# Changes
## Adds support for `DynamicSystemPromptMiddleware`
```py
from langchain.agents.middleware import DynamicSystemPromptMiddleware
from langgraph.runtime import Runtime
from typing_extensions import TypedDict
class Context(TypedDict):
user_name: str
def system_prompt(state: AgentState, runtime: Runtime[Context]) -> str:
user_name = runtime.context.get("user_name", "n/a")
return f"You are a helpful assistant. Always address the user by their name: {user_name}"
middleware = DynamicSystemPromptMiddleware(system_prompt)
```
## Adds support for `runtime` in middleware hooks
```py
class AgentMiddleware(Generic[StateT, ContextT]):
def modify_model_request(
self,
request: ModelRequest,
state: StateT,
runtime: Runtime[ContextT], # Optional runtime parameter
) -> ModelRequest:
# upgrade model if runtime.context.subscription is `top-tier` or whatever
```
## Adds support for omitting state attributes from input / output
schemas
```py
from typing import Annotated, NotRequired
from langchain.agents.middleware.types import PrivateStateAttr, OmitFromInput, OmitFromOutput
class CustomState(AgentState):
# Private field - not in input or output schemas
internal_counter: NotRequired[Annotated[int, PrivateStateAttr]]
# Input-only field - not in output schema
user_input: NotRequired[Annotated[str, OmitFromOutput]]
# Output-only field - not in input schema
computed_result: NotRequired[Annotated[str, OmitFromInput]]
```
## Additionally
* Removes filtering of state before passing into middleware hooks
Typing is not foolproof here, still need to figure out some of the
generics stuff w/ state and context schema extensions for middleware.
TODO:
* More docs for middleware, should hold off on this until other prios
like MCP and deepagents are met
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## 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
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
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
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>
**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>
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:** Aerospike Vector Store has been retired. It is no
longer supported so It should no longer be documented on the Langchain
site.
- **Add tests and docs**: Removes docs for retired Aerospike vector
store.
- **Lint and test**: NA
**Description:**
Raise a more descriptive OutputParserException when JSON parsing results
in a non-dict type. This improves debugging and aligns behavior with
expectations when using expected_keys.
**Issue:**
Fixes#32233
**Twitter handle:**
@yashvtobre
**Testing:**
- Ran make format and make lint from the root directory; both passed
cleanly.
- Attempted make test but no such target exists in the root Makefile.
- Executed tests directly via pytest targeting the relevant test file,
confirming all tests pass except for unrelated async test failures
outside the scope of this change.
**Notes:**
- No additional dependencies introduced.
- Changes are backward compatible and isolated within the output parser
module.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
- **Description:** Currently,
`langchain_core.runnables.graph_mermaid.py` is hardcoded to use
mermaid.ink to render graph diagrams. It would be nice to allow users to
specify a custom URL, e.g. for self-hosted instances of the Mermaid
server.
- **Issue:** [Langchain Forum: allow custom mermaid API
URL](https://forum.langchain.com/t/feature-request-allow-custom-mermaid-api-url/1472)
- **Dependencies:** None
- [X] **Add tests and docs**: Added unit tests using mock requests.
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`.
Minimal example using the feature:
```python
import os
import operator
from pathlib import Path
from typing import Any, Annotated, TypedDict
from langgraph.graph import StateGraph
class State(TypedDict):
messages: Annotated[list[dict[str, Any]], operator.add]
def hello_node(state: State) -> State:
return {"messages": [{"role": "assistant", "content": "pong!"}]}
builder = StateGraph(State)
builder.add_node("hello_node", hello_node)
builder.add_edge("__start__", "hello_node")
builder.add_edge("hello_node", "__end__")
graph = builder.compile()
# Run graph
output = graph.invoke({"messages": [{"role": "user", "content": "ping?"}]})
# Draw graph
Path("graph.png").write_bytes(graph.get_graph().draw_mermaid_png(base_url="https://custom-mermaid.ink"))
```
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
