This PR updates an outdated GitHub Action version.
- Updated `astral-sh/setup-uv` from `v6` to `v7` in
`.github/actions/uv_setup/action.yml`
Looks like this was missed as part of
https://github.com/langchain-ai/langchain/pull/33457 so hopefully safe
to bring it into alignment.
Addresses a flaky test
When executing `exit 1` as a startup command, the shell process
terminates immediately. The code then tries to write a marker command
(`printf '...'`) to stdin, but the pipe is already broken because the
shell has exited, causing `BrokenPipeError`.
* Support sentence-transformers optional dep on python 3.14
* Bump some dep locks to use pre-built wheels instead of building them
(murmurhash, cymem, preshed, thinc, srsly, blis)
* Still not possible to use spacy: even though there are wheels
available, spacy depends on Pydantic v1 which doesn't work on Python
3.14.
* Speeds up installation and CI.
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Bump lock of MarkupSafe to 3.0.3 which has Python 3.14 pre-built wheels.
Speeds up installation and CI.
Co-authored-by: Mason Daugherty <mason@langchain.dev>
The Code of Conduct link was pointing to a non-existent file path.
Updated to use GitHub's community standards tab URL which correctly
displays the Code of Conduct.
Changed from:
https://github.com/langchain-ai/langchain/blob/master/.github/CODE_OF_CONDUCT.md
To:
https://github.com/langchain-ai/langchain/?tab=coc-ov-file
(Replace this entire block of text)
Read the full contributing guidelines:
https://docs.langchain.com/oss/python/contributing/overview
Thank you for contributing to LangChain! Follow these steps to have your
pull request considered as ready for review.
1. PR title: Should follow the format: TYPE(SCOPE): DESCRIPTION
- Examples:
- fix(anthropic): resolve flag parsing error
- feat(core): add multi-tenant support
- test(openai): update API usage tests
- Allowed TYPE and SCOPE values:
https://github.com/langchain-ai/langchain/blob/master/.github/workflows/pr_lint.yml#L15-L33
2. PR description:
- Write 1-2 sentences summarizing the change.
- If this PR addresses a specific issue, please include "Fixes
#ISSUE_NUMBER" in the description to automatically close the issue when
the PR is merged.
- If there are any breaking changes, please clearly describe them.
- If this PR depends on another PR being merged first, please include
"Depends on #PR_NUMBER" inthe description.
3. Run `make format`, `make lint` and `make test` from the root of the
package(s) you've modified.
- We will not consider a PR unless these three are passing in CI.
Additional guidelines:
- We ask that if you use generative AI for your contribution, you
include a disclaimer.
- PRs should not touch more than one package unless absolutely
necessary.
- Do not update the `uv.lock` files unless or add dependencies to
`pyproject.toml` files (even optional ones) unless you have explicit
permission to do so by a maintainer.
Extract strict from kwargs and pass it to convert_to_openai_tool when
converting tools. This ensures that when strict is provided, it's
properly used during tool conversion and removed from kwargs before
calling the parent bind method.
Also extract reasoning_content from API responses and store it in
additional_kwargs for AIMessage objects.
Fixes https://github.com/langchain-ai/langchain/issues/34341 and
https://github.com/langchain-ai/langchain/issues/34342
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
### Description
`ChatPromptTemplate.from_messages` supports multiple tuple formats for
defining message templates. One documented format is `(message class,
template)`, which allows users to specify the message type using the
class directly:
```python
ChatPromptTemplate.from_messages([
(SystemMessage, "You are a helpful assistant named {name}."),
(HumanMessage, "{input}"),
])
```
However, this syntax was broken. Passing a tuple like `(HumanMessage,
"{input}")` would raise a Pydantic validation error because the
conversion logic in `_convert_to_message_template` didn't handle
`BaseMessage` subclasses—it only recognized string-based role
identifiers like `"human"` or `"system"`.
This PR adds the missing branch to detect when the first element of a
tuple is a message class (by checking for the `type` class attribute)
and routes it through `_create_template_from_message_type`, which
already knows how to create the appropriate `MessagePromptTemplate` for
each message type.
### Changes
- Updated `_convert_to_message_template` to properly support `(message
class, template)` tuples
### Testing
Added 16 comprehensive unit tests covering:
- Basic usage with `HumanMessage`, `AIMessage`, and `SystemMessage`
classes
- Integration with `invoke()` method
- Mixed syntax (message class tuples alongside string tuples)
- Multiple template variables
- Edge cases: empty templates, static text (no variables)
- Correct extraction of `input_variables`
- Partial variables support
- Combination with `MessagesPlaceholder`
- Mustache template format
- Template operations: `append()`, `extend()`, concatenation, and
slicing
- Special characters and unicode in templates
### Issue
Fixes#33791
### Dependencies
None
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
This PR adds a regression test covering the JSON Schema `$ref` pattern
found in
MCP-style schemas, where a `$ref` points into a list-based structure
such as:
#/properties/body/anyOf/1/properties/Message/properties/bccRecipients/items
This pattern historically failed due to incorrect handling of numeric
list
components in `_retrieve_ref`. The underlying bug has since been fixed,
and
this test ensures coverage so we don't regress on list-index `$ref`
resolution.
The new test (`test_dereference_refs_list_index_items_ref_mcp_like`)
verifies:
- correct traversal into `anyOf[1]`
- proper dereferencing of `items.$ref`
- no errors thrown
- `ccRecipients.items` is identical to the resolved schema of
`bccRecipients.items`
No code changes are included, just the one test — this PR adds coverage
to preserve the expected
behavior and documents support for this real-world MCP schema pattern.
Related to #32012.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Description
Fixed `BaseCallbackManager.merge()` method to correctly preserve the
distinction between `handlers` and `inheritable_handlers` during merge
operations.
Previously, the merge method was using `add_handler()` which incorrectly
added handlers to both lists when `inherit=True`, causing
cross-contamination between regular and inheritable handlers.
The fix directly passes the combined handler lists to the constructor
instead of using `add_handler()`, ensuring proper separation is
maintained.
## Issue
Fixes#32028
## Dependencies
None
## Testing
- Modified existing test `test_merge_preserves_handler_distinction()` to
verify handlers remain properly separated after merge
## Checklist
- [x] **Breaking Changes**: No breaking changes - only fixes incorrect
behavior
- [x] **Type Hints**: All functions have complete type annotations
- [x] **Tests**: Fix is fully tested with existing unit test
- [x] **Security**: No security implications
- [x] **Documentation**: No documentation changes needed - bug fix only
- [x] **Code Quality**: Passes lint and format checks
- [x] **Commit Message**: Follows Conventional Commits format
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
* FIxed where possible
* Used `cast` when not possible to fix
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Problem
The `draw_mermaid_png()` function fails with HTTP 400 when using named
background colors like `white`. This is because named colors get
prefixed with `!` (e.g., `!white`) but this special character is not
URL-encoded before being added to the API URL.
As reported in #34444, the URL parameter `bgColor=!white` causes
mermaid.ink to return a 400 Bad Request error.
## Solution
URL-encode the `background_color` parameter using `urllib.parse.quote()`
before constructing the API URL. This ensures special characters like
`!` are properly encoded as `%21`.
## Changes
- Added `import urllib.parse`
- URL-encode `background_color` value with
`urllib.parse.quote(str(background_color), safe="")`
- Added 2 unit tests:
- `test_mermaid_bgcolor_url_encoding`: Verifies named colors are
properly encoded
- `test_mermaid_bgcolor_hex_not_encoded`: Verifies hex colors work
correctly
## Testing
```bash
pytest tests/unit_tests/runnables/test_graph.py::test_mermaid_bgcolor_url_encoding -v
pytest tests/unit_tests/runnables/test_graph.py::test_mermaid_bgcolor_hex_not_encoded -v
```
Both tests pass.
Fixes#34444
---
*This contribution was made with AI assistance (Claude).*
Co-authored-by: Mr-Neutr0n <mrneutron@users.noreply.github.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
URL-encode the bgColor parameter to fix 400 errors from mermaid.ink API.
The `!` character in `!white` was not encoded, causing API failures.
Fixes#34444
Moves `_ORIGIN_MAP` dict from inside `_py_38_safe_origin()` to module
level constant. This avoids dict allocation on every function call,
reducing garbage collection pressure during frequent tool conversions.
The function is called during typed dict to pydantic model conversion
which happens during tool binding and invocation - a hot path in
LangChain.
**Testing:** `make lint` passes
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
* Fixed a few TC
* Added a few Pydantic classes to
`flake8-type-checking.runtime-evaluated-base-classes` (not as much as I
would have imagined)
* Added a few `noqa: TC`
* Activated TC rules
Moves hex color validation regex from inside
`_render_mermaid_using_api()` to module-level constant
`_HEX_COLOR_PATTERN`. This avoids recompiling the regex on every
function call, improving performance when rendering multiple Mermaid
graphs.
**Testing:**
- `make lint` passes
- `make test` passes
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Summary
Enhances the `init_chat_model` function with comprehensive input
validation, improved model inference patterns, and better error handling
to provide a significantly improved user experience.
## Changes Made
- ✅ **Input Validation**: Added comprehensive type and value checking
for all parameters
- ✅ **Enhanced Model Inference**: Improved pattern matching with
case-insensitive support and new model patterns
- ✅ **Better Error Messages**: Detailed error messages with examples and
documentation links
- ✅ **Comprehensive Tests**: Added extensive test coverage for all new
functionality
- ✅ **Documentation**: Enhanced docstrings and examples
## Backward Compatibility
All changes are fully backward compatible. No breaking changes
introduced.
## Testing
- Added 6 new test functions covering input validation, model inference,
and error handling
- All existing tests continue to pass
- Comprehensive parametrized testing for various model patterns
## User Experience Improvements
- Better error messages help users quickly resolve configuration issues
- Enhanced model inference reduces the need to specify providers
explicitly
- Comprehensive input validation catches issues early with helpful
guidance
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Summary
Fixes#34247
When using `Annotated[type, Field(description="...")]` syntax with the
`@tool` decorator, field descriptions were being lost during schema
generation. The `_get_annotation_description()` function only checked
for string annotations but not for Pydantic `FieldInfo` objects.
## Changes
- Extended `_get_annotation_description()` to also extract descriptions
from `FieldInfo` objects within `Annotated` types
- Added import for `pydantic.fields.FieldInfo`
- Added unit test to verify `Field(description=...)` is preserved
## Why this approach
The fix is minimal and targeted - it extends the existing description
extraction logic rather than restructuring the schema generation. This
maintains backward compatibility while supporting both annotation
styles:
```python
# Both now work correctly:
topic: Annotated[str, "The research topic"] # existing
topic: Annotated[str, Field(description="...")] # now fixed
```
## Known limitation
This fix only handles `pydantic.fields.FieldInfo` (Pydantic v2). The v1
compatibility layer (`pydantic.v1.fields.FieldInfo`) is a different
class and will not have descriptions extracted. This is intentional:
- Pydantic v1 is deprecated; users should migrate to v2
- The v1 compat layer exists for legacy model migration, not new tool
definitions
- Duck-typing on `description` attribute could match unintended objects
If v1 `Field` support is needed, it can be addressed in a follow-up PR
with explicit handling.
## Testing
- Added `test_tool_field_description_preserved()` covering required and
optional params
- Verified existing `test_tool_annotated_descriptions` still passes
- Lint and type checks pass
---
> [!NOTE]
> This PR was developed with AI agent assistance (Factory/Droid).
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Summary
- Fixes issue where Pydantic default values from `args_schema` were not
passed to tool functions when the caller omits optional arguments
- Modified `_parse_input()` in `libs/core/langchain_core/tools/base.py`
to include fields with non-None defaults
- Added unit tests to verify default args behavior for both sync and
async tools
## Problem
When a tool has an `args_schema` with default values:
```python
class SearchArgs(BaseModel):
query: str = Field(..., description="Search query")
page: int = Field(default=1, description="Page number")
size: int = Field(default=10, description="Results per page")
@tool("search", args_schema=SearchArgs)
def search_tool(query: str, page: int, size: int) -> str:
return f"query={query}, page={page}, size={size}"
# This threw: TypeError: search_tool() missing 2 required positional arguments
search_tool.invoke({"query": "test"})
```
The defaults from `args_schema` were being discarded because
`_parse_input()` filtered validated results to only include keys from
the original input.
## Solution
Changed the filtering logic to:
1. Include all fields that were in the original input (validated)
2. Also include fields with non-None defaults from the Pydantic schema
This applies user-defined defaults (like `Field(default=1)`) while
excluding synthetic fields from `*args`/`**kwargs` which have
`default=None`.
## Test plan
- [x] Added `test_tool_args_schema_default_values` - tests sync tool
with defaults
- [x] Added `test_tool_args_schema_default_values_async` - tests async
tool with defaults
- [x] All existing tests pass (150 passed, 4 skipped)
- [x] Lint passes
Fixes#34384
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Summary
Fixes#33970
`get_buffer_string` was only checking for the deprecated `function_call`
field in `additional_kwargs`, which modern LLM providers no longer
return. This fix updates the function to check for the modern
`tool_calls` field first, falling back to `function_call` for legacy
compatibility.
## Changes
- Check `AIMessage.tool_calls` first (modern standard)
- Fall back to `additional_kwargs["function_call"]` (legacy support)
- Added 3 unit tests covering tool_calls, empty content, and precedence
behavior
## Testing
```python
# Before fix: tool_calls info was lost
msg = AIMessage(content="Hi", tool_calls=[{"name": "search", ...}])
get_buffer_string([msg]) # "AI: Hi" (no tool info)
# After fix: tool_calls are included
get_buffer_string([msg]) # "AI: Hi[{\"name\": \"search\", ...}]"
```
- All existing `get_buffer_string` tests pass
- Legacy `function_call` behavior preserved
---
> [!NOTE]
> This PR was developed with AI agent assistance (Factory/Droid).
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
