## Summary
This PR fixes several bugs and improves the example code in
`BaseChatMessageHistory` docstring that would prevent it from working
correctly.
### Bugs Fixed
- **Critical bug**: Fixed `json.dump(messages, f)` →
`json.dump(serialized, f)` - was using wrong variable
- **NameError**: Fixed bare variable references to use
`self.storage_path` and `self.session_id`
- **Missing imports**: Added required imports (`json`, `os`, message
converters) to make example runnable
### Improvements
- Added missing type hints following project standards (`messages() ->
list[BaseMessage]`, `clear() -> None`)
- Added robust error handling with `FileNotFoundError` exception
handling
- Added directory creation with `os.makedirs(exist_ok=True)` to prevent
path errors
- Improved performance: `json.load(f)` instead of `json.loads(f.read())`
- Added explicit UTF-8 encoding to all file operations
- Updated stores.py to use modern union syntax (`int | None` vs
`Optional[int]`)
### Test Plan
- [x] Code passes linting (`ruff check`)
- [x] Example code now has all required imports and proper syntax
- [x] Fixed variable references prevent runtime errors
- [x] Follows project's type annotation standards
The example code in the docstring is now fully functional and follows
LangChain's coding standards.
---------
Co-authored-by: sadiqkhzn <sadiqkhzn@users.noreply.github.com>
# Main changes / new features
## Better support for parallel tool calls
1. Support for multiple tool calls requiring human input
2. Support for combination of tool calls requiring human input + those
that are auto-approved
3. Support structured output w/ tool calls requiring human input
4. Support structured output w/ standard tool calls
## Shortcut for allowed actions
Adds a shortcut where tool config can be specified as a `bool`, meaning
"all actions allowed"
```py
HumanInTheLoopMiddleware(tool_configs={"expensive_tool": True})
```
## A few design decisions here
* We only raise one interrupt w/ all `HumanInterrupt`s, currently we
won't be able to execute all tools until all of these are resolved. This
isn't super blocking bc we can't re-invoke the model until all tools
have finished execution. That being said, if you have a long running
auto-approved tool, this could slow things down.
## TODOs
* Ideally, we would rename `accept` -> `approve`
* Ideally, we would rename `respond` -> `reject`
* Docs update (@sydney-runkle to own)
* In another PR I'd like to refactor testing to have one file for each
prebuilt middleware :)
Fast follow to https://github.com/langchain-ai/langchain/pull/32962
which was deemed as too breaking
The standard PDF input test was creating file content blocks without a
filename field.
This caused a warning when the OpenAI block translator processed the
message for LangSmith tracing, since OpenAI requires filenames for file
inputs.
Adds documentation for the integration langchain-scraperapi, which
contains 3 tools using the ScraperAPI service.
The tools give AI agents the ability to
Scrape the web and return HTML/text/markdown
Perform Google search and return json output
Perform Amazon search and return json output
For reference, here is the official repo for langchain_scraperapi:
https://github.com/scraperapi/langchain-scraperapi
Description:
Add a docstring to _load_map_reduce_chain in chains/summarize/ to
explain the purpose of the prompt argument and document function
parameters. This addresses an existing TODO in the codebase.
Issue:
N/A (documentation improvement only)
Dependencies:
None
**Description:**
Add a docstring to `_load_stuff_chain` in `chains/summarize/` to explain
the purpose of the `prompt` argument and document function parameters.
This addresses an existing TODO in the codebase.
**Issue:**
N/A (documentation improvement only)
**Dependencies:**
None
## 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 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
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>
**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
