mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-17 21:25:46 +02:00
Merge branch 'main' into v1
This commit is contained in:
Sydney Runkle
@@ -1,29 +1,29 @@
|
||||
name: "\U0001F41B Bug Report"
|
||||
description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
|
||||
description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
|
||||
labels: [pending,bug]
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: >
|
||||
value: |
|
||||
Thank you for taking the time to file a bug report.
|
||||
|
||||
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
|
||||
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
|
||||
|
||||
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
|
||||
if there's another way to solve your problem:
|
||||
|
||||
[LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
|
||||
[LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
|
||||
[LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
|
||||
[LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
|
||||
[GitHub search](https://github.com/langchain-ai/langgraph),
|
||||
* [LangChain Forum](https://forum.langchain.com/),
|
||||
* [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
|
||||
* [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
|
||||
* [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
|
||||
* [GitHub search](https://github.com/langchain-ai/langgraph),
|
||||
- type: checkboxes
|
||||
id: checks
|
||||
attributes:
|
||||
label: Checked other resources
|
||||
description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
|
||||
options:
|
||||
- label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
|
||||
- label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
|
||||
required: true
|
||||
- label: I added a clear and detailed title that summarizes the issue.
|
||||
required: true
|
||||
@@ -38,7 +38,7 @@ body:
|
||||
attributes:
|
||||
label: Example Code
|
||||
description: |
|
||||
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
|
||||
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
|
||||
placeholder: |
|
||||
from langgraph.graph import StateGraph
|
||||
|
||||
@@ -78,7 +78,7 @@ body:
|
||||
attributes:
|
||||
label: System Info
|
||||
description: |
|
||||
python -m langchain_core.sys_info
|
||||
Run on your machine: `python -m langchain_core.sys_info`
|
||||
placeholder: |
|
||||
python -m langchain_core.sys_info
|
||||
validations:
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
blank_issues_enabled: false
|
||||
version: 2.1
|
||||
contact_links:
|
||||
- name: Feature Request
|
||||
url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
|
||||
about: Suggest a feature or an idea
|
||||
- name: LangChain Forum
|
||||
url: https://forum.langchain.com/
|
||||
about: General community discussions and support
|
||||
about: General community discussions, support, and feature requests
|
||||
|
||||
@@ -1,22 +1,22 @@
|
||||
name: 🔒 Privileged
|
||||
description: You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
|
||||
description: You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
Thanks for your interest in LangChain! 🚀
|
||||
|
||||
If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
|
||||
|
||||
You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
|
||||
or are a regular contributor to LangChain with previous merged merged pull requests.
|
||||
Thanks for your interest in LangGraph! 🚀
|
||||
|
||||
If you are not a LangGraph maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
|
||||
|
||||
You are a LangGraph maintainer if you maintain any of the packages inside of the LangGraph repository
|
||||
or are a regular contributor to LangGraph with previous merged merged pull requests.
|
||||
- type: checkboxes
|
||||
id: privileged
|
||||
attributes:
|
||||
label: Privileged issue
|
||||
description: Confirm that you are allowed to create an issue here.
|
||||
options:
|
||||
- label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
|
||||
- label: I am a LangGraph maintainer, or was asked directly by a LangGraph maintainer to create an issue here.
|
||||
required: true
|
||||
- type: textarea
|
||||
id: content
|
||||
|
||||
@@ -0,0 +1,31 @@
|
||||
Thank you for contributing to LangGraph! 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):
|
||||
- langgraph, docs, cli, checkpoint, checkpoint-postgres, checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
|
||||
- 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.
|
||||
- **Issue:** the issue # it fixes, if applicable
|
||||
- **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://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) 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.
|
||||
@@ -0,0 +1,11 @@
|
||||
LangChain
|
||||
LangGraph
|
||||
LangSmith
|
||||
thead
|
||||
stdio
|
||||
nd
|
||||
jupyter
|
||||
lets
|
||||
lite
|
||||
uis
|
||||
deque
|
||||
@@ -34,10 +34,16 @@
|
||||
id: extract_ignore_words
|
||||
|
||||
- name: Codespell
|
||||
uses: codespell-project/actions-codespell@v2
|
||||
uses: codespell-project/actions-codespell@v2.0
|
||||
with:
|
||||
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
|
||||
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
|
||||
ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
|
||||
# We do this to avoid spellchecking cell outputs
|
||||
- name: Codespell Notebooks
|
||||
run: make codespell
|
||||
run: make codespell
|
||||
|
||||
- name: Codespell LangGraph Library
|
||||
run: |
|
||||
# Change to root directory to check the main LangGraph library
|
||||
cd ..
|
||||
codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map,*.pyc,__pycache__/*" --ignore-words-list="${{ steps.extract_ignore_words.outputs.ignore_words_list }}" libs/langgraph/langgraph/
|
||||
+8
-9
@@ -9,7 +9,7 @@ Here are some things to keep in mind for all types of contributions:
|
||||
- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
|
||||
- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
|
||||
- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
|
||||
- If you would like comments or feedback, please open an issue or discussion and tag a maintainer.
|
||||
- If you would like comments or feedback, please tag a maintainer.
|
||||
- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
|
||||
- Look for duplicate PRs or issues that have already been opened before opening a new one.
|
||||
- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
|
||||
@@ -20,7 +20,7 @@ For bug fixes, please open up an issue before proposing a fix to ensure the prop
|
||||
|
||||
### New features
|
||||
|
||||
For new features, please start a new [discussion](https://github.com/langchain-ai/langgraph/discussions), where the maintainers will help with scoping out the necessary changes.
|
||||
For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
|
||||
|
||||
## Contribute Documentation
|
||||
|
||||
@@ -111,7 +111,6 @@ in a more abstract way than how-to guides or tutorials, and should be geared tow
|
||||
gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
|
||||
impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
|
||||
|
||||
|
||||
To quote the Diataxis website:
|
||||
|
||||
> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
|
||||
@@ -187,9 +186,9 @@ Be concise, including in code samples.
|
||||
|
||||
## Setup
|
||||
|
||||
LangChain documentation consists of two components:
|
||||
LangGraph documentation consists of two components:
|
||||
|
||||
1. Main Documentation: Hosted at [https://langchain-ai.github.io](https://langchain-ai.github.io/langgraph/),
|
||||
1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
|
||||
this comprehensive resource serves as the primary user-facing documentation.
|
||||
It covers a wide array of topics, including tutorials, use cases, integrations,
|
||||
and more, offering extensive guidance on building with LangGraph.
|
||||
@@ -250,17 +249,17 @@ make serve-docs
|
||||
|
||||
#### Linting
|
||||
|
||||
The documentation is linted from the **monorepo root**. To lint it, run the following from there:
|
||||
To spell check the docs, run the following from the `docs` directory:
|
||||
|
||||
```bash
|
||||
make spellcheck
|
||||
codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
|
||||
```
|
||||
|
||||
### ️In-code Documentation
|
||||
|
||||
The in-code documentation is autogenerated from docstrings.
|
||||
|
||||
For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
|
||||
For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
|
||||
|
||||
We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
|
||||
|
||||
@@ -291,4 +290,4 @@ def my_function(arg1: int, arg2: str) -> float:
|
||||
This is a description of the return value.
|
||||
"""
|
||||
return 3.14
|
||||
```
|
||||
```
|
||||
|
||||
@@ -73,7 +73,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
|
||||
|
||||
- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
|
||||
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
|
||||
- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
|
||||
- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
|
||||
- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
|
||||
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
|
||||
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
|
||||
@@ -81,4 +81,4 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
|
||||
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
|
||||
+41
-14
@@ -55,14 +55,16 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
|
||||
|
||||
=== "In a workflow"
|
||||
|
||||
```python
|
||||
```python title="Workflow using MCP tools with ToolNode"
|
||||
from langchain_mcp_adapters.client import MultiServerMCPClient
|
||||
from langgraph.graph import StateGraph, MessagesState, START
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
|
||||
from langchain.chat_models import init_chat_model
|
||||
model = init_chat_model("openai:gpt-4.1")
|
||||
from langgraph.graph import StateGraph, MessagesState, START, END
|
||||
from langgraph.prebuilt import ToolNode
|
||||
|
||||
# Initialize the model
|
||||
model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
|
||||
|
||||
# Set up MCP client
|
||||
client = MultiServerMCPClient(
|
||||
{
|
||||
"math": {
|
||||
@@ -80,22 +82,47 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
|
||||
)
|
||||
tools = await client.get_tools()
|
||||
|
||||
def call_model(state: MessagesState):
|
||||
response = model.bind_tools(tools).invoke(state["messages"])
|
||||
return {"messages": response}
|
||||
# Bind tools to model
|
||||
model_with_tools = model.bind_tools(tools)
|
||||
|
||||
# Create ToolNode
|
||||
tool_node = ToolNode(tools)
|
||||
|
||||
def should_continue(state: MessagesState):
|
||||
messages = state["messages"]
|
||||
last_message = messages[-1]
|
||||
if last_message.tool_calls:
|
||||
return "tools"
|
||||
return END
|
||||
|
||||
# Define call_model function
|
||||
async def call_model(state: MessagesState):
|
||||
messages = state["messages"]
|
||||
response = await model_with_tools.ainvoke(messages)
|
||||
return {"messages": [response]}
|
||||
|
||||
# Build the graph
|
||||
builder = StateGraph(MessagesState)
|
||||
builder.add_node(call_model)
|
||||
builder.add_node(ToolNode(tools))
|
||||
builder.add_node("call_model", call_model)
|
||||
builder.add_node("tools", tool_node)
|
||||
|
||||
builder.add_edge(START, "call_model")
|
||||
builder.add_conditional_edges(
|
||||
"call_model",
|
||||
tools_condition,
|
||||
should_continue,
|
||||
)
|
||||
builder.add_edge("tools", "call_model")
|
||||
|
||||
# Compile the graph
|
||||
graph = builder.compile()
|
||||
math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
|
||||
weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
|
||||
|
||||
# Test the graph
|
||||
math_response = await graph.ainvoke(
|
||||
{"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
|
||||
)
|
||||
weather_response = await graph.ainvoke(
|
||||
{"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
@@ -148,4 +175,4 @@ if __name__ == "__main__":
|
||||
|
||||
- [MCP documentation](https://modelcontextprotocol.io/introduction)
|
||||
- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
|
||||
- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)
|
||||
- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)
|
||||
|
||||
@@ -4,6 +4,28 @@
|
||||
|
||||
---
|
||||
|
||||
## v0.2.93 (2025-07-16)
|
||||
- Removed the GIN index for run metadata to improve performance.
|
||||
|
||||
## v0.2.92 (2025-07-16)
|
||||
- Enabled copying functionality for blobs and checkpoints, improving data management flexibility.
|
||||
|
||||
## v0.2.91 (2025-07-16)
|
||||
- Reduced writes to the `checkpoint_blobs` table by inlining small values (null, numeric, str, etc.). This means we don't need to store extra values for channels that haven't been updated.
|
||||
|
||||
## v0.2.90 (2025-07-16)
|
||||
- Improve checkpoint writes via node-local background queueing.
|
||||
|
||||
|
||||
## v0.2.89 (2025-07-15)
|
||||
- Decoupled checkpoint writing from thread/run state by removing foreign keys and updated logger to prevent timeout-related failures.
|
||||
|
||||
## v0.2.88 (2025-07-14)
|
||||
- Removed the foreign key constraint for `thread` in the `run` table to simplify database schema.
|
||||
|
||||
## v0.2.87 (2025-07-14)
|
||||
- Added more detailed logs for Redis worker signaling to improve debugging.
|
||||
|
||||
## v0.2.86 (2025-07-11)
|
||||
- Honored tool descriptions in the `/mcp` endpoint to align with expected functionality.
|
||||
|
||||
|
||||
@@ -298,7 +298,7 @@ print(graph.invoke({"x": 5}, stream_mode='updates')) # (2)!
|
||||
[{'expensive_node': {'result': 10}, '__metadata__': {'cached': True}}]
|
||||
```
|
||||
|
||||
1. First run takes the full second to run (due to mocked expensive computation).
|
||||
1. First run takes two seconds to run (due to mocked expensive computation).
|
||||
2. Second run utilizes cache and returns quickly.
|
||||
|
||||
## Edges
|
||||
|
||||
@@ -31,12 +31,12 @@ To leverage custom authentication and access user-level metadata in your deploym
|
||||
api_key = headers.get("x-api-key")
|
||||
if not api_key or not is_valid_key(api_key):
|
||||
raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid API key")
|
||||
|
||||
# Fetch user-specific tokens from your secret store
|
||||
|
||||
# Fetch user-specific tokens from your secret store
|
||||
user_tokens = await fetch_user_tokens(api_key)
|
||||
|
||||
return { # (2)!
|
||||
"identity": api_key, # fetch user ID from LangSmith
|
||||
"identity": api_key, # fetch user ID from LangSmith
|
||||
"github_token" : user_tokens.github_token
|
||||
"jira_token" : user_tokens.jira_token
|
||||
# ... custom fields/secrets here
|
||||
@@ -50,14 +50,14 @@ To leverage custom authentication and access user-level metadata in your deploym
|
||||
|
||||
```json hl_lines="7-9"
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"agent": "./agent.py:graph"
|
||||
},
|
||||
"env": ".env",
|
||||
"auth": {
|
||||
},
|
||||
"env": ".env",
|
||||
"auth": {
|
||||
"path": "./auth.py:my_auth"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
@@ -80,7 +80,7 @@ To leverage custom authentication and access user-level metadata in your deploym
|
||||
|
||||
```python
|
||||
from langgraph.pregel.remote import RemoteGraph
|
||||
|
||||
|
||||
my_token = "your-token" # In practice, you would generate a signed token with your auth provider
|
||||
remote_graph = RemoteGraph(
|
||||
"agent",
|
||||
@@ -133,15 +133,44 @@ To allow an agent to perform authenticated actions on behalf of the user, access
|
||||
def my_node(state, config):
|
||||
user_config = config["configurable"].get("langgraph_auth_user")
|
||||
# token was resolved during the @auth.authenticate function
|
||||
token = user_config.get("github_token","")
|
||||
token = user_config.get("github_token","")
|
||||
...
|
||||
```
|
||||
|
||||
!!! note
|
||||
Fetch user credentials from a secure secret store. Storing secrets in graph state is not recommended.
|
||||
|
||||
### Authorizing a Studio user
|
||||
|
||||
By default, if you add custom authorization on your resources, this will also apply to interactions made from the Studio. If you want, you can handle logged-in Studio users differently by checking [is_studio_user()](../../reference/functions/sdk_auth.isStudioUser.html).
|
||||
|
||||
!!! note
|
||||
`is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.
|
||||
|
||||
```python
|
||||
from langgraph_sdk.auth import is_studio_user, Auth
|
||||
auth = Auth()
|
||||
|
||||
# ... Setup authenticate, etc.
|
||||
|
||||
@auth.on
|
||||
async def add_owner(
|
||||
ctx: Auth.types.AuthContext,
|
||||
value: dict # The payload being sent to this access method
|
||||
) -> dict: # Returns a filter dict that restricts access to resources
|
||||
if is_studio_user(ctx.user):
|
||||
return {}
|
||||
|
||||
filters = {"owner": ctx.user.identity}
|
||||
metadata = value.setdefault("metadata", {})
|
||||
metadata.update(filters)
|
||||
return filters
|
||||
```
|
||||
|
||||
Only use this if you want to permit developer access to a graph deployed on the managed LangGraph Platform SaaS.
|
||||
|
||||
## Learn more
|
||||
|
||||
* [Authentication & Access Control](../../concepts/auth.md)
|
||||
* [LangGraph Platform](../../concepts/langgraph_platform.md)
|
||||
* [Setting up custom authentication tutorial](../../tutorials/auth/getting_started.md)
|
||||
- [Authentication & Access Control](../../concepts/auth.md)
|
||||
- [LangGraph Platform](../../concepts/langgraph_platform.md)
|
||||
- [Setting up custom authentication tutorial](../../tutorials/auth/getting_started.md)
|
||||
|
||||
@@ -1194,7 +1194,7 @@ from IPython.display import Image, display
|
||||
display(Image(graph.get_graph().draw_mermaid_png()))
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
# Call the graph: here we call it to generate a list of jokes
|
||||
@@ -1446,7 +1446,7 @@ Recursion Error
|
||||
display(Image(graph.get_graph().draw_mermaid_png()))
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
This graph looks complex, but can be conceptualized as loop of [supersteps](../concepts/low_level.md#graphs):
|
||||
|
||||
|
||||
@@ -0,0 +1,21 @@
|
||||
# Observability
|
||||
|
||||
Use [LangSmith](https://smith.langchain.com/) to visualize the execution of a LangGraph application and do the following:
|
||||
|
||||
- [Enable tracing for your application](#enable-tracing-for-your-application).
|
||||
- [Debug a locally running application](../cloud/how-tos/clone_traces_studio.md).
|
||||
- [Evaluate the application performance](../agents/evals.md).
|
||||
- [Monitor the application](https://docs.smith.langchain.com/observability/how_to_guides/dashboards).
|
||||
|
||||
To get started, sign up for a free account at [LangSmith](https://smith.langchain.com/).
|
||||
|
||||
## Enable tracing for your application
|
||||
|
||||
To use LangSmith with your LangGraph application, enable tracing:
|
||||
|
||||
```python
|
||||
export LANGSMITH_TRACING=true
|
||||
export LANGSMITH_API_KEY=<your-api-key>
|
||||
```
|
||||
|
||||
For more information, see [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph).
|
||||
+3
-2
@@ -157,8 +157,9 @@ nav:
|
||||
- Overview: concepts/mcp.md
|
||||
- Use MCP: agents/mcp.md
|
||||
- Server API: concepts/server-mcp.md
|
||||
- Evaluation:
|
||||
- Basic implementation: agents/evals.md
|
||||
- Observability:
|
||||
- Overview: how-tos/observability.md
|
||||
- Evaluate performance: agents/evals.md
|
||||
- Platform-only capabilities:
|
||||
- LangGraph Platform:
|
||||
- Overview: concepts/langgraph_platform.md
|
||||
|
||||
+3
-1
@@ -112,4 +112,6 @@ extend-include = ["*.ipynb"]
|
||||
[tool.codespell]
|
||||
# https://mypy.readthedocs.io/en/stable/config_file.html
|
||||
# comma-separated list
|
||||
ignore-words-list = "infor"
|
||||
ignore-words-list = "infor,thead,stdio,nd,jupyter,lets,lite,uis,deque"
|
||||
# Exclude generated files and directories
|
||||
skip = "*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map"
|
||||
|
||||
@@ -286,6 +286,7 @@ class PostgresSaver(BasePostgresSaver):
|
||||
checkpoint_ns = configurable.pop("checkpoint_ns")
|
||||
checkpoint_id = configurable.pop("checkpoint_id", None)
|
||||
copy = checkpoint.copy()
|
||||
copy["channel_values"] = copy["channel_values"].copy()
|
||||
next_config = {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
@@ -294,16 +295,28 @@ class PostgresSaver(BasePostgresSaver):
|
||||
}
|
||||
}
|
||||
|
||||
# inline primitive values in checkpoint table
|
||||
# others are stored in blobs table
|
||||
blob_values = {}
|
||||
for k, v in checkpoint["channel_values"].items():
|
||||
if v is None or isinstance(v, (str, int, float, bool)):
|
||||
pass
|
||||
else:
|
||||
blob_values[k] = copy["channel_values"].pop(k)
|
||||
|
||||
with self._cursor(pipeline=True) as cur:
|
||||
cur.executemany(
|
||||
self.UPSERT_CHECKPOINT_BLOBS_SQL,
|
||||
self._dump_blobs(
|
||||
thread_id,
|
||||
checkpoint_ns,
|
||||
copy.pop("channel_values"), # type: ignore[misc]
|
||||
new_versions,
|
||||
),
|
||||
)
|
||||
if blob_versions := {
|
||||
k: v for k, v in new_versions.items() if k in blob_values
|
||||
}:
|
||||
cur.executemany(
|
||||
self.UPSERT_CHECKPOINT_BLOBS_SQL,
|
||||
self._dump_blobs(
|
||||
thread_id,
|
||||
checkpoint_ns,
|
||||
blob_values,
|
||||
blob_versions,
|
||||
),
|
||||
)
|
||||
cur.execute(
|
||||
self.UPSERT_CHECKPOINTS_SQL,
|
||||
(
|
||||
@@ -436,7 +449,10 @@ class PostgresSaver(BasePostgresSaver):
|
||||
},
|
||||
{
|
||||
**value["checkpoint"],
|
||||
"channel_values": self._load_blobs(value["channel_values"]),
|
||||
"channel_values": {
|
||||
**value["checkpoint"].get("channel_values"),
|
||||
**self._load_blobs(value["channel_values"]),
|
||||
},
|
||||
},
|
||||
value["metadata"],
|
||||
(
|
||||
|
||||
@@ -243,6 +243,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
|
||||
checkpoint_id = configurable.pop("checkpoint_id", None)
|
||||
|
||||
copy = checkpoint.copy()
|
||||
copy["channel_values"] = copy["channel_values"].copy()
|
||||
next_config = {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
@@ -251,17 +252,29 @@ class AsyncPostgresSaver(BasePostgresSaver):
|
||||
}
|
||||
}
|
||||
|
||||
# inline primitive values in checkpoint table
|
||||
# others are stored in blobs table
|
||||
blob_values = {}
|
||||
for k, v in checkpoint["channel_values"].items():
|
||||
if v is None or isinstance(v, (str, int, float, bool)):
|
||||
pass
|
||||
else:
|
||||
blob_values[k] = copy["channel_values"].pop(k)
|
||||
|
||||
async with self._cursor(pipeline=True) as cur:
|
||||
await cur.executemany(
|
||||
self.UPSERT_CHECKPOINT_BLOBS_SQL,
|
||||
await asyncio.to_thread(
|
||||
self._dump_blobs,
|
||||
thread_id,
|
||||
checkpoint_ns,
|
||||
copy.pop("channel_values"), # type: ignore[misc]
|
||||
new_versions,
|
||||
),
|
||||
)
|
||||
if blob_versions := {
|
||||
k: v for k, v in new_versions.items() if k in blob_values
|
||||
}:
|
||||
await cur.executemany(
|
||||
self.UPSERT_CHECKPOINT_BLOBS_SQL,
|
||||
await asyncio.to_thread(
|
||||
self._dump_blobs,
|
||||
thread_id,
|
||||
checkpoint_ns,
|
||||
blob_values,
|
||||
blob_versions,
|
||||
),
|
||||
)
|
||||
await cur.execute(
|
||||
self.UPSERT_CHECKPOINTS_SQL,
|
||||
(
|
||||
@@ -395,7 +408,10 @@ class AsyncPostgresSaver(BasePostgresSaver):
|
||||
},
|
||||
{
|
||||
**value["checkpoint"],
|
||||
"channel_values": self._load_blobs(value["channel_values"]),
|
||||
"channel_values": {
|
||||
**value["checkpoint"].get("channel_values"),
|
||||
**self._load_blobs(value["channel_values"]),
|
||||
},
|
||||
},
|
||||
value["metadata"],
|
||||
(
|
||||
|
||||
@@ -756,6 +756,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
|
||||
If provided, will create a connection pool and use it instead of a single connection.
|
||||
This overrides the `pipeline` argument.
|
||||
index: The index configuration for the store.
|
||||
ttl: The TTL configuration for the store.
|
||||
|
||||
Returns:
|
||||
PostgresStore: A new PostgresStore instance.
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "langgraph-checkpoint-postgres"
|
||||
version = "2.0.22"
|
||||
version = "2.0.23"
|
||||
description = "Library with a Postgres implementation of LangGraph checkpoint saver."
|
||||
authors = []
|
||||
requires-python = ">=3.9"
|
||||
|
||||
Generated
+26
-26
@@ -28,11 +28,11 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.6.15"
|
||||
version = "2025.7.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b", size = 158753, upload-time = "2025-06-15T02:45:51.329Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/de/8a/c729b6b60c66a38f590c4e774decc4b2ec7b0576be8f1aa984a53ffa812a/certifi-2025.7.9.tar.gz", hash = "sha256:c1d2ec05395148ee10cf672ffc28cd37ea0ab0d99f9cc74c43e588cbd111b079", size = 160386, upload-time = "2025-07-09T02:13:58.874Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057", size = 157650, upload-time = "2025-06-15T02:45:49.977Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/66/f3/80a3f974c8b535d394ff960a11ac20368e06b736da395b551a49ce950cce/certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39", size = 159230, upload-time = "2025-07-09T02:13:57.007Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -334,7 +334,7 @@ dev = [
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint-postgres"
|
||||
version = "2.0.22"
|
||||
version = "2.0.23"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "langgraph-checkpoint" },
|
||||
@@ -381,7 +381,7 @@ dev = [
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.4.4"
|
||||
version = "0.4.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "httpx" },
|
||||
@@ -392,9 +392,9 @@ dependencies = [
|
||||
{ name = "requests-toolbelt" },
|
||||
{ name = "zstandard" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/20/c8/8d2e0fc438d2d3d8d4300f7684ea30a754344ed00d7ba9cc2705241d2a5f/langsmith-0.4.4.tar.gz", hash = "sha256:70c53bbff24a7872e88e6fa0af98270f4986a6e364f9e85db1cc5636defa4d66", size = 352105, upload-time = "2025-06-27T19:20:36.207Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5c/92/7885823f3d13222f57773921f0da19b37d628c64607491233dc853a0f6ea/langsmith-0.4.5.tar.gz", hash = "sha256:49444bd8ccd4e46402f1b9ff1d686fa8e3a31b175e7085e72175ab8ec6164a34", size = 352235, upload-time = "2025-07-10T22:08:04.505Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1d/33/a3337eb70d795495a299a1640d7a75f17fb917155a64309b96106e7b9452/langsmith-0.4.4-py3-none-any.whl", hash = "sha256:014c68329bd085bd6c770a6405c61bb6881f82eb554ce8c4d1984b0035fd1716", size = 367687, upload-time = "2025-06-27T19:20:33.839Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/10/ad3107b666c3203b7938d10ea6b8746b9735c399cf737a51386d58e41d34/langsmith-0.4.5-py3-none-any.whl", hash = "sha256:4167717a2cccc4dff5809dbddc439628e836f6fd13d4fdb31ea013bc8d5cfaf5", size = 367795, upload-time = "2025-07-10T22:08:02.548Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -977,27 +977,27 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.12.2"
|
||||
version = "0.12.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6c/3d/d9a195676f25d00dbfcf3cf95fdd4c685c497fcfa7e862a44ac5e4e96480/ruff-0.12.2.tar.gz", hash = "sha256:d7b4f55cd6f325cb7621244f19c873c565a08aff5a4ba9c69aa7355f3f7afd3e", size = 4432239, upload-time = "2025-07-03T16:40:19.566Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c3/2a/43955b530c49684d3c38fcda18c43caf91e99204c2a065552528e0552d4f/ruff-0.12.3.tar.gz", hash = "sha256:f1b5a4b6668fd7b7ea3697d8d98857390b40c1320a63a178eee6be0899ea2d77", size = 4459341, upload-time = "2025-07-11T13:21:16.086Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/74/b6/2098d0126d2d3318fd5bec3ad40d06c25d377d95749f7a0c5af17129b3b1/ruff-0.12.2-py3-none-linux_armv6l.whl", hash = "sha256:093ea2b221df1d2b8e7ad92fc6ffdca40a2cb10d8564477a987b44fd4008a7be", size = 10369761, upload-time = "2025-07-03T16:39:38.847Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b1/4b/5da0142033dbe155dc598cfb99262d8ee2449d76920ea92c4eeb9547c208/ruff-0.12.2-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:09e4cf27cc10f96b1708100fa851e0daf21767e9709e1649175355280e0d950e", size = 11155659, upload-time = "2025-07-03T16:39:42.294Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3e/21/967b82550a503d7c5c5c127d11c935344b35e8c521f52915fc858fb3e473/ruff-0.12.2-py3-none-macosx_11_0_arm64.whl", hash = "sha256:8ae64755b22f4ff85e9c52d1f82644abd0b6b6b6deedceb74bd71f35c24044cc", size = 10537769, upload-time = "2025-07-03T16:39:44.75Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/33/91/00cff7102e2ec71a4890fb7ba1803f2cdb122d82787c7d7cf8041fe8cbc1/ruff-0.12.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3eb3a6b2db4d6e2c77e682f0b988d4d61aff06860158fdb413118ca133d57922", size = 10717602, upload-time = "2025-07-03T16:39:47.652Z" },
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Generated
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-68
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||||
|
||||
[[package]]
|
||||
|
||||
@@ -73,7 +73,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
|
||||
|
||||
- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
|
||||
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
|
||||
- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
|
||||
- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
|
||||
- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
|
||||
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
|
||||
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
|
||||
@@ -81,4 +81,4 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
|
||||
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
|
||||
@@ -185,6 +185,7 @@ def task(
|
||||
warnings.warn(
|
||||
"`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
|
||||
category=LangGraphDeprecatedSinceV05,
|
||||
stacklevel=2,
|
||||
)
|
||||
if retry_policy is None:
|
||||
retry_policy = retry # type: ignore[assignment]
|
||||
@@ -397,6 +398,7 @@ class entrypoint(Generic[ContextT]):
|
||||
warnings.warn(
|
||||
"`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
|
||||
category=LangGraphDeprecatedSinceV05,
|
||||
stacklevel=2,
|
||||
)
|
||||
if retry_policy is None:
|
||||
retry_policy = cast("RetryPolicy | Sequence[RetryPolicy]", retry)
|
||||
|
||||
@@ -190,7 +190,7 @@ class PregelRunner:
|
||||
tb = tb.tb_next
|
||||
exc.__traceback__ = tb
|
||||
raise
|
||||
if not futures: # maybe `t` schuduled another task
|
||||
if not futures: # maybe `t` scheduled another task
|
||||
return
|
||||
else:
|
||||
tasks = () # don't reschedule this task
|
||||
@@ -326,7 +326,7 @@ class PregelRunner:
|
||||
tb = tb.tb_next
|
||||
exc.__traceback__ = tb
|
||||
raise
|
||||
if not futures: # maybe `t` schuduled another task
|
||||
if not futures: # maybe `t` scheduled another task
|
||||
return
|
||||
else:
|
||||
tasks = () # don't reschedule this task
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "langgraph"
|
||||
version = "0.5.2"
|
||||
version = "0.5.3"
|
||||
description = "Building stateful, multi-actor applications with LLMs"
|
||||
authors = []
|
||||
requires-python = ">=3.9"
|
||||
@@ -106,3 +106,7 @@ packages = ["langgraph"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
addopts = "--full-trace --strict-markers --strict-config --durations=5 --snapshot-warn-unused"
|
||||
|
||||
[tool.codespell]
|
||||
# Ignore words specific to the LangGraph library code
|
||||
ignore-words-list = "infor,thead,stdio,nd,jupyter,lets,lite,uis,deque,langgraph,langchain,pydantic,typing,async,await,coroutine,iterable,iterables,serializable,deserializable,checkpointer,checkpointing,stateful,statefulness,prebuilt,prebuilt,supervisor,supervisory,swarm,swarming,multiactor,multiactors,subgraph,subgraphs,workflow,workflows,streaming,streamable,streamed,streamer,streamers,streaming,streamable,streamed,streamer,streamers"
|
||||
|
||||
Generated
+37
-37
@@ -181,11 +181,11 @@ wheels = [
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||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
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wheels = [
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|
||||
[[package]]
|
||||
@@ -1192,7 +1192,7 @@ wheels = [
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||||
|
||||
[[package]]
|
||||
name = "langgraph"
|
||||
version = "0.5.2"
|
||||
version = "0.5.3"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "langchain-core" },
|
||||
@@ -1271,7 +1271,7 @@ dev = [
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-api"
|
||||
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||||
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||||
{ name = "cloudpickle", marker = "python_full_version >= '3.11'" },
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{ url = "https://files.pythonhosted.org/packages/93/72/08f9e826085b1f57c9a0226e48acb27643ff19b61516a34c6cab9d6ff3fa/ruff-0.12.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:883d844967bffff5ab28bba1a4d246c1a1b2933f48cb9840f3fdc5111c603b07", size = 10661020, upload-time = "2025-07-11T13:20:55.799Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/80/a0/68da1250d12893466c78e54b4a0ff381370a33d848804bb51279367fc688/ruff-0.12.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:2120d3aa855ff385e0e562fdee14d564c9675edbe41625c87eeab744a7830d12", size = 10246300, upload-time = "2025-07-11T13:20:58.222Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6a/22/5f0093d556403e04b6fd0984fc0fb32fbb6f6ce116828fd54306a946f444/ruff-0.12.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6b16647cbb470eaf4750d27dddc6ebf7758b918887b56d39e9c22cce2049082b", size = 11263119, upload-time = "2025-07-11T13:21:01.503Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/92/c9/f4c0b69bdaffb9968ba40dd5fa7df354ae0c73d01f988601d8fac0c639b1/ruff-0.12.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e1417051edb436230023575b149e8ff843a324557fe0a265863b7602df86722f", size = 11746990, upload-time = "2025-07-11T13:21:04.524Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/fe/84/7cc7bd73924ee6be4724be0db5414a4a2ed82d06b30827342315a1be9e9c/ruff-0.12.3-py3-none-win32.whl", hash = "sha256:dfd45e6e926deb6409d0616078a666ebce93e55e07f0fb0228d4b2608b2c248d", size = 10589263, upload-time = "2025-07-11T13:21:07.148Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/07/87/c070f5f027bd81f3efee7d14cb4d84067ecf67a3a8efb43aadfc72aa79a6/ruff-0.12.3-py3-none-win_amd64.whl", hash = "sha256:a946cf1e7ba3209bdef039eb97647f1c77f6f540e5845ec9c114d3af8df873e7", size = 11695072, upload-time = "2025-07-11T13:21:11.004Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e0/30/f3eaf6563c637b6e66238ed6535f6775480db973c836336e4122161986fc/ruff-0.12.3-py3-none-win_arm64.whl", hash = "sha256:5f9c7c9c8f84c2d7f27e93674d27136fbf489720251544c4da7fb3d742e011b1", size = 10805855, upload-time = "2025-07-11T13:21:13.547Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -3076,11 +3076,11 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "types-python-dateutil"
|
||||
version = "2.9.0.20250516"
|
||||
version = "2.9.0.20250708"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ef/88/d65ed807393285204ab6e2801e5d11fbbea811adcaa979a2ed3b67a5ef41/types_python_dateutil-2.9.0.20250516.tar.gz", hash = "sha256:13e80d6c9c47df23ad773d54b2826bd52dbbb41be87c3f339381c1700ad21ee5", size = 13943, upload-time = "2025-05-16T03:06:58.385Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c9/95/6bdde7607da2e1e99ec1c1672a759d42f26644bbacf939916e086db34870/types_python_dateutil-2.9.0.20250708.tar.gz", hash = "sha256:ccdbd75dab2d6c9696c350579f34cffe2c281e4c5f27a585b2a2438dd1d5c8ab", size = 15834, upload-time = "2025-07-08T03:14:03.382Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c5/3f/b0e8db149896005adc938a1e7f371d6d7e9eca4053a29b108978ed15e0c2/types_python_dateutil-2.9.0.20250516-py3-none-any.whl", hash = "sha256:2b2b3f57f9c6a61fba26a9c0ffb9ea5681c9b83e69cd897c6b5f668d9c0cab93", size = 14356, upload-time = "2025-05-16T03:06:57.249Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/72/52/43e70a8e57fefb172c22a21000b03ebcc15e47e97f5cb8495b9c2832efb4/types_python_dateutil-2.9.0.20250708-py3-none-any.whl", hash = "sha256:4d6d0cc1cc4d24a2dc3816024e502564094497b713f7befda4d5bc7a8e3fd21f", size = 17724, upload-time = "2025-07-08T03:14:02.593Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
.PHONY: all format lint test test_watch integration_tests spell_check spell_fix benchmark profile
|
||||
.PHONY: all format lint test test-fast test_watch integration_tests spell_check spell_fix benchmark profile
|
||||
|
||||
# Default target executed when no arguments are given to make.
|
||||
all: help
|
||||
@@ -15,14 +15,17 @@ stop-postgres:
|
||||
|
||||
TEST ?= .
|
||||
|
||||
test-fast:
|
||||
LANGGRAPH_TEST_FAST=1 uv run pytest $(TEST)
|
||||
|
||||
test:
|
||||
make start-postgres && uv run pytest $(TEST); \
|
||||
make start-postgres && LANGGRAPH_TEST_FAST=0 uv run pytest $(TEST); \
|
||||
EXIT_CODE=$$?; \
|
||||
make stop-postgres; \
|
||||
exit $$EXIT_CODE
|
||||
|
||||
test_watch:
|
||||
make start-postgres && uv run ptw $(TEST); \
|
||||
make start-postgres && LANGGRAPH_TEST_FAST=0 uv run ptw $(TEST); \
|
||||
EXIT_CODE=$$?; \
|
||||
make stop-postgres; \
|
||||
exit $$EXIT_CODE
|
||||
@@ -74,5 +77,6 @@ help:
|
||||
@echo '-- TESTS --'
|
||||
@echo 'coverage - run unit tests and generate coverage report'
|
||||
@echo 'test - run unit tests'
|
||||
@echo 'test-fast - run unit tests with in-memory checkpointer only'
|
||||
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
||||
@echo 'test_watch - run unit tests in watch mode'
|
||||
|
||||
@@ -1,3 +1,36 @@
|
||||
"""Tool execution node for LangGraph workflows.
|
||||
|
||||
This module provides prebuilt functionality for executing tools in LangGraph.
|
||||
|
||||
Tools are functions that models can call to interact with external systems,
|
||||
APIs, databases, or perform computations.
|
||||
|
||||
The module implements several key design patterns:
|
||||
- Parallel execution of multiple tool calls for efficiency
|
||||
- Robust error handling with customizable error messages
|
||||
- State injection for tools that need access to graph state
|
||||
- Store injection for tools that need persistent storage
|
||||
- Command-based state updates for advanced control flow
|
||||
|
||||
Key Components:
|
||||
ToolNode: Main class for executing tools in LangGraph workflows
|
||||
InjectedState: Annotation for injecting graph state into tools
|
||||
InjectedStore: Annotation for injecting persistent store into tools
|
||||
tools_condition: Utility function for conditional routing based on tool calls
|
||||
|
||||
Typical Usage:
|
||||
```python
|
||||
from langchain_core.tools import tool
|
||||
from langgraph.prebuilt import ToolNode
|
||||
|
||||
@tool
|
||||
def my_tool(x: int) -> str:
|
||||
return f"Result: {x}"
|
||||
|
||||
tool_node = ToolNode([my_tool])
|
||||
```
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import inspect
|
||||
import json
|
||||
@@ -49,6 +82,24 @@ TOOL_CALL_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
|
||||
|
||||
|
||||
def msg_content_output(output: Any) -> Union[str, list[dict]]:
|
||||
"""Convert tool output to valid message content format.
|
||||
|
||||
LangChain ToolMessages accept either string content or a list of content blocks.
|
||||
This function ensures tool outputs are properly formatted for message consumption
|
||||
by attempting to preserve structured data when possible, falling back to JSON
|
||||
serialization or string conversion.
|
||||
|
||||
Args:
|
||||
output: The raw output from a tool execution. Can be any type.
|
||||
|
||||
Returns:
|
||||
Either a string representation of the output or a list of content blocks
|
||||
if the output is already in the correct format for structured content.
|
||||
|
||||
Note:
|
||||
This function prioritizes backward compatibility by defaulting to JSON
|
||||
serialization rather than supporting all possible message content formats.
|
||||
"""
|
||||
if isinstance(output, str):
|
||||
return output
|
||||
elif isinstance(output, list) and all(
|
||||
@@ -58,9 +109,10 @@ def msg_content_output(output: Any) -> Union[str, list[dict]]:
|
||||
]
|
||||
):
|
||||
return output
|
||||
# Technically a list of strings is also valid message content but it's not currently
|
||||
# well tested that all chat models support this. And for backwards compatibility
|
||||
# we want to make sure we don't break any existing ToolNode usage.
|
||||
# Technically a list of strings is also valid message content, but it's
|
||||
# not currently well tested that all chat models support this.
|
||||
# And for backwards compatibility we want to make sure we don't break
|
||||
# any existing ToolNode usage.
|
||||
else:
|
||||
try:
|
||||
return json.dumps(output, ensure_ascii=False)
|
||||
@@ -78,6 +130,30 @@ def _handle_tool_error(
|
||||
tuple[type[Exception], ...],
|
||||
],
|
||||
) -> str:
|
||||
"""Generate error message content based on exception handling configuration.
|
||||
|
||||
This function centralizes error message generation logic, supporting different
|
||||
error handling strategies configured via the ToolNode's handle_tool_errors
|
||||
parameter.
|
||||
|
||||
Args:
|
||||
e: The exception that occurred during tool execution.
|
||||
flag: Configuration for how to handle the error. Can be:
|
||||
- bool: If True, use default error template
|
||||
- str: Use this string as the error message
|
||||
- Callable: Call this function with the exception to get error message
|
||||
- tuple: Not used in this context (handled by caller)
|
||||
|
||||
Returns:
|
||||
A string containing the error message to include in the ToolMessage.
|
||||
|
||||
Raises:
|
||||
ValueError: If flag is not one of the supported types.
|
||||
|
||||
Note:
|
||||
The tuple case is handled by the caller through exception type checking,
|
||||
not by this function directly.
|
||||
"""
|
||||
if isinstance(flag, (bool, tuple)):
|
||||
content = TOOL_CALL_ERROR_TEMPLATE.format(error=repr(e))
|
||||
elif isinstance(flag, str):
|
||||
@@ -93,6 +169,29 @@ def _handle_tool_error(
|
||||
|
||||
|
||||
def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception], ...]:
|
||||
"""Infer exception types handled by a custom error handler function.
|
||||
|
||||
This function analyzes the type annotations of a custom error handler to determine
|
||||
which exception types it's designed to handle. This enables type-safe error handling
|
||||
where only specific exceptions are caught and processed by the handler.
|
||||
|
||||
Args:
|
||||
handler: A callable that takes an exception and returns an error message string.
|
||||
The first parameter (after self/cls if present) should be type-annotated
|
||||
with the exception type(s) to handle.
|
||||
|
||||
Returns:
|
||||
A tuple of exception types that the handler can process. Returns (Exception,)
|
||||
if no specific type information is available for backward compatibility.
|
||||
|
||||
Raises:
|
||||
ValueError: If the handler's annotation contains non-Exception types or
|
||||
if Union types contain non-Exception types.
|
||||
|
||||
Note:
|
||||
This function supports both single exception types and Union types for
|
||||
handlers that need to handle multiple exception types differently.
|
||||
"""
|
||||
sig = inspect.signature(handler)
|
||||
params = list(sig.parameters.values())
|
||||
if params:
|
||||
@@ -111,8 +210,9 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
|
||||
return tuple(args)
|
||||
else:
|
||||
raise ValueError(
|
||||
"All types in the error handler error annotation must be Exception types. "
|
||||
"For example, `def custom_handler(e: Union[ValueError, TypeError])`. "
|
||||
"All types in the error handler error annotation must be "
|
||||
"Exception types. For example, "
|
||||
"`def custom_handler(e: Union[ValueError, TypeError])`. "
|
||||
f"Got '{first_param.annotation}' instead."
|
||||
)
|
||||
|
||||
@@ -121,13 +221,16 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
|
||||
return (exception_type,)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Arbitrary types are not supported in the error handler signature. "
|
||||
"Please annotate the error with either a specific Exception type or a union of Exception types. "
|
||||
"For example, `def custom_handler(e: ValueError)` or `def custom_handler(e: Union[ValueError, TypeError])`. "
|
||||
f"Arbitrary types are not supported in the error handler "
|
||||
f"signature. Please annotate the error with either a "
|
||||
f"specific Exception type or a union of Exception types. "
|
||||
"For example, `def custom_handler(e: ValueError)` or "
|
||||
"`def custom_handler(e: Union[ValueError, TypeError])`. "
|
||||
f"Got '{exception_type}' instead."
|
||||
)
|
||||
|
||||
# If no type information is available, return (Exception,) for backwards compatibility.
|
||||
# If no type information is available, return (Exception,)
|
||||
# for backwards compatibility.
|
||||
return (Exception,)
|
||||
|
||||
|
||||
@@ -141,60 +244,72 @@ class ToolNode(RunnableCallable):
|
||||
Tool calls can also be passed directly as a list of `ToolCall` dicts.
|
||||
|
||||
Args:
|
||||
tools: A sequence of tools that can be invoked by the ToolNode.
|
||||
name: The name of the ToolNode in the graph. Defaults to "tools".
|
||||
tags: Optional tags to associate with the node. Defaults to None.
|
||||
handle_tool_errors: How to handle tool errors raised by tools inside the node. Defaults to True.
|
||||
Must be one of the following:
|
||||
tools: A sequence of tools that can be invoked by this node. Tools can be
|
||||
BaseTool instances or plain functions that will be converted to tools.
|
||||
name: The name identifier for this node in the graph. Used for debugging
|
||||
and visualization. Defaults to "tools".
|
||||
tags: Optional metadata tags to associate with the node for filtering
|
||||
and organization. Defaults to None.
|
||||
handle_tool_errors: Configuration for error handling during tool execution.
|
||||
Defaults to True. Supports multiple strategies:
|
||||
|
||||
- True: all errors will be caught and
|
||||
a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
|
||||
- str: all errors will be caught and
|
||||
a ToolMessage with the string value of 'handle_tool_errors' will be returned.
|
||||
- tuple[type[Exception], ...]: exceptions in the tuple will be caught and
|
||||
a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
|
||||
- Callable[..., str]: exceptions from the signature of the callable will be caught and
|
||||
a ToolMessage with the string value of the result of the 'handle_tool_errors' callable will be returned.
|
||||
- False: none of the errors raised by the tools will be caught
|
||||
messages_key: The state key in the input that contains the list of messages.
|
||||
The same key will be used for the output from the ToolNode.
|
||||
Defaults to "messages".
|
||||
- True: Catch all errors and return a ToolMessage with the default
|
||||
error template containing the exception details.
|
||||
- str: Catch all errors and return a ToolMessage with this custom
|
||||
error message string.
|
||||
- tuple[type[Exception], ...]: Only catch exceptions of the specified
|
||||
types and return default error messages for them.
|
||||
- Callable[..., str]: Catch exceptions matching the callable's signature
|
||||
and return the string result of calling it with the exception.
|
||||
- False: Disable error handling entirely, allowing exceptions to propagate.
|
||||
|
||||
The `ToolNode` is roughly analogous to:
|
||||
messages_key: The key in the state dictionary that contains the message list.
|
||||
This same key will be used for the output ToolMessages. Defaults to "messages".
|
||||
|
||||
```python
|
||||
tools_by_name = {tool.name: tool for tool in tools}
|
||||
def tool_node(state: dict):
|
||||
result = []
|
||||
for tool_call in state["messages"][-1].tool_calls:
|
||||
tool = tools_by_name[tool_call["name"]]
|
||||
observation = tool.invoke(tool_call["args"])
|
||||
result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
|
||||
return {"messages": result}
|
||||
```
|
||||
Example:
|
||||
Basic usage with simple tools:
|
||||
|
||||
Tool calls can also be passed directly to a ToolNode. This can be useful when using
|
||||
the Send API, e.g., in a conditional edge:
|
||||
```python
|
||||
from langgraph.prebuilt import ToolNode
|
||||
from langchain_core.tools import tool
|
||||
|
||||
```python
|
||||
def example_conditional_edge(state: dict) -> List[Send]:
|
||||
tool_calls = state["messages"][-1].tool_calls
|
||||
# If tools rely on state or store variables (whose values are not generated
|
||||
# directly by a model), you can inject them into the tool calls.
|
||||
tool_calls = [
|
||||
tool_node.inject_tool_args(call, state, store)
|
||||
for call in last_message.tool_calls
|
||||
]
|
||||
return [Send("tools", [tool_call]) for tool_call in tool_calls]
|
||||
```
|
||||
@tool
|
||||
def calculator(a: int, b: int) -> int:
|
||||
\"\"\"Add two numbers.\"\"\"
|
||||
return a + b
|
||||
|
||||
Important:
|
||||
- The input state can be one of the following:
|
||||
- A dict with a messages key containing a list of messages.
|
||||
- A list of messages.
|
||||
- A list of tool calls.
|
||||
- If operating on a message list, the last message must be an `AIMessage` with
|
||||
`tool_calls` populated.
|
||||
tool_node = ToolNode([calculator])
|
||||
```
|
||||
|
||||
Custom error handling:
|
||||
|
||||
```python
|
||||
def handle_math_errors(e: ZeroDivisionError) -> str:
|
||||
return "Cannot divide by zero!"
|
||||
|
||||
tool_node = ToolNode([calculator], handle_tool_errors=handle_math_errors)
|
||||
```
|
||||
|
||||
Direct tool call execution:
|
||||
|
||||
```python
|
||||
tool_calls = [{"name": "calculator", "args": {"a": 5, "b": 3}, "id": "1", "type": "tool_call"}]
|
||||
result = tool_node.invoke(tool_calls)
|
||||
```
|
||||
|
||||
Note:
|
||||
The ToolNode expects input in one of three formats:
|
||||
1. A dictionary with a messages key containing a list of messages
|
||||
2. A list of messages directly
|
||||
3. A list of tool call dictionaries
|
||||
|
||||
When using message formats, the last message must be an AIMessage with
|
||||
tool_calls populated. The node automatically extracts and processes these
|
||||
tool calls concurrently.
|
||||
|
||||
For advanced use cases involving state injection or store access, tools
|
||||
can be annotated with InjectedState or InjectedStore to receive graph
|
||||
context automatically.
|
||||
"""
|
||||
|
||||
name: str = "ToolNode"
|
||||
@@ -210,6 +325,15 @@ class ToolNode(RunnableCallable):
|
||||
] = True,
|
||||
messages_key: str = "messages",
|
||||
) -> None:
|
||||
"""Initialize the ToolNode with the provided tools and configuration.
|
||||
|
||||
Args:
|
||||
tools: Sequence of tools to make available for execution.
|
||||
name: Node name for graph identification.
|
||||
tags: Optional metadata tags.
|
||||
handle_tool_errors: Error handling configuration.
|
||||
messages_key: State key containing messages.
|
||||
"""
|
||||
super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
|
||||
self.tools_by_name: dict[str, BaseTool] = {}
|
||||
self.tool_to_state_args: dict[str, dict[str, Optional[str]]] = {}
|
||||
@@ -542,20 +666,38 @@ class ToolNode(RunnableCallable):
|
||||
],
|
||||
store: Optional[BaseStore],
|
||||
) -> ToolCall:
|
||||
"""Injects the state and store into the tool call.
|
||||
"""Inject graph state and store into tool call arguments.
|
||||
|
||||
Tool arguments with types annotated as `InjectedState` and `InjectedStore` are
|
||||
ignored in tool schemas for generation purposes. This method injects them into
|
||||
tool calls for tool invocation.
|
||||
This method enables tools to access graph context that should not be controlled
|
||||
by the model. Tools can declare dependencies on graph state or persistent storage
|
||||
using InjectedState and InjectedStore annotations. This method automatically
|
||||
identifies these dependencies and injects the appropriate values.
|
||||
|
||||
The injection process preserves the original tool call structure while adding
|
||||
the necessary context arguments. This allows tools to be both model-callable
|
||||
and context-aware without exposing internal state management to the model.
|
||||
|
||||
Args:
|
||||
tool_call: The tool call to inject state and store into.
|
||||
input: The input state
|
||||
to inject.
|
||||
store: The store to inject.
|
||||
tool_call: The tool call dictionary to augment with injected arguments.
|
||||
Must contain 'name', 'args', 'id', and 'type' fields.
|
||||
input: The current graph state to inject into tools requiring state access.
|
||||
Can be a message list, state dictionary, or BaseModel instance.
|
||||
store: The persistent store instance to inject into tools requiring storage.
|
||||
Will be None if no store is configured for the graph.
|
||||
|
||||
Returns:
|
||||
ToolCall: The tool call with injected state and store.
|
||||
A new ToolCall dictionary with the same structure as the input but with
|
||||
additional arguments injected based on the tool's annotation requirements.
|
||||
|
||||
Raises:
|
||||
ValueError: If a tool requires store injection but no store is provided,
|
||||
or if state injection requirements cannot be satisfied.
|
||||
|
||||
Note:
|
||||
This method is automatically called during tool execution but can also
|
||||
be used manually when working with the Send API or custom routing logic.
|
||||
The injection is performed on a copy of the tool call to avoid mutating
|
||||
the original.
|
||||
"""
|
||||
if tool_call["name"] not in self.tools_by_name:
|
||||
return tool_call
|
||||
@@ -626,55 +768,66 @@ def tools_condition(
|
||||
state: Union[list[AnyMessage], dict[str, Any], BaseModel],
|
||||
messages_key: str = "messages",
|
||||
) -> Literal["tools", "__end__"]:
|
||||
"""Use in the conditional_edge to route to the ToolNode if the last message
|
||||
"""Conditional routing function for tool-calling workflows.
|
||||
|
||||
has tool calls. Otherwise, route to the end.
|
||||
This utility function implements the standard conditional logic for ReAct-style
|
||||
agents: if the last AI message contains tool calls, route to the tool execution
|
||||
node; otherwise, end the workflow. This pattern is fundamental to most tool-calling
|
||||
agent architectures.
|
||||
|
||||
The function handles multiple state formats commonly used in LangGraph applications,
|
||||
making it flexible for different graph designs while maintaining consistent behavior.
|
||||
|
||||
Args:
|
||||
state: The state to check for
|
||||
tool calls. Must have a list of messages (MessageGraph) or have the
|
||||
"messages" key (StateGraph).
|
||||
state: The current graph state to examine for tool calls. Supported formats:
|
||||
- List of messages (for MessageGraph)
|
||||
- Dictionary containing a messages key (for StateGraph)
|
||||
- BaseModel instance with a messages attribute
|
||||
messages_key: The key or attribute name containing the message list in the state.
|
||||
This allows customization for graphs using different state schemas.
|
||||
Defaults to "messages".
|
||||
|
||||
Returns:
|
||||
The next node to route to.
|
||||
Either "tools" if tool calls are present in the last AI message, or "__end__"
|
||||
to terminate the workflow. These are the standard routing destinations for
|
||||
tool-calling conditional edges.
|
||||
|
||||
Raises:
|
||||
ValueError: If no messages can be found in the provided state format.
|
||||
|
||||
Examples:
|
||||
Create a custom ReAct-style agent with tools.
|
||||
Example:
|
||||
Basic usage in a ReAct agent:
|
||||
|
||||
```pycon
|
||||
>>> from langchain_anthropic import ChatAnthropic
|
||||
>>> from langchain_core.tools import tool
|
||||
...
|
||||
>>> from langgraph.graph import StateGraph
|
||||
>>> from langgraph.prebuilt import ToolNode, tools_condition
|
||||
>>> from langgraph.graph.message import add_messages
|
||||
...
|
||||
>>> from typing import Annotated
|
||||
>>> from typing_extensions import TypedDict
|
||||
...
|
||||
>>> @tool
|
||||
>>> def divide(a: float, b: float) -> int:
|
||||
... \"\"\"Return a / b.\"\"\"
|
||||
... return a / b
|
||||
...
|
||||
>>> llm = ChatAnthropic(model="claude-3-haiku-20240307")
|
||||
>>> tools = [divide]
|
||||
...
|
||||
>>> class State(TypedDict):
|
||||
... messages: Annotated[list, add_messages]
|
||||
>>>
|
||||
>>> graph_builder = StateGraph(State)
|
||||
>>> graph_builder.add_node("tools", ToolNode(tools))
|
||||
>>> graph_builder.add_node("chatbot", lambda state: {"messages":llm.bind_tools(tools).invoke(state['messages'])})
|
||||
>>> graph_builder.add_edge("tools", "chatbot")
|
||||
>>> graph_builder.add_conditional_edges(
|
||||
... "chatbot", tools_condition
|
||||
... )
|
||||
>>> graph_builder.set_entry_point("chatbot")
|
||||
>>> graph = graph_builder.compile()
|
||||
>>> graph.invoke({"messages": {"role": "user", "content": "What's 329993 divided by 13662?"}})
|
||||
```python
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.prebuilt import ToolNode, tools_condition
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
class State(TypedDict):
|
||||
messages: list
|
||||
|
||||
graph = StateGraph(State)
|
||||
graph.add_node("llm", call_model)
|
||||
graph.add_node("tools", ToolNode([my_tool]))
|
||||
graph.add_conditional_edges(
|
||||
"llm",
|
||||
tools_condition, # Routes to "tools" or "__end__"
|
||||
{"tools": "tools", "__end__": "__end__"}
|
||||
)
|
||||
```
|
||||
|
||||
Custom messages key:
|
||||
|
||||
```python
|
||||
def custom_condition(state):
|
||||
return tools_condition(state, messages_key="chat_history")
|
||||
```
|
||||
|
||||
Note:
|
||||
This function is designed to work seamlessly with ToolNode and standard
|
||||
LangGraph patterns. It expects the last message to be an AIMessage when
|
||||
tool calls are present, which is the standard output format for tool-calling
|
||||
language models.
|
||||
"""
|
||||
if isinstance(state, list):
|
||||
ai_message = state[-1]
|
||||
@@ -690,16 +843,18 @@ def tools_condition(
|
||||
|
||||
|
||||
class InjectedState(InjectedToolArg):
|
||||
"""Annotation for a Tool arg that is meant to be populated with the graph state.
|
||||
"""Annotation for injecting graph state into tool arguments.
|
||||
|
||||
Any Tool argument annotated with InjectedState will be hidden from a tool-calling
|
||||
model, so that the model doesn't attempt to generate the argument. If using
|
||||
ToolNode, the appropriate graph state field will be automatically injected into
|
||||
the model-generated tool args.
|
||||
This annotation enables tools to access graph state without exposing state
|
||||
management details to the language model. Tools annotated with InjectedState
|
||||
receive state data automatically during execution while remaining invisible
|
||||
to the model's tool-calling interface.
|
||||
|
||||
Args:
|
||||
field: The key from state to insert. If None, the entire state is expected to
|
||||
be passed in.
|
||||
field: Optional key to extract from the state dictionary. If None, the entire
|
||||
state is injected. If specified, only that field's value is injected.
|
||||
This allows tools to request specific state components rather than
|
||||
processing the full state structure.
|
||||
|
||||
Example:
|
||||
```python
|
||||
@@ -746,6 +901,15 @@ class InjectedState(InjectedToolArg):
|
||||
ToolMessage(content='bar2', name='foo_tool', tool_call_id='2')
|
||||
]
|
||||
```
|
||||
|
||||
Note:
|
||||
- InjectedState arguments are automatically excluded from tool schemas
|
||||
presented to language models
|
||||
- ToolNode handles the injection process during execution
|
||||
- Tools can mix regular arguments (controlled by the model) with injected
|
||||
arguments (controlled by the system)
|
||||
- State injection occurs after the model generates tool calls but before
|
||||
tool execution
|
||||
""" # noqa: E501
|
||||
|
||||
def __init__(self, field: Optional[str] = None) -> None:
|
||||
@@ -753,61 +917,97 @@ class InjectedState(InjectedToolArg):
|
||||
|
||||
|
||||
class InjectedStore(InjectedToolArg):
|
||||
"""Annotation for a Tool arg that is meant to be populated with LangGraph store.
|
||||
"""Annotation for injecting persistent store into tool arguments.
|
||||
|
||||
Any Tool argument annotated with InjectedStore will be hidden from a tool-calling
|
||||
model, so that the model doesn't attempt to generate the argument. If using
|
||||
ToolNode, the appropriate store field will be automatically injected into
|
||||
the model-generated tool args. Note: if a graph is compiled with a store object,
|
||||
the store will be automatically propagated to the tools with InjectedStore args
|
||||
when using ToolNode.
|
||||
This annotation enables tools to access LangGraph's persistent storage system
|
||||
without exposing storage details to the language model. Tools annotated with
|
||||
InjectedStore receive the store instance automatically during execution while
|
||||
remaining invisible to the model's tool-calling interface.
|
||||
|
||||
The store provides persistent, cross-session data storage that tools can use
|
||||
for maintaining context, user preferences, or any other data that needs to
|
||||
persist beyond individual workflow executions.
|
||||
|
||||
!!! Warning
|
||||
`InjectedStore` annotation requires `langchain-core >= 0.3.8`
|
||||
|
||||
Example:
|
||||
```python
|
||||
from typing import Any
|
||||
from typing_extensions import Annotated
|
||||
|
||||
from langchain_core.messages import AIMessage
|
||||
from langchain_core.tools import tool
|
||||
|
||||
from langgraph.store.memory import InMemoryStore
|
||||
from langgraph.prebuilt import InjectedStore, ToolNode
|
||||
|
||||
store = InMemoryStore()
|
||||
store.put(("values",), "foo", {"bar": 2})
|
||||
@tool
|
||||
def save_preference(
|
||||
key: str,
|
||||
value: str,
|
||||
store: Annotated[Any, InjectedStore()]
|
||||
) -> str:
|
||||
\"\"\"Save user preference to persistent storage.\"\"\"
|
||||
store.put(("preferences",), key, value)
|
||||
return f"Saved {key} = {value}"
|
||||
|
||||
@tool
|
||||
def store_tool(x: int, my_store: Annotated[Any, InjectedStore()]) -> str:
|
||||
'''Do something with store.'''
|
||||
stored_value = my_store.get(("values",), "foo").value["bar"]
|
||||
return stored_value + x
|
||||
|
||||
node = ToolNode([store_tool])
|
||||
|
||||
tool_call = {"name": "store_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
|
||||
state = {
|
||||
"messages": [AIMessage("", tool_calls=[tool_call])],
|
||||
}
|
||||
|
||||
node.invoke(state, store=store)
|
||||
def get_preference(
|
||||
key: str,
|
||||
store: Annotated[Any, InjectedStore()]
|
||||
) -> str:
|
||||
\"\"\"Retrieve user preference from persistent storage.\"\"\"
|
||||
result = store.get(("preferences",), key)
|
||||
return result.value if result else "Not found"
|
||||
```
|
||||
|
||||
```pycon
|
||||
{
|
||||
"messages": [
|
||||
ToolMessage(content='3', name='store_tool', tool_call_id='1'),
|
||||
]
|
||||
}
|
||||
Usage with ToolNode and graph compilation:
|
||||
|
||||
```python
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.store.memory import InMemoryStore
|
||||
|
||||
store = InMemoryStore()
|
||||
tool_node = ToolNode([save_preference, get_preference])
|
||||
|
||||
graph = StateGraph(State)
|
||||
graph.add_node("tools", tool_node)
|
||||
compiled_graph = graph.compile(store=store) # Store is injected automatically
|
||||
```
|
||||
|
||||
Cross-session persistence:
|
||||
|
||||
```python
|
||||
# First session
|
||||
result1 = graph.invoke({"messages": [HumanMessage("Save my favorite color as blue")]})
|
||||
|
||||
# Later session - data persists
|
||||
result2 = graph.invoke({"messages": [HumanMessage("What's my favorite color?")]})
|
||||
```
|
||||
|
||||
Note:
|
||||
- InjectedStore arguments are automatically excluded from tool schemas
|
||||
presented to language models
|
||||
- The store instance is automatically injected by ToolNode during execution
|
||||
- Tools can access namespaced storage using the store's get/put methods
|
||||
- Store injection requires the graph to be compiled with a store instance
|
||||
- Multiple tools can share the same store instance for data consistency
|
||||
""" # noqa: E501
|
||||
|
||||
|
||||
def _is_injection(
|
||||
type_arg: Any, injection_type: Union[Type[InjectedState], Type[InjectedStore]]
|
||||
) -> bool:
|
||||
"""Check if a type argument represents an injection annotation.
|
||||
|
||||
This utility function determines whether a type annotation indicates that
|
||||
an argument should be injected with state or store data. It handles both
|
||||
direct annotations and nested annotations within Union or Annotated types.
|
||||
|
||||
Args:
|
||||
type_arg: The type argument to check for injection annotations.
|
||||
injection_type: The injection type to look for (InjectedState or InjectedStore).
|
||||
|
||||
Returns:
|
||||
True if the type argument contains the specified injection annotation.
|
||||
"""
|
||||
if isinstance(type_arg, injection_type) or (
|
||||
isinstance(type_arg, type) and issubclass(type_arg, injection_type)
|
||||
):
|
||||
@@ -819,6 +1019,19 @@ def _is_injection(
|
||||
|
||||
|
||||
def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
|
||||
"""Extract state injection mappings from tool annotations.
|
||||
|
||||
This function analyzes a tool's input schema to identify arguments that should
|
||||
be injected with graph state. It processes InjectedState annotations to build
|
||||
a mapping of tool argument names to state field names.
|
||||
|
||||
Args:
|
||||
tool: The tool to analyze for state injection requirements.
|
||||
|
||||
Returns:
|
||||
A dictionary mapping tool argument names to state field names. If a field
|
||||
name is None, the entire state should be injected for that argument.
|
||||
"""
|
||||
full_schema = tool.get_input_schema()
|
||||
tool_args_to_state_fields: dict = {}
|
||||
|
||||
@@ -845,6 +1058,22 @@ def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
|
||||
|
||||
|
||||
def _get_store_arg(tool: BaseTool) -> Optional[str]:
|
||||
"""Extract store injection argument from tool annotations.
|
||||
|
||||
This function analyzes a tool's input schema to identify the argument that
|
||||
should be injected with the graph store. Only one store argument is supported
|
||||
per tool.
|
||||
|
||||
Args:
|
||||
tool: The tool to analyze for store injection requirements.
|
||||
|
||||
Returns:
|
||||
The name of the argument that should receive the store injection, or None
|
||||
if no store injection is required.
|
||||
|
||||
Raises:
|
||||
ValueError: If a tool argument has multiple InjectedStore annotations.
|
||||
"""
|
||||
full_schema = tool.get_input_schema()
|
||||
for name, type_ in get_all_basemodel_annotations(full_schema).items():
|
||||
injections = [
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
import os
|
||||
from collections.abc import AsyncIterator, Iterator
|
||||
from uuid import UUID
|
||||
|
||||
@@ -29,6 +30,55 @@ from tests.conftest_store import (
|
||||
|
||||
pytest.register_assert_rewrite("tests.memory_assert")
|
||||
|
||||
# Global variables for checkpointer and store configurations
|
||||
FAST_MODE = os.getenv("LANGGRAPH_TEST_FAST", "true").lower() in ("true", "1", "yes")
|
||||
|
||||
SYNC_CHECKPOINTER_PARAMS = (
|
||||
["memory"]
|
||||
if FAST_MODE
|
||||
else [
|
||||
"memory",
|
||||
"sqlite",
|
||||
"postgres",
|
||||
"postgres_pipe",
|
||||
"postgres_pool",
|
||||
]
|
||||
)
|
||||
|
||||
ASYNC_CHECKPOINTER_PARAMS = (
|
||||
["memory"]
|
||||
if FAST_MODE
|
||||
else [
|
||||
"memory",
|
||||
"sqlite_aio",
|
||||
"postgres_aio",
|
||||
"postgres_aio_pipe",
|
||||
"postgres_aio_pool",
|
||||
]
|
||||
)
|
||||
|
||||
SYNC_STORE_PARAMS = (
|
||||
["in_memory"]
|
||||
if FAST_MODE
|
||||
else [
|
||||
"in_memory",
|
||||
"postgres",
|
||||
"postgres_pipe",
|
||||
"postgres_pool",
|
||||
]
|
||||
)
|
||||
|
||||
ASYNC_STORE_PARAMS = (
|
||||
["in_memory"]
|
||||
if FAST_MODE
|
||||
else [
|
||||
"in_memory",
|
||||
"postgres_aio",
|
||||
"postgres_aio_pipe",
|
||||
"postgres_aio_pool",
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def anyio_backend():
|
||||
@@ -48,7 +98,7 @@ def deterministic_uuids(mocker: MockerFixture) -> MockerFixture:
|
||||
|
||||
@pytest.fixture(
|
||||
scope="function",
|
||||
params=["in_memory", "postgres", "postgres_pipe", "postgres_pool"],
|
||||
params=SYNC_STORE_PARAMS,
|
||||
)
|
||||
def sync_store(request: pytest.FixtureRequest) -> Iterator[BaseStore]:
|
||||
store_name = request.param
|
||||
@@ -72,7 +122,7 @@ def sync_store(request: pytest.FixtureRequest) -> Iterator[BaseStore]:
|
||||
|
||||
@pytest.fixture(
|
||||
scope="function",
|
||||
params=["in_memory", "postgres_aio", "postgres_aio_pipe", "postgres_aio_pool"],
|
||||
params=ASYNC_STORE_PARAMS,
|
||||
)
|
||||
async def async_store(request: pytest.FixtureRequest) -> AsyncIterator[BaseStore]:
|
||||
store_name = request.param
|
||||
@@ -96,13 +146,7 @@ async def async_store(request: pytest.FixtureRequest) -> AsyncIterator[BaseStore
|
||||
|
||||
@pytest.fixture(
|
||||
scope="function",
|
||||
params=[
|
||||
"memory",
|
||||
"sqlite",
|
||||
"postgres",
|
||||
"postgres_pipe",
|
||||
"postgres_pool",
|
||||
],
|
||||
params=SYNC_CHECKPOINTER_PARAMS,
|
||||
)
|
||||
def sync_checkpointer(
|
||||
request: pytest.FixtureRequest,
|
||||
@@ -129,13 +173,7 @@ def sync_checkpointer(
|
||||
|
||||
@pytest.fixture(
|
||||
scope="function",
|
||||
params=[
|
||||
"memory",
|
||||
"sqlite_aio",
|
||||
"postgres_aio",
|
||||
"postgres_aio_pipe",
|
||||
"postgres_aio_pool",
|
||||
],
|
||||
params=ASYNC_CHECKPOINTER_PARAMS,
|
||||
)
|
||||
async def async_checkpointer(
|
||||
request: pytest.FixtureRequest,
|
||||
|
||||
Generated
+28
-28
@@ -40,11 +40,11 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.6.15"
|
||||
version = "2025.7.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
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{ url = "https://files.pythonhosted.org/packages/e0/30/f3eaf6563c637b6e66238ed6535f6775480db973c836336e4122161986fc/ruff-0.12.3-py3-none-win_arm64.whl", hash = "sha256:5f9c7c9c8f84c2d7f27e93674d27136fbf489720251544c4da7fb3d742e011b1", size = 10805855, upload-time = "2025-07-11T13:21:13.547Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
@@ -335,8 +335,10 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
|
||||
@typing.overload
|
||||
def __call__(
|
||||
self,
|
||||
fn: _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
|
||||
| _ActionHandler[dict[str, typing.Any]],
|
||||
fn: (
|
||||
_ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
|
||||
| _ActionHandler[dict[str, typing.Any]]
|
||||
),
|
||||
) -> _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]: ...
|
||||
|
||||
@typing.overload
|
||||
@@ -352,9 +354,11 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
fn: _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
|
||||
| _ActionHandler[dict[str, typing.Any]]
|
||||
| None = None,
|
||||
fn: (
|
||||
_ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
|
||||
| _ActionHandler[dict[str, typing.Any]]
|
||||
| None
|
||||
) = None,
|
||||
*,
|
||||
resources: str | Sequence[str] | None = None,
|
||||
actions: str | Sequence[str] | None = None,
|
||||
@@ -476,9 +480,13 @@ class _StoreOn:
|
||||
def __call__(
|
||||
self,
|
||||
*,
|
||||
actions: typing.Literal["put", "get", "search", "list_namespaces", "delete"]
|
||||
| Sequence[typing.Literal["put", "get", "search", "list_namespaces", "delete"]]
|
||||
| None = None,
|
||||
actions: (
|
||||
typing.Literal["put", "get", "search", "list_namespaces", "delete"]
|
||||
| Sequence[
|
||||
typing.Literal["put", "get", "search", "list_namespaces", "delete"]
|
||||
]
|
||||
| None
|
||||
) = None,
|
||||
) -> Callable[[AHO], AHO]: ...
|
||||
|
||||
@typing.overload
|
||||
@@ -488,9 +496,13 @@ class _StoreOn:
|
||||
self,
|
||||
fn: AHO | None = None,
|
||||
*,
|
||||
actions: typing.Literal["put", "get", "search", "list_namespaces", "delete"]
|
||||
| Sequence[typing.Literal["put", "get", "search", "list_namespaces", "delete"]]
|
||||
| None = None,
|
||||
actions: (
|
||||
typing.Literal["put", "get", "search", "list_namespaces", "delete"]
|
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| Sequence[
|
||||
typing.Literal["put", "get", "search", "list_namespaces", "delete"]
|
||||
]
|
||||
| None
|
||||
) = None,
|
||||
) -> AHO | Callable[[AHO], AHO]:
|
||||
"""Register a handler for specific resources and actions.
|
||||
|
||||
@@ -708,4 +720,12 @@ def _validate_handler(fn: Callable[..., typing.Any]) -> None:
|
||||
)
|
||||
|
||||
|
||||
def is_studio_user(user: types.MinimalUser | types.User | types.UserDict) -> bool:
|
||||
return (
|
||||
isinstance(user, types.StudioUser)
|
||||
or isinstance(user, dict)
|
||||
and user.get("kind") == "StudioUser"
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["Auth", "types", "exceptions"]
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "langgraph-sdk"
|
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version = "0.1.72"
|
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version = "0.1.73"
|
||||
description = "SDK for interacting with LangGraph API"
|
||||
authors = []
|
||||
requires-python = ">=3.9"
|
||||
|
||||
Generated
+56
-56
@@ -19,11 +19,11 @@ wheels = [
|
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|
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[[package]]
|
||||
name = "certifi"
|
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version = "2025.6.15"
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version = "2025.7.14"
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2", size = 162722, upload-time = "2025-07-14T03:29:26.863Z" },
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]
|
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|
||||
[[package]]
|
||||
@@ -119,7 +119,7 @@ wheels = [
|
||||
|
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[[package]]
|
||||
name = "langgraph-sdk"
|
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version = "0.1.72"
|
||||
version = "0.1.73"
|
||||
source = { editable = "." }
|
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dependencies = [
|
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{ name = "httpx" },
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@@ -156,7 +156,7 @@ dev = [
|
||||
|
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[[package]]
|
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name = "mypy"
|
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version = "1.16.1"
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version = "1.17.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "mypy-extensions" },
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@@ -164,39 +164,39 @@ dependencies = [
|
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{ name = "tomli", marker = "python_full_version < '3.11'" },
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{ name = "typing-extensions" },
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wheels = [
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[[package]]
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Reference in New Issue
Block a user