The async version of the test should use the `ayield_keys` method
instead of `yield_keys`.
Otherwise tools such as `blockbuster` may trigger on a blocking call.
* Create usage metadata on
[`message_delta`](https://docs.anthropic.com/en/docs/build-with-claude/streaming#event-types)
instead of at the beginning. Consequently, token counts are not included
during streaming but instead at the end. This allows for accurate
reporting of server-side tool usage (important for billing)
* Add some clarifying comments
* Fix some outstanding Pylance warnings
* Remove unnecessary `text` popping in thinking blocks
* Also now correctly reports `input_cache_read`/`input_cache_creation`
as a result
When citations are returned from streaming, they include a `file_id:
null` field in their `content_block_location` structure.
When these citations are passed back to the API in subsequent messages,
the API rejects them with "Extra inputs are not permitted" for the
`file_id` field.
## Description
This PR adds support for custom header patterns in
`MarkdownHeaderTextSplitter`, allowing users to define non-standard
Markdown header formats (like `**Header**`) and specify their hierarchy
levels.
**Issue:** Fixes#22738
**Dependencies:** None - this change has no new dependencies
**Key Changes:**
- Added optional `custom_header_patterns` parameter to support
non-standard header formats
- Enable splitting on patterns like `**Header**` and `***Header***`
- Maintain full backward compatibility with existing usage
- Added comprehensive tests for custom and mixed header scenarios
## Example Usage
```python
from langchain_text_splitters import MarkdownHeaderTextSplitter
headers_to_split_on = [
("**", "Chapter"),
("***", "Section"),
]
custom_header_patterns = {
"**": 1, # Level 1 headers
"***": 2, # Level 2 headers
}
splitter = MarkdownHeaderTextSplitter(
headers_to_split_on=headers_to_split_on,
custom_header_patterns=custom_header_patterns,
)
# Now **Chapter 1** is treated as a level 1 header
# And ***Section 1.1*** is treated as a level 2 header
```
## Testing
- ✅ Added unit tests for custom header patterns
- ✅ Added tests for mixed standard and custom headers
- ✅ All existing tests pass (backward compatibility maintained)
- ✅ Linting and formatting checks pass
---
The implementation provides a flexible solution while maintaining the
simplicity of the existing API. Users can continue using the splitter
exactly as before, with the new functionality being entirely opt-in
through the `custom_header_patterns` parameter.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Claude <noreply@anthropic.com>
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
```
Enhance the integrations table by adding the `js:
'@langchain/community'` reference for several packages and updating the
titles of specific integrations to avoid improper capitalization
Supersedes #32408
Description:
This PR ensures that tool calls without explicitly provided `args` will
default to an empty dictionary (`{}`), allowing tools with no parameters
(e.g. `def foo() -> str`) to be registered and invoked without
validation errors. This change improves compatibility with agent
frameworks that may omit the `args` field when generating tool calls.
Issue:
See
[langgraph#5722](https://github.com/langchain-ai/langgraph/issues/5722)
–
LangGraph currently emits tool calls without `args`, which leads to
validation errors
when tools with no parameters are invoked. This PR ensures compatibility
by defaulting
`args` to `{}` when missing.
Dependencies:
None
---------
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.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- core, cli, langchain, standard-tests, docs, anthropic, chroma,
deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama,
openai, perplexity, prompty, qdrant, xai
- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes#123)
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **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.
---------
Signed-off-by: jitokim <pigberger70@gmail.com>
Co-authored-by: jito <pigberger70@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- **Description:** Integrated the Scrapeless package to enable Langchain
users to seamlessly incorporate Scrapeless into their agents.
- **Dependencies:** None
- **Twitter handle:** [Scrapelessteam](https://x.com/Scrapelessteam)
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. 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>
# Description
This PR updates the docs for the
[langchain-anchorbrowser](https://pypi.org/project/langchain-anchorbrowser/)
package. It adds a few tools
[Anchor Browser](https://anchorbrowser.io/?utm=langchain) is the
platform for AI Agentic browser automation, which solves the challenge
of automating workflows for web applications that lack APIs or have
limited API coverage. It simplifies the creation, deployment, and
management of browser-based automations, transforming complex web
interactions into simple API endpoints.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
