fix: return JSON parse error to model instead of dispatching with empty args (#2342)
When the model produces malformed JSON in tool call arguments, the agent
loop was setting args={} and dispatching the tool anyway, wasting an
iteration and producing a confusing downstream error. Now the error is
returned directly as the tool result so the model can retry with valid JSON.
Co-authored-by: alireza78a <alireza78.crypto@gmail.com>
This commit is contained in:
Teknium
@@ -346,78 +346,89 @@ class HermesAgentLoop:
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tool_name, turn + 1,
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)
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else:
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# Parse arguments and dispatch
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# Parse arguments
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try:
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args = json.loads(tool_args_raw)
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except json.JSONDecodeError:
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args = {}
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except json.JSONDecodeError as e:
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args = None
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tool_result = json.dumps(
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{"error": f"Invalid JSON in tool arguments: {e}. Please retry with valid JSON."}
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)
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tool_errors.append(ToolError(
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turn=turn + 1, tool_name=tool_name,
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arguments=tool_args_raw[:200],
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error=f"Invalid JSON: {e}",
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tool_result=tool_result,
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))
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logger.warning(
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"Invalid JSON in tool call arguments for '%s': %s",
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tool_name, tool_args_raw[:200],
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)
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try:
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if tool_name == "terminal":
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backend = os.getenv("TERMINAL_ENV", "local")
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cmd_preview = args.get("command", "")[:80]
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logger.info(
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"[%s] $ %s", self.task_id[:8], cmd_preview,
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)
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# Dispatch tool only if arguments parsed successfully
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if args is not None:
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try:
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if tool_name == "terminal":
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backend = os.getenv("TERMINAL_ENV", "local")
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cmd_preview = args.get("command", "")[:80]
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logger.info(
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"[%s] $ %s", self.task_id[:8], cmd_preview,
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)
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tool_submit_time = _time.monotonic()
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tool_submit_time = _time.monotonic()
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# Todo tool -- handle locally (needs per-loop TodoStore)
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if tool_name == "todo":
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tool_result = _todo_tool(
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todos=args.get("todos"),
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merge=args.get("merge", False),
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store=_todo_store,
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)
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tool_elapsed = _time.monotonic() - tool_submit_time
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elif tool_name == "memory":
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tool_result = json.dumps({"error": "Memory is not available in RL environments."})
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tool_elapsed = _time.monotonic() - tool_submit_time
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elif tool_name == "session_search":
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tool_result = json.dumps({"error": "Session search is not available in RL environments."})
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tool_elapsed = _time.monotonic() - tool_submit_time
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else:
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# Run tool calls in a thread pool so backends that
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# use asyncio.run() internally (modal, docker, daytona) get
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# a clean event loop instead of deadlocking.
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loop = asyncio.get_event_loop()
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# Capture current tool_name/args for the lambda
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_tn, _ta, _tid = tool_name, args, self.task_id
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tool_result = await loop.run_in_executor(
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_tool_executor,
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lambda: handle_function_call(
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_tn, _ta, task_id=_tid,
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user_task=_user_task,
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),
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)
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tool_elapsed = _time.monotonic() - tool_submit_time
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# Todo tool -- handle locally (needs per-loop TodoStore)
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if tool_name == "todo":
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tool_result = _todo_tool(
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todos=args.get("todos"),
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merge=args.get("merge", False),
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store=_todo_store,
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)
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tool_elapsed = _time.monotonic() - tool_submit_time
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elif tool_name == "memory":
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tool_result = json.dumps({"error": "Memory is not available in RL environments."})
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tool_elapsed = _time.monotonic() - tool_submit_time
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elif tool_name == "session_search":
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tool_result = json.dumps({"error": "Session search is not available in RL environments."})
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tool_elapsed = _time.monotonic() - tool_submit_time
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else:
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# Run tool calls in a thread pool so backends that
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# use asyncio.run() internally (modal, docker, daytona) get
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# a clean event loop instead of deadlocking.
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loop = asyncio.get_event_loop()
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# Capture current tool_name/args for the lambda
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_tn, _ta, _tid = tool_name, args, self.task_id
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tool_result = await loop.run_in_executor(
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_tool_executor,
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lambda: handle_function_call(
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_tn, _ta, task_id=_tid,
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user_task=_user_task,
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),
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)
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tool_elapsed = _time.monotonic() - tool_submit_time
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# Log slow tools and thread pool stats for debugging
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pool_active = _tool_executor._work_queue.qsize()
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if tool_elapsed > 30:
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logger.warning(
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"[%s] turn %d: %s took %.1fs (pool queue=%d)",
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self.task_id[:8], turn + 1, tool_name,
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tool_elapsed, pool_active,
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# Log slow tools and thread pool stats for debugging
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pool_active = _tool_executor._work_queue.qsize()
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if tool_elapsed > 30:
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logger.warning(
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"[%s] turn %d: %s took %.1fs (pool queue=%d)",
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self.task_id[:8], turn + 1, tool_name,
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tool_elapsed, pool_active,
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)
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except Exception as e:
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tool_result = json.dumps(
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{"error": f"Tool execution failed: {type(e).__name__}: {str(e)}"}
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)
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tool_errors.append(ToolError(
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turn=turn + 1, tool_name=tool_name,
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arguments=tool_args_raw[:200],
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error=f"{type(e).__name__}: {str(e)}",
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tool_result=tool_result,
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))
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logger.error(
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"Tool '%s' execution failed on turn %d: %s",
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tool_name, turn + 1, e,
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)
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except Exception as e:
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tool_result = json.dumps(
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{"error": f"Tool execution failed: {type(e).__name__}: {str(e)}"}
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)
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tool_errors.append(ToolError(
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turn=turn + 1, tool_name=tool_name,
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arguments=tool_args_raw[:200],
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error=f"{type(e).__name__}: {str(e)}",
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tool_result=tool_result,
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))
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logger.error(
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"Tool '%s' execution failed on turn %d: %s",
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tool_name, turn + 1, e,
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)
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# Also check if the tool returned an error in its JSON result
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try:
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