## Summary
Add XML format option for `get_buffer_string()` to provide unambiguous
message serialization. This fixes role prefix ambiguity when message
content contains strings like "Human:" or "AI:".
Fixes#34786
## Changes
- Add `format="xml"` parameter with proper XML escaping using
`quoteattr()` for attributes
- Add explicit validation for format parameter (raises `ValueError` for
invalid values)
- Add comprehensive tests for XML format edge cases
<img width="1952" height="706" alt="image"
src="https://github.com/user-attachments/assets/1cd6f887-9365-43cf-a532-72d7addd8bad"
/>
<img width="2786" height="776" alt="image"
src="https://github.com/user-attachments/assets/a07b0db0-519c-46d7-b34b-b404237d812b"
/>
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
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>
## 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>
### Description:
earlier we have to use like below:
```python
from langchain_core.messages import trim_messages
from langchain_core.messages.utils import count_tokens_approximately
trim_messages(..., token_counter=count_tokens_approximately)
```
Now can be used as like this also
```python
from langchain_core.messages import trim_messages
trim_messages(..., token_counter="approximate")
```
- [x] **Added tests**
- [x] **Lint and test**: Run this as I made change in langchain/core, uv
run --group test pytest tests/unit_tests/messages/test_utils.py -v
<img width="1006" height="66" alt="image"
src="https://github.com/user-attachments/assets/c6938c29-a781-4e7f-871b-8e888ee764b7"
/>
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
With this we get the correct types for `_runnable_support` annotated
functions.
* return list[BaseMessage] when messages is not None
* return Runnable when messages is None
* typing of function args
- Support thinking blocks in core's `convert_to_openai_messages` (pass
through instead of error)
- Ignore thinking blocks in ChatOpenAI (instead of error)
- Support Anthropic-style image blocks in ChatOpenAI
---
Standard integration tests include a `supports_anthropic_inputs`
property which is currently enabled only for tests on `ChatAnthropic`.
This test enforces compatibility with message histories of the form:
```
- system message
- human message
- AI message with tool calls specified only through `tool_use` content blocks
- human message containing `tool_result` and an additional `text` block
```
It additionally checks support for Anthropic-style image inputs if
`supports_image_inputs` is enabled.
Here we change this test, such that if you enable
`supports_anthropic_inputs`:
- You support AI messages with text and `tool_use` content blocks
- You support Anthropic-style image inputs (if `supports_image_inputs`
is enabled)
- You support thinking content blocks.
That is, we add a test case for thinking content blocks, but we also
remove the requirement of handling tool results within HumanMessages
(motivated by existing agent abstractions, which should all return
ToolMessage). We move that requirement to a ChatAnthropic-specific test.
- Convert developer openai messages to SystemMessage
- store additional_kwargs={"__openai_role__": "developer"} so that the
correct role can be reconstructed if needed
- update ChatOpenAI to read in openai_role
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Last week Anthropic released version 0.39.0 of its python sdk, which
enabled support for Python 3.13. This release deleted a legacy
`client.count_tokens` method, which we currently access during init of
the `Anthropic` LLM. Anthropic has replaced this functionality with the
[client.beta.messages.count_tokens()
API](https://github.com/anthropics/anthropic-sdk-python/pull/726).
To enable support for `anthropic >= 0.39.0` and Python 3.13, here we
drop support for the legacy token counting method, and add support for
the new method via `ChatAnthropic.get_num_tokens_from_messages`.
To fully support the token counting API, we update the signature of
`get_num_tokens_from_message` to accept tools everywhere.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
