Keep no-prose assistant tool-call messages through _sanitize_llm_messages (#862)
cb13d09 made _append_tool_results emit content=None (JSON null) for a follow-up
assistant message that carries only tool_calls and no prose, because Gemini's
OpenAI-compatible endpoint and Ollama reject tool_calls alongside an
empty-string content with HTTP 400.
But _sanitize_llm_messages strips None values and then required "content" on
every message, so it dropped that assistant message entirely — leaving the
role:"tool" result dangling with no parent tool_calls, which breaks the
follow-up round for every provider (and regresses ones that accepted "" before,
since the message is now removed rather than sent). cb13d09's tests covered
_append_tool_results in isolation, so the sanitizer interaction was uncaught.
Make the sanitizer role-aware: assistant messages survive with content OR
tool_calls, and a tool-calls-only assistant message gets an explicit
content=None re-added so the provider receives spec-correct `content: null`.
tool messages still require content + tool_call_id; user/system still require
content.
Adds tests/test_llm_core_sanitize_tool_calls.py, which drives the real producer
(_append_tool_results) into the sanitizer and asserts the assistant tool-call
message survives with its tool result paired. Red before this change, green
after.
This commit is contained in:
@@ -502,14 +502,37 @@ def _parse_anthropic_response(data: dict) -> str:
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def _sanitize_llm_messages(messages: List[Dict]) -> List[Dict]:
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"""Strip Odysseus-only metadata before sending messages to providers."""
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"""Strip Odysseus-only metadata before sending messages to providers.
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Per the OpenAI chat format: user/system messages must have content; a tool
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message needs content + tool_call_id; an assistant message may carry content,
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tool_calls, or both. The old guard required content on every message, which
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dropped a valid assistant message that has only tool_calls — e.g. the
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follow-up message _append_tool_results builds for a no-prose native tool call
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(content=None, since Gemini/Ollama reject tool_calls alongside ""). Dropping
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it leaves the tool result dangling and breaks the next round.
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"""
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allowed = {"role", "content", "name", "tool_call_id", "tool_calls", "function_call"}
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cleaned = []
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for msg in messages or []:
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if not isinstance(msg, dict):
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continue
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item = {k: v for k, v in msg.items() if k in allowed and v is not None}
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if "role" in item and "content" in item:
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role = item.get("role")
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if not role:
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continue
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if role == "assistant":
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# Re-add an explicit content=None when the message is tool-calls-only
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# (the None was stripped above) so the provider gets the spec-correct
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# `content: null`, not an omitted key.
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if "content" not in item and item.get("tool_calls"):
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item["content"] = None
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if "content" in item or item.get("tool_calls"):
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cleaned.append(item)
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elif role == "tool":
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if "content" in item and "tool_call_id" in item:
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cleaned.append(item)
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elif "content" in item:
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cleaned.append(item)
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return cleaned
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61
tests/test_llm_core_sanitize_tool_calls.py
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61
tests/test_llm_core_sanitize_tool_calls.py
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@@ -0,0 +1,61 @@
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"""Regression test: _sanitize_llm_messages must not drop the no-prose
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assistant tool-call message.
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Commit cb13d09 changed _append_tool_results so that when the model emits ONLY
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tool calls (no prose), the follow-up assistant message carries content=None
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(JSON null) instead of "" — Google Gemini's OpenAI-compatible endpoint and
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Ollama reject tool_calls alongside an empty-string content with HTTP 400.
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But _sanitize_llm_messages drops None values (`v is not None`) and then required
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"content" to be present, so it dropped that assistant message entirely — leaving
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a dangling role:"tool" result with no parent tool_calls. That re-breaks native
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tool-calling on the follow-up round (and regresses providers that accepted ""
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before, since the message is now removed instead of sent). cb13d09's tests only
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exercised _append_tool_results in isolation, so the sanitizer interaction went
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uncaught.
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This test drives the real producer (_append_tool_results) into the sanitizer.
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"""
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import sys
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from unittest.mock import MagicMock
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# Mock heavy dependencies before importing (mirrors tests/test_agent_loop.py).
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for mod in [
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'sqlalchemy', 'sqlalchemy.orm', 'sqlalchemy.ext', 'sqlalchemy.ext.declarative',
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'sqlalchemy.ext.hybrid', 'sqlalchemy.sql', 'sqlalchemy.sql.expression',
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'src.database', 'src.agent_tools', 'core.models', 'core.database',
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]:
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if mod not in sys.modules:
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sys.modules[mod] = MagicMock()
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from src.agent_loop import _append_tool_results
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from src.llm_core import _sanitize_llm_messages
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def test_sanitize_keeps_no_prose_assistant_tool_call_message():
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native = [{"id": "call_1", "name": "web_fetch",
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"arguments": '{"url": "https://example.com"}'}]
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messages = []
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# Model emitted only a tool call, no prose -> _append_tool_results sets the
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# assistant message's content to None (cb13d09).
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_append_tool_results(messages, "", native, [{}], ["page text"],
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used_native=True, round_num=1)
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assert messages[0]["role"] == "assistant"
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assert messages[0]["content"] is None # producer contract (cb13d09)
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out = _sanitize_llm_messages(messages)
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roles = [m["role"] for m in out]
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# The assistant tool-call message must survive sanitization, otherwise the
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# following tool result is dangling and the provider call breaks.
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assert "assistant" in roles, (
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"sanitize dropped the no-prose assistant tool-call message; the tool "
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"result is left dangling"
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)
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assistant = next(m for m in out if m["role"] == "assistant")
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assert assistant.get("tool_calls"), "assistant tool_calls were lost"
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# Faithful to cb13d09: keep explicit JSON null rather than an omitted key.
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assert assistant["content"] is None
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# Pairing intact: the tool result references the assistant's tool_call id.
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tool = next(m for m in out if m["role"] == "tool")
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assert tool["tool_call_id"] == assistant["tool_calls"][0]["id"]
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