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Merge pull request #922 from langchain-ai/yogesh/add-error-normalization-middleware
feat: add error normalization middleware for tool and model calls [Closes #904]
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commit
b8caf4c4a1
4 changed files with 111 additions and 2 deletions
3
apps/agent/agent/middleware/__init__.py
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3
apps/agent/agent/middleware/__init__.py
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@ -0,0 +1,3 @@
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from .tool_error_handler import ToolErrorMiddleware
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__all__ = ["ToolErrorMiddleware"]
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104
apps/agent/agent/middleware/tool_error_handler.py
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104
apps/agent/agent/middleware/tool_error_handler.py
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@ -0,0 +1,104 @@
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"""Tool error handling middleware.
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Wraps all tool calls in try/except so that unhandled exceptions are
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returned as error ToolMessages instead of crashing the agent run.
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"""
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from __future__ import annotations
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import json
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import logging
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from collections.abc import Awaitable, Callable
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from langchain.agents.middleware.types import (
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AgentMiddleware,
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AgentState,
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)
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from langchain_core.messages import ToolMessage
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from langgraph.prebuilt.tool_node import ToolCallRequest
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from langgraph.types import Command
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logger = logging.getLogger(__name__)
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def _get_name(candidate: object) -> str | None:
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if not candidate:
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return None
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if isinstance(candidate, str):
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return candidate
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if isinstance(candidate, dict):
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name = candidate.get("name")
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else:
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name = getattr(candidate, "name", None)
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return name if isinstance(name, str) and name else None
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def _extract_tool_name(request: ToolCallRequest | None) -> str | None:
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if request is None:
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return None
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for attr in ("tool_call", "tool_name", "name"):
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name = _get_name(getattr(request, attr, None))
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if name:
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return name
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return None
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def _to_error_payload(e: Exception, request: ToolCallRequest | None = None) -> dict[str, str]:
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data: dict[str, str] = {
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"error": str(e),
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"error_type": e.__class__.__name__,
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"status": "error",
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}
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tool_name = _extract_tool_name(request)
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if tool_name:
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data["name"] = tool_name
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return data
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def _get_tool_call_id(request: ToolCallRequest) -> str | None:
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if isinstance(request.tool_call, dict):
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return request.tool_call.get("id")
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return None
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class ToolErrorMiddleware(AgentMiddleware):
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"""Normalize tool execution errors into predictable payloads.
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Catches any exception thrown during a tool call and converts it into
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a ToolMessage with status="error" so the LLM can see the failure and
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self-correct, rather than crashing the entire agent run.
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"""
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state_schema = AgentState
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def wrap_tool_call(
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self,
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request: ToolCallRequest,
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handler: Callable[[ToolCallRequest], ToolMessage | Command],
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) -> ToolMessage | Command:
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try:
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return handler(request)
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except Exception as e:
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logger.exception("Error during tool call handling; request=%r", request)
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data = _to_error_payload(e, request)
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return ToolMessage(
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content=json.dumps(data),
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tool_call_id=_get_tool_call_id(request),
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status="error",
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)
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async def awrap_tool_call(
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self,
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request: ToolCallRequest,
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handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
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) -> ToolMessage | Command:
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try:
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return await handler(request)
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except Exception as e:
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logger.exception("Error during tool call handling; request=%r", request)
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data = _to_error_payload(e, request)
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return ToolMessage(
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content=json.dumps(data),
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tool_call_id=_get_tool_call_id(request),
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status="error",
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)
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@ -26,13 +26,14 @@ warnings.filterwarnings("ignore", message=".*Pydantic V1.*", category=UserWarnin
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# Now safe to import agent (which imports LangChain modules)
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from deepagents import create_deep_agent
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from .protocol import SandboxBackendProtocol
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# Local import for encryption
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from langchain_anthropic import ChatAnthropic
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from .encryption import decrypt_token
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from .middleware import ToolErrorMiddleware
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from .prompt import construct_system_prompt
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from .protocol import SandboxBackendProtocol
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from .tools import commit_and_open_pr, fetch_url, http_request
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@ -959,6 +960,7 @@ async def get_agent(config: RunnableConfig) -> Pregel: # noqa: PLR0915
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tools=[http_request, fetch_url, commit_and_open_pr],
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backend=sandbox_backend,
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middleware=[
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ToolErrorMiddleware(),
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check_message_queue_before_model,
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post_to_linear_after_model,
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open_pr_if_needed,
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@ -71,7 +71,7 @@ def get_service_jwt_token_for_user(
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LINEAR_TEAM_TO_REPO: dict[str, dict[str, str]] = {
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"Brace's test workspace": {"owner": "langchain-ai", "name": "open-swe"},
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"Yogesh-dev": {"owner": "aran-yogesh", "name": "nimedge"},
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"Yogesh-dev": {"owner": "aran-yogesh", "name": "TalkBack"},
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}
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