Add a "Model references in docs and examples" subsection to `AGENTS.md`
and `CLAUDE.md` under Documentation standards. Codifies that docstrings
and examples should use current GA model names, not stale ones — and
explicitly draws the line between updating illustrative references
(encouraged) and changing shipped code defaults (breaking change,
separate process).
Pin all remaining GitHub Actions references to full-length commit SHAs,
matching the convention already established by third-party actions in
this repo. This is a prerequisite for enabling GitHub's "Require actions
to be pinned to a full-length commit SHA" repository ruleset, which
mitigates tag-hijacking supply chain attacks.
Automated refresh of model profile data for all in-monorepo partner
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Resolve symlinks before validating file extensions in the deprecated
`save()` method on prompt classes.
Credit to Jeff Ponte (@JDP-Security) for reporting the symlink
resolution issue.
Convert the `working-directory` input in the release workflow from a
free-text string to a dropdown of known package paths.
## Changes
- Change `working-directory` from `type: string` to `type: choice` in
`_release.yml`, enumerating all 21 releasable packages under `libs/` and
`libs/partners/`
- Add `check-release-options` CI job in `check_diffs.yml` that runs a
pytest script to assert the dropdown options match directories
containing a `pyproject.toml`
Fixes#34610
---
This PR resolves an issue where `ChatOllama` would raise an `unexpected
keyword argument 'response_format'` error when used with `create_agent`
or when passed an OpenAI-style `response_format`.
When using `create_agent` (especially with models like `gpt-oss`),
LangChain creates a `response_format` argument (e.g., `{"type":
"json_schema", ...}`). `ChatOllama` previously passed this argument
directly to the underlying Ollama client, which does not support
`response_format` and instead expects a `format` parameter.
## The Fix
I updated `_chat_params` in
`libs/partners/ollama/langchain_ollama/chat_models.py` to:
1. Intercept the `response_format` argument.
2. Map it to the native Ollama `format` parameter:
* `{"type": "json_schema", "json_schema": {"schema": ...}}` ->
`format=schema`
* `{"type": "json_object"}` -> `format="json"`
3. Remove `response_format` from the kwargs passed to the client.
## Validation
* **Reproduction Script**: Verified the fix with a script covering
`json_schema`, `json_object`, and explicit `format` priority scenarios.
* **New Tests**: Added 3 new unit tests to
`libs/partners/ollama/tests/unit_tests/test_chat_models.py` covering
these scenarios.
* **Regression**: Ran the full test suite (`make -C libs/partners/ollama
test`), passing 29 tests (previously 26).
* **Lint/Format**: Verified with `make lint_package` and `make format`.
---------
Co-authored-by: Mohan Kumar Sagadevan <mohankumarsagadevan@Mohans-MacBook-Air.local>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Closes#36177.
---
Ollama's deserialization path already captures `"thinking"` content as
`additional_kwargs["reasoning_content"]` on `AIMessage`, but the reverse
direction — serializing back to the Ollama wire format — was missing.
This means multi-turn conversations with reasoning models like
`deepseek-r1` would silently drop the chain-of-thought, breaking agents
that need prior reasoning preserved across turns.
Fixes#36564
The method modifies messages[idx] in-place when converting v1 format
content. Add messages = list(messages) to create a shallow copy before
any mutations.
1 line change in libs/partners/ollama/langchain_ollama/chat_models.py
Co-authored-by: bahtya <bahtyar153@qq.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Fixes#34623
Add `dimensions` field to `OllamaEmbeddings` to allow users to specify
output embedding size for models that support variable dimensions . The
field is passed
directly to the Ollama client's `embed()` call for both sync and async
methods.
**How I verified it works:**
- Ran unit tests: `python -m pytest tests/unit_tests/ -v`
- Ran integration tests against a live Ollama instance:
`OLLAMA_HOST=http://ollama:11434 python -m pytest
tests/integration_tests/ -v`
- Confirmed that passing `dimensions=768` no longer raises
`extra_forbidden`
Pydantic validation error and returns embeddings of the expected size.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Fixes#33986.
Summary:
- Normalize scheme-less `base_url` values (e.g., `ollama:11434`) by
defaulting to `http://` when the input resembles `host:port`.
- Preserve and merge `Authorization` headers when `userinfo` credentials
are present, both for sync and async clients.
- Add unit tests covering scheme-less host:port and scheme-less userinfo
credentials.
Implementation details:
- Update `parse_url_with_auth` to accept scheme-less endpoints,
producing a cleaned URL with explicit scheme and extracted auth headers.
- No changes required in `OllamaLLM`, `ChatOllama`, or
`OllamaEmbeddings`—they already consume the cleaned URL and headers.
Why:
- Previously, scheme-less inputs caused `parse_url_with_auth` to return
`(None, None)`, leading Ollama clients to fall back to defaults and
ignore the provided `base_url`.
Tests:
- Extended `libs/partners/ollama/tests/unit_tests/test_auth.py` to cover
the new cases.
Notes:
- Default scheme chosen is `http` to match common Ollama local
deployments. Users can still explicitly provide `https://` when
appropriate.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Closes#34207
---
Expose log probabilities from the Ollama Python SDK through
`ChatOllama`. The ollama client already returns a `logprobs` field on
chat responses for supported models, but `ChatOllama` had no way to
request or surface it.
## Changes
- Add `logprobs` and `top_logprobs` fields to `ChatOllama`, forwarded to
the client via `_build_chat_params`. Setting `top_logprobs` without
`logprobs=True` auto-enables it with a warning; setting it with
`logprobs=False` raises a `ValueError`
- Surface per-token logprobs on intermediate streaming chunks (both sync
`_create_chat_stream` and async `_create_async_chat_stream`) via
`response_metadata["logprobs"]`, accumulated into the final response on
`invoke()`
- Bump minimum `ollama` SDK from `>=0.6.0` to `>=0.6.1` — the version
that added logprobs support
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
GitHub issue forms render the "Area (Required)" field label at
inconsistent heading levels — some issues get `### Area` (h3), others
get `## Area` (h2). The `auto-label-by-package` workflow's regex was
hardcoded to `### Area`, silently skipping issues with `## Area`
headings and never applying package labels (e.g.
[#2471](https://github.com/langchain-ai/deepagents/issues/2471)).
The `init_chat_model` docstring lists supported `model_provider` values,
but `baseten` and `litellm` were missing despite both being present in
`_BUILTIN_PROVIDERS` since they were added. This adds the two missing
entries to keep the docstring in sync with the registry.
Automated refresh of model profile data for all in-monorepo partner
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🤖 Generated by the `refresh_model_profiles` workflow.
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Automated refresh of model profile data for all in-monorepo partner
integrations via `langchain-profiles refresh`.
🤖 Generated by the `refresh_model_profiles` workflow.
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
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Adds serialization mappings for `ChatBedrockConverse` and `BedrockLLM`
to unblock standard tests on `langchain-core>=1.2.5` (context:
[langchain-aws#821](https://github.com/langchain-ai/langchain-aws/pull/821)).
Also introduces a class-specific validator system in
`langchain_core.load` that blocks deserialization of AWS Bedrock models
when `endpoint_url` or `base_url` parameters are present, preventing
SSRF attacks via crafted serialized payloads.
Closes#34645
## Changes
- Add `ChatBedrockConverse` and `BedrockLLM` entries to
`SERIALIZABLE_MAPPING` in `mapping.py`, mapping legacy paths to their
`langchain_aws` import locations
- Add `validators.py` with `_bedrock_validator` — rejects
deserialization kwargs containing `endpoint_url` or `base_url` for all
Bedrock-related classes (`ChatBedrock`, `BedrockChat`,
`ChatBedrockConverse`, `ChatAnthropicBedrock`, `BedrockLLM`, `Bedrock`)
- `CLASS_INIT_VALIDATORS` registry covers both serialized (legacy) keys
and resolved import paths from `ALL_SERIALIZABLE_MAPPINGS`, preventing
bypass via direct-path payloads
- Move kwargs extraction and all validator checks
(`CLASS_INIT_VALIDATORS` + `init_validator`) in `Reviver.__call__` to
run **before** `importlib.import_module()` — fail fast on security
violations before executing third-party code
- Class-specific validators are independent of `init_validator` and
cannot be disabled by passing `init_validator=None`
## Testing
- `test_validator_registry_keys_in_serializable_mapping` — structural
invariant test ensuring every `CLASS_INIT_VALIDATORS` key exists in
`ALL_SERIALIZABLE_MAPPINGS`
- 10 end-to-end `load()` tests covering all Bedrock class paths (legacy
aliases, resolved import paths, `ChatAnthropicBedrock`,
`init_validator=None` bypass attempt)
- Unit tests for `_bedrock_validator` covering `endpoint_url`,
`base_url`, both params, and safe kwargs
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Register `ChatBaseten` from `langchain_baseten` in the core
serialization mapping so it can round-trip through `loads`/`dumps`.
Without this entry, serialized `ChatBaseten` objects fail to
deserialize.
Fix broken VCR cassette playback in `langchain-openai` integration tests
and add a CI job to prevent regressions. Two independent bugs made all
VCR-backed tests fail: `before_record_request` redacts URIs to
`**REDACTED**` but `match_on` still included `uri` (so playback never
matched), and a typo-fix commit (`c9f51aef85`) changed test input
strings without re-recording cassettes (so `json_body` matching also
failed).
Python's `or` operator treats `0` as falsy, so
`token_usage.get("total_tokens") or fallback` silently replaces a
provider-reported `total_tokens=0` with the computed sum of input +
output tokens. Providers can legitimately report zero tokens (e.g.,
cached responses, empty completions).
The same pattern exists in the dual-key lookups for
`input_tokens`/`output_tokens` in Groq and OpenRouter. While current
APIs don't return both key formats simultaneously (making the `or`-chain
functionally correct today), the semantics are still wrong; `0` should
not fall through to a fallback.
## Changes
- Replace `x.get(key) or fallback` with explicit `is not None` checks in
`_create_usage_metadata` across `langchain-openai`, `langchain-groq`,
and `langchain-openrouter` for `input_tokens`, `output_tokens`, and
`total_tokens`
- Fix a concrete bug in the `total_tokens` path: a provider-reported `0`
was silently replaced by the computed sum
- Harden dual-key lookups in Groq and OpenRouter to correctly preserve
zero values from the preferred key, should both key formats ever coexist
- Update OpenAI's single-key extraction for consistency — the old `or 0`
pattern happened to produce correct results (`0 or 0 == 0`) but was
semantically wrong
After reopening a PR and removing the `missing-issue-link` label, the
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check persists until the contributor pushes again. This adds a
best-effort re-run of the failed check so the PR's status clears
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