Making LangChain’s agent tool binding compatible with Claude’s extended thinking

The same binary-gated discipline Hermes Labs offers as a diagnostic pipeline for private stacks also governs how it produces a public upstream fix: pass a gate, or stop. No gate is skippable. No gate is judgment-graded. The intent is that an opinionated process produces a falsifiable artifact at each step, so the diff at the end is one a maintainer can adopt without re-deriving the reasoning. The gates below describe that discipline as it bears on this fix.

The internal artifact set for this fix is on file. Sharing the relevant slices is a service deliverable on request.

The cause sits at a specific seam: bind_tools() passed a forcing tool_choice through to a thinking-enabled request with no guard. The fix is the minimum change that closes that seam. After tool_choice normalization, if thinking is enabled and the choice forces tool use, drop the choice and warn. The precedent surfaced in Gate 2 set the shape of the fix: the structured-output path already omits tool_choice under thinking for the same API reason, so reviewers could check the guard against code already in the repo rather than against a novel pattern.

if (
    self.thinking is not None
    and self.thinking.get("type") == "enabled"
    and "tool_choice" in kwargs
    and kwargs["tool_choice"].get("type") in ("any", "tool")
):
    warnings.warn(
        "tool_choice is forced but thinking is enabled. The Anthropic "
        "API does not support forced tool use with thinking. "
        "Dropping tool_choice to avoid an API error. ...",
        stacklevel=2,
    )
    del kwargs["tool_choice"]

The production change is 20 lines in chat_models.py (the warnings module was already imported there; the only added import was import warnings in the test file). It is paired with two test functions covering eight scenarios across the forced variants, the "auto" passthrough, and the thinking-off, None, and disabled cases. The warning matters as much as the guard: dropping tool_choice means tool calls are no longer guaranteed on that request, so the caller is told explicitly rather than left to discover it. Credit to the reporter for the accurate root cause and clean repro, and to the LangChain maintainers for the structured-output guard that gave the fix its shape. Hermes's contribution was the engineering: generalizing the guard to the binding boundary so every forcing caller is covered, proving which tool_choice values had to be dropped versus preserved, showing the change was inert for every unaffected agent, and landing it through review.

For a team running Claude agents, this is the failure shape that is most expensive to diagnose in-house: a provider constraint, enforced at the API boundary, triggered by a value set two layers up in a framework you did not write. In the reported case nothing flagged it before the request was sent: the types were sound and the binding passed both settings through, so the signal was a live request that came back 400. And the error message, while accurate, is misleading, because it names tool_choice and not the binding that forced it. Teams burn hours bisecting their own agent graph before the cause turns out to be a one-line interaction between two correct-looking settings, which in the reported case surfaced as the API rejecting the request.

This case is public, so the credit is shared. The reporter supplied the diagnosis. The engineering held up in a maintainer's review and merged. When Hermes Labs audits a private stack, the same discipline turns to discovery, and the deliverable is the receipts: the reproduction, the falsification log that rules out competing causes, the failing-then-passing regression tests, and an explicit list of what was checked — that auto stays untouched, that thinking-off agents see no change, and that the change is inert everywhere the failure does not apply. A buyer can read it cold and decide whether the failure applies to their stack and whether the audit checked the right things, without re-running the investigation.

This is the same pipeline Hermes Labs runs whether the target is a model binding, a prompt template, an agent harness, a retrieval layer, or a tool definition. The systematic failure categories vary; the gate structure does not.

Seeing this in your own system? A paid diagnosis applies the same method to your system and symptom. The tools, papers and receipts stay free.

If you run Claude agents in production, the failures that cost the most are the ones that look like your code but live in a framework binding or a provider constraint. A paid diagnosis can apply this method to your stack under agreed terms.

References: #35539 · #35544 · PR filed under our founder's GitHub handle, roli-lpci · Upstream contributions: /open-source/contributions