feat: restore forced tool call to prevent premature run stops (#1622)

Restore the ensure_no_empty_msg middleware and the always-call-a-tool system-prompt instruction that #1535 removed. When the model emits a message with no tool call (and hasn't already messaged the user or confirmed completion), the middleware re-injects a no_op / confirming_completion tool call so the run continues instead of ending mid-task.

Shipping to test whether it fixes runs that stop halfway through.

Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>

AGENTS.md

@@ -68,12 +68,13 @@ Configured in `agent/server.py:get_agent`, runs around every model call (in this
 3. `ToolErrorMiddleware` — catches tool exceptions and surfaces them as tool messages.
 4. `check_message_queue_before_model` — pulls Linear comments / Slack messages that arrived mid-run from the thread queue and injects them as user messages before the next LLM call. This is what makes "message the agent while it's working" work.
 5. `SlackAssistantStatusMiddleware` — keeps the Slack "assistant is typing"-style status up to date around model calls.
-6. `notify_step_limit_reached` — after-agent hook that posts a Slack reply when the agent hits the step limit, so the user gets a clear signal instead of silence.
-7. `SandboxCircuitBreakerMiddleware` — trips the agent out of repeated sandbox failures instead of looping.
-8. `ModelFallbackMiddleware` (optional) — added only when `LLM_FALLBACK_MODEL_ID` or the per-model default fallback differs from the primary model.
-9. `SanitizeThinkingBlocksMiddleware` — strips malformed empty Anthropic thinking blocks immediately before provider calls.
+6. `ensure_no_empty_msg` — after-model hook; when the model emits a message with no tool call (and hasn't already messaged the user or confirmed completion) it re-injects a synthetic `no_op` / `confirming_completion` tool call so the run continues instead of ending prematurely.
+7. `notify_step_limit_reached` — after-agent hook that posts a Slack reply when the agent hits the step limit, so the user gets a clear signal instead of silence.
+8. `SandboxCircuitBreakerMiddleware` — trips the agent out of repeated sandbox failures instead of looping.
+9. `ModelFallbackMiddleware` (optional) — added only when `LLM_FALLBACK_MODEL_ID` or the per-model default fallback differs from the primary model.
+10. `SanitizeThinkingBlocksMiddleware` — strips malformed empty Anthropic thinking blocks immediately before provider calls.
 
-The agent ends its turn naturally when the model emits a final message with no tool call; there is intentionally no middleware that forces a tool call on every turn.
+The system prompt instructs the agent to call a tool every turn, and `ensure_no_empty_msg` re-injects a tool call when it doesn't — together these keep runs from stopping partway through a task.
 
 Other middleware exists in `agent/middleware/` (`ExcludeToolsMiddleware`) but isn't wired into the default agent. The reviewer uses a leaner stack: `SanitizeToolInputsMiddleware`, `ModelCallLimitMiddleware`, `ToolErrorMiddleware`, `SlackAssistantStatusMiddleware`, `SanitizeThinkingBlocksMiddleware`.
 

CLAUDE.md

@@ -65,11 +65,12 @@ Configured in `agent/server.py:get_agent`, runs around every model call (in this
 3. `ToolErrorMiddleware` — catches tool exceptions and surfaces them as tool messages.
 4. `check_message_queue_before_model` — pulls Linear comments / Slack messages that arrived mid-run from the thread queue and injects them as user messages before the next LLM call. This is what makes "message the agent while it's working" work.
 5. `SlackAssistantStatusMiddleware` — keeps the Slack "assistant is typing"-style status up to date around model calls.
-6. `notify_step_limit_reached` — after-agent hook that posts a Slack reply when the agent hits the step limit, so the user gets a clear signal instead of silence.
-7. `SandboxCircuitBreakerMiddleware` — trips the agent out of repeated sandbox failures instead of looping.
-8. `ModelFallbackMiddleware` (optional, last) — added only when `LLM_FALLBACK_MODEL_ID` or the per-model default fallback differs from the primary model.
+6. `ensure_no_empty_msg` — after-model hook; when the model emits a message with no tool call (and hasn't already messaged the user or confirmed completion) it re-injects a synthetic `no_op` / `confirming_completion` tool call so the run continues instead of ending prematurely.
+7. `notify_step_limit_reached` — after-agent hook that posts a Slack reply when the agent hits the step limit, so the user gets a clear signal instead of silence.
+8. `SandboxCircuitBreakerMiddleware` — trips the agent out of repeated sandbox failures instead of looping.
+9. `ModelFallbackMiddleware` (optional, last) — added only when `LLM_FALLBACK_MODEL_ID` or the per-model default fallback differs from the primary model.
 
-The agent ends its turn naturally when the model emits a final message with no tool call; there is intentionally no middleware that forces a tool call on every turn.
+The system prompt instructs the agent to call a tool every turn, and `ensure_no_empty_msg` re-injects a tool call when it doesn't — together these keep runs from stopping partway through a task.
 
 Other middleware exists in `agent/middleware/` (`ExcludeToolsMiddleware`) but isn't wired into the default agent. The reviewer uses a leaner stack: `SanitizeToolInputsMiddleware`, `ModelCallLimitMiddleware`, `ToolErrorMiddleware`, `SlackAssistantStatusMiddleware`.
 

CUSTOMIZATION.md

@@ -17,6 +17,7 @@ return create_deep_agent(
     middleware=[
         ToolErrorMiddleware(),
         check_message_queue_before_model,
+        ensure_no_empty_msg,
         notify_step_limit_reached,
     ],
 )
@@ -465,6 +466,7 @@ Middleware hooks run around the agent loop. Open SWE includes:
 |---|---|---|
 | `ToolErrorMiddleware` | Tool error handler | Catches and formats tool errors |
 | `check_message_queue_before_model` | Before model | Injects follow-up messages that arrived mid-run |
+| `ensure_no_empty_msg` | After model | Re-injects a tool call when the model stops without one, so runs don't end prematurely |
 | `notify_step_limit_reached` | After agent | Posts a Slack reply when the agent hits the model-call limit |
 
 There is intentionally no after-agent middleware that opens a PR for the agent. The agent is responsible for committing, pushing, opening/updating the draft PR, and replying in the source channel. If you want a deterministic backstop for your fork, add an `@after_agent` hook here.
@@ -490,6 +492,7 @@ Then add it to the middleware list:
 middleware=[
     ToolErrorMiddleware(),
     check_message_queue_before_model,
+    ensure_no_empty_msg,
     notify_step_limit_reached,
     run_ci_check,  # new middleware
 ],

agent/middleware/__init__.py

@@ -1,4 +1,5 @@
 from .check_message_queue import check_message_queue_before_model
+from .ensure_no_empty_msg import ensure_no_empty_msg
 from .exclude_tools import ExcludeToolsMiddleware
 from .model_fallback import ModelFallbackMiddleware
 from .notify_step_limit import notify_step_limit_reached
@@ -27,6 +28,7 @@ __all__ = [
     "SandboxCircuitBreakerMiddleware",
     "SlackAssistantStatusMiddleware",
     "check_message_queue_before_model",
+    "ensure_no_empty_msg",
     "notify_step_limit_reached",
     "refresh_github_proxy_before_model",
     "settle_review_check_on_exit",

agent/middleware/ensure_no_empty_msg.py

@@ -0,0 +1,124 @@
+from typing import Any
+from uuid import uuid4
+
+from langchain.agents.middleware import AgentState, after_model
+from langchain_core.messages import AnyMessage, ToolMessage
+from langgraph.config import get_config
+from langgraph.runtime import Runtime
+
+from .check_message_queue import DASHBOARD_HANDOFF_MARKER
+
+_DASHBOARD_SOURCE = "dashboard"
+
+
+def get_every_message_since_last_human(state: AgentState) -> list[AnyMessage]:
+    messages = state["messages"]
+    last_human_idx = -1
+    for i in range(len(messages) - 1, -1, -1):
+        if messages[i].type == "human":
+            last_human_idx = i
+            break
+    return messages[last_human_idx + 1 :]
+
+
+def check_if_model_messaged_user(messages: list[AnyMessage]) -> bool:
+    for msg in messages:
+        if msg.type == "tool" and msg.name in [
+            "slack_thread_reply",
+            "linear_comment",
+        ]:
+            return True
+    return False
+
+
+def check_if_confirming_completion(messages: list[AnyMessage]) -> bool:
+    for msg in messages:
+        if msg.type == "tool" and msg.name == "confirming_completion":
+            return True
+    return False
+
+
+def check_if_no_op(messages: list[AnyMessage]) -> bool:
+    for msg in messages:
+        if msg.type == "tool" and msg.name == "no_op":
+            return True
+    return False
+
+
+def _content_contains_text(content: object, text: str) -> bool:
+    if isinstance(content, str):
+        return text in content
+    if not isinstance(content, list):
+        return False
+    for block in content:
+        if isinstance(block, dict) and text in str(block.get("text", "")):
+            return True
+    return False
+
+
+def _last_human_is_dashboard_handoff(state: AgentState) -> bool:
+    for msg in reversed(state["messages"]):
+        if msg.type == "human":
+            return _content_contains_text(msg.content, DASHBOARD_HANDOFF_MARKER)
+    return False
+
+
+def _is_dashboard_source() -> bool:
+    try:
+        config = get_config()
+    except RuntimeError:
+        return False
+    configurable = config.get("configurable", {})
+    if not isinstance(configurable, dict):
+        return False
+    return configurable.get("source") == _DASHBOARD_SOURCE
+
+
+@after_model
+def ensure_no_empty_msg(state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
+    last_msg = state["messages"][-1]
+    has_contents = bool(last_msg.text)
+    has_tool_calls = bool(last_msg.tool_calls)
+    if not has_tool_calls and not has_contents:
+        messages_since_last_human = get_every_message_since_last_human(state)
+        if check_if_no_op(messages_since_last_human):
+            return None
+
+        if check_if_model_messaged_user(messages_since_last_human):
+            return None
+
+        tc_id = str(uuid4())
+        last_msg.tool_calls = [{"name": "no_op", "args": {}, "id": tc_id}]
+        no_op_tool_msg = ToolMessage(
+            content="No operation performed."
+            + "Please continue with the task, ensuring you ALWAYS call at least one tool in"
+            + " every message unless you are absolutely sure the task has been fully completed.",
+            tool_call_id=tc_id,
+        )
+
+        return {"messages": [last_msg, no_op_tool_msg]}
+
+    if has_contents and not has_tool_calls:
+        messages_since_last_human = get_every_message_since_last_human(state)
+
+        if (
+            check_if_model_messaged_user(messages_since_last_human)
+            or check_if_confirming_completion(messages_since_last_human)
+            or _is_dashboard_source()
+            or _last_human_is_dashboard_handoff(state)
+        ):
+            return None
+
+        tc_id = str(uuid4())
+        last_msg.tool_calls = [{"name": "confirming_completion", "args": {}, "id": tc_id}]
+        no_op_tool_msg = ToolMessage(
+            content="Confirming task completion. I see you did not call a tool, which would end the task, however you haven't called a tool to message the user or open a pull request."
+            + "This may indicate premature termination - please ensure you fully complete the task before ending it. "
+            + "If you do not call any tools it will end the task.",
+            name="confirming_completion",
+            tool_call_id=tc_id,
+        )
+
+        return {"messages": [last_msg, no_op_tool_msg]}
+
+    return None

agent/prompt.py

@@ -83,7 +83,10 @@ OPEN_SWE_SHARED_BASE = """You are **Open SWE**, an open-source agent built on La
 ### Communication
 
 - Focus on the substance and keep summaries brief. Use light markdown (`###`/`####` headings, bold, code) — avoid `#`/`##` titles.
-- When delegated work to a subagent: the calling agent only sees your final message, so make it the complete answer."""
+- When delegated work to a subagent: the calling agent only sees your final message, so make it the complete answer.
+
+IMPORTANT: You must ALWAYS call a tool in EVERY SINGLE TURN. If you don't call a tool, the session will end and you won't be able to resume without the user manually restarting you.
+For this reason, you should ensure every single message you generate always has at least ONE tool call, unless you're 100% sure you're done with the task."""
 
 
 WORKING_ENV_SECTION = """### Working Environment

agent/server.py

@@ -64,6 +64,7 @@ from .middleware import (
     ToolErrorMiddleware,
     WorkflowPushGuardMiddleware,
     check_message_queue_before_model,
+    ensure_no_empty_msg,
     notify_step_limit_reached,
     refresh_github_proxy_before_model,
 )
@@ -851,6 +852,7 @@ async def get_agent(config: RunnableConfig) -> Pregel:
             refresh_github_proxy_before_model,
             check_message_queue_before_model,
             SlackAssistantStatusMiddleware(),
+            ensure_no_empty_msg,
             notify_step_limit_reached,
             SandboxCircuitBreakerMiddleware(),
             *fallback_middleware,

tests/test_ensure_no_empty_msg.py

@@ -0,0 +1,231 @@
+from unittest.mock import MagicMock, patch
+
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+
+from agent.middleware.check_message_queue import DASHBOARD_HANDOFF_INSTRUCTION
+from agent.middleware.ensure_no_empty_msg import (
+    check_if_confirming_completion,
+    check_if_model_messaged_user,
+    ensure_no_empty_msg,
+    get_every_message_since_last_human,
+)
+
+
+class TestGetEveryMessageSinceLastHuman:
+    def test_returns_messages_after_last_human(self) -> None:
+        state = {
+            "messages": [
+                HumanMessage(content="first human"),
+                AIMessage(content="ai response"),
+                HumanMessage(content="second human"),
+                AIMessage(content="final ai"),
+            ]
+        }
+
+        result = get_every_message_since_last_human(state)
+
+        assert len(result) == 1
+        assert result[0].content == "final ai"
+
+    def test_returns_all_messages_when_no_human(self) -> None:
+        state = {
+            "messages": [
+                AIMessage(content="ai 1"),
+                AIMessage(content="ai 2"),
+            ]
+        }
+
+        result = get_every_message_since_last_human(state)
+
+        assert len(result) == 2
+        assert result[0].content == "ai 1"
+        assert result[1].content == "ai 2"
+
+    def test_returns_empty_when_human_is_last(self) -> None:
+        state = {
+            "messages": [
+                AIMessage(content="ai response"),
+                HumanMessage(content="human last"),
+            ]
+        }
+
+        result = get_every_message_since_last_human(state)
+
+        assert len(result) == 0
+
+    def test_returns_multiple_messages_after_human(self) -> None:
+        state = {
+            "messages": [
+                HumanMessage(content="human"),
+                AIMessage(content="ai 1"),
+                ToolMessage(content="tool result", tool_call_id="123"),
+                AIMessage(content="ai 2"),
+            ]
+        }
+
+        result = get_every_message_since_last_human(state)
+
+        assert len(result) == 3
+        assert result[0].content == "ai 1"
+        assert result[1].content == "tool result"
+        assert result[2].content == "ai 2"
+
+
+class TestCheckIfModelMessagedUser:
+    def test_returns_true_for_slack_thread_reply(self) -> None:
+        messages = [
+            ToolMessage(content="sent", tool_call_id="123", name="slack_thread_reply"),
+        ]
+
+        assert check_if_model_messaged_user(messages) is True
+
+    def test_returns_true_for_linear_comment(self) -> None:
+        messages = [
+            ToolMessage(content="commented", tool_call_id="123", name="linear_comment"),
+        ]
+
+        assert check_if_model_messaged_user(messages) is True
+
+    def test_returns_false_for_other_tools(self) -> None:
+        messages = [
+            ToolMessage(content="result", tool_call_id="123", name="bash"),
+            ToolMessage(content="result", tool_call_id="456", name="read_file"),
+        ]
+
+        assert check_if_model_messaged_user(messages) is False
+
+    def test_returns_false_for_empty_list(self) -> None:
+        assert check_if_model_messaged_user([]) is False
+
+
+class TestCheckIfConfirmingCompletion:
+    def test_returns_true_when_confirming_completion_called(self) -> None:
+        messages = [
+            ToolMessage(content="confirmed", tool_call_id="123", name="confirming_completion"),
+        ]
+
+        assert check_if_confirming_completion(messages) is True
+
+    def test_returns_false_for_other_tools(self) -> None:
+        messages = [
+            ToolMessage(content="result", tool_call_id="123", name="bash"),
+        ]
+
+        assert check_if_confirming_completion(messages) is False
+
+    def test_returns_false_for_empty_list(self) -> None:
+        assert check_if_confirming_completion([]) is False
+
+    def test_finds_confirming_completion_among_other_messages(self) -> None:
+        messages = [
+            AIMessage(content="working"),
+            ToolMessage(content="done", tool_call_id="1", name="bash"),
+            ToolMessage(content="confirmed", tool_call_id="2", name="confirming_completion"),
+            AIMessage(content="finished"),
+        ]
+
+        assert check_if_confirming_completion(messages) is True
+
+
+class TestEnsureNoEmptyMsgNotify:
+    def _make_runtime(self) -> MagicMock:
+        return MagicMock()
+
+    def test_returns_none_when_user_messaged(self) -> None:
+        empty_ai = AIMessage(content="")
+        state = {
+            "messages": [
+                HumanMessage(content="fix the bug"),
+                ToolMessage(content="message sent", tool_call_id="1", name="slack_thread_reply"),
+                empty_ai,
+            ]
+        }
+
+        result = ensure_no_empty_msg.after_model(state, self._make_runtime())
+
+        assert result is None
+
+    def test_returns_none_with_linear_comment(self) -> None:
+        empty_ai = AIMessage(content="")
+        state = {
+            "messages": [
+                HumanMessage(content="fix the bug"),
+                ToolMessage(content="commented", tool_call_id="1", name="linear_comment"),
+                empty_ai,
+            ]
+        }
+
+        result = ensure_no_empty_msg.after_model(state, self._make_runtime())
+
+        assert result is None
+
+    def test_injects_no_op_when_user_not_messaged(self) -> None:
+        empty_ai = AIMessage(content="")
+        state = {
+            "messages": [
+                HumanMessage(content="fix the bug"),
+                ToolMessage(content="result", tool_call_id="1", name="bash"),
+                empty_ai,
+            ]
+        }
+
+        result = ensure_no_empty_msg.after_model(state, self._make_runtime())
+
+        assert result is not None
+        assert len(result["messages"]) == 2
+        assert result["messages"][0].tool_calls[0]["name"] == "no_op"
+
+    def test_returns_none_when_only_user_messaged(self) -> None:
+        empty_ai = AIMessage(content="")
+        state = {
+            "messages": [
+                HumanMessage(content="fix the bug"),
+                ToolMessage(content="message sent", tool_call_id="1", name="slack_thread_reply"),
+                empty_ai,
+            ]
+        }
+
+        result = ensure_no_empty_msg.after_model(state, self._make_runtime())
+
+        assert result is None
+
+    def test_skips_confirming_completion_for_dashboard_source(self) -> None:
+        ai = AIMessage(content="Hi! How can I help?")
+        state = {
+            "messages": [
+                HumanMessage(content="hello"),
+                ai,
+            ]
+        }
+
+        with patch(
+            "agent.middleware.ensure_no_empty_msg.get_config",
+            return_value={"configurable": {"source": "dashboard"}},
+        ):
+            result = ensure_no_empty_msg.after_model(state, self._make_runtime())
+
+        assert result is None
+        assert not ai.tool_calls
+
+    def test_skips_confirming_completion_for_dashboard_handoff(self) -> None:
+        ai = AIMessage(content="Done in web.")
+        state = {
+            "messages": [
+                HumanMessage(
+                    content=[
+                        {"type": "text", "text": DASHBOARD_HANDOFF_INSTRUCTION},
+                        {"type": "text", "text": "continue in web"},
+                    ]
+                ),
+                ai,
+            ]
+        }
+
+        with patch(
+            "agent.middleware.ensure_no_empty_msg.get_config",
+            return_value={"configurable": {"source": "slack"}},
+        ):
+            result = ensure_no_empty_msg.after_model(state, self._make_runtime())
+
+        assert result is None
+        assert not ai.tool_calls