", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1321, + "cache_read_input_tokens": 53830, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01US9nFuJ3WMbDtRFF5YkDjS", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1280,1380]}" + } + ], + "id": "msg_01UxyDBjM1rbm8cnpqgoDGPi", + "usage_metadata": { + "input_tokens": 1174, + "output_tokens": 195, + "total_tokens": 1369, + "input_token_details": { + "cache_read": 53830, + "cache_creation": 1321 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1280, 1380] + }, + "id": "toolu_01US9nFuJ3WMbDtRFF5YkDjS", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "eae858c3-0d51-44e9-b488-0277b2632f43", + "tool_call_id": "toolu_01US9nFuJ3WMbDtRFF5YkDjS", + "content": "1280: name=name,\n1281: )\n1282: \n1283: # Define the function that determines whether to continue or not\n1284: def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n1285: messages = _get_state_value(state, \"messages\")\n1286: last_message = messages[-1]\n1287: # If there is no function call, then we finish\n1288: if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n1289: if post_model_hook is not None:\n1290: return \"post_model_hook\"\n1291: elif response_format is not None:\n1292: return \"generate_structured_response\"\n1293: else:\n1294: return END\n1295: # Otherwise if there is, we continue\n1296: else:\n1297: if version == \"v1\":\n1298: return \"tools\"\n1299: elif version == \"v2\":\n1300: if post_model_hook is not None:\n1301: return \"post_model_hook\"\n1302: return [\n1303: Send(\n1304: \"tools\",\n1305: ToolCallWithContext(\n1306: __type=\"tool_call_with_context\",\n1307: tool_call=tool_call,\n1308: state=state,\n1309: ),\n1310: )\n1311: for tool_call in last_message.tool_calls\n1312: ]\n1313: \n1314: # Define a new graph\n1315: workflow = StateGraph(\n1316: state_schema=state_schema or AgentState, context_schema=context_schema\n1317: )\n1318: \n1319: # Define the two nodes we will cycle between\n1320: workflow.add_node(\n1321: \"agent\",\n1322: RunnableCallable(call_model, acall_model),\n1323: input_schema=input_schema,\n1324: )\n1325: workflow.add_node(\"tools\", tool_node)\n1326: \n1327: # Optionally add a pre-model hook node that will be called\n1328: # every time before the \"agent\" (LLM-calling node)\n1329: if pre_model_hook is not None:\n1330: workflow.add_node(\"pre_model_hook\", pre_model_hook) # type: ignore[arg-type]\n1331: workflow.add_edge(\"pre_model_hook\", \"agent\")\n1332: entrypoint = \"pre_model_hook\"\n1333: else:\n1334: entrypoint = \"agent\"\n1335: \n1336: # Set the entrypoint as `agent`\n1337: # This means that this node is the first one called\n1338: workflow.set_entry_point(entrypoint)\n1339: \n1340: agent_paths = []\n1341: post_model_hook_paths = [entrypoint, \"tools\"]\n1342: \n1343: # Add a post model hook node if post_model_hook is provided\n1344: if post_model_hook is not None:\n1345: workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]\n1346: agent_paths.append(\"post_model_hook\")\n1347: workflow.add_edge(\"agent\", \"post_model_hook\")\n1348: else:\n1349: agent_paths.append(\"tools\")\n1350: \n1351: # Add a structured output node if response_format is provided\n1352: if response_format is not None:\n1353: workflow.add_node(\n1354: \"generate_structured_response\",\n1355: RunnableCallable(\n1356: generate_structured_response,\n1357: agenerate_structured_response,\n1358: ),\n1359: )\n1360: if post_model_hook is not None:\n1361: post_model_hook_paths.append(\"generate_structured_response\")\n1362: else:\n1363: agent_paths.append(\"generate_structured_response\")\n1364: else:\n1365: if post_model_hook is not None:\n1366: post_model_hook_paths.append(END)\n1367: else:\n1368: agent_paths.append(END)\n1369: \n1370: if post_model_hook is not None:\n1371: \n1372: def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n1373: \"\"\"Route to the next node after post_model_hook.\n1374: \n1375: Routes to one of:\n1376: * \"tools\": if there are pending tool calls without a corresponding message.\n1377: * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\n1378: * END: if no pending tool calls exist and no response_format is specified.\n1379: \"\"\"\n1380: ", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to implement the `_AgentBuilder.build` method as outlined in the current task. This is the final method that assembles the complete graph based on all configuration options. Let me examine the original implementation and implement this method." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_014PPoc3azxxkjs7hZmWqJFG", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def build(self) -> CompiledStateGraph:\\n \\\"\\\"\\\"Assemble the complete graph based on all configuration options.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def build(self) -> CompiledStateGraph:\\n \\\"\\\"\\\"Assemble the complete graph based on all configuration options.\\\"\\\"\\\"\\n # Setup model and tools first\\n self._setup_model_and_tools()\\n \\n # Handle tool-calling vs non-tool-calling workflows\\n if not self.tool_calling_enabled:\\n # Create StateGraph with proper schema\\n workflow = StateGraph(state_schema=self.state_schema, context_schema=self.context_schema)\\n \\n # Add agent node with _create_model_node\\n agent_node = self._create_model_node()\\n workflow.add_node(\\\"agent\\\", agent_node, input_schema=agent_node.input_schema)\\n \\n # Set up hooks and get entrypoint\\n entrypoint = self._setup_hooks(workflow)\\n workflow.set_entry_point(entrypoint)\\n\\n # Add structured response node if needed\\n structured_response_node = self._create_structured_response_node()\\n if structured_response_node is not None:\\n workflow.add_node(\\\"generate_structured_response\\\", structured_response_node)\\n if self.post_model_hook is not None:\\n workflow.add_edge(\\\"post_model_hook\\\", \\\"generate_structured_response\\\")\\n else:\\n workflow.add_edge(\\\"agent\\\", \\\"generate_structured_response\\\")\\n\\n # Compile with all provided options\\n return workflow.compile(\\n checkpointer=self.checkpointer,\\n store=self.store,\\n interrupt_before=self.interrupt_before,\\n interrupt_after=self.interrupt_after,\\n debug=self.debug,\\n name=self.name,\\n )\\n\\n # Tool-calling workflow\\n # Create StateGraph with proper schema\\n workflow = StateGraph(\\n state_schema=self.state_schema or AgentState, \\n context_schema=self.context_schema\\n )\\n\\n # Add agent node with _create_model_node\\n agent_node = self._create_model_node()\\n workflow.add_node(\\\"agent\\\", agent_node, input_schema=agent_node.input_schema)\\n \\n # Add tools node\\n workflow.add_node(\\\"tools\\\", self.tool_node)\\n\\n # Set up hooks and get entrypoint\\n entrypoint = self._setup_hooks(workflow)\\n workflow.set_entry_point(entrypoint)\\n\\n # Set up path mappings for conditional edges\\n agent_paths = []\\n post_model_hook_paths = [entrypoint, \\\"tools\\\"]\\n\\n # Configure agent paths based on post_model_hook\\n if self.post_model_hook is not None:\\n agent_paths.append(\\\"post_model_hook\\\")\\n else:\\n agent_paths.append(\\\"tools\\\")\\n\\n # Add structured response node if needed\\n structured_response_node = self._create_structured_response_node()\\n if structured_response_node is not None:\\n workflow.add_node(\\\"generate_structured_response\\\", structured_response_node)\\n if self.post_model_hook is not None:\\n post_model_hook_paths.append(\\\"generate_structured_response\\\")\\n else:\\n agent_paths.append(\\\"generate_structured_response\\\")\\n else:\\n if self.post_model_hook is not None:\\n post_model_hook_paths.append(END)\\n else:\\n agent_paths.append(END)\\n\\n # Set up all conditional edges with proper path mappings\\n # Add conditional edges for agent node\\n should_continue = self._create_model_router()\\n workflow.add_conditional_edges(\\n \\\"agent\\\",\\n should_continue,\\n path_map=agent_paths,\\n )\\n\\n # Add conditional edges for post_model_hook if present\\n if self.post_model_hook is not None:\\n workflow.add_conditional_edges(\\n \\\"post_model_hook\\\",\\n self.post_model_hook_router,\\n path_map=post_model_hook_paths,\\n )\\n\\n # Add conditional edges for tools node based on return_direct\\n tools_router = self._create_tools_router()\\n if tools_router is not None:\\n workflow.add_conditional_edges(\\n \\\"tools\\\", \\n tools_router, \\n path_map=[entrypoint, END]\\n )\\n else:\\n workflow.add_edge(\\\"tools\\\", entrypoint)\\n\\n # Compile with all provided options\\n return workflow.compile(\\n checkpointer=self.checkpointer,\\n store=self.store,\\n interrupt_before=self.interrupt_before,\\n interrupt_after=self.interrupt_after,\\n debug=self.debug,\\n name=self.name,\\n )\"}" + } + ], + "additional_kwargs": { + "id": "msg_01Qb93tPaCrHSjT1assUDNZh", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1520, + "cache_read_input_tokens": 55151, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_014PPoc3azxxkjs7hZmWqJFG", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" def build(self) -> CompiledStateGraph:\\n \\\"\\\"\\\"Assemble the complete graph based on all configuration options.\\\"\\\"\\\"\\n # Implementation will be added in next task\\n pass\", \"new_str\": \" def build(self) -> CompiledStateGraph:\\n \\\"\\\"\\\"Assemble the complete graph based on all configuration options.\\\"\\\"\\\"\\n # Setup model and tools first\\n self._setup_model_and_tools()\\n \\n # Handle tool-calling vs non-tool-calling workflows\\n if not self.tool_calling_enabled:\\n # Create StateGraph with proper schema\\n workflow = StateGraph(state_schema=self.state_schema, context_schema=self.context_schema)\\n \\n # Add agent node with _create_model_node\\n agent_node = self._create_model_node()\\n workflow.add_node(\\\"agent\\\", agent_node, input_schema=agent_node.input_schema)\\n \\n # Set up hooks and get entrypoint\\n entrypoint = self._setup_hooks(workflow)\\n workflow.set_entry_point(entrypoint)\\n\\n # Add structured response node if needed\\n structured_response_node = self._create_structured_response_node()\\n if structured_response_node is not None:\\n workflow.add_node(\\\"generate_structured_response\\\", structured_response_node)\\n if self.post_model_hook is not None:\\n workflow.add_edge(\\\"post_model_hook\\\", \\\"generate_structured_response\\\")\\n else:\\n workflow.add_edge(\\\"agent\\\", \\\"generate_structured_response\\\")\\n\\n # Compile with all provided options\\n return workflow.compile(\\n checkpointer=self.checkpointer,\\n store=self.store,\\n interrupt_before=self.interrupt_before,\\n interrupt_after=self.interrupt_after,\\n debug=self.debug,\\n name=self.name,\\n )\\n\\n # Tool-calling workflow\\n # Create StateGraph with proper schema\\n workflow = StateGraph(\\n state_schema=self.state_schema or AgentState, \\n context_schema=self.context_schema\\n )\\n\\n # Add agent node with _create_model_node\\n agent_node = self._create_model_node()\\n workflow.add_node(\\\"agent\\\", agent_node, input_schema=agent_node.input_schema)\\n \\n # Add tools node\\n workflow.add_node(\\\"tools\\\", self.tool_node)\\n\\n # Set up hooks and get entrypoint\\n entrypoint = self._setup_hooks(workflow)\\n workflow.set_entry_point(entrypoint)\\n\\n # Set up path mappings for conditional edges\\n agent_paths = []\\n post_model_hook_paths = [entrypoint, \\\"tools\\\"]\\n\\n # Configure agent paths based on post_model_hook\\n if self.post_model_hook is not None:\\n agent_paths.append(\\\"post_model_hook\\\")\\n else:\\n agent_paths.append(\\\"tools\\\")\\n\\n # Add structured response node if needed\\n structured_response_node = self._create_structured_response_node()\\n if structured_response_node is not None:\\n workflow.add_node(\\\"generate_structured_response\\\", structured_response_node)\\n if self.post_model_hook is not None:\\n post_model_hook_paths.append(\\\"generate_structured_response\\\")\\n else:\\n agent_paths.append(\\\"generate_structured_response\\\")\\n else:\\n if self.post_model_hook is not None:\\n post_model_hook_paths.append(END)\\n else:\\n agent_paths.append(END)\\n\\n # Set up all conditional edges with proper path mappings\\n # Add conditional edges for agent node\\n should_continue = self._create_model_router()\\n workflow.add_conditional_edges(\\n \\\"agent\\\",\\n should_continue,\\n path_map=agent_paths,\\n )\\n\\n # Add conditional edges for post_model_hook if present\\n if self.post_model_hook is not None:\\n workflow.add_conditional_edges(\\n \\\"post_model_hook\\\",\\n self.post_model_hook_router,\\n path_map=post_model_hook_paths,\\n )\\n\\n # Add conditional edges for tools node based on return_direct\\n tools_router = self._create_tools_router()\\n if tools_router is not None:\\n workflow.add_conditional_edges(\\n \\\"tools\\\", \\n tools_router, \\n path_map=[entrypoint, END]\\n )\\n else:\\n workflow.add_edge(\\\"tools\\\", entrypoint)\\n\\n # Compile with all provided options\\n return workflow.compile(\\n checkpointer=self.checkpointer,\\n store=self.store,\\n interrupt_before=self.interrupt_before,\\n interrupt_after=self.interrupt_after,\\n debug=self.debug,\\n name=self.name,\\n )\"}" + } + ], + "id": "msg_01Qb93tPaCrHSjT1assUDNZh", + "usage_metadata": { + "input_tokens": 1173, + "output_tokens": 1377, + "total_tokens": 2550, + "input_token_details": { + "cache_read": 55151, + "cache_creation": 1520 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " def build(self) -> CompiledStateGraph:\n \"\"\"Assemble the complete graph based on all configuration options.\"\"\"\n # Implementation will be added in next task\n pass", + "new_str": " def build(self) -> CompiledStateGraph:\n \"\"\"Assemble the complete graph based on all configuration options.\"\"\"\n # Setup model and tools first\n self._setup_model_and_tools()\n \n # Handle tool-calling vs non-tool-calling workflows\n if not self.tool_calling_enabled:\n # Create StateGraph with proper schema\n workflow = StateGraph(state_schema=self.state_schema, context_schema=self.context_schema)\n \n # Add agent node with _create_model_node\n agent_node = self._create_model_node()\n workflow.add_node(\"agent\", agent_node, input_schema=agent_node.input_schema)\n \n # Set up hooks and get entrypoint\n entrypoint = self._setup_hooks(workflow)\n workflow.set_entry_point(entrypoint)\n\n # Add structured response node if needed\n structured_response_node = self._create_structured_response_node()\n if structured_response_node is not None:\n workflow.add_node(\"generate_structured_response\", structured_response_node)\n if self.post_model_hook is not None:\n workflow.add_edge(\"post_model_hook\", \"generate_structured_response\")\n else:\n workflow.add_edge(\"agent\", \"generate_structured_response\")\n\n # Compile with all provided options\n return workflow.compile(\n checkpointer=self.checkpointer,\n store=self.store,\n interrupt_before=self.interrupt_before,\n interrupt_after=self.interrupt_after,\n debug=self.debug,\n name=self.name,\n )\n\n # Tool-calling workflow\n # Create StateGraph with proper schema\n workflow = StateGraph(\n state_schema=self.state_schema or AgentState, \n context_schema=self.context_schema\n )\n\n # Add agent node with _create_model_node\n agent_node = self._create_model_node()\n workflow.add_node(\"agent\", agent_node, input_schema=agent_node.input_schema)\n \n # Add tools node\n workflow.add_node(\"tools\", self.tool_node)\n\n # Set up hooks and get entrypoint\n entrypoint = self._setup_hooks(workflow)\n workflow.set_entry_point(entrypoint)\n\n # Set up path mappings for conditional edges\n agent_paths = []\n post_model_hook_paths = [entrypoint, \"tools\"]\n\n # Configure agent paths based on post_model_hook\n if self.post_model_hook is not None:\n agent_paths.append(\"post_model_hook\")\n else:\n agent_paths.append(\"tools\")\n\n # Add structured response node if needed\n structured_response_node = self._create_structured_response_node()\n if structured_response_node is not None:\n workflow.add_node(\"generate_structured_response\", structured_response_node)\n if self.post_model_hook is not None:\n post_model_hook_paths.append(\"generate_structured_response\")\n else:\n agent_paths.append(\"generate_structured_response\")\n else:\n if self.post_model_hook is not None:\n post_model_hook_paths.append(END)\n else:\n agent_paths.append(END)\n\n # Set up all conditional edges with proper path mappings\n # Add conditional edges for agent node\n should_continue = self._create_model_router()\n workflow.add_conditional_edges(\n \"agent\",\n should_continue,\n path_map=agent_paths,\n )\n\n # Add conditional edges for post_model_hook if present\n if self.post_model_hook is not None:\n workflow.add_conditional_edges(\n \"post_model_hook\",\n self.post_model_hook_router,\n path_map=post_model_hook_paths,\n )\n\n # Add conditional edges for tools node based on return_direct\n tools_router = self._create_tools_router()\n if tools_router is not None:\n workflow.add_conditional_edges(\n \"tools\", \n tools_router, \n path_map=[entrypoint, END]\n )\n else:\n workflow.add_edge(\"tools\", entrypoint)\n\n # Compile with all provided options\n return workflow.compile(\n checkpointer=self.checkpointer,\n store=self.store,\n interrupt_before=self.interrupt_before,\n interrupt_after=self.interrupt_after,\n debug=self.debug,\n name=self.name,\n )" + }, + "id": "toolu_014PPoc3azxxkjs7hZmWqJFG", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "533e734e-2732-4a42-92c1-35ef98a90b9a", + "tool_call_id": "toolu_014PPoc3azxxkjs7hZmWqJFG", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 8) - implementing the `_AgentBuilder.build` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_012jwc4vQ4mR7yX4DGHtM5NA", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [759,780]}" + } + ], + "additional_kwargs": { + "id": "msg_01SzcSpwEuTPBJfkJAgKdpC7", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1428, + "cache_read_input_tokens": 56671, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_012jwc4vQ4mR7yX4DGHtM5NA", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [759,780]}" + } + ], + "id": "msg_01SzcSpwEuTPBJfkJAgKdpC7", + "usage_metadata": { + "input_tokens": 1172, + "output_tokens": 177, + "total_tokens": 1349, + "input_token_details": { + "cache_read": 56671, + "cache_creation": 1428 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [759, 780] + }, + "id": "toolu_012jwc4vQ4mR7yX4DGHtM5NA", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "beab573c-188c-4035-b7d6-1dd8604a3298", + "tool_call_id": "toolu_012jwc4vQ4mR7yX4DGHtM5NA", + "content": "759: def build(self) -> CompiledStateGraph:\n760: \"\"\"Assemble the complete graph based on all configuration options.\"\"\"\n761: # Setup model and tools first\n762: self._setup_model_and_tools()\n763: \n764: # Handle tool-calling vs non-tool-calling workflows\n765: if not self.tool_calling_enabled:\n766: # Create StateGraph with proper schema\n767: workflow = StateGraph(state_schema=self.state_schema, context_schema=self.context_schema)\n768: \n769: # Add agent node with _create_model_node\n770: agent_node = self._create_model_node()\n771: workflow.add_node(\"agent\", agent_node, input_schema=agent_node.input_schema)\n772: \n773: # Set up hooks and get entrypoint\n774: entrypoint = self._setup_hooks(workflow)\n775: workflow.set_entry_point(entrypoint)\n776: \n777: # Add structured response node if needed\n778: structured_response_node = self._create_structured_response_node()\n779: if structured_response_node is not None:\n780: workflow.add_node(\"generate_structured_response\", structured_response_node)", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 8) - implementing the `_AgentBuilder.build` method. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_011APb8YTVsrT8fFLBpb1Af5", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [840,870]}" + } + ], + "additional_kwargs": { + "id": "msg_01GSSnaT6Wndgo5mFPsKAK8T", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 502, + "cache_read_input_tokens": 58099, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_011APb8YTVsrT8fFLBpb1Af5", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [840,870]}" + } + ], + "id": "msg_01GSSnaT6Wndgo5mFPsKAK8T", + "usage_metadata": { + "input_tokens": 1171, + "output_tokens": 177, + "total_tokens": 1348, + "input_token_details": { + "cache_read": 58099, + "cache_creation": 502 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [840, 870] + }, + "id": "toolu_011APb8YTVsrT8fFLBpb1Af5", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "db86b9f8-e1b1-41e0-8dea-bb3791c48a64", + "tool_call_id": "toolu_011APb8YTVsrT8fFLBpb1Af5", + "content": "840: should_continue = self._create_model_router()\n841: workflow.add_conditional_edges(\n842: \"agent\",\n843: should_continue,\n844: path_map=agent_paths,\n845: )\n846: \n847: # Add conditional edges for post_model_hook if present\n848: if self.post_model_hook is not None:\n849: workflow.add_conditional_edges(\n850: \"post_model_hook\",\n851: self.post_model_hook_router,\n852: path_map=post_model_hook_paths,\n853: )\n854: \n855: # Add conditional edges for tools node based on return_direct\n856: tools_router = self._create_tools_router()\n857: if tools_router is not None:\n858: workflow.add_conditional_edges(\n859: \"tools\", \n860: tools_router, \n861: path_map=[entrypoint, END]\n862: )\n863: else:\n864: workflow.add_edge(\"tools\", entrypoint)\n865: \n866: # Compile with all provided options\n867: return workflow.compile(\n868: checkpointer=self.checkpointer,\n869: store=self.store,\n870: interrupt_before=self.interrupt_before,", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 8) - implementing the `_AgentBuilder.build` method. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **Create StateGraph with proper schema** - Done:\n- Creates StateGraph with `self.state_schema` and `self.context_schema`\n- Handles both tool-calling and non-tool-calling workflows\n\n✅ **Add agent node with _create_model_node** - Done:\n- Calls `self._create_model_node()` to get the agent node\n- Adds it to workflow with proper input schema\n\n✅ **Handle tool-calling vs non-tool-calling workflows** - Done:\n- Separate logic paths for `self.tool_calling_enabled`\n- Non-tool-calling: simpler workflow without tools node\n- Tool-calling: full workflow with tools node and conditional routing\n\n✅ **Add structured response node if needed** - Done:\n- Calls `self._create_structured_response_node()` \n- Adds node if not None with proper edge connections\n\n✅ **Set up all conditional edges with proper path mappings** - Done:\n- Agent conditional edges with `should_continue` router\n- Post model hook conditional edges if present\n- Tools conditional edges based on return_direct behavior\n- Proper path mappings for all routing scenarios\n\n✅ **Compile with all provided options** - Done:\n- Compiles with `checkpointer`, `store`, `interrupt_before`, `interrupt_after`, `debug`, `name`\n\nThe implementation correctly extracts and implements all the graph assembly logic from the original `create_react_agent` function, including proper handling of both tool-calling and non-tool-calling workflows, conditional edge setup, and compilation with all options." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01JmiFU2k9wEX2rM5V1btCKF", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder.build` method with all required functionality. The method first calls _setup_model_and_tools() to initialize models and tools, then handles both tool-calling and non-tool-calling workflows separately. For non-tool-calling workflows, it creates a simpler StateGraph with agent node, hooks, and optional structured response node. For tool-calling workflows, it creates a full StateGraph with agent and tools nodes, sets up proper path mappings for conditional edges (agent_paths and post_model_hook_paths), adds conditional edges for agent routing using _create_model_router(), post_model_hook routing if present, and tools routing based on return_direct behavior using _create_tools_router(). The method compiles the graph with all provided options (checkpointer, store, interrupt_before, interrupt_after, debug, name) and returns a CompiledStateGraph. The implementation faithfully extracts and replicates all the graph assembly logic from the original create_react_agent function, handling all edge cases and routing scenarios correctly.\"}" + } + ], + "additional_kwargs": { + "id": "msg_01DEXVdQFjaBAmte4Pf4QPxe", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 527, + "cache_read_input_tokens": 58601, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01JmiFU2k9wEX2rM5V1btCKF", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully implemented the `_AgentBuilder.build` method with all required functionality. The method first calls _setup_model_and_tools() to initialize models and tools, then handles both tool-calling and non-tool-calling workflows separately. For non-tool-calling workflows, it creates a simpler StateGraph with agent node, hooks, and optional structured response node. For tool-calling workflows, it creates a full StateGraph with agent and tools nodes, sets up proper path mappings for conditional edges (agent_paths and post_model_hook_paths), adds conditional edges for agent routing using _create_model_router(), post_model_hook routing if present, and tools routing based on return_direct behavior using _create_tools_router(). The method compiles the graph with all provided options (checkpointer, store, interrupt_before, interrupt_after, debug, name) and returns a CompiledStateGraph. The implementation faithfully extracts and replicates all the graph assembly logic from the original create_react_agent function, handling all edge cases and routing scenarios correctly.\"}" + } + ], + "id": "msg_01DEXVdQFjaBAmte4Pf4QPxe", + "usage_metadata": { + "input_tokens": 1171, + "output_tokens": 700, + "total_tokens": 1871, + "input_token_details": { + "cache_read": 58601, + "cache_creation": 527 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully implemented the `_AgentBuilder.build` method with all required functionality. The method first calls _setup_model_and_tools() to initialize models and tools, then handles both tool-calling and non-tool-calling workflows separately. For non-tool-calling workflows, it creates a simpler StateGraph with agent node, hooks, and optional structured response node. For tool-calling workflows, it creates a full StateGraph with agent and tools nodes, sets up proper path mappings for conditional edges (agent_paths and post_model_hook_paths), adds conditional edges for agent routing using _create_model_router(), post_model_hook routing if present, and tools routing based on return_direct behavior using _create_tools_router(). The method compiles the graph with all provided options (checkpointer, store, interrupt_before, interrupt_after, debug, name) and returns a CompiledStateGraph. The implementation faithfully extracts and replicates all the graph assembly logic from the original create_react_agent function, handling all edge cases and routing scenarios correctly." + }, + "id": "toolu_01JmiFU2k9wEX2rM5V1btCKF", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "d1e68b98-5ec6-44ce-8f75-b57514204c9d", + "tool_call_id": "toolu_01JmiFU2k9wEX2rM5V1btCKF", + "content": "Saved task status as completed for task Implement _AgentBuilder.build method to create StateGraph with proper schema, add agent node with _create_model_node, handle tool-calling vs non-tool-calling workflows, add structured response node if needed, set up all conditional edges with proper path mappings, and compile with all provided options (checkpointer, store, interrupts, debug, name)", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me first examine the current function to understand its signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01RuoUjh8ijGWQxeygTYSW8X", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [875,920]}" + } + ], + "additional_kwargs": { + "id": "msg_01LZdcNbiXKVmF23b25sDAY2", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 797, + "cache_read_input_tokens": 59128, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01RuoUjh8ijGWQxeygTYSW8X", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [875,920]}" + } + ], + "id": "msg_01LZdcNbiXKVmF23b25sDAY2", + "usage_metadata": { + "input_tokens": 1170, + "output_tokens": 179, + "total_tokens": 1349, + "input_token_details": { + "cache_read": 59128, + "cache_creation": 797 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [875, 920] + }, + "id": "toolu_01RuoUjh8ijGWQxeygTYSW8X", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "3a02b042-1591-4d79-a59a-a4cfd5ddab89", + "tool_call_id": "toolu_01RuoUjh8ijGWQxeygTYSW8X", + "content": "875: \n876: \n877: def create_react_agent(\n878: model: Union[\n879: str,\n880: LanguageModelLike,\n881: Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],\n882: Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],\n883: ],\n884: tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],\n885: *,\n886: prompt: Optional[Prompt] = None,\n887: response_format: Optional[\n888: Union[StructuredResponseSchema, tuple[str, StructuredResponseSchema]]\n889: ] = None,\n890: pre_model_hook: Optional[RunnableLike] = None,\n891: post_model_hook: Optional[RunnableLike] = None,\n892: state_schema: Optional[StateSchemaType] = None,\n893: context_schema: Optional[Type[Any]] = None,\n894: checkpointer: Optional[Checkpointer] = None,\n895: store: Optional[BaseStore] = None,\n896: interrupt_before: Optional[list[str]] = None,\n897: interrupt_after: Optional[list[str]] = None,\n898: debug: bool = False,\n899: version: Literal[\"v1\", \"v2\"] = \"v2\",\n900: name: Optional[str] = None,\n901: **deprecated_kwargs: Any,\n902: ) -> CompiledStateGraph:\n903: \"\"\"Creates an agent graph that calls tools in a loop until a stopping condition is met.\n904: \n905: For more details on using `create_react_agent`, visit [Agents](https://langchain-ai.github.io/langgraph/agents/overview/) documentation.\n906: \n907: Args:\n908: model: The language model for the agent. Supports static and dynamic\n909: model selection.\n910: \n911: - **Static model**: A chat model instance (e.g., `ChatOpenAI()`) or\n912: string identifier (e.g., `\"openai:gpt-4\"`)\n913: - **Dynamic model**: A callable with signature\n914: `(state, runtime) -> BaseChatModel` that returns different models\n915: based on runtime context\n916: \n917: Dynamic functions receive graph state and runtime, enabling\n918: context-dependent model selection. Must return a `BaseChatModel`\n919: instance. For tool calling, bind tools using `.bind_tools()`.\n920: Bound tools must be a subset of the `tools` parameter.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me first examine the current function to understand its signature and docstring, then replace the body." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01Ttf9rDstEobR8PqXS5fiq4", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [877,950]}" + } + ], + "additional_kwargs": { + "id": "msg_017M8YtEEivJgb9ipgCpzXwS", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 852, + "cache_read_input_tokens": 59925, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01Ttf9rDstEobR8PqXS5fiq4", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [877,950]}" + } + ], + "id": "msg_017M8YtEEivJgb9ipgCpzXwS", + "usage_metadata": { + "input_tokens": 1169, + "output_tokens": 184, + "total_tokens": 1353, + "input_token_details": { + "cache_read": 59925, + "cache_creation": 852 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [877, 950] + }, + "id": "toolu_01Ttf9rDstEobR8PqXS5fiq4", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "d9e8bb80-41ee-411c-920c-92dd0de1c693", + "tool_call_id": "toolu_01Ttf9rDstEobR8PqXS5fiq4", + "content": "877: def create_react_agent(\n878: model: Union[\n879: str,\n880: LanguageModelLike,\n881: Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],\n882: Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],\n883: ],\n884: tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],\n885: *,\n886: prompt: Optional[Prompt] = None,\n887: response_format: Optional[\n888: Union[StructuredResponseSchema, tuple[str, StructuredResponseSchema]]\n889: ] = None,\n890: pre_model_hook: Optional[RunnableLike] = None,\n891: post_model_hook: Optional[RunnableLike] = None,\n892: state_schema: Optional[StateSchemaType] = None,\n893: context_schema: Optional[Type[Any]] = None,\n894: checkpointer: Optional[Checkpointer] = None,\n895: store: Optional[BaseStore] = None,\n896: interrupt_before: Optional[list[str]] = None,\n897: interrupt_after: Optional[list[str]] = None,\n898: debug: bool = False,\n899: version: Literal[\"v1\", \"v2\"] = \"v2\",\n900: name: Optional[str] = None,\n901: **deprecated_kwargs: Any,\n902: ) -> CompiledStateGraph:\n903: \"\"\"Creates an agent graph that calls tools in a loop until a stopping condition is met.\n904: \n905: For more details on using `create_react_agent`, visit [Agents](https://langchain-ai.github.io/langgraph/agents/overview/) documentation.\n906: \n907: Args:\n908: model: The language model for the agent. Supports static and dynamic\n909: model selection.\n910: \n911: - **Static model**: A chat model instance (e.g., `ChatOpenAI()`) or\n912: string identifier (e.g., `\"openai:gpt-4\"`)\n913: - **Dynamic model**: A callable with signature\n914: `(state, runtime) -> BaseChatModel` that returns different models\n915: based on runtime context\n916: \n917: Dynamic functions receive graph state and runtime, enabling\n918: context-dependent model selection. Must return a `BaseChatModel`\n919: instance. For tool calling, bind tools using `.bind_tools()`.\n920: Bound tools must be a subset of the `tools` parameter.\n921: \n922: Dynamic model example:\n923: ```python\n924: from dataclasses import dataclass\n925: \n926: @dataclass\n927: class ModelContext:\n928: model_name: str = \"gpt-3.5-turbo\"\n929: \n930: # Instantiate models globally\n931: gpt4_model = ChatOpenAI(model=\"gpt-4\")\n932: gpt35_model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n933: \n934: def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI:\n935: model_name = runtime.context.model_name\n936: model = gpt4_model if model_name == \"gpt-4\" else gpt35_model\n937: return model.bind_tools(tools)\n938: ```\n939: \n940: !!! note \"Dynamic Model Requirements\"\n941: Ensure returned models have appropriate tools bound via\n942: `.bind_tools()` and support required functionality. Bound tools\n943: must be a subset of those specified in the `tools` parameter.\n944: \n945: tools: A list of tools or a ToolNode instance.\n946: If an empty list is provided, the agent will consist of a single LLM node without tool calling.\n947: prompt: An optional prompt for the LLM. Can take a few different forms:\n948: \n949: - str: This is converted to a SystemMessage and added to the beginning of the list of messages in state[\"messages\"].\n950: - SystemMessage: this is added to the beginning of the list of messages in state[\"messages\"].", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me first find the current function body and then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01NCcwLPcS9fvfYKqNJM2zhE", + "name": "grep", + "input": "{\"query\": \"def create_react_agent\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 5}" + } + ], + "additional_kwargs": { + "id": "msg_017QUKkJQHrofzuTzgMTd6iS", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 1272, + "cache_read_input_tokens": 60777, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01NCcwLPcS9fvfYKqNJM2zhE", + "index": 1, + "name": "grep", + "args": "{\"query\": \"def create_react_agent\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 5}" + } + ], + "id": "msg_017QUKkJQHrofzuTzgMTd6iS", + "usage_metadata": { + "input_tokens": 1169, + "output_tokens": 180, + "total_tokens": 1349, + "input_token_details": { + "cache_read": 60777, + "cache_creation": 1272 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "def create_react_agent", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 5 + }, + "id": "toolu_01NCcwLPcS9fvfYKqNJM2zhE", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "f0a59060-a91d-438e-adde-682cbfe7d289", + "tool_call_id": "toolu_01NCcwLPcS9fvfYKqNJM2zhE", + "content": "libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n872- debug=self.debug,\r\n873- name=self.name,\r\n874- )\r\n875-\r\n876-\r\n877:def create_react_agent(\r\n878- model: Union[\r\n879- str,\r\n880- LanguageModelLike,\r\n881- Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],\r\n882- Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me first find the current function body and then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01BNMSqnZDKcCKADJ7rJc2hm", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1100,1150]}" + } + ], + "additional_kwargs": { + "id": "msg_01Ahww7bH6LdKZ6Tfq4fj5wH", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 325, + "cache_read_input_tokens": 62049, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01BNMSqnZDKcCKADJ7rJc2hm", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1100,1150]}" + } + ], + "id": "msg_01Ahww7bH6LdKZ6Tfq4fj5wH", + "usage_metadata": { + "input_tokens": 1169, + "output_tokens": 190, + "total_tokens": 1359, + "input_token_details": { + "cache_read": 62049, + "cache_creation": 325 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1100, 1150] + }, + "id": "toolu_01BNMSqnZDKcCKADJ7rJc2hm", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "30b173ff-6fdf-472e-91e8-3311b2f77990", + "tool_call_id": "toolu_01BNMSqnZDKcCKADJ7rJc2hm", + "content": "1100: if state_schema is not None:\n1101: required_keys = {\"messages\", \"remaining_steps\"}\n1102: if response_format is not None:\n1103: required_keys.add(\"structured_response\")\n1104: \n1105: schema_keys = set(get_type_hints(state_schema))\n1106: if missing_keys := required_keys - set(schema_keys):\n1107: raise ValueError(f\"Missing required key(s) {missing_keys} in state_schema\")\n1108: \n1109: if state_schema is None:\n1110: state_schema = (\n1111: AgentStateWithStructuredResponse\n1112: if response_format is not None\n1113: else AgentState\n1114: )\n1115: \n1116: llm_builtin_tools: list[dict] = []\n1117: if isinstance(tools, ToolNode):\n1118: tool_classes = list(tools.tools_by_name.values())\n1119: tool_node = tools\n1120: else:\n1121: llm_builtin_tools = [t for t in tools if isinstance(t, dict)]\n1122: tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])\n1123: tool_classes = list(tool_node.tools_by_name.values())\n1124: \n1125: is_dynamic_model = not isinstance(model, (str, Runnable)) and callable(model)\n1126: is_async_dynamic_model = is_dynamic_model and inspect.iscoroutinefunction(model)\n1127: \n1128: tool_calling_enabled = len(tool_classes) > 0\n1129: \n1130: if not is_dynamic_model:\n1131: if isinstance(model, str):\n1132: try:\n1133: from langchain.chat_models import ( # type: ignore[import-not-found]\n1134: init_chat_model,\n1135: )\n1136: except ImportError:\n1137: raise ImportError(\n1138: \"Please install langchain (`pip install langchain`) to \"\n1139: \"use ':' string syntax for `model` parameter.\"\n1140: )\n1141: \n1142: model = cast(BaseChatModel, init_chat_model(model))\n1143: \n1144: if (\n1145: _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools)) # type: ignore[arg-type]\n1146: and len(tool_classes + llm_builtin_tools) > 0\n1147: ):\n1148: model = cast(BaseChatModel, model).bind_tools(\n1149: tool_classes + llm_builtin_tools # type: ignore[operator]\n1150: )", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me first find the current function body and then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_016XehJcpMV8D3Md1WFN67GS", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1080,1120]}" + } + ], + "additional_kwargs": { + "id": "msg_01HJP6AUxcxqBQyMZ7yUFtsQ", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 946, + "cache_read_input_tokens": 62374, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_016XehJcpMV8D3Md1WFN67GS", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1080,1120]}" + } + ], + "id": "msg_01HJP6AUxcxqBQyMZ7yUFtsQ", + "usage_metadata": { + "input_tokens": 1169, + "output_tokens": 190, + "total_tokens": 1359, + "input_token_details": { + "cache_read": 62374, + "cache_creation": 946 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1080, 1120] + }, + "id": "toolu_016XehJcpMV8D3Md1WFN67GS", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "279ca25b-53c9-418d-a334-9859e7a3192c", + "tool_call_id": "toolu_016XehJcpMV8D3Md1WFN67GS", + "content": "1080: for chunk in graph.stream(inputs, stream_mode=\"updates\"):\n1081: print(chunk)\n1082: ```\n1083: \"\"\"\n1084: if (\n1085: config_schema := deprecated_kwargs.pop(\"config_schema\", MISSING)\n1086: ) is not MISSING:\n1087: warn(\n1088: \"`config_schema` is no longer supported. Use `context_schema` instead.\",\n1089: category=LangGraphDeprecatedSinceV10,\n1090: )\n1091: \n1092: if context_schema is not None:\n1093: context_schema = config_schema\n1094: \n1095: if version not in (\"v1\", \"v2\"):\n1096: raise ValueError(\n1097: f\"Invalid version {version}. Supported versions are 'v1' and 'v2'.\"\n1098: )\n1099: \n1100: if state_schema is not None:\n1101: required_keys = {\"messages\", \"remaining_steps\"}\n1102: if response_format is not None:\n1103: required_keys.add(\"structured_response\")\n1104: \n1105: schema_keys = set(get_type_hints(state_schema))\n1106: if missing_keys := required_keys - set(schema_keys):\n1107: raise ValueError(f\"Missing required key(s) {missing_keys} in state_schema\")\n1108: \n1109: if state_schema is None:\n1110: state_schema = (\n1111: AgentStateWithStructuredResponse\n1112: if response_format is not None\n1113: else AgentState\n1114: )\n1115: \n1116: llm_builtin_tools: list[dict] = []\n1117: if isinstance(tools, ToolNode):\n1118: tool_classes = list(tools.tools_by_name.values())\n1119: tool_node = tools\n1120: else:", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me first find the exact line numbers where the function body starts and ends, then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_013EJR8o3wvFypZa1FcUFDzz", + "name": "grep", + "input": "{\"query\": \"\\\"\\\"\\\"Creates an agent graph that calls tools in a loop until a stopping condition is met.\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 5}" + } + ], + "additional_kwargs": { + "id": "msg_01G1XQPAFgQLUUtf6v1SXMXt", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 708, + "cache_read_input_tokens": 63320, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_013EJR8o3wvFypZa1FcUFDzz", + "index": 1, + "name": "grep", + "args": "{\"query\": \"\\\"\\\"\\\"Creates an agent graph that calls tools in a loop until a stopping condition is met.\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 5}" + } + ], + "id": "msg_01G1XQPAFgQLUUtf6v1SXMXt", + "usage_metadata": { + "input_tokens": 1168, + "output_tokens": 198, + "total_tokens": 1366, + "input_token_details": { + "cache_read": 63320, + "cache_creation": 708 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "\"\"\"Creates an agent graph that calls tools in a loop until a stopping condition is met.", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 5 + }, + "id": "toolu_013EJR8o3wvFypZa1FcUFDzz", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "7031246e-dca9-4065-a883-fde7223486a4", + "tool_call_id": "toolu_013EJR8o3wvFypZa1FcUFDzz", + "content": "libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n898- debug: bool = False,\r\n899- version: Literal[\"v1\", \"v2\"] = \"v2\",\r\n900- name: Optional[str] = None,\r\n901- **deprecated_kwargs: Any,\r\n902-) -> CompiledStateGraph:\r\n903: \"\"\"Creates an agent graph that calls tools in a loop until a stopping condition is met.\r\n904-\r\n905- For more details on using `create_react_agent`, visit [Agents](https://langchain-ai.github.io/langgraph/agents/overview/) documentation.\r\n906-\r\n907- Args:\r\n908- model: The language model for the agent. Supports static and dynamic\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me find the exact line numbers where the function body starts and ends, then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01KyEwuba5xL2EDNfr6JQ9sx", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1080,1090]}" + } + ], + "additional_kwargs": { + "id": "msg_017LHMMm1YNGtJmPDHhNZz5Y", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 390, + "cache_read_input_tokens": 64028, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01KyEwuba5xL2EDNfr6JQ9sx", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1080,1090]}" + } + ], + "id": "msg_017LHMMm1YNGtJmPDHhNZz5Y", + "usage_metadata": { + "input_tokens": 1168, + "output_tokens": 196, + "total_tokens": 1364, + "input_token_details": { + "cache_read": 64028, + "cache_creation": 390 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1080, 1090] + }, + "id": "toolu_01KyEwuba5xL2EDNfr6JQ9sx", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "4633ce95-f467-4072-a3e5-15b23adc2b48", + "tool_call_id": "toolu_01KyEwuba5xL2EDNfr6JQ9sx", + "content": "1080: for chunk in graph.stream(inputs, stream_mode=\"updates\"):\n1081: print(chunk)\n1082: ```\n1083: \"\"\"\n1084: if (\n1085: config_schema := deprecated_kwargs.pop(\"config_schema\", MISSING)\n1086: ) is not MISSING:\n1087: warn(\n1088: \"`config_schema` is no longer supported. Use `context_schema` instead.\",\n1089: category=LangGraphDeprecatedSinceV10,\n1090: )", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me find the exact line numbers where the function body starts and ends, then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01T8ae7trWfFGJ1d9s11qNmV", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1480,1490]}" + } + ], + "additional_kwargs": { + "id": "msg_01Sg3xkYkKxEf7YSTGB3ggBp", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 342, + "cache_read_input_tokens": 64418, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01T8ae7trWfFGJ1d9s11qNmV", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1480,1490]}" + } + ], + "id": "msg_01Sg3xkYkKxEf7YSTGB3ggBp", + "usage_metadata": { + "input_tokens": 1168, + "output_tokens": 196, + "total_tokens": 1364, + "input_token_details": { + "cache_read": 64418, + "cache_creation": 342 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1480, 1490] + }, + "id": "toolu_01T8ae7trWfFGJ1d9s11qNmV", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "0672f431-4a48-49ac-b6de-99fb1f1b3673", + "tool_call_id": "toolu_01T8ae7trWfFGJ1d9s11qNmV", + "content": "1480: agent_paths.append(END)\n1481: \n1482: if post_model_hook is not None:\n1483: \n1484: def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n1485: \"\"\"Route to the next node after post_model_hook.\n1486: \n1487: Routes to one of:\n1488: * \"tools\": if there are pending tool calls without a corresponding message.\n1489: * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\n1490: * END: if no pending tool calls exist and no response_format is specified.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me find the exact line numbers where the function body starts and ends, then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01W52Q4CmupJrYx6B1iZ6gFx", + "name": "grep", + "input": "{\"query\": \"return workflow.compile\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 5}" + } + ], + "additional_kwargs": { + "id": "msg_0146821E4jKRLxWRUXUeKD6s", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 373, + "cache_read_input_tokens": 64760, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01W52Q4CmupJrYx6B1iZ6gFx", + "index": 1, + "name": "grep", + "args": "{\"query\": \"return workflow.compile\", \"file_types\": [\".py\"], \"include_files\": \"*chat_agent_executor.py\", \"context_lines\": 5}" + } + ], + "id": "msg_0146821E4jKRLxWRUXUeKD6s", + "usage_metadata": { + "input_tokens": 1167, + "output_tokens": 184, + "total_tokens": 1351, + "input_token_details": { + "cache_read": 64760, + "cache_creation": 373 + } + }, + "tool_calls": [ + { + "name": "grep", + "args": { + "query": "return workflow.compile", + "file_types": [".py"], + "include_files": "*chat_agent_executor.py", + "context_lines": 5 + }, + "id": "toolu_01W52Q4CmupJrYx6B1iZ6gFx", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "43430c4f-c989-4cc5-a8a9-fd3d2f46cbb7", + "tool_call_id": "toolu_01W52Q4CmupJrYx6B1iZ6gFx", + "content": "libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\r\n782- workflow.add_edge(\"post_model_hook\", \"generate_structured_response\")\r\n783- else:\r\n784- workflow.add_edge(\"agent\", \"generate_structured_response\")\r\n785-\r\n786- # Compile with all provided options\r\n787: return workflow.compile(\r\n788- checkpointer=self.checkpointer,\r\n789- store=self.store,\r\n790- interrupt_before=self.interrupt_before,\r\n791- interrupt_after=self.interrupt_after,\r\n792- debug=self.debug,\r\n--\r\n862- )\r\n863- else:\r\n864- workflow.add_edge(\"tools\", entrypoint)\r\n865-\r\n866- # Compile with all provided options\r\n867: return workflow.compile(\r\n868- checkpointer=self.checkpointer,\r\n869- store=self.store,\r\n870- interrupt_before=self.interrupt_before,\r\n871- interrupt_after=self.interrupt_after,\r\n872- debug=self.debug,\r\n--\r\n1381- if post_model_hook is not None:\r\n1382- workflow.add_edge(\"post_model_hook\", \"generate_structured_response\")\r\n1383- else:\r\n1384- workflow.add_edge(\"agent\", \"generate_structured_response\")\r\n1385-\r\n1386: return workflow.compile(\r\n1387- checkpointer=checkpointer,\r\n1388- store=store,\r\n1389- interrupt_before=interrupt_before,\r\n1390- interrupt_after=interrupt_after,\r\n1391- debug=debug,\r\n--\r\n1555- workflow.add_edge(\"tools\", entrypoint)\r\n1556-\r\n1557- # Finally, we compile it!\r\n1558- # This compiles it into a LangChain Runnable,\r\n1559- # meaning you can use it as you would any other runnable\r\n1560: return workflow.compile(\r\n1561- checkpointer=checkpointer,\r\n1562- store=store,\r\n1563- interrupt_before=interrupt_before,\r\n1564- interrupt_after=interrupt_after,\r\n1565- debug=debug,\r\n", + "name": "grep", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me find the exact line numbers where the function body starts and ends, then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01PYkNuZXRi3B4eJrthoNzsz", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1560,1570]}" + } + ], + "additional_kwargs": { + "id": "msg_01LKnJNxDXdzBo5caGFQPP1f", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 744, + "cache_read_input_tokens": 65133, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01PYkNuZXRi3B4eJrthoNzsz", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1560,1570]}" + } + ], + "id": "msg_01LKnJNxDXdzBo5caGFQPP1f", + "usage_metadata": { + "input_tokens": 1167, + "output_tokens": 196, + "total_tokens": 1363, + "input_token_details": { + "cache_read": 65133, + "cache_creation": 744 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1560, 1570] + }, + "id": "toolu_01PYkNuZXRi3B4eJrthoNzsz", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "6f8c8e6e-b582-42c2-b60d-324c34ac98d5", + "tool_call_id": "toolu_01PYkNuZXRi3B4eJrthoNzsz", + "content": "1560: return workflow.compile(\n1561: checkpointer=checkpointer,\n1562: store=store,\n1563: interrupt_before=interrupt_before,\n1564: interrupt_after=interrupt_after,\n1565: debug=debug,\n1566: name=name,\n1567: )\n1568: \n1569: \n1570: # Keep for backwards compatibility", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "I need to refactor the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me find the exact line numbers where the function body starts and ends, then replace it while preserving the exact same function signature and docstring." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01NGeTGWsyunCwGaq17UpEDB", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" \\\"\\\"\\\"\\n if (\\n config_schema := deprecated_kwargs.pop(\\\"config_schema\\\", MISSING)\\n ) is not MISSING:\\n warn(\\n \\\"`config_schema` is no longer supported. Use `context_schema` instead.\\\",\\n category=LangGraphDeprecatedSinceV10,\\n )\\n\\n if context_schema is not None:\\n context_schema = config_schema\\n\\n if version not in (\\\"v1\\\", \\\"v2\\\"):\\n raise ValueError(\\n f\\\"Invalid version {version}. Supported versions are 'v1' and 'v2'.\\\"\\n )\\n\\n if state_schema is not None:\\n required_keys = {\\\"messages\\\", \\\"remaining_steps\\\"}\\n if response_format is not None:\\n required_keys.add(\\\"structured_response\\\")\\n\\n schema_keys = set(get_type_hints(state_schema))\\n if missing_keys := required_keys - set(schema_keys):\\n raise ValueError(f\\\"Missing required key(s) {missing_keys} in state_schema\\\")\\n\\n if state_schema is None:\\n state_schema = (\\n AgentStateWithStructuredResponse\\n if response_format is not None\\n else AgentState\\n )\\n\\n llm_builtin_tools: list[dict] = []\\n if isinstance(tools, ToolNode):\\n tool_classes = list(tools.tools_by_name.values())\\n tool_node = tools\\n else:\\n llm_builtin_tools = [t for t in tools if isinstance(t, dict)]\\n tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])\\n tool_classes = list(tool_node.tools_by_name.values())\\n\\n is_dynamic_model = not isinstance(model, (str, Runnable)) and callable(model)\\n is_async_dynamic_model = is_dynamic_model and inspect.iscoroutinefunction(model)\\n\\n tool_calling_enabled = len(tool_classes) > 0\\n\\n if not is_dynamic_model:\\n if isinstance(model, str):\\n try:\\n from langchain.chat_models import ( # type: ignore[import-not-found]\\n init_chat_model,\\n )\\n except ImportError:\\n raise ImportError(\\n \\\"Please install langchain (`pip install langchain`) to \\\"\\n \\\"use ':' string syntax for `model` parameter.\\\"\\n )\\n\\n model = cast(BaseChatModel, init_chat_model(model))\\n\\n if (\\n _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools)) # type: ignore[arg-type]\\n and len(tool_classes + llm_builtin_tools) > 0\\n ):\\n model = cast(BaseChatModel, model).bind_tools(\\n tool_classes + llm_builtin_tools # type: ignore[operator]\\n )\\n\\n static_model: Optional[Runnable] = _get_prompt_runnable(prompt) | model # type: ignore[operator]\\n else:\\n # For dynamic models, we'll create the runnable at runtime\\n static_model = None\\n\\n # If any of the tools are configured to return_directly after running,\\n # our graph needs to check if these were called\\n should_return_direct = {t.name for t in tool_classes if t.return_direct}\\n\\n def _resolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if is_dynamic_model:\\n return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\\n else:\\n return static_model\\n\\n async def _aresolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Async resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if is_async_dynamic_model:\\n resolved_model = await model(state, runtime) # type: ignore[misc,operator]\\n return _get_prompt_runnable(prompt) | resolved_model\\n elif is_dynamic_model:\\n return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\\n else:\\n return static_model\\n\\n def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\\n has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\\n all_tools_return_direct = (\\n all(call[\\\"name\\\"] in should_return_direct for call in response.tool_calls)\\n if isinstance(response, AIMessage)\\n else False\\n )\\n remaining_steps = _get_state_value(state, \\\"remaining_steps\\\", None)\\n is_last_step = _get_state_value(state, \\\"is_last_step\\\", False)\\n return (\\n (remaining_steps is None and is_last_step and has_tool_calls)\\n or (\\n remaining_steps is not None\\n and remaining_steps < 1\\n and all_tools_return_direct\\n )\\n or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\\n )\\n\\n def _get_model_input_state(state: StateSchema) -> StateSchema:\\n if pre_model_hook is not None:\\n messages = (\\n _get_state_value(state, \\\"llm_input_messages\\\")\\n ) or _get_state_value(state, \\\"messages\\\")\\n error_msg = f\\\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\\\"\\n else:\\n messages = _get_state_value(state, \\\"messages\\\")\\n error_msg = (\\n f\\\"Expected input to call_model to have 'messages' key, but got {state}\\\"\\n )\\n\\n if messages is None:\\n raise ValueError(error_msg)\\n\\n _validate_chat_history(messages)\\n # we're passing messages under `messages` key, as this is expected by the prompt\\n if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\\n state.messages = messages # type: ignore\\n else:\\n state[\\\"messages\\\"] = messages # type: ignore\\n\\n return state\\n\\n # Define the function that calls the model\\n def call_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or \\\"\\n \\\"provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n model_input = _get_model_input_state(state)\\n\\n if is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n dynamic_model = _resolve_model(state, runtime)\\n response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, static_model.invoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = name\\n\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n async def acall_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n model_input = _get_model_input_state(state)\\n\\n if is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n # (supports both sync and async)\\n dynamic_model = await _aresolve_model(state, runtime)\\n response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, await static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = name\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n input_schema: StateSchemaType\\n if pre_model_hook is not None:\\n # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\\n if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\\n # For Pydantic schemas\\n from pydantic import create_model\\n\\n input_schema = create_model(\\n \\\"CallModelInputSchema\\\",\\n llm_input_messages=(list[AnyMessage], ...),\\n __base__=state_schema,\\n )\\n else:\\n # For TypedDict schemas\\n class CallModelInputSchema(state_schema): # type: ignore\\n llm_input_messages: list[AnyMessage]\\n\\n input_schema = CallModelInputSchema\\n else:\\n input_schema = state_schema\\n\\n def generate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = response_format\\n if isinstance(response_format, tuple):\\n system_prompt, structured_response_schema = response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n resolved_model = _resolve_model(state, runtime)\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = model_with_structured_output.invoke(messages, config)\\n return {\\\"structured_response\\\": response}\\n\\n async def agenerate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = response_format\\n if isinstance(response_format, tuple):\\n system_prompt, structured_response_schema = response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n resolved_model = await _aresolve_model(state, runtime)\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = await model_with_structured_output.ainvoke(messages, config)\\n return {\\\"structured_response\\\": response}\\n\\n if not tool_calling_enabled:\\n # Define a new graph\\n workflow = StateGraph(state_schema=state_schema, context_schema=context_schema)\\n workflow.add_node(\\n \\\"agent\\\",\\n RunnableCallable(call_model, acall_model),\\n input_schema=input_schema,\\n )\\n if pre_model_hook is not None:\\n workflow.add_node(\\\"pre_model_hook\\\", pre_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"pre_model_hook\\\", \\\"agent\\\")\\n entrypoint = \\\"pre_model_hook\\\"\\n else:\\n entrypoint = \\\"agent\\\"\\n\\n workflow.set_entry_point(entrypoint)\\n\\n if post_model_hook is not None:\\n workflow.add_node(\\\"post_model_hook\\\", post_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"agent\\\", \\\"post_model_hook\\\")\\n\\n if response_format is not None:\\n workflow.add_node(\\n \\\"generate_structured_response\\\",\\n RunnableCallable(\\n generate_structured_response,\\n agenerate_structured_response,\\n ),\\n )\\n if post_model_hook is not None:\\n workflow.add_edge(\\\"post_model_hook\\\", \\\"generate_structured_response\\\")\\n else:\\n workflow.add_edge(\\\"agent\\\", \\\"generate_structured_response\\\")\\n\\n return workflow.compile(\\n checkpointer=checkpointer,\\n store=store,\\n interrupt_before=interrupt_before,\\n interrupt_after=interrupt_after,\\n debug=debug,\\n name=name,\\n )\\n\\n # Define the function that determines whether to continue or not\\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\\n messages = _get_state_value(state, \\\"messages\\\")\\n last_message = messages[-1]\\n # If there is no function call, then we finish\\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\\n if post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n elif response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n # Otherwise if there is, we continue\\n else:\\n if version == \\\"v1\\\":\\n return \\\"tools\\\"\\n elif version == \\\"v2\\\":\\n if post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in last_message.tool_calls\\n ]\\n\\n # Define a new graph\\n workflow = StateGraph(\\n state_schema=state_schema or AgentState, context_schema=context_schema\\n )\\n\\n # Define the two nodes we will cycle between\\n workflow.add_node(\\n \\\"agent\\\",\\n RunnableCallable(call_model, acall_model),\\n input_schema=input_schema,\\n )\\n workflow.add_node(\\\"tools\\\", tool_node)\\n\\n # Optionally add a pre-model hook node that will be called\\n # every time before the \\\"agent\\\" (LLM-calling node)\\n if pre_model_hook is not None:\\n workflow.add_node(\\\"pre_model_hook\\\", pre_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"pre_model_hook\\\", \\\"agent\\\")\\n entrypoint = \\\"pre_model_hook\\\"\\n else:\\n entrypoint = \\\"agent\\\"\\n\\n # Set the entrypoint as `agent`\\n # This means that this node is the first one called\\n workflow.set_entry_point(entrypoint)\\n\\n agent_paths = []\\n post_model_hook_paths = [entrypoint, \\\"tools\\\"]\\n\\n # Add a post model hook node if post_model_hook is provided\\n if post_model_hook is not None:\\n workflow.add_node(\\\"post_model_hook\\\", post_model_hook) # type: ignore[arg-type]\\n agent_paths.append(\\\"post_model_hook\\\")\\n workflow.add_edge(\\\"agent\\\", \\\"post_model_hook\\\")\\n else:\\n agent_paths.append(\\\"tools\\\")\\n\\n # Add a structured output node if response_format is provided\\n if response_format is not None:\\n workflow.add_node(\\n \\\"generate_structured_response\\\",\\n RunnableCallable(\\n generate_structured_response,\\n agenerate_structured_response,\\n ),\\n )\\n if post_model_hook is not None:\\n post_model_hook_paths.append(\\\"generate_structured_response\\\")\\n else:\\n agent_paths.append(\\\"generate_structured_response\\\")\\n else:\\n if post_model_hook is not None:\\n post_model_hook_paths.append(END)\\n else:\\n agent_paths.append(END)\\n\\n if post_model_hook is not None:\\n\\n def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\\n \\\"\\\"\\\"Route to the next node after post_model_hook.\\n\\n Routes to one of:\\n * \\\"tools\\\": if there are pending tool calls without a corresponding message.\\n * \\\"generate_structured_response\\\": if no pending tool calls exist and response_format is specified.\\n * END: if no pending tool calls exist and no response_format is specified.\\n \\\"\\\"\\\"\\n\\n messages = _get_state_value(state, \\\"messages\\\")\\n tool_messages = [\\n m.tool_call_id for m in messages if isinstance(m, ToolMessage)\\n ]\\n last_ai_message = next(\\n m for m in reversed(messages) if isinstance(m, AIMessage)\\n )\\n pending_tool_calls = [\\n c for c in last_ai_message.tool_calls if c[\\\"id\\\"] not in tool_messages\\n ]\\n\\n if pending_tool_calls:\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in pending_tool_calls\\n ]\\n elif isinstance(messages[-1], ToolMessage):\\n return entrypoint\\n elif response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n\\n workflow.add_conditional_edges(\\n \\\"post_model_hook\\\",\\n post_model_hook_router, # type: ignore[arg-type]\\n path_map=post_model_hook_paths,\\n )\\n\\n workflow.add_conditional_edges(\\n \\\"agent\\\",\\n should_continue, # type: ignore[arg-type]\\n path_map=agent_paths,\\n )\\n\\n def route_tool_responses(state: StateSchema) -> str:\\n for m in reversed(_get_state_value(state, \\\"messages\\\")):\\n if not isinstance(m, ToolMessage):\\n break\\n if m.name in should_return_direct:\\n return END\\n\\n # handle a case of parallel tool calls where\\n # the tool w/ `return_direct` was executed in a different `Send`\\n if isinstance(m, AIMessage) and m.tool_calls:\\n if any(call[\\\"name\\\"] in should_return_direct for call in m.tool_calls):\\n return END\\n\\n return entrypoint\\n\\n if should_return_direct:\\n workflow.add_conditional_edges(\\n \\\"tools\\\", route_tool_responses, path_map=[entrypoint, END]\\n )\\n else:\\n workflow.add_edge(\\\"tools\\\", entrypoint)\\n\\n # Finally, we compile it!\\n # This compiles it into a LangChain Runnable,\\n # meaning you can use it as you would any other runnable\\n return workflow.compile(\\n checkpointer=checkpointer,\\n store=store,\\n interrupt_before=interrupt_before,\\n interrupt_after=interrupt_after,\\n debug=debug,\\n name=name,\\n )\", \"new_str\": \" \\\"\\\"\\\"\\n # Parameter validation for deprecated kwargs\\n if deprecated_kwargs:\\n raise ValueError(f\\\"Unexpected keyword arguments: {list(deprecated_kwargs.keys())}\\\")\\n \\n # _AgentBuilder instantiation with all parameters\\n builder = _AgentBuilder(\\n model=model,\\n tools=tools,\\n prompt=prompt,\\n response_format=response_format,\\n pre_model_hook=pre_model_hook,\\n post_model_hook=post_model_hook,\\n state_schema=state_schema,\\n context_schema=context_schema,\\n checkpointer=checkpointer,\\n store=store,\\n interrupt_before=interrupt_before,\\n interrupt_after=interrupt_after,\\n debug=debug,\\n version=version,\\n name=name,\\n )\\n \\n # Return builder.build()\\n return builder.build()\"}" + } + ], + "additional_kwargs": { + "id": "msg_01D4BPPVGU7xXQ5qYvEqXra9", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 306, + "cache_read_input_tokens": 65877, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01NGeTGWsyunCwGaq17UpEDB", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"str_replace\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"old_str\": \" \\\"\\\"\\\"\\n if (\\n config_schema := deprecated_kwargs.pop(\\\"config_schema\\\", MISSING)\\n ) is not MISSING:\\n warn(\\n \\\"`config_schema` is no longer supported. Use `context_schema` instead.\\\",\\n category=LangGraphDeprecatedSinceV10,\\n )\\n\\n if context_schema is not None:\\n context_schema = config_schema\\n\\n if version not in (\\\"v1\\\", \\\"v2\\\"):\\n raise ValueError(\\n f\\\"Invalid version {version}. Supported versions are 'v1' and 'v2'.\\\"\\n )\\n\\n if state_schema is not None:\\n required_keys = {\\\"messages\\\", \\\"remaining_steps\\\"}\\n if response_format is not None:\\n required_keys.add(\\\"structured_response\\\")\\n\\n schema_keys = set(get_type_hints(state_schema))\\n if missing_keys := required_keys - set(schema_keys):\\n raise ValueError(f\\\"Missing required key(s) {missing_keys} in state_schema\\\")\\n\\n if state_schema is None:\\n state_schema = (\\n AgentStateWithStructuredResponse\\n if response_format is not None\\n else AgentState\\n )\\n\\n llm_builtin_tools: list[dict] = []\\n if isinstance(tools, ToolNode):\\n tool_classes = list(tools.tools_by_name.values())\\n tool_node = tools\\n else:\\n llm_builtin_tools = [t for t in tools if isinstance(t, dict)]\\n tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])\\n tool_classes = list(tool_node.tools_by_name.values())\\n\\n is_dynamic_model = not isinstance(model, (str, Runnable)) and callable(model)\\n is_async_dynamic_model = is_dynamic_model and inspect.iscoroutinefunction(model)\\n\\n tool_calling_enabled = len(tool_classes) > 0\\n\\n if not is_dynamic_model:\\n if isinstance(model, str):\\n try:\\n from langchain.chat_models import ( # type: ignore[import-not-found]\\n init_chat_model,\\n )\\n except ImportError:\\n raise ImportError(\\n \\\"Please install langchain (`pip install langchain`) to \\\"\\n \\\"use ':' string syntax for `model` parameter.\\\"\\n )\\n\\n model = cast(BaseChatModel, init_chat_model(model))\\n\\n if (\\n _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools)) # type: ignore[arg-type]\\n and len(tool_classes + llm_builtin_tools) > 0\\n ):\\n model = cast(BaseChatModel, model).bind_tools(\\n tool_classes + llm_builtin_tools # type: ignore[operator]\\n )\\n\\n static_model: Optional[Runnable] = _get_prompt_runnable(prompt) | model # type: ignore[operator]\\n else:\\n # For dynamic models, we'll create the runnable at runtime\\n static_model = None\\n\\n # If any of the tools are configured to return_directly after running,\\n # our graph needs to check if these were called\\n should_return_direct = {t.name for t in tool_classes if t.return_direct}\\n\\n def _resolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if is_dynamic_model:\\n return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\\n else:\\n return static_model\\n\\n async def _aresolve_model(\\n state: StateSchema, runtime: Runtime[ContextT]\\n ) -> LanguageModelLike:\\n \\\"\\\"\\\"Async resolve the model to use, handling both static and dynamic models.\\\"\\\"\\\"\\n if is_async_dynamic_model:\\n resolved_model = await model(state, runtime) # type: ignore[misc,operator]\\n return _get_prompt_runnable(prompt) | resolved_model\\n elif is_dynamic_model:\\n return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\\n else:\\n return static_model\\n\\n def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\\n has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\\n all_tools_return_direct = (\\n all(call[\\\"name\\\"] in should_return_direct for call in response.tool_calls)\\n if isinstance(response, AIMessage)\\n else False\\n )\\n remaining_steps = _get_state_value(state, \\\"remaining_steps\\\", None)\\n is_last_step = _get_state_value(state, \\\"is_last_step\\\", False)\\n return (\\n (remaining_steps is None and is_last_step and has_tool_calls)\\n or (\\n remaining_steps is not None\\n and remaining_steps < 1\\n and all_tools_return_direct\\n )\\n or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\\n )\\n\\n def _get_model_input_state(state: StateSchema) -> StateSchema:\\n if pre_model_hook is not None:\\n messages = (\\n _get_state_value(state, \\\"llm_input_messages\\\")\\n ) or _get_state_value(state, \\\"messages\\\")\\n error_msg = f\\\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\\\"\\n else:\\n messages = _get_state_value(state, \\\"messages\\\")\\n error_msg = (\\n f\\\"Expected input to call_model to have 'messages' key, but got {state}\\\"\\n )\\n\\n if messages is None:\\n raise ValueError(error_msg)\\n\\n _validate_chat_history(messages)\\n # we're passing messages under `messages` key, as this is expected by the prompt\\n if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\\n state.messages = messages # type: ignore\\n else:\\n state[\\\"messages\\\"] = messages # type: ignore\\n\\n return state\\n\\n # Define the function that calls the model\\n def call_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or \\\"\\n \\\"provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n model_input = _get_model_input_state(state)\\n\\n if is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n dynamic_model = _resolve_model(state, runtime)\\n response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, static_model.invoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = name\\n\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n async def acall_model(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n model_input = _get_model_input_state(state)\\n\\n if is_dynamic_model:\\n # Resolve dynamic model at runtime and apply prompt\\n # (supports both sync and async)\\n dynamic_model = await _aresolve_model(state, runtime)\\n response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\\n else:\\n response = cast(AIMessage, await static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\\n\\n # add agent name to the AIMessage\\n response.name = name\\n if _are_more_steps_needed(state, response):\\n return {\\n \\\"messages\\\": [\\n AIMessage(\\n id=response.id,\\n content=\\\"Sorry, need more steps to process this request.\\\",\\n )\\n ]\\n }\\n # We return a list, because this will get added to the existing list\\n return {\\\"messages\\\": [response]}\\n\\n input_schema: StateSchemaType\\n if pre_model_hook is not None:\\n # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\\n if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\\n # For Pydantic schemas\\n from pydantic import create_model\\n\\n input_schema = create_model(\\n \\\"CallModelInputSchema\\\",\\n llm_input_messages=(list[AnyMessage], ...),\\n __base__=state_schema,\\n )\\n else:\\n # For TypedDict schemas\\n class CallModelInputSchema(state_schema): # type: ignore\\n llm_input_messages: list[AnyMessage]\\n\\n input_schema = CallModelInputSchema\\n else:\\n input_schema = state_schema\\n\\n def generate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n if is_async_dynamic_model:\\n msg = (\\n \\\"Async model callable provided but agent invoked synchronously. \\\"\\n \\\"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\\\"\\n )\\n raise RuntimeError(msg)\\n\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = response_format\\n if isinstance(response_format, tuple):\\n system_prompt, structured_response_schema = response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n resolved_model = _resolve_model(state, runtime)\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = model_with_structured_output.invoke(messages, config)\\n return {\\\"structured_response\\\": response}\\n\\n async def agenerate_structured_response(\\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\\n ) -> StateSchema:\\n messages = _get_state_value(state, \\\"messages\\\")\\n structured_response_schema = response_format\\n if isinstance(response_format, tuple):\\n system_prompt, structured_response_schema = response_format\\n messages = [SystemMessage(content=system_prompt)] + list(messages)\\n\\n resolved_model = await _aresolve_model(state, runtime)\\n model_with_structured_output = _get_model(\\n resolved_model\\n ).with_structured_output(\\n cast(StructuredResponseSchema, structured_response_schema)\\n )\\n response = await model_with_structured_output.ainvoke(messages, config)\\n return {\\\"structured_response\\\": response}\\n\\n if not tool_calling_enabled:\\n # Define a new graph\\n workflow = StateGraph(state_schema=state_schema, context_schema=context_schema)\\n workflow.add_node(\\n \\\"agent\\\",\\n RunnableCallable(call_model, acall_model),\\n input_schema=input_schema,\\n )\\n if pre_model_hook is not None:\\n workflow.add_node(\\\"pre_model_hook\\\", pre_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"pre_model_hook\\\", \\\"agent\\\")\\n entrypoint = \\\"pre_model_hook\\\"\\n else:\\n entrypoint = \\\"agent\\\"\\n\\n workflow.set_entry_point(entrypoint)\\n\\n if post_model_hook is not None:\\n workflow.add_node(\\\"post_model_hook\\\", post_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"agent\\\", \\\"post_model_hook\\\")\\n\\n if response_format is not None:\\n workflow.add_node(\\n \\\"generate_structured_response\\\",\\n RunnableCallable(\\n generate_structured_response,\\n agenerate_structured_response,\\n ),\\n )\\n if post_model_hook is not None:\\n workflow.add_edge(\\\"post_model_hook\\\", \\\"generate_structured_response\\\")\\n else:\\n workflow.add_edge(\\\"agent\\\", \\\"generate_structured_response\\\")\\n\\n return workflow.compile(\\n checkpointer=checkpointer,\\n store=store,\\n interrupt_before=interrupt_before,\\n interrupt_after=interrupt_after,\\n debug=debug,\\n name=name,\\n )\\n\\n # Define the function that determines whether to continue or not\\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\\n messages = _get_state_value(state, \\\"messages\\\")\\n last_message = messages[-1]\\n # If there is no function call, then we finish\\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\\n if post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n elif response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n # Otherwise if there is, we continue\\n else:\\n if version == \\\"v1\\\":\\n return \\\"tools\\\"\\n elif version == \\\"v2\\\":\\n if post_model_hook is not None:\\n return \\\"post_model_hook\\\"\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in last_message.tool_calls\\n ]\\n\\n # Define a new graph\\n workflow = StateGraph(\\n state_schema=state_schema or AgentState, context_schema=context_schema\\n )\\n\\n # Define the two nodes we will cycle between\\n workflow.add_node(\\n \\\"agent\\\",\\n RunnableCallable(call_model, acall_model),\\n input_schema=input_schema,\\n )\\n workflow.add_node(\\\"tools\\\", tool_node)\\n\\n # Optionally add a pre-model hook node that will be called\\n # every time before the \\\"agent\\\" (LLM-calling node)\\n if pre_model_hook is not None:\\n workflow.add_node(\\\"pre_model_hook\\\", pre_model_hook) # type: ignore[arg-type]\\n workflow.add_edge(\\\"pre_model_hook\\\", \\\"agent\\\")\\n entrypoint = \\\"pre_model_hook\\\"\\n else:\\n entrypoint = \\\"agent\\\"\\n\\n # Set the entrypoint as `agent`\\n # This means that this node is the first one called\\n workflow.set_entry_point(entrypoint)\\n\\n agent_paths = []\\n post_model_hook_paths = [entrypoint, \\\"tools\\\"]\\n\\n # Add a post model hook node if post_model_hook is provided\\n if post_model_hook is not None:\\n workflow.add_node(\\\"post_model_hook\\\", post_model_hook) # type: ignore[arg-type]\\n agent_paths.append(\\\"post_model_hook\\\")\\n workflow.add_edge(\\\"agent\\\", \\\"post_model_hook\\\")\\n else:\\n agent_paths.append(\\\"tools\\\")\\n\\n # Add a structured output node if response_format is provided\\n if response_format is not None:\\n workflow.add_node(\\n \\\"generate_structured_response\\\",\\n RunnableCallable(\\n generate_structured_response,\\n agenerate_structured_response,\\n ),\\n )\\n if post_model_hook is not None:\\n post_model_hook_paths.append(\\\"generate_structured_response\\\")\\n else:\\n agent_paths.append(\\\"generate_structured_response\\\")\\n else:\\n if post_model_hook is not None:\\n post_model_hook_paths.append(END)\\n else:\\n agent_paths.append(END)\\n\\n if post_model_hook is not None:\\n\\n def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\\n \\\"\\\"\\\"Route to the next node after post_model_hook.\\n\\n Routes to one of:\\n * \\\"tools\\\": if there are pending tool calls without a corresponding message.\\n * \\\"generate_structured_response\\\": if no pending tool calls exist and response_format is specified.\\n * END: if no pending tool calls exist and no response_format is specified.\\n \\\"\\\"\\\"\\n\\n messages = _get_state_value(state, \\\"messages\\\")\\n tool_messages = [\\n m.tool_call_id for m in messages if isinstance(m, ToolMessage)\\n ]\\n last_ai_message = next(\\n m for m in reversed(messages) if isinstance(m, AIMessage)\\n )\\n pending_tool_calls = [\\n c for c in last_ai_message.tool_calls if c[\\\"id\\\"] not in tool_messages\\n ]\\n\\n if pending_tool_calls:\\n return [\\n Send(\\n \\\"tools\\\",\\n ToolCallWithContext(\\n __type=\\\"tool_call_with_context\\\",\\n tool_call=tool_call,\\n state=state,\\n ),\\n )\\n for tool_call in pending_tool_calls\\n ]\\n elif isinstance(messages[-1], ToolMessage):\\n return entrypoint\\n elif response_format is not None:\\n return \\\"generate_structured_response\\\"\\n else:\\n return END\\n\\n workflow.add_conditional_edges(\\n \\\"post_model_hook\\\",\\n post_model_hook_router, # type: ignore[arg-type]\\n path_map=post_model_hook_paths,\\n )\\n\\n workflow.add_conditional_edges(\\n \\\"agent\\\",\\n should_continue, # type: ignore[arg-type]\\n path_map=agent_paths,\\n )\\n\\n def route_tool_responses(state: StateSchema) -> str:\\n for m in reversed(_get_state_value(state, \\\"messages\\\")):\\n if not isinstance(m, ToolMessage):\\n break\\n if m.name in should_return_direct:\\n return END\\n\\n # handle a case of parallel tool calls where\\n # the tool w/ `return_direct` was executed in a different `Send`\\n if isinstance(m, AIMessage) and m.tool_calls:\\n if any(call[\\\"name\\\"] in should_return_direct for call in m.tool_calls):\\n return END\\n\\n return entrypoint\\n\\n if should_return_direct:\\n workflow.add_conditional_edges(\\n \\\"tools\\\", route_tool_responses, path_map=[entrypoint, END]\\n )\\n else:\\n workflow.add_edge(\\\"tools\\\", entrypoint)\\n\\n # Finally, we compile it!\\n # This compiles it into a LangChain Runnable,\\n # meaning you can use it as you would any other runnable\\n return workflow.compile(\\n checkpointer=checkpointer,\\n store=store,\\n interrupt_before=interrupt_before,\\n interrupt_after=interrupt_after,\\n debug=debug,\\n name=name,\\n )\", \"new_str\": \" \\\"\\\"\\\"\\n # Parameter validation for deprecated kwargs\\n if deprecated_kwargs:\\n raise ValueError(f\\\"Unexpected keyword arguments: {list(deprecated_kwargs.keys())}\\\")\\n \\n # _AgentBuilder instantiation with all parameters\\n builder = _AgentBuilder(\\n model=model,\\n tools=tools,\\n prompt=prompt,\\n response_format=response_format,\\n pre_model_hook=pre_model_hook,\\n post_model_hook=post_model_hook,\\n state_schema=state_schema,\\n context_schema=context_schema,\\n checkpointer=checkpointer,\\n store=store,\\n interrupt_before=interrupt_before,\\n interrupt_after=interrupt_after,\\n debug=debug,\\n version=version,\\n name=name,\\n )\\n \\n # Return builder.build()\\n return builder.build()\"}" + } + ], + "id": "msg_01D4BPPVGU7xXQ5qYvEqXra9", + "usage_metadata": { + "input_tokens": 1167, + "output_tokens": 5354, + "total_tokens": 6521, + "input_token_details": { + "cache_read": 65877, + "cache_creation": 306 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "str_replace", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "old_str": " \"\"\"\n if (\n config_schema := deprecated_kwargs.pop(\"config_schema\", MISSING)\n ) is not MISSING:\n warn(\n \"`config_schema` is no longer supported. Use `context_schema` instead.\",\n category=LangGraphDeprecatedSinceV10,\n )\n\n if context_schema is not None:\n context_schema = config_schema\n\n if version not in (\"v1\", \"v2\"):\n raise ValueError(\n f\"Invalid version {version}. Supported versions are 'v1' and 'v2'.\"\n )\n\n if state_schema is not None:\n required_keys = {\"messages\", \"remaining_steps\"}\n if response_format is not None:\n required_keys.add(\"structured_response\")\n\n schema_keys = set(get_type_hints(state_schema))\n if missing_keys := required_keys - set(schema_keys):\n raise ValueError(f\"Missing required key(s) {missing_keys} in state_schema\")\n\n if state_schema is None:\n state_schema = (\n AgentStateWithStructuredResponse\n if response_format is not None\n else AgentState\n )\n\n llm_builtin_tools: list[dict] = []\n if isinstance(tools, ToolNode):\n tool_classes = list(tools.tools_by_name.values())\n tool_node = tools\n else:\n llm_builtin_tools = [t for t in tools if isinstance(t, dict)]\n tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])\n tool_classes = list(tool_node.tools_by_name.values())\n\n is_dynamic_model = not isinstance(model, (str, Runnable)) and callable(model)\n is_async_dynamic_model = is_dynamic_model and inspect.iscoroutinefunction(model)\n\n tool_calling_enabled = len(tool_classes) > 0\n\n if not is_dynamic_model:\n if isinstance(model, str):\n try:\n from langchain.chat_models import ( # type: ignore[import-not-found]\n init_chat_model,\n )\n except ImportError:\n raise ImportError(\n \"Please install langchain (`pip install langchain`) to \"\n \"use ':' string syntax for `model` parameter.\"\n )\n\n model = cast(BaseChatModel, init_chat_model(model))\n\n if (\n _should_bind_tools(model, tool_classes, num_builtin=len(llm_builtin_tools)) # type: ignore[arg-type]\n and len(tool_classes + llm_builtin_tools) > 0\n ):\n model = cast(BaseChatModel, model).bind_tools(\n tool_classes + llm_builtin_tools # type: ignore[operator]\n )\n\n static_model: Optional[Runnable] = _get_prompt_runnable(prompt) | model # type: ignore[operator]\n else:\n # For dynamic models, we'll create the runnable at runtime\n static_model = None\n\n # If any of the tools are configured to return_directly after running,\n # our graph needs to check if these were called\n should_return_direct = {t.name for t in tool_classes if t.return_direct}\n\n def _resolve_model(\n state: StateSchema, runtime: Runtime[ContextT]\n ) -> LanguageModelLike:\n \"\"\"Resolve the model to use, handling both static and dynamic models.\"\"\"\n if is_dynamic_model:\n return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\n else:\n return static_model\n\n async def _aresolve_model(\n state: StateSchema, runtime: Runtime[ContextT]\n ) -> LanguageModelLike:\n \"\"\"Async resolve the model to use, handling both static and dynamic models.\"\"\"\n if is_async_dynamic_model:\n resolved_model = await model(state, runtime) # type: ignore[misc,operator]\n return _get_prompt_runnable(prompt) | resolved_model\n elif is_dynamic_model:\n return _get_prompt_runnable(prompt) | model(state, runtime) # type: ignore[operator]\n else:\n return static_model\n\n def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:\n has_tool_calls = isinstance(response, AIMessage) and response.tool_calls\n all_tools_return_direct = (\n all(call[\"name\"] in should_return_direct for call in response.tool_calls)\n if isinstance(response, AIMessage)\n else False\n )\n remaining_steps = _get_state_value(state, \"remaining_steps\", None)\n is_last_step = _get_state_value(state, \"is_last_step\", False)\n return (\n (remaining_steps is None and is_last_step and has_tool_calls)\n or (\n remaining_steps is not None\n and remaining_steps < 1\n and all_tools_return_direct\n )\n or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)\n )\n\n def _get_model_input_state(state: StateSchema) -> StateSchema:\n if pre_model_hook is not None:\n messages = (\n _get_state_value(state, \"llm_input_messages\")\n ) or _get_state_value(state, \"messages\")\n error_msg = f\"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}\"\n else:\n messages = _get_state_value(state, \"messages\")\n error_msg = (\n f\"Expected input to call_model to have 'messages' key, but got {state}\"\n )\n\n if messages is None:\n raise ValueError(error_msg)\n\n _validate_chat_history(messages)\n # we're passing messages under `messages` key, as this is expected by the prompt\n if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\n state.messages = messages # type: ignore\n else:\n state[\"messages\"] = messages # type: ignore\n\n return state\n\n # Define the function that calls the model\n def call_model(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n if is_async_dynamic_model:\n msg = (\n \"Async model callable provided but agent invoked synchronously. \"\n \"Use agent.ainvoke() or agent.astream(), or \"\n \"provide a sync model callable.\"\n )\n raise RuntimeError(msg)\n\n model_input = _get_model_input_state(state)\n\n if is_dynamic_model:\n # Resolve dynamic model at runtime and apply prompt\n dynamic_model = _resolve_model(state, runtime)\n response = cast(AIMessage, dynamic_model.invoke(model_input, config)) # type: ignore[arg-type]\n else:\n response = cast(AIMessage, static_model.invoke(model_input, config)) # type: ignore[union-attr]\n\n # add agent name to the AIMessage\n response.name = name\n\n if _are_more_steps_needed(state, response):\n return {\n \"messages\": [\n AIMessage(\n id=response.id,\n content=\"Sorry, need more steps to process this request.\",\n )\n ]\n }\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}\n\n async def acall_model(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n model_input = _get_model_input_state(state)\n\n if is_dynamic_model:\n # Resolve dynamic model at runtime and apply prompt\n # (supports both sync and async)\n dynamic_model = await _aresolve_model(state, runtime)\n response = cast(AIMessage, await dynamic_model.ainvoke(model_input, config)) # type: ignore[arg-type]\n else:\n response = cast(AIMessage, await static_model.ainvoke(model_input, config)) # type: ignore[union-attr]\n\n # add agent name to the AIMessage\n response.name = name\n if _are_more_steps_needed(state, response):\n return {\n \"messages\": [\n AIMessage(\n id=response.id,\n content=\"Sorry, need more steps to process this request.\",\n )\n ]\n }\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}\n\n input_schema: StateSchemaType\n if pre_model_hook is not None:\n # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'\n if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):\n # For Pydantic schemas\n from pydantic import create_model\n\n input_schema = create_model(\n \"CallModelInputSchema\",\n llm_input_messages=(list[AnyMessage], ...),\n __base__=state_schema,\n )\n else:\n # For TypedDict schemas\n class CallModelInputSchema(state_schema): # type: ignore\n llm_input_messages: list[AnyMessage]\n\n input_schema = CallModelInputSchema\n else:\n input_schema = state_schema\n\n def generate_structured_response(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n if is_async_dynamic_model:\n msg = (\n \"Async model callable provided but agent invoked synchronously. \"\n \"Use agent.ainvoke() or agent.astream(), or provide a sync model callable.\"\n )\n raise RuntimeError(msg)\n\n messages = _get_state_value(state, \"messages\")\n structured_response_schema = response_format\n if isinstance(response_format, tuple):\n system_prompt, structured_response_schema = response_format\n messages = [SystemMessage(content=system_prompt)] + list(messages)\n\n resolved_model = _resolve_model(state, runtime)\n model_with_structured_output = _get_model(\n resolved_model\n ).with_structured_output(\n cast(StructuredResponseSchema, structured_response_schema)\n )\n response = model_with_structured_output.invoke(messages, config)\n return {\"structured_response\": response}\n\n async def agenerate_structured_response(\n state: StateSchema, runtime: Runtime[ContextT], config: RunnableConfig\n ) -> StateSchema:\n messages = _get_state_value(state, \"messages\")\n structured_response_schema = response_format\n if isinstance(response_format, tuple):\n system_prompt, structured_response_schema = response_format\n messages = [SystemMessage(content=system_prompt)] + list(messages)\n\n resolved_model = await _aresolve_model(state, runtime)\n model_with_structured_output = _get_model(\n resolved_model\n ).with_structured_output(\n cast(StructuredResponseSchema, structured_response_schema)\n )\n response = await model_with_structured_output.ainvoke(messages, config)\n return {\"structured_response\": response}\n\n if not tool_calling_enabled:\n # Define a new graph\n workflow = StateGraph(state_schema=state_schema, context_schema=context_schema)\n workflow.add_node(\n \"agent\",\n RunnableCallable(call_model, acall_model),\n input_schema=input_schema,\n )\n if pre_model_hook is not None:\n workflow.add_node(\"pre_model_hook\", pre_model_hook) # type: ignore[arg-type]\n workflow.add_edge(\"pre_model_hook\", \"agent\")\n entrypoint = \"pre_model_hook\"\n else:\n entrypoint = \"agent\"\n\n workflow.set_entry_point(entrypoint)\n\n if post_model_hook is not None:\n workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]\n workflow.add_edge(\"agent\", \"post_model_hook\")\n\n if response_format is not None:\n workflow.add_node(\n \"generate_structured_response\",\n RunnableCallable(\n generate_structured_response,\n agenerate_structured_response,\n ),\n )\n if post_model_hook is not None:\n workflow.add_edge(\"post_model_hook\", \"generate_structured_response\")\n else:\n workflow.add_edge(\"agent\", \"generate_structured_response\")\n\n return workflow.compile(\n checkpointer=checkpointer,\n store=store,\n interrupt_before=interrupt_before,\n interrupt_after=interrupt_after,\n debug=debug,\n name=name,\n )\n\n # Define the function that determines whether to continue or not\n def should_continue(state: StateSchema) -> Union[str, list[Send]]:\n messages = _get_state_value(state, \"messages\")\n last_message = messages[-1]\n # If there is no function call, then we finish\n if not isinstance(last_message, AIMessage) or not last_message.tool_calls:\n if post_model_hook is not None:\n return \"post_model_hook\"\n elif response_format is not None:\n return \"generate_structured_response\"\n else:\n return END\n # Otherwise if there is, we continue\n else:\n if version == \"v1\":\n return \"tools\"\n elif version == \"v2\":\n if post_model_hook is not None:\n return \"post_model_hook\"\n return [\n Send(\n \"tools\",\n ToolCallWithContext(\n __type=\"tool_call_with_context\",\n tool_call=tool_call,\n state=state,\n ),\n )\n for tool_call in last_message.tool_calls\n ]\n\n # Define a new graph\n workflow = StateGraph(\n state_schema=state_schema or AgentState, context_schema=context_schema\n )\n\n # Define the two nodes we will cycle between\n workflow.add_node(\n \"agent\",\n RunnableCallable(call_model, acall_model),\n input_schema=input_schema,\n )\n workflow.add_node(\"tools\", tool_node)\n\n # Optionally add a pre-model hook node that will be called\n # every time before the \"agent\" (LLM-calling node)\n if pre_model_hook is not None:\n workflow.add_node(\"pre_model_hook\", pre_model_hook) # type: ignore[arg-type]\n workflow.add_edge(\"pre_model_hook\", \"agent\")\n entrypoint = \"pre_model_hook\"\n else:\n entrypoint = \"agent\"\n\n # Set the entrypoint as `agent`\n # This means that this node is the first one called\n workflow.set_entry_point(entrypoint)\n\n agent_paths = []\n post_model_hook_paths = [entrypoint, \"tools\"]\n\n # Add a post model hook node if post_model_hook is provided\n if post_model_hook is not None:\n workflow.add_node(\"post_model_hook\", post_model_hook) # type: ignore[arg-type]\n agent_paths.append(\"post_model_hook\")\n workflow.add_edge(\"agent\", \"post_model_hook\")\n else:\n agent_paths.append(\"tools\")\n\n # Add a structured output node if response_format is provided\n if response_format is not None:\n workflow.add_node(\n \"generate_structured_response\",\n RunnableCallable(\n generate_structured_response,\n agenerate_structured_response,\n ),\n )\n if post_model_hook is not None:\n post_model_hook_paths.append(\"generate_structured_response\")\n else:\n agent_paths.append(\"generate_structured_response\")\n else:\n if post_model_hook is not None:\n post_model_hook_paths.append(END)\n else:\n agent_paths.append(END)\n\n if post_model_hook is not None:\n\n def post_model_hook_router(state: StateSchema) -> Union[str, list[Send]]:\n \"\"\"Route to the next node after post_model_hook.\n\n Routes to one of:\n * \"tools\": if there are pending tool calls without a corresponding message.\n * \"generate_structured_response\": if no pending tool calls exist and response_format is specified.\n * END: if no pending tool calls exist and no response_format is specified.\n \"\"\"\n\n messages = _get_state_value(state, \"messages\")\n tool_messages = [\n m.tool_call_id for m in messages if isinstance(m, ToolMessage)\n ]\n last_ai_message = next(\n m for m in reversed(messages) if isinstance(m, AIMessage)\n )\n pending_tool_calls = [\n c for c in last_ai_message.tool_calls if c[\"id\"] not in tool_messages\n ]\n\n if pending_tool_calls:\n return [\n Send(\n \"tools\",\n ToolCallWithContext(\n __type=\"tool_call_with_context\",\n tool_call=tool_call,\n state=state,\n ),\n )\n for tool_call in pending_tool_calls\n ]\n elif isinstance(messages[-1], ToolMessage):\n return entrypoint\n elif response_format is not None:\n return \"generate_structured_response\"\n else:\n return END\n\n workflow.add_conditional_edges(\n \"post_model_hook\",\n post_model_hook_router, # type: ignore[arg-type]\n path_map=post_model_hook_paths,\n )\n\n workflow.add_conditional_edges(\n \"agent\",\n should_continue, # type: ignore[arg-type]\n path_map=agent_paths,\n )\n\n def route_tool_responses(state: StateSchema) -> str:\n for m in reversed(_get_state_value(state, \"messages\")):\n if not isinstance(m, ToolMessage):\n break\n if m.name in should_return_direct:\n return END\n\n # handle a case of parallel tool calls where\n # the tool w/ `return_direct` was executed in a different `Send`\n if isinstance(m, AIMessage) and m.tool_calls:\n if any(call[\"name\"] in should_return_direct for call in m.tool_calls):\n return END\n\n return entrypoint\n\n if should_return_direct:\n workflow.add_conditional_edges(\n \"tools\", route_tool_responses, path_map=[entrypoint, END]\n )\n else:\n workflow.add_edge(\"tools\", entrypoint)\n\n # Finally, we compile it!\n # This compiles it into a LangChain Runnable,\n # meaning you can use it as you would any other runnable\n return workflow.compile(\n checkpointer=checkpointer,\n store=store,\n interrupt_before=interrupt_before,\n interrupt_after=interrupt_after,\n debug=debug,\n name=name,\n )", + "new_str": " \"\"\"\n # Parameter validation for deprecated kwargs\n if deprecated_kwargs:\n raise ValueError(f\"Unexpected keyword arguments: {list(deprecated_kwargs.keys())}\")\n \n # _AgentBuilder instantiation with all parameters\n builder = _AgentBuilder(\n model=model,\n tools=tools,\n prompt=prompt,\n response_format=response_format,\n pre_model_hook=pre_model_hook,\n post_model_hook=post_model_hook,\n state_schema=state_schema,\n context_schema=context_schema,\n checkpointer=checkpointer,\n store=store,\n interrupt_before=interrupt_before,\n interrupt_after=interrupt_after,\n debug=debug,\n version=version,\n name=name,\n )\n \n # Return builder.build()\n return builder.build()" + }, + "id": "toolu_01NGeTGWsyunCwGaq17UpEDB", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "71ad7ec3-e97f-4355-bbc1-66cf3b5d479f", + "tool_call_id": "toolu_01NGeTGWsyunCwGaq17UpEDB", + "content": "Successfully replaced text in /home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py at exactly one location.", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 9) - refactoring the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me verify that all the required functionality has been implemented correctly:" + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_01UX2xuCoHaFeeKWkPptP5QT", + "name": "str_replace_based_edit_tool", + "input": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1083,1110]}" + } + ], + "additional_kwargs": { + "id": "msg_01BKTq6Kv9HgiS1khtDpoSJs", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 5405, + "cache_read_input_tokens": 66183, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_01UX2xuCoHaFeeKWkPptP5QT", + "index": 1, + "name": "str_replace_based_edit_tool", + "args": "{\"command\": \"view\", \"path\": \"/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py\", \"view_range\": [1083,1110]}" + } + ], + "id": "msg_01BKTq6Kv9HgiS1khtDpoSJs", + "usage_metadata": { + "input_tokens": 1166, + "output_tokens": 195, + "total_tokens": 1361, + "input_token_details": { + "cache_read": 66183, + "cache_creation": 5405 + } + }, + "tool_calls": [ + { + "name": "str_replace_based_edit_tool", + "args": { + "command": "view", + "path": "/home/daytona/langgraph/libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py", + "view_range": [1083, 1110] + }, + "id": "toolu_01UX2xuCoHaFeeKWkPptP5QT", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "e03e38e2-3e8b-446e-bbb0-f380a39d6bc5", + "tool_call_id": "toolu_01UX2xuCoHaFeeKWkPptP5QT", + "content": "1083: \"\"\"\n1084: # Parameter validation for deprecated kwargs\n1085: if deprecated_kwargs:\n1086: raise ValueError(f\"Unexpected keyword arguments: {list(deprecated_kwargs.keys())}\")\n1087: \n1088: # _AgentBuilder instantiation with all parameters\n1089: builder = _AgentBuilder(\n1090: model=model,\n1091: tools=tools,\n1092: prompt=prompt,\n1093: response_format=response_format,\n1094: pre_model_hook=pre_model_hook,\n1095: post_model_hook=post_model_hook,\n1096: state_schema=state_schema,\n1097: context_schema=context_schema,\n1098: checkpointer=checkpointer,\n1099: store=store,\n1100: interrupt_before=interrupt_before,\n1101: interrupt_after=interrupt_after,\n1102: debug=debug,\n1103: version=version,\n1104: name=name,\n1105: )\n1106: \n1107: # Return builder.build()\n1108: return builder.build()\n1109: \n1110: ", + "name": "str_replace_based_edit_tool", + "status": "success", + "additional_kwargs": {}, + "response_metadata": {} + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "AIMessageChunk"], + "kwargs": { + "content": [ + { + "index": 0, + "type": "text", + "text": "Perfect! I can see that I've successfully completed the current task (index 9) - refactoring the `create_react_agent` function to replace its entire function body with a call to `_AgentBuilder`. Let me verify that all the required functionality has been implemented correctly:\n\n✅ **Parameter validation for deprecated kwargs** - Done:\n- Added check for `deprecated_kwargs` and raises ValueError if any unexpected kwargs are present\n\n✅ **_AgentBuilder instantiation with all parameters** - Done:\n- Creates `_AgentBuilder` instance with all parameters from the function signature\n- Passes all parameters: model, tools, prompt, response_format, pre_model_hook, post_model_hook, state_schema, context_schema, checkpointer, store, interrupt_before, interrupt_after, debug, version, name\n\n✅ **Return builder.build()** - Done:\n- Calls and returns the result of `builder.build()`\n\n✅ **Preserve exact same function signature and docstring** - Done:\n- Function signature remains unchanged\n- Docstring remains unchanged\n- Only the function body has been replaced\n\nThe refactor successfully replaces the large (~700 lines) function body with a clean, maintainable implementation that delegates to the `_AgentBuilder` class while preserving all existing functionality and the public API." + }, + { + "index": 1, + "type": "tool_use", + "id": "toolu_012XVkG2kMdKbK2oBHr69qK1", + "name": "mark_task_completed", + "input": "{\"completed_task_summary\": \"Successfully refactored the `create_react_agent` function by replacing its entire function body (originally ~700 lines) with a clean implementation that validates deprecated kwargs, instantiates `_AgentBuilder` with all parameters (model, tools, prompt, response_format, pre_model_hook, post_model_hook, state_schema, context_schema, checkpointer, store, interrupt_before, interrupt_after, debug, version, name), and returns `builder.build()`. The refactor preserves the exact same function signature and docstring while delegating all the complex logic to the `_AgentBuilder` class. This transformation maintains full backward compatibility and all existing functionality while significantly improving code maintainability and readability by encapsulating the agent construction logic in a dedicated helper class.\"}" + } + ], + "additional_kwargs": { + "id": "msg_01H6bw18X1hZboc8jaUzaLuC", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-20250514", + "stop_reason": "tool_use", + "stop_sequence": null + }, + "response_metadata": { + "usage": { + "cache_creation_input_tokens": 506, + "cache_read_input_tokens": 71588, + "service_tier": "standard" + } + }, + "tool_call_chunks": [ + { + "id": "toolu_012XVkG2kMdKbK2oBHr69qK1", + "index": 1, + "name": "mark_task_completed", + "args": "{\"completed_task_summary\": \"Successfully refactored the `create_react_agent` function by replacing its entire function body (originally ~700 lines) with a clean implementation that validates deprecated kwargs, instantiates `_AgentBuilder` with all parameters (model, tools, prompt, response_format, pre_model_hook, post_model_hook, state_schema, context_schema, checkpointer, store, interrupt_before, interrupt_after, debug, version, name), and returns `builder.build()`. The refactor preserves the exact same function signature and docstring while delegating all the complex logic to the `_AgentBuilder` class. This transformation maintains full backward compatibility and all existing functionality while significantly improving code maintainability and readability by encapsulating the agent construction logic in a dedicated helper class.\"}" + } + ], + "id": "msg_01H6bw18X1hZboc8jaUzaLuC", + "usage_metadata": { + "input_tokens": 1165, + "output_tokens": 528, + "total_tokens": 1693, + "input_token_details": { + "cache_read": 71588, + "cache_creation": 506 + } + }, + "tool_calls": [ + { + "name": "mark_task_completed", + "args": { + "completed_task_summary": "Successfully refactored the `create_react_agent` function by replacing its entire function body (originally ~700 lines) with a clean implementation that validates deprecated kwargs, instantiates `_AgentBuilder` with all parameters (model, tools, prompt, response_format, pre_model_hook, post_model_hook, state_schema, context_schema, checkpointer, store, interrupt_before, interrupt_after, debug, version, name), and returns `builder.build()`. The refactor preserves the exact same function signature and docstring while delegating all the complex logic to the `_AgentBuilder` class. This transformation maintains full backward compatibility and all existing functionality while significantly improving code maintainability and readability by encapsulating the agent construction logic in a dedicated helper class." + }, + "id": "toolu_012XVkG2kMdKbK2oBHr69qK1", + "type": "tool_call" + } + ], + "invalid_tool_calls": [] + } + }, + { + "lc": 1, + "type": "constructor", + "id": ["langchain_core", "messages", "ToolMessage"], + "kwargs": { + "id": "a9ab7b94-8235-422a-ae5e-adf96ce7d953", + "tool_call_id": "toolu_012XVkG2kMdKbK2oBHr69qK1", + "content": "Saved task status as completed for task Refactor create_react_agent function in libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py to replace the entire function body (lines 458-941) with: parameter validation for deprecated kwargs, _AgentBuilder instantiation with all parameters, and return builder.build() while preserving the exact same function signature and docstring", + "name": "mark_task_completed", + "additional_kwargs": {}, + "response_metadata": {} + } + } + ] +} diff --git a/apps/open-swe/src/__tests__/tokens.test.ts b/apps/open-swe/src/__tests__/tokens.test.ts index 9a5cd860..14344059 100644 --- a/apps/open-swe/src/__tests__/tokens.test.ts +++ b/apps/open-swe/src/__tests__/tokens.test.ts @@ -1,10 +1,19 @@ +import fs from "fs"; +import path from "path"; +import { fileURLToPath } from "url"; import { describe, it, expect } from "@jest/globals"; -import { AIMessage, HumanMessage, ToolMessage } from "@langchain/core/messages"; +import { + AIMessage, + coerceMessageLikeToMessage, + HumanMessage, + ToolMessage, +} from "@langchain/core/messages"; import { calculateConversationHistoryTokenCount, getMessagesSinceLastSummary, MAX_INTERNAL_TOKENS, } from "../utils/tokens.js"; +import { GraphState } from "@open-swe/shared/open-swe/types"; describe("calculateConversationHistoryTokenCount", () => { it("should return 0 for empty messages array", async () => { @@ -228,14 +237,20 @@ describe("getMessagesSinceLastSummary", () => { }); it("should return messages after the last summary message", async () => { - const summaryMessage = new AIMessage({ + const summaryAIMessage = new AIMessage({ + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); const messages = [ new HumanMessage({ content: "Message 1" }), - summaryMessage, + summaryAIMessage, + summaryToolMessage, new HumanMessage({ content: "Message 3" }), new AIMessage({ content: "Message 4" }), ]; @@ -246,14 +261,59 @@ describe("getMessagesSinceLastSummary", () => { expect(result[1].content).toBe("Message 4"); }); + it("should return messages after the last summary message, when there are multiple", async () => { + const summaryAIMessage1 = new AIMessage({ + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); + const summaryToolMessage1 = new ToolMessage({ + tool_call_id: "tool-call-id-1", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); + + const summaryAIMessage2 = new AIMessage({ + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); + const summaryToolMessage2 = new ToolMessage({ + tool_call_id: "tool-call-id-1", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); + + const messages = [ + new HumanMessage({ content: "Message 1" }), + summaryAIMessage1, + summaryToolMessage1, + new HumanMessage({ content: "Message 4" }), + new AIMessage({ content: "Message 5" }), + summaryAIMessage2, + summaryToolMessage2, + new HumanMessage({ content: "Message 8" }), + new AIMessage({ content: "Message 9" }), + ]; + + const result = await getMessagesSinceLastSummary(messages); + expect(result).toHaveLength(2); + expect(result[0].content).toBe("Message 8"); + expect(result[1].content).toBe("Message 9"); + }); + it("should exclude hidden messages when option is provided", async () => { const summaryMessage = new AIMessage({ content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "Visible message" }), new HumanMessage({ content: "Hidden message", @@ -276,9 +336,15 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "Message 1" }), new AIMessage({ content: "Message 2" }), new HumanMessage({ content: "Message 3" }), @@ -298,9 +364,15 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "Message 1" }), new HumanMessage({ content: "Hidden message", @@ -325,6 +397,11 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const aiMessageWithToolCalls = new AIMessage({ content: "I'll help you with that", @@ -344,6 +421,7 @@ describe("getMessagesSinceLastSummary", () => { const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "First message" }), aiMessageWithToolCalls, toolMessage, @@ -365,6 +443,11 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const aiMessageWithToolCalls = new AIMessage({ content: "I'll use multiple tools", @@ -394,6 +477,7 @@ describe("getMessagesSinceLastSummary", () => { const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "First message" }), aiMessageWithToolCalls, toolMessage1, @@ -416,6 +500,11 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const aiMessageWithToolCalls = new AIMessage({ content: "I'll use a tool", @@ -435,6 +524,7 @@ describe("getMessagesSinceLastSummary", () => { const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "First message" }), aiMessageWithToolCalls, toolMessage, @@ -455,6 +545,11 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const aiMessageWithMultipleToolCalls = new AIMessage({ content: "I'll use multiple tools to help you", @@ -494,6 +589,7 @@ describe("getMessagesSinceLastSummary", () => { const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "First message" }), aiMessageWithMultipleToolCalls, searchToolMessage, @@ -522,6 +618,11 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const aiMessageWithMultipleToolCalls = new AIMessage({ content: "I'll use two tools", @@ -551,6 +652,7 @@ describe("getMessagesSinceLastSummary", () => { const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "First message" }), aiMessageWithMultipleToolCalls, tool1Message, @@ -579,6 +681,11 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }); + const summaryToolMessage = new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }); const aiMessageWithToolCalls = new AIMessage({ content: "I'll use a tool", @@ -598,6 +705,7 @@ describe("getMessagesSinceLastSummary", () => { const messages = [ summaryMessage, + summaryToolMessage, new HumanMessage({ content: "First message" }), aiMessageWithToolCalls, toolMessage, @@ -625,11 +733,41 @@ describe("getMessagesSinceLastSummary", () => { content: "Summary of conversation", additional_kwargs: { summary_message: true }, }), + new ToolMessage({ + tool_call_id: "tool-call-id", + content: "Summary of conversation", + additional_kwargs: { summary_message: true }, + }), ]; const result = await getMessagesSinceLastSummary(messages); expect(result).toHaveLength(0); }); + + it("retains the last summary tool messages from a real trace", async () => { + const __dirname = path.dirname(fileURLToPath(import.meta.url)); + const basePath = path.join(__dirname, "data"); + const inputs: GraphState = JSON.parse( + fs.readFileSync( + path.join(basePath, "summarize-history-input.json"), + "utf-8", + ), + ); + + const conversationHistoryToSummarize = await getMessagesSinceLastSummary( + inputs.internalMessages.map(coerceMessageLikeToMessage), + { + excludeHiddenMessages: true, + excludeCountFromEnd: 20, + }, + ); + + const expectedToolMessageId = "465097e3-3c65-4af1-beb5-c3d9444219fd"; + const toolMessageExists = conversationHistoryToSummarize.find( + (m) => m.id === expectedToolMessageId, + ); + expect(toolMessageExists).not.toBeDefined(); + }); }); describe("MAX_INTERNAL_TOKENS constant", () => { diff --git a/apps/open-swe/src/utils/tokens.ts b/apps/open-swe/src/utils/tokens.ts index c8dd7859..7089afaf 100644 --- a/apps/open-swe/src/utils/tokens.ts +++ b/apps/open-swe/src/utils/tokens.ts @@ -139,12 +139,12 @@ export function getMessagesSinceLastSummaryFunc( excludeCountFromEnd?: number; }, ): BaseMessage[] { - // Find the index of the last summary message - const lastSummaryIndex = messages.findIndex( - (m) => m.additional_kwargs?.summary_message, + // Find the last summary tool message (summary_messages are AI/tool pairs) + const lastSummaryIndex = messages.findLastIndex( + (m) => m.additional_kwargs?.summary_message && isToolMessage(m), ); - // Get all messages after the last summary message + // Get all messages after the last summary_message let messagesAfterLastSummary = lastSummaryIndex >= 0 ? messages.slice(lastSummaryIndex + 1)