fix: recover from empty tool call names (#4008)

* fix: recover from empty tool call names

* test: harden empty tool call recovery

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
This commit is contained in:
Huixin615
2026-07-09 16:40:10 +08:00
committed by GitHub
co-authored by Willem Jiang
parent 5ba25b06ec
commit c30c8ef759
3 changed files with 276 additions and 19 deletions
+1 -1
View File
@@ -224,7 +224,7 @@ Lead-agent middlewares are assembled in strict order across three functions: the
4. **ThreadDataMiddleware** - Creates per-thread directories under the user's isolation scope (`backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); resolves `user_id` via `get_effective_user_id()` (falls back to `"default"` in no-auth mode)
5. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation (lead agent only)
6. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
7. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., user interruption), preserving raw provider tool-call payloads in `additional_kwargs["tool_calls"]`
7. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., user interruption), preserving raw provider tool-call payloads in `additional_kwargs["tool_calls"]`; malformed empty tool-call names are normalized to a recoverable error so strict OpenAI-compatible providers do not reject the next request
8. **LLMErrorHandlingMiddleware** - Normalizes provider/model invocation failures into recoverable assistant-facing errors before later stages run
9. **GuardrailMiddleware** - *(optional, if `guardrails.enabled`)* Pre-tool-call authorization via pluggable `GuardrailProvider`; returns an error ToolMessage on deny. Providers: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md)
10. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution
@@ -30,6 +30,20 @@ logger = logging.getLogger(__name__)
# payloads in invalid tool-call args. Keep recovery error details short so the
# synthetic ToolMessage does not echo large or malformed content back to the model.
_MAX_RECOVERY_ERROR_DETAIL_LEN = 500
_UNKNOWN_TOOL_NAME = "unknown_tool"
_EMPTY_TOOL_NAME_ERROR = "Tool call could not be executed because its name was missing or empty."
def _valid_tool_name(name: object) -> bool:
return isinstance(name, str) and bool(name.strip())
def _normalize_tool_name(name: object) -> str:
return name.strip() if _valid_tool_name(name) else _UNKNOWN_TOOL_NAME
def _has_invalid_tool_name(name: object) -> bool:
return not _valid_tool_name(name)
class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
@@ -54,7 +68,16 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
normalized: list[dict] = []
tool_calls = getattr(msg, "tool_calls", None) or []
normalized.extend(list(tool_calls))
for tool_call in tool_calls:
if not isinstance(tool_call, dict):
logger.debug("Skipping malformed non-dict tool_call in AIMessage: %r", tool_call)
continue
original_name = tool_call.get("name")
normalized_call = dict(tool_call)
normalized_call["name"] = _normalize_tool_name(original_name)
if _has_invalid_tool_name(original_name):
normalized_call["invalid_tool_name"] = True
normalized.append(normalized_call)
raw_tool_calls = (getattr(msg, "additional_kwargs", None) or {}).get("tool_calls") or []
if not tool_calls:
@@ -77,31 +100,36 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
parsed_args = {}
args = parsed_args if isinstance(parsed_args, dict) else {}
normalized.append(
{
"id": raw_tc.get("id"),
"name": name or "unknown",
"args": args if isinstance(args, dict) else {},
}
)
normalized_call = {
"id": raw_tc.get("id"),
"name": _normalize_tool_name(name),
"args": args if isinstance(args, dict) else {},
}
if _has_invalid_tool_name(name):
normalized_call["invalid_tool_name"] = True
normalized.append(normalized_call)
for invalid_tc in getattr(msg, "invalid_tool_calls", None) or []:
if not isinstance(invalid_tc, dict):
continue
normalized.append(
{
"id": invalid_tc.get("id"),
"name": invalid_tc.get("name") or "unknown",
"args": {},
"invalid": True,
"error": invalid_tc.get("error"),
}
)
original_name = invalid_tc.get("name")
normalized_call = {
"id": invalid_tc.get("id"),
"name": _normalize_tool_name(original_name),
"args": {},
"invalid": True,
"error": invalid_tc.get("error"),
}
if _has_invalid_tool_name(original_name):
normalized_call["invalid_tool_name"] = True
normalized.append(normalized_call)
return normalized
@staticmethod
def _synthetic_tool_message_content(tool_call: dict) -> str:
if tool_call.get("invalid_tool_name"):
return f"[{_EMPTY_TOOL_NAME_ERROR} Use one of the available tool names when retrying.]"
if tool_call.get("invalid"):
name = tool_call.get("name")
error = tool_call.get("error")
@@ -125,6 +153,69 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
return "[Tool call could not be executed because its arguments were invalid.]"
return "[Tool call was interrupted and did not return a result.]"
@staticmethod
def _sanitize_ai_message_tool_names(msg):
"""Return an AIMessage with model-bound tool-call names made non-empty."""
if getattr(msg, "type", None) != "ai":
return msg
changed = False
update: dict = {}
tool_calls = getattr(msg, "tool_calls", None)
if tool_calls:
structured_changed = False
sanitized_tool_calls = []
for tool_call in tool_calls:
if not isinstance(tool_call, dict):
sanitized_tool_calls.append(tool_call)
continue
name = tool_call.get("name")
sanitized = dict(tool_call)
normalized_name = _normalize_tool_name(name)
if sanitized.get("name") != normalized_name:
sanitized["name"] = normalized_name
structured_changed = True
sanitized_tool_calls.append(sanitized)
if structured_changed:
update["tool_calls"] = sanitized_tool_calls
changed = True
additional_kwargs = dict(getattr(msg, "additional_kwargs", {}) or {})
raw_tool_calls = additional_kwargs.get("tool_calls")
if isinstance(raw_tool_calls, list):
raw_changed = False
sanitized_raw_tool_calls = []
for raw_tool_call in raw_tool_calls:
if not isinstance(raw_tool_call, dict):
sanitized_raw_tool_calls.append(raw_tool_call)
continue
sanitized_raw = dict(raw_tool_call)
function = sanitized_raw.get("function")
if isinstance(function, dict):
sanitized_function = dict(function)
normalized_name = _normalize_tool_name(sanitized_function.get("name"))
if sanitized_function.get("name") != normalized_name:
sanitized_function["name"] = normalized_name
sanitized_raw["function"] = sanitized_function
raw_changed = True
else:
normalized_name = _normalize_tool_name(sanitized_raw.get("name"))
if sanitized_raw.get("name") != normalized_name:
sanitized_raw["name"] = normalized_name
raw_changed = True
sanitized_raw_tool_calls.append(sanitized_raw)
if raw_changed:
additional_kwargs["tool_calls"] = sanitized_raw_tool_calls
update["additional_kwargs"] = additional_kwargs
changed = True
if not changed:
return msg
return msg.model_copy(update=update)
def _build_patched_messages(self, messages: list) -> list | None:
"""Return messages with tool results grouped after their tool-call AIMessage.
@@ -151,10 +242,13 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
if isinstance(msg, ToolMessage) and msg.tool_call_id in tool_call_ids:
continue
patched.append(msg)
sanitized_msg = self._sanitize_ai_message_tool_names(msg)
patched.append(sanitized_msg)
if getattr(msg, "type", None) != "ai":
continue
# Intentionally inspect the original message so empty names can be
# classified before the sanitized message replaces them.
for tc in self._message_tool_calls(msg):
tc_id = tc.get("id")
if not tc_id:
@@ -163,6 +257,8 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
tool_msg_queue = tool_messages_by_id.get(tc_id)
existing_tool_msg = tool_msg_queue.popleft() if tool_msg_queue else None
if existing_tool_msg is not None:
if tc.get("invalid_tool_name") and _has_invalid_tool_name(existing_tool_msg.name):
existing_tool_msg = existing_tool_msg.model_copy(update={"name": tc["name"]})
patched.append(existing_tool_msg)
else:
patched.append(
@@ -5,6 +5,10 @@ from unittest.mock import AsyncMock, MagicMock
import pytest
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
# Intentional private import: these tests lock the OpenAI serialization boundary
# that strict providers reject when assistant tool-call names are empty.
from langchain_openai.chat_models.base import _convert_message_to_dict
from deerflow.agents.middlewares.dangling_tool_call_middleware import (
DanglingToolCallMiddleware,
)
@@ -59,6 +63,27 @@ class TestBuildPatchedMessagesNoPatch:
]
assert mw._build_patched_messages(msgs) is None
def test_valid_tool_call_names_are_sanitization_noop(self):
mw = DanglingToolCallMiddleware()
msgs = [
AIMessage(
content="",
tool_calls=[_tc("bash", "call_1")],
additional_kwargs={
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {"name": "bash", "arguments": "{}"},
}
]
},
),
_tool_msg("call_1", "bash"),
]
assert mw._build_patched_messages(msgs) is None
class TestBuildPatchedMessagesPatching:
def test_single_dangling_call(self):
@@ -158,6 +183,142 @@ class TestBuildPatchedMessagesPatching:
assert patched[1].name == "bash"
assert patched[1].status == "error"
def test_empty_structured_tool_call_name_is_sanitized(self):
mw = DanglingToolCallMiddleware()
msgs = [_ai_with_tool_calls([_tc("", "empty_name_call")])]
patched = mw._build_patched_messages(msgs)
assert patched is not None
assert patched[0].tool_calls[0]["name"] == "unknown_tool"
payload = _convert_message_to_dict(patched[0])
assert payload["tool_calls"][0]["function"]["name"] == "unknown_tool"
assert isinstance(patched[1], ToolMessage)
assert patched[1].tool_call_id == "empty_name_call"
assert patched[1].name == "unknown_tool"
assert patched[1].status == "error"
assert "name was missing or empty" in patched[1].content
@pytest.mark.parametrize(
"raw_tool_call",
[
{"id": "missing_name_call", "type": "function", "function": {"arguments": "{}"}},
{"id": "non_string_name_call", "type": "function", "function": {"name": 42, "arguments": "{}"}},
],
)
def test_malformed_raw_provider_tool_call_name_is_sanitized(self, raw_tool_call):
mw = DanglingToolCallMiddleware()
msgs = [
AIMessage.model_construct(
content="",
type="ai",
tool_calls=[],
invalid_tool_calls=[],
additional_kwargs={"tool_calls": [raw_tool_call]},
response_metadata={},
)
]
patched = mw._build_patched_messages(msgs)
assert patched is not None
assert patched[0].additional_kwargs["tool_calls"][0]["function"]["name"] == "unknown_tool"
payload = _convert_message_to_dict(patched[0])
assert payload["tool_calls"][0]["function"]["name"] == "unknown_tool"
assert patched[1].name == "unknown_tool"
assert patched[1].status == "error"
def test_existing_tool_result_still_sanitizes_empty_structured_tool_call_name(self):
mw = DanglingToolCallMiddleware()
msgs = [
_ai_with_tool_calls([_tc(" ", "empty_name_call")]),
ToolMessage(content="Error: invalid tool", tool_call_id="empty_name_call", name=""),
]
patched = mw._build_patched_messages(msgs)
assert patched is not None
assert patched[0].tool_calls[0]["name"] == "unknown_tool"
payload = _convert_message_to_dict(patched[0])
assert payload["tool_calls"][0]["function"]["name"] == "unknown_tool"
assert patched[1].tool_call_id == "empty_name_call"
assert patched[1].name == "unknown_tool"
def test_raw_provider_tool_call_empty_function_name_is_sanitized(self):
mw = DanglingToolCallMiddleware()
msgs = [
AIMessage(
content="",
tool_calls=[],
additional_kwargs={
"tool_calls": [
{
"id": "raw_empty_name_call",
"type": "function",
"function": {"name": "", "arguments": "{}"},
}
]
},
)
]
patched = mw._build_patched_messages(msgs)
assert patched is not None
raw_tool_call = patched[0].additional_kwargs["tool_calls"][0]
assert raw_tool_call["function"]["name"] == "unknown_tool"
payload = _convert_message_to_dict(patched[0])
assert payload["tool_calls"][0]["function"]["name"] == "unknown_tool"
assert patched[1].tool_call_id == "raw_empty_name_call"
assert patched[1].name == "unknown_tool"
assert patched[1].status == "error"
assert "name was missing or empty" in patched[1].content
def test_valid_structured_call_with_empty_raw_provider_name_is_sanitized(self):
mw = DanglingToolCallMiddleware()
msgs = [
AIMessage.model_construct(
content="",
type="ai",
tool_calls=[_tc("bash", "call_1")],
invalid_tool_calls=[],
additional_kwargs={
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {"name": "", "arguments": "{}"},
}
]
},
response_metadata={},
),
_tool_msg("call_1", "bash"),
]
patched = mw._build_patched_messages(msgs)
assert patched is not None
assert patched[0].tool_calls[0]["name"] == "bash"
raw_tool_call = patched[0].additional_kwargs["tool_calls"][0]
assert raw_tool_call["function"]["name"] == "unknown_tool"
payload = _convert_message_to_dict(patched[0])
assert payload["tool_calls"][0]["function"]["name"] == "bash"
assert patched[1].tool_call_id == "call_1"
assert patched[1].name == "bash"
def test_empty_name_invalid_tool_call_uses_name_recovery_message(self):
mw = DanglingToolCallMiddleware()
msgs = [_ai_with_invalid_tool_calls([_invalid_tc(name="", tc_id="empty_invalid_call")])]
patched = mw._build_patched_messages(msgs)
assert patched is not None
assert patched[1].tool_call_id == "empty_invalid_call"
assert patched[1].name == "unknown_tool"
assert "name was missing or empty" in patched[1].content
assert "arguments were invalid" not in patched[1].content
def test_non_adjacent_tool_result_is_moved_next_to_tool_call(self):
middleware = DanglingToolCallMiddleware()
msgs = [