Supersedes #32461
Fixed incorrect input token reporting during streaming when tools are
used. Previously, input tokens were counted at `message_start` before
tool execution, leading to inaccurate counts. Now input tokens are
properly deferred until `message_delta` (completion), aligning with
Anthropic's billing model and SDK expectations.
**Before Fix:**
- Streaming with tools: Input tokens = 0 ❌
- Non-streaming with tools: Input tokens = 472 ✅
**After Fix:**
- Streaming with tools: Input tokens = 472 ✅
- Non-streaming with tools: Input tokens = 472 ✅
Aligns with Anthropic's SDK expectations. The SDK handles input token
updates in `message_delta` events:
```python
# https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/lib/streaming/_messages.py
if event.usage.input_tokens is not None:
current_snapshot.usage.input_tokens = event.usage.input_tokens
```
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
fix#30146
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
- [x] **Lint and test**: 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.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
```python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-haiku-latest")
caching_llm = llm.bind(cache_control={"type": "ephemeral"})
caching_llm.invoke(
[
HumanMessage("..."),
AIMessage("..."),
HumanMessage("..."), # <-- final message / content block gets cache annotation
]
)
```
Potentially useful given's Anthropic's [incremental
caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#continuing-a-multi-turn-conversation)
capabilities:
> During each turn, we mark the final block of the final message with
cache_control so the conversation can be incrementally cached. The
system will automatically lookup and use the longest previously cached
prefix for follow-up messages.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
**TL;DR much of the provided `Makefile` targets were broken, and any
time I wanted to preview changes locally I either had to refer to a
command Chester gave me or try waiting on a Vercel preview deployment.
With this PR, everything should behave like normal.**
Significant updates to the `Makefile` and documentation files, focusing
on improving usability, adding clear messaging, and fixing/enhancing
documentation workflows.
### Updates to `Makefile`:
#### Enhanced build and cleaning processes:
- Added informative messages (e.g., "📚 Building LangChain
documentation...") to makefile targets like `docs_build`, `docs_clean`,
and `api_docs_build` for better user feedback during execution.
- Introduced a `clean-cache` target to the `docs` `Makefile` to clear
cached dependencies and ensure clean builds.
#### Improved dependency handling:
- Modified `install-py-deps` to create a `.venv/deps_installed` marker,
preventing redundant/duplicate dependency installations and improving
efficiency.
#### Streamlined file generation and infrastructure setup:
- Added caching for the LangServe README download and parallelized
feature table generation
- Added user-friendly completion messages for targets like `copy-infra`
and `render`.
#### Documentation server updates:
- Enhanced the `start` target with messages indicating server start and
URL for local documentation viewing.
---
### Documentation Improvements:
#### Content clarity and consistency:
- Standardized section titles for consistency across documentation
files.
[[1]](diffhunk://#diff-9b1a85ea8a9dcf79f58246c88692cd7a36316665d7e05a69141cfdc50794c82aL1-R1)
[[2]](diffhunk://#diff-944008ad3a79d8a312183618401fcfa71da0e69c75803eff09b779fc8e03183dL1-R1)
- Refined phrasing and formatting in sections like "Dependency
management" and "Formatting and linting" for better readability.
[[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L6-R6)
[[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L84-R82)
#### Enhanced workflows:
- Updated instructions for building and viewing documentation locally,
including tips for specifying server ports and handling API reference
previews.
[[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L60-R94)
[[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126)
- Expanded guidance on cleaning documentation artifacts and using
linting tools effectively.
[[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126)
[[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142)
#### API reference documentation:
- Improved instructions for generating and formatting in-code
documentation, highlighting best practices for docstring writing.
[[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142)
[[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L144-R186)
---
### Minor Changes:
- Added support for a new package name (`langchain_v1`) in the API
documentation generation script.
- Fixed minor capitalization and formatting issues in documentation
files.
[[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L40-R40)
[[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L166-R160)
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This PR addresses the common issue where users struggle to pass custom
parameters to OpenAI-compatible APIs like LM Studio, vLLM, and others.
The problem occurs when users try to use `model_kwargs` for custom
parameters, which causes API errors.
## Problem
Users attempting to pass custom parameters (like LM Studio's `ttl`
parameter) were getting errors:
```python
# ❌ This approach fails
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
model_kwargs={"ttl": 5} # Causes TypeError: unexpected keyword argument 'ttl'
)
```
## Solution
The `extra_body` parameter is the correct way to pass custom parameters
to OpenAI-compatible APIs:
```python
# ✅ This approach works correctly
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 5} # Custom parameters go in extra_body
)
```
## Changes Made
1. **Enhanced Documentation**: Updated the `extra_body` parameter
docstring with comprehensive examples for LM Studio, vLLM, and other
providers
2. **Added Documentation Section**: Created a new "OpenAI-compatible
APIs" section in the main class docstring with practical examples
3. **Unit Tests**: Added tests to verify `extra_body` functionality
works correctly:
- `test_extra_body_parameter()`: Verifies custom parameters are included
in request payload
- `test_extra_body_with_model_kwargs()`: Ensures `extra_body` and
`model_kwargs` work together
4. **Clear Guidance**: Documented when to use `extra_body` vs
`model_kwargs`
## Examples Added
**LM Studio with TTL (auto-eviction):**
```python
ChatOpenAI(
base_url="http://localhost:1234/v1",
api_key="lm-studio",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 300} # Auto-evict after 5 minutes
)
```
**vLLM with custom sampling:**
```python
ChatOpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY",
model="meta-llama/Llama-2-7b-chat-hf",
extra_body={
"use_beam_search": True,
"best_of": 4
}
)
```
## Why This Works
- `model_kwargs` parameters are passed directly to the OpenAI client's
`create()` method, causing errors for non-standard parameters
- `extra_body` parameters are included in the HTTP request body, which
is exactly what OpenAI-compatible APIs expect for custom parameters
Fixes#32115.
<!-- START COPILOT CODING AGENT TIPS -->
---
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$200 gift card! Click
[here](https://survey.alchemer.com/s3/8343779/Copilot-Coding-agent) to
start the survey.
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Multiple models were
[retired](https://docs.anthropic.com/en/docs/about-claude/model-deprecations#model-status)
yesterday.
Tests remain broken until we figure out what to do with the legacy
Anthropic LLM integration— currently uses their (legacy) text
completions API, for which there appear to be no remaining supported
models.
**PR message**: Not sure if I put the check at the right spot, but I
thought throwing the error before the loop made sense to me.
**Description:** Checks if there are only system messages using
AnthropicChat model and throws an error if it's the case. Check Issue
for more details
**Issue:** #30764
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Anthropic updated how they report token counts during streaming today.
See changes to `MessageDeltaUsage` in [this
commit](2da00f26c5 (diff-1a396eba0cd9cd8952dcdb58049d3b13f6b7768ead1411888d66e28211f7bfc5)).
It's clean and simple to grab these fields from the final
`message_delta` event. However, some of them are typed as Optional, and
language
[here](e42451ab3f/src/anthropic/lib/streaming/_messages.py (L462))
suggests they may not always be present. So here we take the required
field from the `message_delta` event as we were doing previously, and
ignore the rest.
- **Description:** `ChatAnthropic.get_num_tokens_from_messages` does not
currently receive `kwargs` and pass those on to
`self._client.beta.messages.count_tokens`. This is a problem if you need
to pass specific options to `count_tokens`, such as the `thinking`
option. This PR fixes that.
- **Issue:** N/A
- **Dependencies:** None
- **Twitter handle:** @bengladwell
Co-authored-by: ccurme <chester.curme@gmail.com>
