## Description
This PR adds documentation for the new ZeusDB vector store integration
with LangChain.
## Motivation
ZeusDB is a high-performance vector database (Python/Rust backend)
designed for AI applications that need fast similarity search and
real-time vector ops. This integration brings ZeusDB's capabilities to
the LangChain ecosystem, giving developers another production-oriented
option for vector storage and retrieval.
**Key Features:**
- **User-Friendly Python API**: Intuitive interface that integrates
seamlessly with Python ML workflows
- **High Performance**: Powered by a robust Rust backend for
lightning-fast vector operations
- **Enterprise Logging**: Comprehensive logging capabilities for
monitoring and debugging production systems
- **Advanced Features**: Includes product quantization and persistence
capabilities
- **AI-Optimized**: Purpose-built for modern AI applications and RAG
pipelines
## Changes
- Added provider documentation:
`docs/docs/integrations/providers/zeusdb.mdx` (installation, setup).
- Added vector store documentation:
`docs/docs/integrations/vectorstores/zeusdb.ipynb` (quickstart for
creating/querying a ZeusDBVectorStore).
- Registered langchain-zeusdb in `libs/packages.yml` for discovery.
## Target users
- AI/ML engineers building RAG pipelines
- Data scientists working with large document collections
- Developers needing high-throughput vector search
- Teams requiring near real-time vector operations
## Testing
- Followed LangChain's "How to add standard tests to an integration"
guidance.
- Code passes format, lint, and test checks locally.
- Tested with LangChain Core 0.3.74
- Works with Python 3.10 to 3.13
## Package Information
**PyPI:** https://pypi.org/project/langchain-zeusdb
**Github:** https://github.com/ZeusDB/langchain-zeusdb
## Summary
- Add comprehensive type hints to the MyInMemoryStore example code in
BaseStore docstring
- Improve documentation quality and educational value for developers
- Align with LangChain's coding standards requiring type hints on all
Python code
## Changes Made
- Added return type annotations to all methods (__init__, mget, mset,
mdelete, yield_keys)
- Added parameter type annotations using proper generic types (Sequence,
Iterator)
- Added instance variable type annotation for the store attribute
- Used modern Python union syntax (str | None) for optional types
## Test Plan
- Verified Python syntax validity with ast.parse()
- No functional changes to actual code, only documentation improvements
- Example code now follows best practices and coding standards
This change improves the educational value of the example code and
ensures consistency with LangChain's requirement that "All Python code
MUST include type hints and return types" as specified in the
development guidelines.
---------
Co-authored-by: sadiqkhzn <sadiqkhzn@users.noreply.github.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
**Description:**
Introduces documentation notebooks for AI/ML API integration covering
the following use cases:
- Chat models (`ChatAimlapi`)
- Text completion models (`AimlapiLLM`)
- Provider usage examples
- Text embedding models (`AimlapiEmbeddings`)
Additionally, adds the `langchain-aimlapi` package entry to
`libs/packages.yml` for package management.
This PR aims to provide a comprehensive starting point for developers
integrating AI/ML API models with LangChain via the new
`langchain-aimlapi` package.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** @aimlapi
---
### **To-Do Before Submitting PR:**
* [x] Run `make format`
* [x] Run `make lint`
* [x] Confirm all documentation notebooks are in
`docs/docs/integrations/`
* [x] Double-check `libs/packages.yml` has the correct repo path
* [x] Confirm no `pyproject.toml` modifications were made unnecessarily
Co-authored-by: Mason Daugherty <mason@langchain.dev>
**Description:**
This PR updates the free searches per month from **100** to **250** and
renames SerpAPI to [SerpApi](https://serpapi.com/) to prevent confusion.
Add import API keys and enhance usage instructions in the Jupyter
notebook
**Issue:** N/A
**Dependencies:** N/A
- [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.
**Description:**
This PR updated links to the latest Anthropic documentation. Changes
include revised links for model overview, tool usage, web search tool,
text editor tool, and more.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
N/A
- **Description:** The `langchain-yugabytedb` package implementations of
core LangChain abstractions using `YugabyteDB` Distributed SQL Database.
YugabyteDB is a cloud-native distributed PostgreSQL-compatible database
that combines strong consistency with ultra-resilience, seamless
scalability, geo-distribution, and highly flexible data locality to
deliver business-critical, transactional applications.
[YugabyteDB](https://www.yugabyte.com/ai/) combines the power of the
`pgvector` PostgreSQL extension with an inherently distributed
architecture. This future-proofed foundation helps you build GenAI
applications using RAG retrieval that demands high-performance vector
search.
- [ ] **tests and docs**:
1. `langchain-yugabytedb`
[github](https://github.com/yugabyte/langchain-yugabytedb) repo.
2. YugabyteDB VectorStore example notebook showing its use. It lives in
`langchain/docs/docs/integrations/vectorstores/yugabytedb.ipynb`
directory.
3. Running `langchain-yugabytedb` unit tests
- Setting up a Development Environment
This document details how to set up a local development environment that
will
allow you to contribute changes to the project.
Acquire sources and create virtualenv.
```shell
git clone https://github.com/yugabyte/langchain-yugabytedb
cd langchain-yugabytedb
uv venv --python=3.13
source .venv/bin/activate
```
Install package in editable mode.
```shell
uv pip install pipx
pipx install poetry
poetry install
uv pip install pytest pytest_asyncio pytest-timeout langchain-core langchain_tests sqlalchemy psycopg psycopg-binary numpy pgvector
```
Start YugabyteDB RF-1 Universe.
```shell
docker run -d --name yugabyte_node01 --hostname yugabyte01 \
-p 7000:7000 -p 9000:9000 -p 15433:15433 -p 5433:5433 -p 9042:9042 \
yugabytedb/yugabyte:2.25.2.0-b359 bin/yugabyted start --background=false \
--master_flags="allowed_preview_flags_csv=ysql_yb_enable_advisory_locks,ysql_yb_enable_advisory_locks=true" \
--tserver_flags="allowed_preview_flags_csv=ysql_yb_enable_advisory_locks,ysql_yb_enable_advisory_locks=true"
docker exec -it yugabyte_node01 bin/ysqlsh -h yugabyte01 -c "CREATE extension vector;"
```
Invoke test cases.
```shell
pytest -vvv tests/unit_tests/yugabytedb_tests
```
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] **feat(docs)**: add Bigtable Key-value store doc
- [X] **feat(docs)**: add Bigtable Vector store doc
This PR adds a doc for Bigtable and LangChain Key-value store
integration. It contains guides on how to add, delete, get, and yield
key-value pairs from Bigtable Key-value Store for LangChain.
- [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>
# feat(integrations): Add Timbr tools integration
## DESCRIPTION
This PR adds comprehensive documentation and integration support for
Timbr's semantic layer tools in LangChain.
[Timbr](https://timbr.ai/) provides an ontology-driven semantic layer
that enables natural language querying of databases through
business-friendly concepts. It connects raw data to governed business
measures for consistent access across BI, APIs, and AI applications.
[`langchain-timbr`](https://pypi.org/project/langchain-timbr/) is a
Python SDK that extends
[LangChain](https://github.com/WPSemantix/Timbr-GenAI/tree/main/LangChain)
and
[LangGraph](https://github.com/WPSemantix/Timbr-GenAI/tree/main/LangGraph)
with custom agents, chains, and nodes for seamless integration with the
Timbr semantic layer. It enables converting natural language prompts
into optimized semantic-SQL queries and executing them directly against
your data.
**What's Added:**
- Complete integration documentation for `langchain-timbr` package
- Tool documentation page with usage examples and API reference
**Integration Components:**
- `IdentifyTimbrConceptChain` - Identify relevant concepts from user
prompts
- `GenerateTimbrSqlChain` - Generate SQL queries from natural language
- `ValidateTimbrSqlChain` - Validate queries against knowledge graph
schemas
- `ExecuteTimbrQueryChain` - Execute queries against semantic databases
- `GenerateAnswerChain` - Generate human-readable answers from results
## Documentation Added
- `/docs/integrations/providers/timbr.mdx` - Provider overview and
configuration
- `/docs/integrations/tools/timbr.ipynb` - Comprehensive tool usage
examples
## Links
- [PyPI Package](https://pypi.org/project/langchain-timbr/)
- [GitHub Repository](https://github.com/WPSemantix/langchain-timbr)
- [Official
Documentation](https://docs.timbr.ai/doc/docs/integration/langchain-sdk/)
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
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.**
**Description:**
Add documentation for Qwen integration in LangChain, including setup
instructions, usage examples, and configuration details. Update related
qwq documentation to reflect current best practices and improve clarity
for users.
This PR enhances the documentation ecosystem by:
- Adding a new guide for integrating Qwen models
- Updating outdated or incomplete qwq documentation
- Improving structure and readability of relevant sections
**Issue:** N/A
**Dependencies:** None
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
**Description:** Adds documentation for ZenRows integration with
LangChain, including provider overview and detailed tool documentation.
ZenRows is an enterprise-grade web scraping solution that enables
LangChain agents to extract web content at scale with advanced features
like JavaScript rendering, anti-bot bypass, geo-targeting, and multiple
output formats.
This PR includes:
- Provider documentation
(`docs/docs/integrations/providers/zenrows.ipynb`)
- Tool documentation
(`docs/docs/integrations/tools/zenrows_universal_scraper.ipynb`)
- Complete usage examples and API reference links
**Issue:** N/A
**Dependencies:**
- [langchain-zenrows](https://github.com/ZenRows-Hub/langchain-zenrows)
package (external, available on
[PyPI](https://pypi.org/project/langchain-zenrows/))
- No changes to core LangChain dependencies
**LinkedIn handle:** https://www.linkedin.com/company/zenrows/
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Adding Oracle Generative AI as one of the providers for langchain.
Updated the old examples in the documentation with the new working
examples.
---------
Co-authored-by: Vishal Karwande <vishalkarwande@Vishals-MacBook-Pro.local>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
**Description:** Fixes a small typo in `_get_document_with_hash` inside
`libs/core/langchain_core/indexing/api.py`.
**Issue:** N/A (no related issue)
**Dependencies:** None
Especially helpful for the text splitters tests where we're installing
pytorch (expensive and slow slow slow). Should speed up CI by 5-10 mins.
w/o caches, CI taking 20 minutes 😨
w/ caches, CI taking 3 minutes
Taking advantage of [partial
runs](https://codspeed.io/docs/features/partial-runs)!
This should save us minutes on every CI job, we only run codspeed for
libs w/ changes and this doesn't affect benchmarking drops
Oversight when moving back to basic function call for
`modify_model_request` rather than implementation as its own node.
Basic test right now failing on main, passing on this branch
Revealed a gap in testing. Will write up a more robust test suite for
basic middleware features.
### Description
* Replace the Mermaid graph node label escaping logic
(`_escape_node_label`) with `_to_safe_id`, which converts a string into
a unique, Mermaid-compatible node id. Ensures nodes with special
characters always render correctly.
**Before**
* Invalid characters (e.g. `开`) replaced with `_`. Causes collisions
between nodes with names that are the same length and contain all
non-safe characters:
```python
_escape_node_label("开") # '_'
_escape_node_label("始") # '_' same as above, but different character passed in. not a unique mapping.
```
**After**
```python
_to_safe_id("开") # \5f00
_to_safe_id("始") # \59cb unique!
```
### Tests
* Rename `test_graph_mermaid_escape_node_label()` to
`test_graph_mermaid_to_safe_id()` and update function logic to use
`_to_safe_id`
* Add `test_graph_mermaid_special_chars()`
### Issue
Fixeslangchain-ai/langgraph#6036
Reusable workflows are not currently supported by PyPI's Trusted
Publishing
functionality, and are subject to breakage. Users are strongly
encouraged
to avoid using reusable workflows for Trusted Publishing until support
becomes official. Please, do not report bugs if this breaks.
Description: Fixes a bug in RunnableRetry where .batch / .abatch could
return misordered outputs (e.g. inputs [0,1,2] yielding [1,1,2]) when
some items succeeded on an earlier attempt and others were retried. Root
cause: successful results were stored keyed by the index within the
shrinking “pending” subset rather than the original input index, causing
collisions and reordered/duplicated outputs after retries. Fix updates
_batch and _abatch to:
- Track remaining original indices explicitly.
- Call underlying batch/abatch only on remaining inputs.
- Map results back to original indices.
- Preserve final ordering by reconstructing outputs in original
positional order.
Issue: Fixes#21326
Tests:
- Added regression tests: test_retry_batch_preserves_order and
test_async_retry_batch_preserves_order asserting correct ordering after
a single controlled failure + retry.
- Existing retry tests still pass.
Dependencies:
- None added or changed.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This removes langchain-experimental from api reference.
We do not recommend it to users for production use cases, so let's also
deprecate it from documentation
**Description:** Fixes infinite recursion issue in JSON schema
dereferencing when objects contain both $ref and other properties (e.g.,
nullable, description, additionalProperties). This was causing Apollo
MCP server schemas to hang indefinitely during tool binding.
**Problem:**
- Commit fb5da8384 changed the condition from `set(obj.keys()) ==
{"$ref"}` to `"$ref" in set(obj.keys())`
- This caused objects with $ref + other properties to be treated as pure
$ref nodes
- Result: other properties were lost and infinite recursion occurred
with complex schemas
**Solution:**
- Restore pure $ref detection for objects with only $ref key
- Add proper handling for mixed $ref objects that preserves all
properties
- Merge resolved reference content with other properties
- Maintain cycle detection to prevent infinite recursion
**Impact:**
- Fixes Apollo MCP server schema integration
- Resolves tool binding infinite recursion with complex GraphQL schemas
- Preserves backward compatibility with existing functionality
- No performance impact - actually improves handling of complex schemas
**Issue:** Fixes#32511
**Dependencies:** None
**Testing:**
- Added comprehensive unit tests covering mixed $ref scenarios
- All existing tests pass (1326 passed, 0 failed)
- Tested with realistic Apollo GraphQL schemas
- Stress tested with 100 iterations of complex schemas
**Verification:**
- ✅ `make format` - All files properly formatted
- ✅ `make lint` - All linting checks pass
- ✅ `make test` - All 1326 unit tests pass
- ✅ No breaking changes - full backwards compatibility maintained
---------
Co-authored-by: Marcus <marcus@Marcus-M4-MAX.local>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
On Friday, October 10th, the moonshotai/kimi-k2-instruct model will be
decommissioned in favor of the latest version,
moonshotai/kimi-k2-instruct-0905.
Until then, requests to moonshotai/kimi-k2-instruct will automatically
be routed to moonshotai/kimi-k2-instruct-0905.
# Description
This PR fixes a bug in _recursive_set_additional_properties_false used
in function_calling.convert_to_openai_function.
Previously, schemas with "additionalProperties=True" were not correctly
overridden when strict validation was expected, which could lead to
invalid OpenAI function schemas.
The updated implementation ensures that:
- Any schema with "additionalProperties" already set will now be forced
to False under strict mode.
- Recursive traversal of properties, items, and anyOf is preserved.
- Function signature remains unchanged for backward compatibility.
# Issue
When using tool calling in OpenAI structured output strict mode
(strict=True), 400: "Invalid schema for response_format XXXXX
'additionalProperties' is required to be supplied and to be false" error
raises for the parameter that contains dict type. OpenAI requires
additionalProperties to be set to False.
Some PRs try to resolved the issue.
- PR #25169 introduced _recursive_set_additional_properties_false to
recursively set additionalProperties=False.
- PR #26287 fixed handling of empty parameter tools for OpenAI function
generation.
- PR #30971 added support for Union type arguments in strict mode of
OpenAI function calling / structured output.
Despite these improvements, since Pydantic 2.11, it will always add
`additionalProperties: True` for arbitrary dictionary schemas dict or
Any (https://pydantic.dev/articles/pydantic-v2-11-release#changes).
Schemas that already had additionalProperties=True in such cases were
not being overridden, which this PR addresses to ensure strict mode
behaves correctly in all cases.
# Dependencies
No Changes
---------
Co-authored-by: Zhong, Yu <yzhong@freewheel.com>
This PR adds a new cookbook demonstrating how to build a RAG pipeline
with LangChain and track + evaluate it using MLflow.
Currently not much documentation on LangChain MLflow integration, hope
this can help folks trying to monitor and evaluate their LangChain
applications.
- ArXiv document loader
- In Memory vector store
- LCEL rag pipeline
- MLflow tracing
- MLflow evaluation
Issue:
N/A
Dependencies:
N/A
**Description:**
Updates the Confident AI integration documentation to use modern
patterns and improve code quality. This change:
- Replaces deprecated `DeepEvalCallbackHandler` with the new
`CallbackHandler` from `deepeval.integrations.langchain`
- Updates installation and authentication instructions to match current
best practices
- Adds modern integration examples using LangChain's latest patterns
- Removes deprecated metrics and outdated code examples
- Updates code samples to follow current best practices
The changes make the documentation more maintainable and ensure users
follow the recommended integration patterns.
**Issue:** Fixes#32444
**Dependencies:**
- deepeval
- langchain
- langchain-openai
**Twitter handle:** @Muwinuddin
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Description:
Added "Method Two: Quick Setup (Linux)" section to prerequisites,
providing a curl-based installation method for deploying JaguarDB
without Docker. Retained original Docker setup instructions for
flexibility.
