feat: add DanglingToolCallMiddleware and SubagentLimitMiddleware

Add two new middlewares to improve robustness of the agent pipeline:
- DanglingToolCallMiddleware injects placeholder ToolMessages for
  interrupted tool calls, preventing LLM errors from malformed history
- SubagentLimitMiddleware truncates excess parallel task tool calls at
  the model response level, replacing the runtime check in task_tool

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/src/agents/middlewares/dangling_tool_call_middleware.py

@@ -0,0 +1,74 @@
+"""Middleware to fix dangling tool calls in message history.
+
+A dangling tool call occurs when an AIMessage contains tool_calls but there are
+no corresponding ToolMessages in the history (e.g., due to user interruption or
+request cancellation). This causes LLM errors due to incomplete message format.
+
+This middleware runs before the model call to detect and patch such gaps by
+inserting synthetic ToolMessages with an error indicator.
+"""
+
+import logging
+from typing import override
+
+from langchain.agents import AgentState
+from langchain.agents.middleware import AgentMiddleware
+from langchain_core.messages import ToolMessage
+from langgraph.runtime import Runtime
+
+logger = logging.getLogger(__name__)
+
+
+class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
+    """Inserts placeholder ToolMessages for dangling tool calls before model invocation.
+
+    Scans the message history for AIMessages whose tool_calls lack corresponding
+    ToolMessages, and injects synthetic error responses so the LLM receives a
+    well-formed conversation.
+    """
+
+    def _fix_dangling_tool_calls(self, state: AgentState) -> dict | None:
+        messages = state.get("messages", [])
+        if not messages:
+            return None
+
+        # Collect IDs of all existing ToolMessages
+        existing_tool_msg_ids: set[str] = set()
+        for msg in messages:
+            if isinstance(msg, ToolMessage):
+                existing_tool_msg_ids.add(msg.tool_call_id)
+
+        # Find dangling tool calls and build patch messages
+        patches: list[ToolMessage] = []
+        for msg in messages:
+            if getattr(msg, "type", None) != "ai":
+                continue
+            tool_calls = getattr(msg, "tool_calls", None)
+            if not tool_calls:
+                continue
+            for tc in tool_calls:
+                tc_id = tc.get("id")
+                if tc_id and tc_id not in existing_tool_msg_ids:
+                    patches.append(
+                        ToolMessage(
+                            content="[Tool call was interrupted and did not return a result.]",
+                            tool_call_id=tc_id,
+                            name=tc.get("name", "unknown"),
+                            status="error",
+                        )
+                    )
+                    existing_tool_msg_ids.add(tc_id)
+
+        if not patches:
+            return None
+
+        logger.warning(f"Injecting {len(patches)} placeholder ToolMessage(s) for dangling tool calls")
+        return {"messages": patches}
+
+    @override
+    def before_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._fix_dangling_tool_calls(state)
+
+    @override
+    async def abefore_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._fix_dangling_tool_calls(state)

backend/src/agents/middlewares/subagent_limit_middleware.py

@@ -0,0 +1,61 @@
+"""Middleware to enforce maximum concurrent subagent tool calls per model response."""
+
+import logging
+from typing import override
+
+from langchain.agents import AgentState
+from langchain.agents.middleware import AgentMiddleware
+from langgraph.runtime import Runtime
+
+from src.subagents.executor import MAX_CONCURRENT_SUBAGENTS
+
+logger = logging.getLogger(__name__)
+
+
+class SubagentLimitMiddleware(AgentMiddleware[AgentState]):
+    """Truncates excess 'task' tool calls from a single model response.
+
+    When an LLM generates more than MAX_CONCURRENT_SUBAGENTS parallel task tool calls
+    in one response, this middleware keeps only the first MAX_CONCURRENT_SUBAGENTS and
+    discards the rest. This is more reliable than prompt-based limits.
+    """
+
+    def _truncate_task_calls(self, state: AgentState) -> dict | None:
+        messages = state.get("messages", [])
+        if not messages:
+            return None
+
+        last_msg = messages[-1]
+        if getattr(last_msg, "type", None) != "ai":
+            return None
+
+        tool_calls = getattr(last_msg, "tool_calls", None)
+        if not tool_calls:
+            return None
+
+        # Count task tool calls
+        task_indices = [i for i, tc in enumerate(tool_calls) if tc.get("name") == "task"]
+        if len(task_indices) <= MAX_CONCURRENT_SUBAGENTS:
+            return None
+
+        # Build set of indices to drop (excess task calls beyond the limit)
+        indices_to_drop = set(task_indices[MAX_CONCURRENT_SUBAGENTS:])
+        truncated_tool_calls = [tc for i, tc in enumerate(tool_calls) if i not in indices_to_drop]
+
+        dropped_count = len(indices_to_drop)
+        logger.warning(
+            f"Truncated {dropped_count} excess task tool call(s) from model response "
+            f"(limit: {MAX_CONCURRENT_SUBAGENTS})"
+        )
+
+        # Replace the AIMessage with truncated tool_calls (same id triggers replacement)
+        updated_msg = last_msg.model_copy(update={"tool_calls": truncated_tool_calls})
+        return {"messages": [updated_msg]}
+
+    @override
+    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._truncate_task_calls(state)
+
+    @override
+    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._truncate_task_calls(state)