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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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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When the `require_issue_link` workflow closes a PR and posts an
enforcement comment, that comment was never cleaned up after the
situation resolved — leaving a stale "automatically closed" message
visible on reopened PRs. Now all three resolution paths (maintainer
bypass, author fixing the issue link, and contributor assignment)
minimize the enforcement comment as outdated via GraphQL. The cleanup is
best-effort: failures log a warning but never block the primary workflow
logic (label removal, bypass, reopen).
Tighten the top-level `permissions` default in the release workflow from
`contents: write` to `contents: read`. All 8 jobs already declare their
own `permissions` blocks, so this has zero functional impact — but it
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## Summary
Bumps `pygments` to `>=2.20.0` across all 21 affected packages to
address [CVE-2026-4539](https://github.com/advisories/GHSA-XXXX) — ReDoS
via inefficient GUID regex in Pygments.
- **Severity:** Low
- **Fixed in:** 2.20.0 (was 2.19.2)
- **Change:** Added `pygments>=2.20.0` to `constraint-dependencies` in
`[tool.uv]` for each package, then ran `uv lock --upgrade-package
pygments` to regenerate lock files.
Closes Dependabot alerts #3435–#3455.
## Release Note
Patch deps
### Test Plan
- [x] CI Green 🙏
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
`require_issue_link.yml` auto-closes external PRs when the author isn't
assigned to the linked issue, but there was no trigger to reopen them
when a maintainer later grants the assignment. Contributors had to
manually edit their PR description to trigger re-evaluation — poor UX
that generated repeat questions. This adds a companion workflow that
listens for issue assignment events and reopens matching PRs
automatically.
GitHub issue forms enforce `required: true` checkboxes in the web UI,
but the API bypasses form validation — bots and scripts can open issues
with every box unchecked or skip the template entirely. This adds a
workflow that auto-closes those issues, with an org-membership carve-out
so maintainers can still open free-form issues.
Extends the existing unit test for to verify that a per-invoke override
is visible inside the tool runtime config. This keeps the coverage in
the existing fake-model end-to-end test and exercises both the default
config path and the override path in one place.
Created with [Deep Agents
CLI](https://docs.langchain.com/oss/python/deepagents/cli/overview)
using gpt-5.4 (provider: openai).
