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William Fu-Hinthorn
2025-03-13 09:37:47 -07:00
parent b5a981d82d fc5dde6c55
commit 8729ebc40c
116 changed files with 10538 additions and 5468 deletions
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.github/workflows/_lint.yml
+1 -1
View File
@@ -54,7 +54,7 @@ jobs:
if: steps.changed-files.outputs.all
shell: bash
working-directory: ${{ inputs.working-directory }}
run: poetry lock --check
run: poetry check --lock
- name: Install dependencies
if: steps.changed-files.outputs.all
.github/workflows/_test.yml
+6
View File
@@ -39,6 +39,12 @@ jobs:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
- name: Check Lock
shell: bash
working-directory: ${{ inputs.working-directory }}
run: |
poetry check --lock
- name: Install dependencies
shell: bash
working-directory: ${{ inputs.working-directory }}
.github/workflows/ci.yml
+41
View File
@@ -39,6 +39,7 @@ jobs:
- 'libs/checkpoint-sqlite/**'
- 'libs/checkpoint-postgres/**'
- 'libs/scheduler-kafka/**'
- 'libs/prebuilt/**'
sdk-js:
- 'libs/sdk-js/**'
@@ -56,6 +57,7 @@ jobs:
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/scheduler-kafka",
"libs/prebuilt",
]
if: needs.changes.outputs.python == 'true'
uses: ./.github/workflows/_lint.yml
@@ -74,6 +76,7 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/prebuilt",
]
if: needs.changes.outputs.python == 'true'
uses: ./.github/workflows/_test.yml
@@ -111,6 +114,42 @@ jobs:
- name: Run check_sdk_methods script
run: python .github/scripts/check_sdk_methods.py
check-schema:
needs: changes
if: needs.changes.outputs.python == 'true'
name: "Check CLI schema hasn't changed #${{ matrix.python-version }}"
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.11"
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
uses: "./.github/actions/poetry_setup"
with:
python-version: "3.11"
poetry-version: ${{ env.POETRY_VERSION }}
cache-key: schema-check-cli
- name: Install CLI dependencies
run: |
cd libs/cli
poetry install
- name: Generate schema and check for changes
run: |
cd libs/cli
# Create a temporary copy of the current schema
cp schemas/schema.json schemas/schema.current.json
# Generate new schema
poetry run python generate_schema.py
# Compare the new schema with the original
if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
echo "Error: Langgraph.json configuration schema has changed. Please run 'poetry run python generate_schema.py' in the libs/cli directory and commit the changes."
diff schemas/schema.json schemas/schema.current.json
exit 1
fi
echo "Schema check passed - no changes detected"
integration-test:
needs: changes
if: needs.changes.outputs.python == 'true'
@@ -177,6 +216,8 @@ jobs:
test,
test-langgraph,
test-scheduler-kafka,
check-sdk-methods,
check-schema,
integration-test,
test-js,
]
.github/workflows/release.yml
+5 -1
View File
@@ -195,7 +195,11 @@ jobs:
"$PKG_NAME==$VERSION" \
)
if [[ "$PKG_NAME" == *checkpoint* ]]; then
if [[ "$PKG_NAME" == *prebuilt* ]]; then
poetry run pip install langgraph
fi
if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
# since checkpoint packages are namespace packages, import them with . convention
# i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
README.md
+39 -297
View File
@@ -1,339 +1,81 @@
# 🦜🕸️LangGraph
![Version](https://img.shields.io/pypi/v/langgraph)
[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
[![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
⚡ Building language agents as graphs ⚡
> [!NOTE]
> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
## Overview
LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
stateful, multi-actor applications with LLMs, used to create agent and multi-agent
workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
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.
### Why use LangGraph?
LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
- **Memory**: LangGraph persists arbitrary aspects of your application's state,
supporting memory of conversations and other updates within and across user
interactions;
- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
and resumed, allowing for decisions, validation, and corrections at key stages via
human input.
Standardizing these components allows individuals and teams to focus on the behavior
of their agent, instead of its supporting infrastructure.
Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
the development, deployment, debugging, and monitoring of your applications.
LangGraph integrates seamlessly with
[LangChain](https://python.langchain.com/docs/introduction/) and
[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
To learn more about LangGraph, check out our first LangChain Academy
course, *Introduction to LangGraph*, available for free
[here](https://academy.langchain.com/courses/intro-to-langgraph).
### LangGraph Platform
[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
(includes a free tier).
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
- **Background runs**: Runs agents asynchronously in the background
- **Support for long running agents**: Infrastructure that can handle long running processes
- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
## Installation
```shell
```bash
pip install -U langgraph
```
## Example
Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
```shell
pip install langchain-anthropic
```
```shell
export ANTHROPIC_API_KEY=sk-...
```
Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
```shell
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=lsv2_sk_...
```
The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
<details open>
<summary>High-level implementation</summary>
To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
```python
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
model = ChatAnthropic(model=“claude-3-7-sonnet-latest”)
tools = [search]
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
app = create_react_agent(model, tools, checkpointer=checkpointer)
# Use the agent
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
agent = create_react_agent(model, tools)
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
## Why use LangGraph?
```python
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what about ny"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
```
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
```
</details>
- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
> [!TIP]
> LangGraph is a **low-level** framework that allows you to implement any custom agent
architectures. Click on the low-level implementation below to see how to implement a
tool-calling agent from scratch.
LangGraph is trusted in production and powering agents for companies like:
<details>
<summary>Low-level implementation</summary>
- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
```python
from typing import Literal
## LangGraph’s ecosystem
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
## Pairing with LangGraph Platform
While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
tools = [search]
LangGraph Platform can help engineering teams:
tool_node = ToolNode(tools)
- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
## Additional resources
# Define the function that determines whether to continue or not
def should_continue(state: MessagesState) -> Literal["tools", END]:
messages = state['messages']
last_message = messages[-1]
# If the LLM makes a tool call, then we route to the "tools" node
if last_message.tool_calls:
return "tools"
# Otherwise, we stop (reply to the user)
return END
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
- [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.
- [How-to 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.).
- [API 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.
- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
## Acknowledgements
# Define the function that calls the model
def call_model(state: MessagesState):
messages = state['messages']
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("tools", 'agent')
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable.
# Note that we're (optionally) passing the memory when compiling the graph
app = workflow.compile(checkpointer=checkpointer)
# Use the agent
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
<b>Step-by-step Breakdown</b>:
<details>
<summary>Initialize the model and tools.</summary>
<ul>
<li>
We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
</li>
<li>
We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
</li>
</ul>
</details>
<details>
<summary>Initialize graph with state.</summary>
<ul>
<li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
<li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
</ul>
</details>
<details>
<summary>Define graph nodes.</summary>
There are two main nodes we need:
<ul>
<li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
<li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
</ul>
</details>
<details>
<summary>Define entry point and graph edges.</summary>
First, we need to set the entry point for graph execution - <code>agent</code> node.
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
<ul>
<li>Conditional edge: after the agent is called, we should either:
<ul>
<li>a. Run tools if the agent said to take an action, OR</li>
<li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
</ul>
</li>
<li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
</ul>
</details>
<details>
<summary>Compile the graph.</summary>
<ul>
<li>
When we compile the graph, we turn it into a LangChain
<a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>,
which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code>
with your inputs
</li>
<li>
We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory,
human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> -
a simple in-memory checkpointer
</li>
</ul>
</details>
<details>
<summary>Execute the graph.</summary>
<ol>
<li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
<li>The <code>"agent"</code> node executes, invoking the chat model.</li>
<li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
<li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
<ul>
<li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
<li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
</ul>
</li>
<li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
</ol>
</details>
</details>
## Documentation
* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
## Resources
* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
## Contributing
For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
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.
docs/_scripts/third_party_page/create_third_party_page.py
+7 -3
View File
@@ -83,14 +83,18 @@ def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -
resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
)
rows = [
"| Name | GitHub URL | Description | Weekly Downloads |",
"| --- | --- | --- | --- |",
"| Name | GitHub URL | Description | Weekly Downloads | Stars |",
"| --- | --- | --- | --- | --- |",
]
for package in sorted_packages:
name = f"**{package['name']}**"
repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
stars_badge = (
f"https://img.shields.io/github/stars/{package['repo']}?style=social"
)
stars = f"![GitHub stars]({stars_badge})"
downloads = package["weekly_downloads"] or "-"
row = f"| {name} | {repo_url} | {package['description']} | {downloads} |"
row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
rows.append(row)
markdown_content = MARKDOWN.format(
library_list="\n".join(rows), langgraph_url=langgraph_url
docs/_scripts/third_party_page/packages.yml
+19 -4
View File
@@ -2,13 +2,13 @@
packages:
- name: "trustcall"
repo: "hinthornw/trustcall"
description: "Tenacious tool calling built on LangGraph"
description: "Tenacious tool calling built on LangGraph."
- name: "breeze-agent"
repo: "andrestorres123/breeze-agent"
description: "A streamlined research system built inspired on STORM and built on LangGraph"
description: "A streamlined research system built inspired on STORM and built on LangGraph."
- name: "langgraph-supervisor"
repo: "langchain-ai/langgraph-supervisor"
description: "Build supervisor multi-agent systems with LangGraph"
repo: "langchain-ai/langgraph-supervisor-py"
description: "Build supervisor multi-agent systems with LangGraph."
- name: "langmem"
repo: "langchain-ai/langmem"
description: "Build agents that learn and adapt from interactions over time."
@@ -18,3 +18,18 @@ packages:
- name: "open-deep-research"
repo: "langchain-ai/open_deep_research"
description: "Open source assistant for iterative web research and report writing."
- name: "langgraph-swarm"
repo: "langchain-ai/langgraph-swarm-py"
description: "Build swarm-style multi-agent systems using LangGraph."
- name: "delve-taxonomy-generator"
repo: "andrestorres123/delve"
description: "A taxonomy generator for unstructured data"
- name: "nodeology"
repo: "xyin-anl/Nodeology"
description: "Enable researcher to build scientific workflows easily with simplified interface."
- name: "langgraph-bigtool"
repo: "langchain-ai/langgraph-bigtool"
description: "Build LangGraph agents with large numbers of tools."
- name: "langgraph-reflection"
repo: "langchain-ai/langgraph-reflection"
description: "LangGraph agent that runs a reflection step."
docs/docs/adopters.md
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@@ -1,4 +1,4 @@
# 🦜🕸️ LangGraph Adopters
# 🦜🕸️ Companies using LangGraph
This list of companies using LangGraph and their success stories is compiled from public sources. If your company uses LangGraph, we'd love for you to share your story and add it to the list. You’re also welcome to contribute updates based on publicly available information from other companies, such as blog posts or press releases.
@@ -9,6 +9,7 @@ This list of companies using LangGraph and their success stories is compiled fro
| [AppFolio](https://www.appfolio.com/) | Real Estate | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-appfolio/) |
| [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) |
| [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) |
| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
| [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) |
| [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
| [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
docs/docs/cloud/deployment/semantic_search.md
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@@ -17,7 +17,7 @@ This guide explains how to add semantic search to your LangGraph deployment's cr
...
"store": {
"index": {
"embed": "openai:text-embeddings-3-small",
"embed": "openai:text-embedding-3-small",
"dims": 1536,
"fields": ["$"]
}
@@ -27,7 +27,7 @@ This guide explains how to add semantic search to your LangGraph deployment's cr
This configuration:
- Uses OpenAI's text-embeddings-3-small model for generating embeddings
- Uses OpenAI's text-embedding-3-small model for generating embeddings
- Sets the embedding dimension to 1536 (matching the model's output)
- Indexes all fields in your stored data (`["$"]` means index everything, or specify specific fields like `["text", "metadata.title"]`)
docs/docs/cloud/deployment/setup.md
+5 -6
View File
@@ -36,21 +36,20 @@ Dependencies can optionally be specified in one of the following files: `pyproje
The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
```
langgraph>=0.2.56,<0.3.0
langgraph-checkpoint>=2.0.5,<3.0
langgraph>=0.2.56,<0.4.0
langgraph-sdk>=0.1.53
langgraph-checkpoint>=2.0.15,<3.0
langchain-core>=0.2.38,<0.4.0
langsmith>=0.1.63
orjson>=3.9.7
httpx>=0.25.0
tenacity>=8.0.0
uvicorn>=0.26.0
sse-starlette>=2.1.0
sse-starlette>=2.1.0,<2.2.0
uvloop>=0.18.0
httptools>=0.5.0
jsonschema-rs>=0.16.3
croniter>=1.0.1
jsonschema-rs>=0.20.0
structlog>=23.1.0
redis>=5.0.0,<6.0.0
```
Example `requirements.txt` file:
docs/docs/cloud/deployment/setup_pyproject.md
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View File
@@ -36,21 +36,20 @@ Dependencies can optionally be specified in one of the following files: `pyproje
The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
```
langgraph>=0.2.56,<0.3.0
langgraph-checkpoint>=2.0.5,<3.0
langgraph>=0.2.56,<0.4.0
langgraph-sdk>=0.1.53
langgraph-checkpoint>=2.0.15,<3.0
langchain-core>=0.2.38,<0.4.0
langsmith>=0.1.63
orjson>=3.9.7
httpx>=0.25.0
tenacity>=8.0.0
uvicorn>=0.26.0
sse-starlette>=2.1.0
sse-starlette>=2.1.0,<2.2.0
uvloop>=0.18.0
httptools>=0.5.0
jsonschema-rs>=0.16.3
croniter>=1.0.1
jsonschema-rs>=0.20.0
structlog>=23.1.0
redis>=5.0.0,<6.0.0
```
Example `pyproject.toml` file:
docs/docs/cloud/how-tos/langgraph_to_langgraph_cloud.ipynb
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docs/docs/cloud/how-tos/test_deployment.md
+3 -3
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@@ -1,10 +1,10 @@
# Test Cloud Deployment
# Test LangGraph Platform Deployment
The LangGraph Studio UI connects directly to LangGraph Cloud deployments.
The LangGraph Studio UI connects directly to LangGraph Platform deployments.
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
1. Select an existing deployment to test with LangGraph Studio.
1. In the top-right corner, select `Open LangGraph Studio`.
1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).
docs/docs/cloud/reference/cli.md
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@@ -1,6 +1,6 @@
# LangGraph CLI
The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server as an alternative to the [Studio desktop app](../../concepts/langgraph_studio.md).
The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server.
## Installation
docs/docs/cloud/reference/env_var.md
+6
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@@ -2,6 +2,12 @@
The LangGraph Cloud Server supports specific environment variables for configuring a deployment.
## `DD_API_KEY`
Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.
If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.
## `LANGCHAIN_TRACING_SAMPLING_RATE`
Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.
docs/docs/concepts/faq.md
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@@ -36,7 +36,7 @@ LangGraph is a stateful, orchestration framework that brings added control to ag
| Concurrency Control | Simple threading | Supports double-texting |
| Scheduling | None | Cron scheduling |
| Monitoring | None | Integrated with LangSmith for observability |
| IDE integration | LangGraph Studio for Desktop | LangGraph Studio for Desktop & Cloud |
| IDE integration | LangGraph Studio | LangGraph Studio |
## What are my deployment options for LangGraph Platform?
@@ -62,3 +62,9 @@ Yes! You can use LangGraph with any LLMs. The main reason we use LLMs that suppo
## Does LangGraph work with OSS LLMs?
Yes! LangGraph is totally ambivalent to what LLMs are used under the hood. The main reason we use closed LLMs in most of the tutorials is that they seamlessly support tool calling, while OSS LLMs often don't. But tool calling is not necessary (see [this section](#does-langgraph-work-with-llms-that-dont-support-tool-calling)) so you can totally use LangGraph with OSS LLMs.
## Can I use LangGraph Studio without logging to LangSmith
Yes! You can use the [development version of LangGraph Server](../tutorials/langgraph-platform/local-server.md) to run the backend locally.
This will connect to the studio frontend hosted as part of LangSmith.
If you set an environment variable of `LANGSMITH_TRACING=false` then no traces will be sent to LangSmith.
docs/docs/concepts/langgraph_cli.md
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@@ -4,7 +4,7 @@
- [LangGraph Platform](./langgraph_platform.md)
- [LangGraph Server](./langgraph_server.md)
The LangGraph CLI is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. This offers an alternative to the [LangGraph Studio desktop app](./langgraph_studio.md) for developing and testing agents across all major operating systems (Linux, Windows, MacOS). The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
The LangGraph CLI is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
## Installation
docs/docs/concepts/langgraph_studio.md
+71 -64
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@@ -7,7 +7,7 @@
LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.
With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.
With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.
![](img/lg_studio.png)
@@ -15,7 +15,7 @@ With visual graphs and the ability to edit state, you can better understand agen
The key features of LangGraph Studio are:
- Visualizes your graph
- Visualize your graphs
- Test your graph by running it from the UI
- Debug your agent by [modifying its state and rerunning](human_in_the_loop.md)
- Create and manage [assistants](assistants.md)
@@ -23,86 +23,54 @@ The key features of LangGraph Studio are:
- View and manage [long term memory](memory.md)
- Add node input/outputs to [LangSmith](https://smith.langchain.com/) datasets for testing
## Types
## Getting started
### Development server with web UI
There are two ways to connect your LangGraph app with the studio:
You can [run a local in-memory development server](../tutorials/langgraph-platform/local-server.md) that can be used to connect a local LangGraph app with a web version of the studio.
For example, if you start the local server with `langgraph dev` (running at `http://127.0.0.1:2024` by default), you can connect to the studio by navigating to:
### Deployed Application
If you have deployed your LangGraph application on LangGraph Platform, you can access the studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
### Local Development Server
If you have a LangGraph application that is [running locally in-memory](../tutorials/langgraph-platform/local-server.md), you can connect it to LangGraph Studio in the browser within LangSmith.
By default, starting the local server with `langgraph dev` will run the server at `http://127.0.0.1:2024` and automatically open Studio in your browser. However, you can also manually connect to Studio by either:
1. In LangGraph Platform, clicking the "LangGraph Studio" button and entering the server URL in the dialog that appears.
or
2. Navigating to the URL in your browser:
```
https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
```
See [instructions here](../cloud/reference/cli.md#dev) for more information.
## Related
The web UI version of the studio will connect to your locally running server — your agent is still running locally and never leaves your device.
For more information please see the following:
### Cloud studio
- [LangGraph Studio how-to guides](../how-tos/index.md#langgraph-studio)
- [LangGraph CLI Documentation](../cloud/reference/cli.md)
If you have deployed your LangGraph application on LangGraph Platform (Cloud), you can access the studio as part of that
### Desktop app
LangGraph Studio is available as a [desktop app](https://studio.langchain.com/) for MacOS users.
While in Beta, LangGraph Studio is available for free to all [LangSmith](https://smith.langchain.com/) users on any plan tier.
## Studio FAQs
## LangGraph Studio FAQs
### Why is my project failing to start?
There are a few reasons that your project might fail to start, here are some of the most common ones.
#### Docker issues (desktop only)
LangGraph Studio (desktop) requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
#### Configuration or environment issues
Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables.
!!! Important "Note (desktop only)"
LangGraph Studio Desktop automatically populates `LANGCHAIN_*` environment variables for license verification and tracing, regardless of the contents of the `.env` file. All other environment variables defined in `.env` will be read as normal.
#### Incorrect data region (desktop only)
If you receive a license verification error when attempting to start the LangGraph Server, you may be logged into the incorrect LangSmith data region. Ensure that you're logged into the correct LangSmith data region and ensure that the LangSmith account has access to LangGraph platform.
1. In the top right-hand corner, click the user icon and select `Logout`.
1. At the login screen, click the `Data Region` dropdown menu and select the appropriate data region. Then click `Login to LangSmith`.
A project may fail to start if the configuration file is defined incorrectly, or if required environment variables are missing. See [here](../cloud/reference/cli.md#configuration-file) for how your configuration file should be defined.
### How does interrupt work?
When you select the `Interrupts` dropdown and select a node to interrupt the graph will pause execution before and after (unless the node goes straight to `END`) that node has run. This means that you will be able to both edit the state before the node is ran and the state after the node has ran. This is intended to allow developers more fine-grained control over the behavior of a node and make it easier to observe how the node is behaving. You will not be able to edit the state after the node has ran if the node is the final node in the graph.
### How do I reload the app? (desktop only)
For more information on interrupts and human in the loop, see [here](./human_in_the_loop.md).
If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
### How does automatic rebuilding work? (desktop only)
One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
#### Rebuilds from source code changes
If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
#### Rebuilds from configuration or dependency changes
If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
### Why is my graph taking so long to startup? (desktop only)
The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
## Why are extra edges showing up in my graph?
### Why are extra edges showing up in my graph?
If you don't define your conditional edges carefully, you might notice extra edges appearing in your graph. This is because without proper definition, LangGraph Studio assumes the conditional edge could access all other nodes. In order for this to not be the case, you need to be explicit about how you define the nodes the conditional edge routes to. There are two ways you can do this:
### Solution 1: Include a path map
#### Solution 1: Include a path map
The first way to solve this is to add path maps to your conditional edges. A path map is just a dictionary or array that maps the possible outputs of your router function with the names of the nodes that each output corresponds to. The path map is passed as the third argument to the `add_conditional_edges` function like so:
@@ -120,7 +88,7 @@ The first way to solve this is to add path maps to your conditional edges. A pat
In this case, the routing function returns either True or False, which map to `node_b` and `node_c` respectively.
### Solution 2: Update the typing of the router (Python only)
#### Solution 2: Update the typing of the router (Python only)
Instead of passing a path map, you can also be explicit about the typing of your routing function by specifying the nodes it can map to using the `Literal` python definition. Here is an example of how to define a routing function in that way:
@@ -132,9 +100,48 @@ def routing_function(state: GraphState) -> Literal["node_b","node_c"]:
return "node_c"
```
### Studio Desktop FAQs
## Related
!!! warning "Deprecation Warning"
In order to support a wider range of platforms and users, we now recommend following the above instructions to connect to LangGraph Studio using the development server instead of the desktop app.
For more information please see the following:
The LangGraph Studio Desktop App is a standalone application that allows you to connect to your LangGraph application and visualize and interact with your graph. It is available for MacOS only and requires Docker to be installed.
* [LangGraph Studio how-to guides](../how-tos/index.md#langgraph-studio)
#### Why is my project failing to start?
In addition to the reasons listed above, for the desktop app there are a few more reasons that your project might fail to start:
!!! Important "Note "
LangGraph Studio Desktop automatically populates `LANGCHAIN_*` environment variables for license verification and tracing, regardless of the contents of the `.env` file. All other environment variables defined in `.env` will be read as normal.
##### Docker issues
LangGraph Studio (desktop) requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
##### Incorrect data region
If you receive a license verification error when attempting to start the LangGraph Server, you may be logged into the incorrect LangSmith data region. Ensure that you're logged into the correct LangSmith data region and ensure that the LangSmith account has access to LangGraph platform.
1. In the top right-hand corner, click the user icon and select `Logout`.
1. At the login screen, click the `Data Region` dropdown menu and select the appropriate data region. Then click `Login to LangSmith`.
### How do I reload the app?
If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
### How does automatic rebuilding work?
One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
#### Rebuilds from source code changes
If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
#### Rebuilds from configuration or dependency changes
If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
### Why is my graph taking so long to startup?
The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
docs/docs/concepts/low_level.md
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@@ -310,7 +310,7 @@ graph.add_conditional_edges(START, routing_function, {True: "node_b", False: "no
## `Send`
By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of `State` to exist at the same time. A common example of this is with `map-reduce` design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input `State` to the downstream `Node` should be different (one for each generated object).
By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of `State` to exist at the same time. A common example of this is with [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input `State` to the downstream `Node` should be different (one for each generated object).
To support this design pattern, LangGraph supports returning [`Send`][langgraph.types.Send] objects from conditional edges. `Send` takes two arguments: first is the name of the node, and second is the state to pass to that node.
docs/docs/concepts/persistence.md
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@@ -32,7 +32,7 @@ from typing_extensions import TypedDict
from operator import add
class State(TypedDict):
foo: int
foo: str
bar: Annotated[list[str], add]
def node_a(state: State):
docs/docs/how-tos/create-react-agent-memory.ipynb
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@@ -220,7 +220,7 @@
"id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
"metadata": {},
"source": [
"Notice that when we pass the same the same thread ID, the chat history is preserved"
"Notice that when we pass the same thread ID, the chat history is preserved."
]
},
{
docs/docs/how-tos/index.md
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@@ -59,12 +59,10 @@ LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) i
[Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.
Key workflows:
- [How to wait for user input](human_in_the_loop/wait-user-input.ipynb): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
Other methods:
@@ -290,10 +288,9 @@ Graph execution can take a while, and sometimes users may change their mind abou
LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
- [How to connect to a LangGraph Cloud deployment](../cloud/how-tos/test_deployment.md)
- [How to connect to a LangGraph Platform deployment](../cloud/how-tos/test_deployment.md)
- [How to connect to a local dev server](../how-tos/local-studio.md)
- [How to connect to a local deployment (Docker)](../cloud/how-tos/test_local_deployment.md)
- [How to test your graph in LangGraph Studio (MacOS only)](../cloud/how-tos/invoke_studio.md)
- [How to interact with threads in LangGraph Studio](../cloud/how-tos/threads_studio.md)
- [How to add nodes as dataset examples in LangGraph Studio](../cloud/how-tos/datasets_studio.md)
- [How to engineer prompts in LangGraph Studio](../cloud/how-tos/iterate_graph_studio.md)
@@ -312,4 +309,4 @@ These are the guides for resolving common errors you may find while building wit
These guides provide troubleshooting information for errors that are specific to the LangGraph Platform.
- [INVALID_LICENSE](../troubleshooting/errors/INVALID_LICENSE.md)
- [INVALID_LICENSE](../troubleshooting/errors/INVALID_LICENSE.md)
docs/docs/how-tos/local-studio.md
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@@ -1,15 +1,6 @@
# How to connect a local agent to LangGraph Studio
This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging.
## Connection Options
There are two ways to connect your local agent to LangGraph Studio:
- [Development Server](../concepts/langgraph_studio.md#development-server-with-web-ui): Python package, all platforms, no Docker
- [LangGraph Desktop](../concepts/langgraph_studio.md#desktop-app): Application, Mac only, requires Docker
In this guide we will cover how to use the development server as that is generally an easier and better experience.
This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging using the development server.
## Setup your application
@@ -24,9 +15,8 @@ You will need to make sure to install the `inmem` extras.
???+ note "Minimum version"
The minimum version to use the `inmem` extra with `langgraph-cli` is `0.1.55`.
Python 3.11 or higher is required.
The minimum version to use the `inmem` extra with `langgraph-cli` is `0.1.55`.
Python 3.11 or higher is required.
```shell
pip install -U "langgraph-cli[inmem]"
@@ -41,7 +31,7 @@ pip install -U "langgraph-cli[inmem]"
langgraph dev
```
This will look for the `langgraph.json` file in your current directory.
This will look for the `langgraph.json` file in your current directory.
In there, it will find the paths to the graph(s), and start those up.
It will then automatically connect to the cloud-hosted studio.
@@ -89,4 +79,4 @@ Then attach your preferred debugger:
2. Click + and select "Python Debug Server"
3. Set IDE host name: `localhost`
4. Set port: `5678` (or the port number you chose in the previous step)
5. Click "OK" and start debugging
5. Click "OK" and start debugging
docs/docs/how-tos/memory/add-summary-conversation-history.ipynb
+1
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@@ -10,6 +10,7 @@
"One of the most common use cases for persistence is to use it to keep track of conversation history. This is great - it makes it easy to continue conversations. As conversations get longer and longer, however, this conversation history can build up and take up more and more of the context window. This can often be undesirable as it leads to more expensive and longer calls to the LLM, and potentially ones that error. One way to work around that is to create a summary of the conversation to date, and use that with the past N messages. This guide will go through an example of how to do that.\n",
"\n",
"This will involve a few steps:\n",
"\n",
"- Check if the conversation is too long (can be done by checking number of messages or length of messages)\n",
"- If yes, the create summary (will need a prompt for this)\n",
"- Then remove all except the last N messages\n",
docs/docs/llms.txt
+191
View File
@@ -0,0 +1,191 @@
# LangGraph
## Quickstart
These guides are designed to help you get started with LangGraph.
- [LangGraph Quickstart](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
- [Common Workflows](https://langchain-ai.github.io/langgraph/tutorials/workflows/): Overview of the most common workflows using LLMs implemented with LangGraph.
- [LangGraph Server Quickstart](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
- [Deploy with LangGraph Cloud Quickstart](https://langchain-ai.github.io/langgraph/cloud/quick_start/): Deploy a LangGraph app using LangGraph Cloud.
## Concepts
These guides provide explanations of the key concepts behind the LangGraph framework.
- [Why LangGraph?](https://langchain-ai.github.io/langgraph/concepts/high_level/): Motivation for LangGraph, a library for building agentic applications with LLMs.
- [LangGraph Glossary](https://langchain-ai.github.io/langgraph/concepts/low_level/): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
- [Common Agentic Patterns](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
- [Multi-Agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
- [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/breakpoints/): Breakpoints allow pausing the execution of a graph at specific points. Breakpoints allow stepping through graph execution for debugging purposes.
- [Human-in-the-Loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Explains different ways of integrating human feedback into a LangGraph application.
- [Time Travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/): Time travel allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues.
- [Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.
- [Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
- [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/): `@entrypoint` and `@task` decorators that allow you to add LangGraph functionality to an existing codebase.
- [Durable Execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): LangGraph's built-in [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store.
- [Pregel](https://langchain-ai.github.io/langgraph/concepts/pregel/): Pregel is LangGraph's runtime, which is responsible for managing the execution of LangGraph applications.
- [FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): Frequently asked questions about LangGraph.
## How-tos
Here you’ll find answers to “How do I...?” types of questions.
These guides are **goal-oriented** and concrete.
They're meant to help you complete a specific task.
### Graph API Basics
- [How to update graph state from nodes](https://langchain-ai.github.io/langgraph/how-tos/state-reducers/)
- [How to create a sequence of steps](https://langchain-ai.github.io/langgraph/how-tos/sequence/)
- [How to create branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/branching/)
- [How to create and control loops with recursion limits](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/)
- [How to visualize your graph](https://langchain-ai.github.io/langgraph/how-tos/visualization/)
### Fine-grained Control
These guides demonstrate LangGraph features that grant fine-grained control over the execution of your graph.
- [How to create map-reduce branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/)
- [How to update state and jump to nodes in graphs and subgraphs](https://langchain-ai.github.io/langgraph/how-tos/command/)
- [How to add runtime configuration to your graph](https://langchain-ai.github.io/langgraph/how-tos/configuration/)
- [How to add node retries](https://langchain-ai.github.io/langgraph/how-tos/node-retries/)
- [How to return state before hitting recursion limit](https://langchain-ai.github.io/langgraph/how-tos/return-when-recursion-limit-hits/)
### Persistence
Persistence makes it easy to persist state across graph runs (per-thread persistence) and across threads (cross-thread persistence).
These how-to guides show how to add persistence to your graph.
- [How to add thread-level persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
- [How to add thread-level persistence to a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-persistence/)
- [How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence/)
- [How to use Postgres checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/)
- [How to use MongoDB checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/)
- [How to create a custom checkpointer using Redis](https://langchain-ai.github.io/langgraph/how-tos/persistence_redis/)
See the below guides for how-to add persistence to your workflow using the [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/):
- [How to add thread-level persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/persistence-functional/)
- [How to add cross-thread persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence-functional/)
### Memory
LangGraph makes it easy to manage conversation memory in your graph. These how-to guides show how to implement different strategies for that.
- [How to manage conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/)
- [How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/)
- [How to add summary conversation memory](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/)
- [How to add long-term memory (cross-thread)](https://langchain-ai.github.io/langgraph/how-tos/memory/cross-thread-persistence/)
- [How to use semantic search for long-term memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/)
### Human-in-the-loop
Human-in-the-loop functionality allows you to involve humans in the decision-making process of your graph.
These how-to guides show how to implement human-in-the-loop workflows in your graph.
- [How to wait for user input](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
- [How to review tool calls](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/review-tool-calls/): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
- [How to add static breakpoints](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): Use for debugging purposes. For human-in-the-loop workflows, we recommend the [`interrupt` function](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.interrupt) instead.
- [How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**.
See the below guides for how-to implement human-in-the-loop workflows with the Functional API.
- [How to wait for user input (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/wait-user-input-functional/)
- [How to review tool calls (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/review-tool-calls-functional/)
### Time Travel
[Time travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/time-travel/)
### Streaming
[Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
- [How to stream](https://langchain-ai.github.io/langgraph/how-tos/streaming/)
- [How to stream LLM tokens](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens/)
- [How to stream LLM tokens from specific nodes](https://langchain-ai.github.io/langgraph/how-tos/streaming-specific-nodes/)
- [How to stream data from within a tool](https://langchain-ai.github.io/langgraph/how-tos/streaming-events-from-within-tools/)
- [How to stream from subgraphs](https://langchain-ai.github.io/langgraph/how-tos/streaming-subgraphs/)
- [How to disable streaming for models that don't support it](https://langchain-ai.github.io/langgraph/how-tos/disable-streaming/)
### Tool calling
[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of [chat model](https://python.langchain.com/docs/concepts/chat_models/) API.
It accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
These how-to guides show common patterns for tool calling with LangGraph:
- [How to call tools using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
- [How to handle tool calling errors](https://langchain-ai.github.io/langgraph/how-tos/tool-calling-errors/)
- [How to pass runtime values to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)
- [How to pass config to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-config-to-tools/)
- [How to update graph state from tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/)
- [How to handle large numbers of tools](https://langchain-ai.github.io/langgraph/how-tos/many-tools/)
### Subgraphs
Subgraphs allow you to reuse an existing graph from another graph.
These how-to guides show how to use subgraphs:
- [How to use subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraph/)
- [How to view and update state in subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraphs-manage-state/)
- [How to transform inputs and outputs of a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/)
### Multi-agent
Multi-agent systems are useful to break down complex LLM applications into multiple agents, each responsible for a different part of the application.
These how-to guides show how to implement multi-agent systems in LangGraph:
- [How to implement handoffs between agents](https://langchain-ai.github.io/langgraph/how-tos/agent-handoffs/)
- [How to build a multi-agent network](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/)
- [How to add multi-turn conversation in a multi-agent application](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo/)
### State Management
- [How to use Pydantic model as graph state](https://langchain-ai.github.io/langgraph/how-tos/state-model/)
- [How to define input/output schema for your graph](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/)
- [How to pass private state between nodes inside the graph](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/)
### Other
- [How to run graph asynchronously](https://langchain-ai.github.io/langgraph/how-tos/async/)
- [How to force tool-calling agent to structure output](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/)
- [How to pass custom LangSmith run ID for graph runs](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/)
- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration/)
## Use cases
Explore practical implementations tailored for specific scenarios:
### Chatbots
- [Customer Support](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/): Build a multi-functional support bot for flights, hotels, and car rentals.
- [Prompt Generation from User Requirements](https://langchain-ai.github.io/langgraph/tutorials/chatbots/information-gather-prompting/): Build an information gathering chatbot.
- [Code Assistant](https://langchain-ai.github.io/langgraph/tutorials/code_assistant/langgraph_code_assistant/): Build a code analysis and generation assistant.
### RAG
- [Agentic RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
- [Adaptive RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
- For a version that uses a local LLM: [Adaptive RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/)
- [Corrective RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag/): Uses an LLM to grade the quality of the retrieved information from the given source, and if the quality is low, it will try to retrieve the information from another source. Implementation of: https://arxiv.org/pdf/2401.15884.pdf
- For a version that uses a local LLM: [Corrective RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag_local/)
- [Self-RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/): Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. Implementation of https://arxiv.org/abs/2310.11511.
- For a version that uses a local LLM: [Self-RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag_local/)
- [SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/): Build a SQL agent that can answer questions about a SQL database.
### Multi-Agent Systems
- [Network](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/): Enable two or more agents to collaborate on a task
- [Supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/): Use an LLM to orchestrate and delegate to individual agents
- [Hierarchical Teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/): Orchestrate nested teams of agents to solve problems
docs/docs/tutorials/workflows/index.md
+7 -7
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@@ -184,7 +184,7 @@ As noted in the [Anthropic blog](https://www.anthropic.com/research/building-eff
See our lesson on Prompt Chaining [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/chain.ipynb).
=== "Functional API (beta)"
=== "Functional API"
```python
from langgraph.func import entrypoint, task
@@ -335,7 +335,7 @@ With parallelization, LLMs work simultaneously on a task:
See our lesson on parallelization [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/simple-graph.ipynb).
=== "Functional API (beta)"
=== "Functional API"
```python
@task
@@ -524,7 +524,7 @@ Routing classifies an input and directs it to a followup task. As noted in the [
[Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is RAG workflow that routes questions. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).
=== "Functional API (beta)"
=== "Functional API"
```python
from typing_extensions import Literal
@@ -761,7 +761,7 @@ With orchestrator-worker, an orchestrator breaks down a task and delegates each
[Here](https://github.com/langchain-ai/report-mAIstro) is a project that uses orchestrator-worker for report planning and writing. See our video [here](https://www.youtube.com/watch?v=wSxZ7yFbbas).
=== "Functional API (beta)"
=== "Functional API"
```python
from typing import List
@@ -952,7 +952,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano
[Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is a RAG workflow that grades answers for hallucinations or errors. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).
=== "Functional API (beta)"
=== "Functional API"
```python
# Schema for structured output to use in evaluation
@@ -1161,7 +1161,7 @@ llm_with_tools = llm.bind_tools(tools)
[Here](https://github.com/langchain-ai/memory-agent) is a project that uses a tool calling agent to create / store long-term memories.
=== "Functional API (beta)"
=== "Functional API"
```python
from langgraph.graph import add_messages
@@ -1270,4 +1270,4 @@ LangGraph provides several ways to stream workflow / agent outputs or intermedia
### Deployment
LangGraph provides an easy on-ramp for deployment, observability, and evaluation. See [module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy.
LangGraph provides an easy on-ramp for deployment, observability, and evaluation. See [module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy.
docs/mkdocs.yml
+1 -1
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@@ -359,7 +359,7 @@ nav:
- Resources:
# NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
- Prebuilt Agents: prebuilt.md
- Adopters: adopters.md
- Companies using LangGraph: adopters.md
- FAQ: concepts/faq.md
- Troubleshooting:
- Troubleshooting: troubleshooting/errors/index.md
docs/poetry.lock
Generated
+54 -38
View File
@@ -169,15 +169,15 @@ files = [
[[package]]
name = "anthropic"
version = "0.45.2"
version = "0.47.2"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.8"
groups = ["test"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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[package.dependencies]
@@ -1299,7 +1299,7 @@ version = "0.7.1"
description = "XML bomb protection for Python stdlib modules"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
groups = ["docs", "test"]
groups = ["docs"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
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@@ -3288,21 +3288,20 @@ together = ["langchain-together"]
[[package]]
name = "langchain-anthropic"
version = "0.2.4"
version = "0.3.8"
description = "An integration package connecting AnthropicMessages and LangChain"
optional = false
python-versions = "<4.0,>=3.9"
groups = ["test"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = [
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[package.dependencies]
anthropic = ">=0.30.0,<1"
defusedxml = ">=0.7.1,<0.8.0"
langchain-core = ">=0.3.15,<0.4.0"
anthropic = ">=0.47.0,<1"
langchain-core = ">=0.3.39,<1.0.0"
pydantic = ">=2.7.4,<3.0.0"
[[package]]
@@ -3357,15 +3356,15 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
[[package]]
name = "langchain-core"
version = "0.3.34"
version = "0.3.40"
description = "Building applications with LLMs through composability"
optional = false
python-versions = "<4.0,>=3.9"
groups = ["docs", "test"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = [
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[package.dependencies]
@@ -3474,19 +3473,19 @@ ollama = ">=0.4.4,<1"
[[package]]
name = "langchain-openai"
version = "0.3.4"
version = "0.3.7"
description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = "<4.0,>=3.9"
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markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = [
{file = "langchain_openai-0.3.4-py3-none-any.whl", hash = "sha256:58d0c014620eb92f4f46ff9daf584c2a7794896b1379eb85ad7be8d9f3493b61"},
{file = "langchain_openai-0.3.4.tar.gz", hash = "sha256:c6645745a1d1bf19f21ea6fa473a746bd464053ff57ce563215e6165a0c4b9f1"},
{file = "langchain_openai-0.3.7-py3-none-any.whl", hash = "sha256:0aefc7bdf8e7398d41e09c4313cace816df6438f2aa93d34f79523487310f0da"},
{file = "langchain_openai-0.3.7.tar.gz", hash = "sha256:b8b51a3aaa1cc3bda060651ea41145f7728219e8a7150b5404fb1e8446de9cef"},
]
[package.dependencies]
langchain-core = ">=0.3.34,<1.0.0"
langchain-core = ">=0.3.39,<1.0.0"
openai = ">=1.58.1,<2.0.0"
tiktoken = ">=0.7,<1"
@@ -3508,17 +3507,17 @@ langchain-core = ">=0.3.34,<1.0.0"
[[package]]
name = "langgraph"
version = "0.2.71"
version = "0.3.0"
description = "Building stateful, multi-actor applications with LLMs"
optional = false
python-versions = ">=3.9.0,<4.0"
groups = ["docs", "test"]
groups = ["docs"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = []
develop = true
[package.dependencies]
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
langchain-core = ">=0.1,<0.4"
langgraph-checkpoint = "^2.0.10"
langgraph-sdk = "^0.1.42"
@@ -3528,7 +3527,7 @@ url = "../libs/langgraph"
[[package]]
name = "langgraph-checkpoint"
version = "2.0.13"
version = "2.0.16"
description = "Library with base interfaces for LangGraph checkpoint savers."
optional = false
python-versions = "^3.9.0,<4.0"
@@ -3547,26 +3546,25 @@ url = "../libs/checkpoint"
[[package]]
name = "langgraph-checkpoint-mongodb"
version = "0.1.0"
version = "0.1.1"
description = "Library with a MongoDB implementation of LangGraph checkpoint saver."
optional = false
python-versions = "<4.0.0,>=3.9.0"
python-versions = ">=3.9"
groups = ["test"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = [
{file = "langgraph_checkpoint_mongodb-0.1.0-py3-none-any.whl", hash = "sha256:52f20956b36e0275ff805a1eea1db4c1a7e5e0ffe0a1ade65969004fa1654703"},
{file = "langgraph_checkpoint_mongodb-0.1.0.tar.gz", hash = "sha256:3165c134ad5c82a3fe02fef04c81dcd48a3f5d031e07a9d1cb84457241f76793"},
{file = "langgraph_checkpoint_mongodb-0.1.1-py3-none-any.whl", hash = "sha256:1ff2c3cb2a9139c38ea9cf398659b8b32d6bbfcc4999713b62014431477c5ac5"},
{file = "langgraph_checkpoint_mongodb-0.1.1.tar.gz", hash = "sha256:350d347b0458fb7977231ac1295095bef512458ee0debe09fd394d913b8d89d3"},
]
[package.dependencies]
langgraph = ">=0.2.38,<0.3.0"
langgraph-checkpoint = ">=2.0.0,<3.0.0"
langgraph-checkpoint = ">=2.0.0"
motor = ">3.5.0"
pymongo = ">=4.9.0,<4.10.0"
pymongo = ">=4.9,<4.12"
[[package]]
name = "langgraph-checkpoint-postgres"
version = "2.0.14"
version = "2.0.15"
description = "Library with a Postgres implementation of LangGraph checkpoint saver."
optional = false
python-versions = "^3.9.0,<4.0"
@@ -3576,7 +3574,7 @@ files = []
develop = true
[package.dependencies]
langgraph-checkpoint = "^2.0.10"
langgraph-checkpoint = "^2.0.15"
orjson = ">=3.10.1"
psycopg = "^3.2.0"
psycopg-pool = "^3.2.0"
@@ -3587,7 +3585,7 @@ url = "../libs/checkpoint-postgres"
[[package]]
name = "langgraph-checkpoint-sqlite"
version = "2.0.4"
version = "2.0.5"
description = "Library with a SQLite implementation of LangGraph checkpoint saver."
optional = false
python-versions = "^3.9.0"
@@ -3597,20 +3595,40 @@ files = []
develop = true
[package.dependencies]
aiosqlite = "^0.20.0"
langgraph-checkpoint = "^2.0.10"
aiosqlite = ">=0.20,<0.22"
langgraph-checkpoint = "^2.0.15"
[package.source]
type = "directory"
url = "../libs/checkpoint-sqlite"
[[package]]
name = "langgraph-prebuilt"
version = "1.0.0"
description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
optional = false
python-versions = "^3.9.0,<4.0"
groups = ["docs"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = []
develop = true
[package.dependencies]
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
langgraph = ">=0.3,<0.4"
langgraph-checkpoint = "^2.0.10"
[package.source]
type = "directory"
url = "../libs/prebuilt"
[[package]]
name = "langgraph-sdk"
version = "0.1.51"
version = "0.1.53"
description = "SDK for interacting with LangGraph API"
optional = false
python-versions = "^3.9.0,<4.0"
groups = ["docs", "test"]
groups = ["docs"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = []
develop = true
@@ -5939,7 +5957,6 @@ python-versions = ">=3.8"
groups = ["test"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = [
{file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
{file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
]
@@ -5952,7 +5969,6 @@ python-versions = ">=3.8"
groups = ["test"]
markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
files = [
{file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
{file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
]
@@ -8634,4 +8650,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = "^3.10"
content-hash = "06debb82135affdb2baf1fdcc028c062c236121508d787588cd0de1db2da11e4"
content-hash = "ac9af57c6abaddd1f181551a7bb8194ef3e4491391a0f2dc71417d68e85cb5b3"
docs/pyproject.toml
+3 -2
View File
@@ -13,6 +13,7 @@ hub = "^3.0.1"
[tool.poetry.group.docs.dependencies]
langgraph = { path = "../libs/langgraph/", develop = true }
langgraph-prebuilt = {path = "../libs/prebuilt", develop = true}
langgraph-checkpoint = { path = "../libs/checkpoint/", develop = true }
langgraph-checkpoint-sqlite = { path = "../libs/checkpoint-sqlite", develop = true }
langgraph-checkpoint-postgres = { path = "../libs/checkpoint-postgres", develop = true }
@@ -40,8 +41,8 @@ langchain-cohere = "^0.4.2"
[tool.poetry.group.test.dependencies]
langchain = "^0.3.8"
langchain-openai = "^0.3.0"
langchain-anthropic = "^0.2.1"
langchain-openai = "^0.3.7"
langchain-anthropic = "^0.3.8"
langchain-nomic = "^0.1.3"
langchain-fireworks = "^0.2.0"
langchain-community = "^0.3.0"
libs/checkpoint-postgres/langgraph/store/postgres/aio.py
+2 -2
View File
@@ -20,7 +20,7 @@ from langgraph.store.base import (
)
from langgraph.store.base.batch import AsyncBatchedBaseStore
from langgraph.store.postgres.base import (
_PLACEHOLDER,
PLACEHOLDER,
BasePostgresStore,
PoolConfig,
PostgresIndexConfig,
@@ -360,7 +360,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
for (idx, _), vector in zip(embedding_requests, vectors):
_paramslist = queries[idx][1]
for i in range(len(_paramslist)):
if _paramslist[i] is _PLACEHOLDER:
if _paramslist[i] is PLACEHOLDER:
_paramslist[i] = vector
for (idx, _), (query, params) in zip(search_ops, queries):
libs/checkpoint-postgres/langgraph/store/postgres/base.py
+9 -6
View File
@@ -39,6 +39,7 @@ from langgraph.store.base import (
Result,
SearchItem,
SearchOp,
TTLConfig,
ensure_embeddings,
get_text_at_path,
tokenize_path,
@@ -370,7 +371,7 @@ class BasePostgresStore(Generic[C]):
if op.query and self.index_config:
embedding_requests.append((idx, op.query))
score_operator, post_operator = _get_distance_operator(self)
score_operator, post_operator = get_distance_operator(self)
vector_type = (
cast(PostgresIndexConfig, self.index_config)
.get("ann_index_config", {})
@@ -430,10 +431,10 @@ class BasePostgresStore(Generic[C]):
OFFSET %s
"""
params = [
_PLACEHOLDER, # Vector placeholder
PLACEHOLDER, # Vector placeholder
*ns_args,
*filter_params,
_PLACEHOLDER,
PLACEHOLDER,
expanded_limit,
op.limit,
op.offset,
@@ -622,6 +623,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]
] = None,
index: Optional[PostgresIndexConfig] = None,
ttl: Optional[TTLConfig] = None,
) -> None:
super().__init__()
self._deserializer = deserializer
@@ -634,6 +636,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
self.embeddings, self.index_config = _ensure_index_config(self.index_config)
else:
self.embeddings = None
self.ttl_config = ttl
@classmethod
@contextmanager
@@ -828,7 +831,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
for (idx, _), embedding in zip(embedding_requests, embeddings):
_paramslist = queries[idx][1]
for i in range(len(_paramslist)):
if _paramslist[i] is _PLACEHOLDER:
if _paramslist[i] is PLACEHOLDER:
_paramslist[i] = embedding
for (idx, _), (query, params) in zip(search_ops, queries):
@@ -1055,7 +1058,7 @@ def _decode_ns_bytes(namespace: Union[str, bytes, list]) -> tuple[str, ...]:
return tuple(namespace.split("."))
def _get_distance_operator(store: Any) -> tuple[str, str]:
def get_distance_operator(store: Any) -> tuple[str, str]:
"""Get the distance operator and score expression based on config."""
# Note: Today, we are not using ANN indices due to restrictions
# on PGVector's support for mixing vector and non-vector filters
@@ -1121,4 +1124,4 @@ def _ensure_index_config(
return embeddings, index_config
_PLACEHOLDER = object()
PLACEHOLDER = object()
libs/checkpoint-postgres/poetry.lock
Generated
+629 -480
View File
File diff suppressed because it is too large Load Diff
libs/checkpoint-postgres/pyproject.toml
+1 -1
View File
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langgraph-checkpoint-postgres"
version = "2.0.15"
version = "2.0.16"
description = "Library with a Postgres implementation of LangGraph checkpoint saver."
authors = []
license = "MIT"
libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py
+1
View File
@@ -530,6 +530,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
for idx, (channel, value) in enumerate(writes)
],
)
await self.conn.commit()
def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str:
"""Generate the next version ID for a channel.
libs/checkpoint-sqlite/poetry.lock
Generated
+8 -8
View File
@@ -1,23 +1,23 @@
# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
[[package]]
name = "aiosqlite"
version = "0.20.0"
version = "0.21.0"
description = "asyncio bridge to the standard sqlite3 module"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
{file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
{file = "aiosqlite-0.21.0-py3-none-any.whl", hash = "sha256:2549cf4057f95f53dcba16f2b64e8e2791d7e1adedb13197dd8ed77bb226d7d0"},
{file = "aiosqlite-0.21.0.tar.gz", hash = "sha256:131bb8056daa3bc875608c631c678cda73922a2d4ba8aec373b19f18c17e7aa3"},
]
[package.dependencies]
typing_extensions = ">=4.0"
[package.extras]
dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"]
docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
dev = ["attribution (==1.7.1)", "black (==24.3.0)", "build (>=1.2)", "coverage[toml] (==7.6.10)", "flake8 (==7.0.0)", "flake8-bugbear (==24.12.12)", "flit (==3.10.1)", "mypy (==1.14.1)", "ufmt (==2.5.1)", "usort (==1.0.8.post1)"]
docs = ["sphinx (==8.1.3)", "sphinx-mdinclude (==0.6.1)"]
[[package]]
name = "annotated-types"
@@ -1043,4 +1043,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.1"
python-versions = "^3.9.0"
content-hash = "e6d3ca9bce723c05f4c5ae9dc4bee872f7581b7763680b34112f1d280f5a9b0a"
content-hash = "21896b8d3d283d95bc3988aa93f06faf5c47dadc2a8822e5a35672b9cb054693"
libs/checkpoint-sqlite/pyproject.toml
+2 -2
View File
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langgraph-checkpoint-sqlite"
version = "2.0.5"
version = "2.0.6"
description = "Library with a SQLite implementation of LangGraph checkpoint saver."
authors = []
license = "MIT"
@@ -11,7 +11,7 @@ packages = [{ include = "langgraph" }]
[tool.poetry.dependencies]
python = "^3.9.0"
langgraph-checkpoint = "^2.0.15"
aiosqlite = "^0.20.0"
aiosqlite = ">=0.20,<0.22"
[tool.poetry.group.dev.dependencies]
ruff = "^0.6.2"
libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py
+12 -2
View File
@@ -487,7 +487,12 @@ def _msgpack_ext_hook(code: int, data: bytes) -> Any:
except Exception:
return cls.construct(**tup[2])
except Exception:
return
# for pydantic objects we can't find/reconstruct
# let's return the kwargs dict instead
try:
return tup[2]
except NameError:
return
elif code == EXT_PYDANTIC_V2:
try:
tup = msgpack.unpackb(
@@ -500,7 +505,12 @@ def _msgpack_ext_hook(code: int, data: bytes) -> Any:
except Exception:
return cls.model_construct(**tup[2])
except Exception:
return
# for pydantic objects we can't find/reconstruct
# let's return the kwargs dict instead
try:
return tup[2]
except NameError:
return
def _msgpack_enc(data: Any) -> bytes:
libs/checkpoint/langgraph/store/base/__init__.py
+209 -12
View File
@@ -11,9 +11,19 @@ Core types:
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, Iterable, Literal, NamedTuple, Optional, TypedDict, Union, cast
from typing import (
Any,
Iterable,
Literal,
NamedTuple,
Optional,
TypedDict,
Union,
cast,
)
from langchain_core.embeddings import Embeddings
from typing_extensions import override
from langgraph.store.base.embed import (
AEmbeddingsFunc,
@@ -24,6 +34,20 @@ from langgraph.store.base.embed import (
)
class NotProvided:
"""Sentinel singleton."""
def __bool__(self) -> Literal[False]:
return False
@override
def __repr__(self) -> str:
return "NOT_GIVEN"
NOT_PROVIDED = NotProvided()
class Item:
"""Represents a stored item with metadata.
@@ -59,7 +83,7 @@ class Item:
else created_at
)
self.updated_at = (
datetime.fromisoformat(cast(str, created_at))
datetime.fromisoformat(cast(str, updated_at))
if isinstance(updated_at, str)
else updated_at
)
@@ -166,6 +190,13 @@ class GetOp(NamedTuple):
"doc456" # For a document
```
"""
refresh_ttl: bool = True
"""Whether to refresh TTLs for the returned item.
If no TTL was specified for the original item(s),
or if TTL support is not enabled for your adapter,
this argument is ignored.
"""
class SearchOp(NamedTuple):
@@ -260,6 +291,13 @@ class SearchOp(NamedTuple):
- "technical documentation about REST APIs"
- "machine learning papers from 2023"
"""
refresh_ttl: bool = True
"""Whether to refresh TTLs for the returned item.
If no TTL was specified for the original item(s),
or if TTL support is not enabled for your adapter,
this argument is ignored.
"""
# Type representing a namespace path that can include wildcards
@@ -463,6 +501,15 @@ class PutOp(NamedTuple):
]
```
"""
ttl: Optional[float] = None
"""Controls the TTL (time-to-live) for the item in minutes.
If provided, and if the store you are using supports this feature, the item
will expire this many minutes after it was last accessed. The expiration timer
refreshes on both read operations (get/search) and write operations (put/update).
When the TTL expires, the item will be scheduled for deletion on a best-effort basis.
Defaults to None (no expiration).
"""
Op = Union[GetOp, SearchOp, PutOp, ListNamespacesOp]
@@ -473,6 +520,25 @@ class InvalidNamespaceError(ValueError):
"""Provided namespace is invalid."""
class TTLConfig(TypedDict, total=False):
"""Configuration for TTL (time-to-live) behavior in the store."""
refresh_on_read: bool
"""Default behavior for refreshing TTLs on read operations (GET and SEARCH).
If True, TTLs will be refreshed on read operations (get/search) by default.
This can be overridden per-operation by explicitly setting refresh_ttl.
Defaults to True if not configured.
"""
default_ttl: Optional[float]
"""Default TTL (time-to-live) in minutes for new items.
If provided, new items will expire after this many minutes after their last access.
The expiration timer refreshes on both read and write operations.
Defaults to None (no expiration).
"""
class IndexConfig(TypedDict, total=False):
"""Configuration for indexing documents for semantic search in the store.
@@ -612,8 +678,14 @@ class BaseStore(ABC):
by providing an `index` configuration at creation time. Without this
configuration, semantic search is disabled and any `index` arguments
to storage operations will have no effect.
Similarly, TTL (time-to-live) support is disabled by default.
Subclasses must explicitly set `supports_ttl = True` to enable this feature.
"""
supports_ttl: bool = False
ttl_config: Optional[TTLConfig] = None
__slots__ = ("__weakref__",)
@abstractmethod
@@ -640,17 +712,28 @@ class BaseStore(ABC):
The order of results matches the order of input operations.
"""
def get(self, namespace: tuple[str, ...], key: str) -> Optional[Item]:
def get(
self,
namespace: tuple[str, ...],
key: str,
*,
refresh_ttl: Optional[bool] = None,
) -> Optional[Item]:
"""Retrieve a single item.
Args:
namespace: Hierarchical path for the item.
key: Unique identifier within the namespace.
refresh_ttl: Whether to refresh TTLs for the returned item.
If None (default), uses the store's default refresh_ttl setting.
If no TTL is specified, this argument is ignored.
Returns:
The retrieved item or None if not found.
"""
return self.batch([GetOp(namespace, key)])[0]
return self.batch(
[GetOp(namespace, str(key), _ensure_refresh(self.ttl_config, refresh_ttl))]
)[0]
def search(
self,
@@ -661,6 +744,7 @@ class BaseStore(ABC):
filter: Optional[dict[str, Any]] = None,
limit: int = 10,
offset: int = 0,
refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]:
"""Search for items within a namespace prefix.
@@ -670,6 +754,8 @@ class BaseStore(ABC):
filter: Key-value pairs to filter results.
limit: Maximum number of items to return.
offset: Number of items to skip before returning results.
refresh_ttl: Whether to refresh TTLs for the returned items.
If no TTL is specified, this argument is ignored.
Returns:
List of items matching the search criteria.
@@ -707,7 +793,18 @@ class BaseStore(ABC):
Note: Natural language search support depends on your store implementation
and requires proper embedding configuration.
"""
return self.batch([SearchOp(namespace_prefix, filter, limit, offset, query)])[0]
return self.batch(
[
SearchOp(
namespace_prefix,
filter,
limit,
offset,
query,
_ensure_refresh(self.ttl_config, refresh_ttl),
)
]
)[0]
def put(
self,
@@ -715,6 +812,8 @@ class BaseStore(ABC):
key: str,
value: dict[str, Any],
index: Optional[Union[Literal[False], list[str]]] = None,
*,
ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
) -> None:
"""Store or update an item in the store.
@@ -735,12 +834,20 @@ class BaseStore(ABC):
- Nested fields: "metadata.title"
- Array access: "chapters[*].content" (each indexed separately)
- Specific indices: "authors[0].name"
ttl: Time to live in minutes. Support for this argument depends on your store adapter.
If specified, the item will expire after this many minutes from when it was last accessed.
None means no expiration. Expired runs will be deleted opportunistically.
By default, the expiration timer refreshes on both read operations (get/search)
and write operations (put/update), whenever the item is included in the operation.
Note:
Indexing support depends on your store implementation.
If you do not initialize the store with indexing capabilities,
the `index` parameter will be ignored.
Similarly, TTL support depends on the specific store implementation.
Some implementations may not support expiration of items.
???+ example "Examples"
Store item. Indexing depends on how you configure the store.
```python
@@ -759,7 +866,22 @@ class BaseStore(ABC):
```
"""
_validate_namespace(namespace)
self.batch([PutOp(namespace, key, value, index=index)])
if ttl not in (NOT_PROVIDED, None) and not self.supports_ttl:
raise NotImplementedError(
f"TTL is not supported by {self.__class__.__name__}. "
f"Use a store implementation that supports TTL or set ttl=None."
)
self.batch(
[
PutOp(
namespace,
str(key),
value,
index=index,
ttl=_ensure_ttl(self.ttl_config, ttl),
)
]
)
def delete(self, namespace: tuple[str, ...], key: str) -> None:
"""Delete an item.
@@ -768,7 +890,7 @@ class BaseStore(ABC):
namespace: Hierarchical path for the item.
key: Unique identifier within the namespace.
"""
self.batch([PutOp(namespace, key, None)])
self.batch([PutOp(namespace, str(key), None, ttl=None)])
def list_namespaces(
self,
@@ -823,7 +945,13 @@ class BaseStore(ABC):
)
return self.batch([op])[0]
async def aget(self, namespace: tuple[str, ...], key: str) -> Optional[Item]:
async def aget(
self,
namespace: tuple[str, ...],
key: str,
*,
refresh_ttl: Optional[bool] = None,
) -> Optional[Item]:
"""Asynchronously retrieve a single item.
Args:
@@ -833,7 +961,17 @@ class BaseStore(ABC):
Returns:
The retrieved item or None if not found.
"""
return (await self.abatch([GetOp(namespace, key)]))[0]
return (
await self.abatch(
[
GetOp(
namespace,
str(key),
_ensure_refresh(self.ttl_config, refresh_ttl),
)
]
)
)[0]
async def asearch(
self,
@@ -844,6 +982,7 @@ class BaseStore(ABC):
filter: Optional[dict[str, Any]] = None,
limit: int = 10,
offset: int = 0,
refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]:
"""Asynchronously search for items within a namespace prefix.
@@ -853,6 +992,9 @@ class BaseStore(ABC):
filter: Key-value pairs to filter results.
limit: Maximum number of items to return.
offset: Number of items to skip before returning results.
refresh_ttl: Whether to refresh TTLs for the returned items.
If None (default), uses the store's TTLConfig.refresh_default setting.
If TTLConfig is not provided or no TTL is specified, this argument is ignored.
Returns:
List of items matching the search criteria.
@@ -892,7 +1034,16 @@ class BaseStore(ABC):
"""
return (
await self.abatch(
[SearchOp(namespace_prefix, filter, limit, offset, query)]
[
SearchOp(
namespace_prefix,
filter,
limit,
offset,
query,
_ensure_refresh(self.ttl_config, refresh_ttl),
)
]
)
)[0]
@@ -902,6 +1053,8 @@ class BaseStore(ABC):
key: str,
value: dict[str, Any],
index: Optional[Union[Literal[False], list[str]]] = None,
*,
ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
) -> None:
"""Asynchronously store or update an item in the store.
@@ -922,12 +1075,20 @@ class BaseStore(ABC):
- Nested fields: "metadata.title"
- Array access: "chapters[*].content" (each indexed separately)
- Specific indices: "authors[0].name"
ttl: Time to live in minutes. Support for this argument depends on your store adapter.
If specified, the item will expire after this many minutes from when it was last accessed.
None means no expiration. Expired runs will be deleted opportunistically.
By default, the expiration timer refreshes on both read operations (get/search)
and write operations (put/update), whenever the item is included in the operation.
Note:
Indexing support depends on your store implementation.
If you do not initialize the store with indexing capabilities,
the `index` parameter will be ignored.
Similarly, TTL support depends on the specific store implementation.
Some implementations may not support expiration of items.
???+ example "Examples"
Store item. Indexing depends on how you configure the store.
```python
@@ -954,7 +1115,22 @@ class BaseStore(ABC):
```
"""
_validate_namespace(namespace)
await self.abatch([PutOp(namespace, key, value, index=index)])
if ttl not in (NOT_PROVIDED, None) and not self.supports_ttl:
raise NotImplementedError(
f"TTL is not supported by {self.__class__.__name__}. "
f"Use a store implementation that supports TTL or set ttl=None."
)
await self.abatch(
[
PutOp(
namespace,
str(key),
value,
index=index,
ttl=_ensure_ttl(self.ttl_config, ttl),
)
]
)
async def adelete(self, namespace: tuple[str, ...], key: str) -> None:
"""Asynchronously delete an item.
@@ -963,7 +1139,7 @@ class BaseStore(ABC):
namespace: Hierarchical path for the item.
key: Unique identifier within the namespace.
"""
await self.abatch([PutOp(namespace, key, None)])
await self.abatch([PutOp(namespace, str(key), None)])
async def alist_namespaces(
self,
@@ -1043,6 +1219,27 @@ def _validate_namespace(namespace: tuple[str, ...]) -> None:
)
def _ensure_refresh(
ttl_config: Optional[TTLConfig], refresh_ttl: Optional[bool] = None
) -> bool:
if refresh_ttl is not None:
return refresh_ttl
if ttl_config is not None:
return ttl_config.get("refresh_on_read", True)
return True
def _ensure_ttl(
ttl_config: Optional[TTLConfig],
ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
) -> Optional[float]:
if ttl is NOT_PROVIDED:
if ttl_config:
return ttl_config.get("default_ttl")
return None
return ttl
__all__ = [
"BaseStore",
"Item",
libs/checkpoint/langgraph/store/base/batch.py
+58 -6
View File
@@ -5,17 +5,21 @@ from collections.abc import Iterable
from typing import Any, Callable, Literal, Optional, TypeVar, Union
from langgraph.store.base import (
NOT_PROVIDED,
BaseStore,
GetOp,
Item,
ListNamespacesOp,
MatchCondition,
NamespacePath,
NotProvided,
Op,
PutOp,
Result,
SearchItem,
SearchOp,
_ensure_refresh,
_ensure_ttl,
_validate_namespace,
)
@@ -68,10 +72,21 @@ class AsyncBatchedBaseStore(BaseStore):
self,
namespace: tuple[str, ...],
key: str,
*,
refresh_ttl: Optional[bool] = None,
) -> Optional[Item]:
assert not self._task.done()
fut = self._loop.create_future()
self._aqueue.put_nowait((fut, GetOp(namespace, key)))
self._aqueue.put_nowait(
(
fut,
GetOp(
namespace,
key,
refresh_ttl=_ensure_refresh(self.ttl_config, refresh_ttl),
),
)
)
return await fut
async def asearch(
@@ -83,11 +98,22 @@ class AsyncBatchedBaseStore(BaseStore):
filter: Optional[dict[str, Any]] = None,
limit: int = 10,
offset: int = 0,
refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]:
assert not self._task.done()
fut = self._loop.create_future()
self._aqueue.put_nowait(
(fut, SearchOp(namespace_prefix, filter, limit, offset, query))
(
fut,
SearchOp(
namespace_prefix,
filter,
limit,
offset,
query,
refresh_ttl=_ensure_refresh(self.ttl_config, refresh_ttl),
),
)
)
return await fut
@@ -97,11 +123,20 @@ class AsyncBatchedBaseStore(BaseStore):
key: str,
value: dict[str, Any],
index: Optional[Union[Literal[False], list[str]]] = None,
*,
ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
) -> None:
assert not self._task.done()
_validate_namespace(namespace)
fut = self._loop.create_future()
self._aqueue.put_nowait((fut, PutOp(namespace, key, value, index)))
self._aqueue.put_nowait(
(
fut,
PutOp(
namespace, key, value, index, ttl=_ensure_ttl(self.ttl_config, ttl)
),
)
)
return await fut
async def adelete(
@@ -149,9 +184,11 @@ class AsyncBatchedBaseStore(BaseStore):
self,
namespace: tuple[str, ...],
key: str,
*,
refresh_ttl: Optional[bool] = None,
) -> Optional[Item]:
return asyncio.run_coroutine_threadsafe(
self.aget(namespace, key=key), self._loop
self.aget(namespace, key=key, refresh_ttl=refresh_ttl), self._loop
).result()
@_check_loop
@@ -164,10 +201,16 @@ class AsyncBatchedBaseStore(BaseStore):
filter: Optional[dict[str, Any]] = None,
limit: int = 10,
offset: int = 0,
refresh_ttl: Optional[bool] = None,
) -> list[SearchItem]:
return asyncio.run_coroutine_threadsafe(
self.asearch(
namespace_prefix, query=query, filter=filter, limit=limit, offset=offset
namespace_prefix,
query=query,
filter=filter,
limit=limit,
offset=offset,
refresh_ttl=refresh_ttl,
),
self._loop,
).result()
@@ -179,10 +222,19 @@ class AsyncBatchedBaseStore(BaseStore):
key: str,
value: dict[str, Any],
index: Optional[Union[Literal[False], list[str]]] = None,
*,
ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
) -> None:
_validate_namespace(namespace)
asyncio.run_coroutine_threadsafe(
self.aput(namespace, key=key, value=value, index=index), self._loop
self.aput(
namespace,
key=key,
value=value,
index=index,
ttl=_ensure_ttl(self.ttl_config, ttl),
),
self._loop,
).result()
@_check_loop
libs/checkpoint/pyproject.toml
+1 -1
View File
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langgraph-checkpoint"
version = "2.0.16"
version = "2.0.19"
description = "Library with base interfaces for LangGraph checkpoint savers."
authors = []
license = "MIT"
libs/checkpoint/tests/test_jsonplus.py
+1 -1
View File
@@ -130,7 +130,7 @@ def test_serde_jsonplus() -> None:
key="my-key",
namespace=("a", "name", " "),
created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
updated_at=datetime(2024, 9, 24, 17, 29, 11, 128397),
),
}
libs/checkpoint/tests/test_store.py
+4 -4
View File
@@ -148,8 +148,8 @@ async def test_async_batch_store(mocker: MockerFixture) -> None:
assert abatch.call_count == 1
assert [tuple(c.args[0]) for c in abatch.call_args_list] == [
(
GetOp(("a",), "b"),
GetOp(("c",), "d"),
GetOp(("a",), "b", refresh_ttl=True),
GetOp(("c",), "d", refresh_ttl=True),
),
]
@@ -467,8 +467,8 @@ async def test_async_batch_store_deduplication(mocker: MockerFixture) -> None:
assert len(abatch.call_args_list) == 1
ops = list(abatch.call_args_list[0].args[1])
assert len(ops) == 2
assert GetOp(("test",), "same") in ops
assert GetOp(("test",), "different") in ops
assert GetOp(("test",), "same", refresh_ttl=True) in ops
assert GetOp(("test",), "different", refresh_ttl=True) in ops
abatch.reset_mock()
libs/cli/generate_schema.py
+226
View File
@@ -0,0 +1,226 @@
#!/usr/bin/env python3
"""
Script to generate a JSON schema for the langgraph-cli Config class.
This script creates a schema.json file that can be referenced in langgraph.json files
to provide IDE autocompletion and validation.
"""
import inspect
import json
import textwrap
from pathlib import Path
import msgspec
from langgraph_cli.config import (
AuthConfig,
Config,
CorsConfig,
HttpConfig,
IndexConfig,
SecurityConfig,
StoreConfig,
)
def add_descriptions_to_schema(schema, cls):
"""Add docstring descriptions to the schema properties."""
if schema.get("description"):
schema["description"] = inspect.cleandoc(schema["description"])
elif class_doc := inspect.getdoc(cls):
schema["description"] = inspect.cleandoc(class_doc)
# Get attribute docstrings from the class
attr_docs = {}
# Also check class annotations for docstrings
source_lines = inspect.getsourcelines(cls)[0]
current_attr = None
docstring_lines = []
for line in source_lines:
line = line.strip()
# Check for attribute definition (TypedDict style)
if ":" in line and not line.startswith("#") and not line.startswith('"""'):
parts = line.split(":", 1)
if len(parts) == 2 and parts[0].strip().isidentifier():
# If we were collecting a docstring, save it for the previous attribute
if current_attr and docstring_lines:
attr_docs[current_attr] = "\n".join(docstring_lines).strip('"')
docstring_lines = []
current_attr = parts[0].strip()
# Check for docstring after attribute
elif line.startswith('"""') and current_attr:
# Start or end of a docstring
if len(line) > 3 and line.endswith('"""'):
# Single line docstring
attr_docs[current_attr] = line.strip('"')
current_attr = None
elif docstring_lines:
# End of multi-line docstring
docstring_lines.append(line.rstrip('"'))
attr_docs[current_attr] = "\n".join(docstring_lines).strip('"')
docstring_lines = []
current_attr = None
else:
# Start of multi-line docstring
docstring_lines.append(line.lstrip('"'))
# Continue multi-line docstring
elif docstring_lines and current_attr:
docstring_lines.append(line.strip('"'))
# Add the last docstring if there is one
if current_attr and docstring_lines:
attr_docs[current_attr] = "\n".join(docstring_lines).strip('"')
# Add descriptions to properties
if "properties" in schema:
for prop_name, prop_schema in schema["properties"].items():
# First try to get from attribute docstrings
if prop_name in attr_docs and "description" not in prop_schema:
prop_schema["description"] = textwrap.dedent(attr_docs[prop_name])
# Fall back to class docstring parsing
elif class_doc:
for line in class_doc.split("\n"):
if line.strip().startswith(
f"{prop_name}:"
) or line.strip().startswith(f'"{prop_name}"'):
description = line.split(":", 1)[1].strip()
if description and "description" not in prop_schema:
prop_schema["description"] = description
break
# Recursively process nested definitions
if "$defs" in schema:
for def_name, def_schema in schema["$defs"].items():
# Find the class that corresponds to this definition
for potential_cls in [
Config,
StoreConfig,
IndexConfig,
AuthConfig,
SecurityConfig,
HttpConfig,
CorsConfig,
]:
if potential_cls.__name__ == def_name:
add_descriptions_to_schema(def_schema, potential_cls)
break
return schema
def generate_schema():
"""Generate a JSON schema for the Config class using msgspec."""
# Generate the basic schema
schema = msgspec.json.schema(Config)
# Add title and description
schema["title"] = "LangGraph CLI Configuration"
schema["description"] = "Configuration schema for langgraph-cli"
# Add docstring descriptions
schema = add_descriptions_to_schema(schema, Config)
# Add constraint that only one of python_version or node_version should be specified
config_schema = schema["$defs"]["Config"]
# Create two subschemas: one with python_version and one with node_version
# Define properties specific to Python projects
python_specific_props = ["python_version", "pip_config_file"]
# Define properties specific to Node.js projects
node_specific_props = ["node_version"]
# Define properties common to both project types
common_props = [
k
for k in config_schema["properties"]
if k not in python_specific_props and k not in node_specific_props
]
# Create Python schema with python_version and pip_config_file
python_schema = {
"type": "object",
"properties": {
# Include Python-specific properties
**{k: config_schema["properties"][k].copy() for k in python_specific_props},
# Include common properties
**{k: config_schema["properties"][k].copy() for k in common_props},
},
"required": ["dependencies", "graphs"],
}
# Add enum constraint for python_version
if "python_version" in python_schema["properties"]:
python_schema["properties"]["python_version"]["enum"] = ["3.11", "3.12"]
# Create Node.js schema with node_version
node_schema = {
"type": "object",
"properties": {
# Include Node-specific properties
**{k: config_schema["properties"][k].copy() for k in node_specific_props},
# Include common properties
**{k: config_schema["properties"][k].copy() for k in common_props},
},
"required": ["node_version", "graphs"],
}
# Add enum constraint for node_version
if "node_version" in node_schema["properties"]:
node_schema["properties"]["node_version"]["anyOf"] = [
{"type": "string", "enum": ["20"]},
{"type": "null"},
]
# Replace the Config schema with a oneOf constraint
config_schema["oneOf"] = [python_schema, node_schema]
# Remove the properties field as it's now defined in the oneOf subschemas
if "properties" in config_schema:
del config_schema["properties"]
return schema
def main():
"""Generate the schema and write it to a file."""
schema = generate_schema()
# Add versioning to the schema
import importlib.metadata
try:
version = importlib.metadata.version("langgraph_cli").split(".")
schema_version = f"v{version[0]}"
except importlib.metadata.PackageNotFoundError:
schema_version = "v1"
# Add version to schema
schema["version"] = schema_version
config_dir = Path(__file__).parent / "schemas"
# Create versioned schema file
versioned_path = config_dir / f"schema.{schema_version}.json"
with open(versioned_path, "w") as f:
json.dump(schema, f, indent=2)
# Also create a latest version
latest_path = config_dir / "schema.json"
with open(latest_path, "w") as f:
json.dump(schema, f, indent=2)
print(f"Schema written to {versioned_path} and {latest_path}")
print(
f"You can now add '$schema: https://raw.githubusercontent.com/langchain-ai/langgraph/refs/heads/main/libs/cli/schemas/schema.json'"
f" or '$schema: https://raw.githubusercontent.com/langchain-ai/langgraph/refs/heads/main/libs/cli/schemas/schema.{schema_version}.json'"
" to your langgraph.json files"
)
if __name__ == "__main__":
main()
libs/cli/langgraph_cli/config.py
+273 -57
View File
@@ -3,7 +3,7 @@ import os
import pathlib
import textwrap
from collections import Counter
from typing import NamedTuple, Optional, TypedDict, Union
from typing import Any, NamedTuple, Optional, TypedDict, Union
import click
@@ -11,13 +11,37 @@ MIN_NODE_VERSION = "20"
MIN_PYTHON_VERSION = "3.11"
class TTLConfig(TypedDict, total=False):
"""Configuration for TTL (time-to-live) behavior in the store."""
refresh_on_read: bool
"""Default behavior for refreshing TTLs on read operations (GET and SEARCH).
If True, TTLs will be refreshed on read operations (get/search) by default.
This can be overridden per-operation by explicitly setting refresh_ttl.
Defaults to True if not configured.
"""
default_ttl: Optional[float]
"""Optional. Default TTL (time-to-live) in minutes for new items.
If provided, all new items will have this TTL unless explicitly overridden.
If omitted, items will have no TTL by default.
"""
class IndexConfig(TypedDict, total=False):
"""Configuration for indexing documents for semantic search in the store."""
"""Configuration for indexing documents for semantic search in the store.
This governs how text is converted into embeddings and stored for vector-based lookups.
"""
dims: int
"""Number of dimensions in the embedding vectors.
"""Required. Dimensionality of the embedding vectors you will store.
Common embedding models have the following dimensions:
Must match the output dimension of your selected embedding model or custom embed function.
If mismatched, you will likely encounter shape/size errors when inserting or querying vectors.
Common embedding model output dimensions:
- openai:text-embedding-3-large: 3072
- openai:text-embedding-3-small: 1536
- openai:text-embedding-ada-002: 1536
@@ -28,42 +52,130 @@ class IndexConfig(TypedDict, total=False):
"""
embed: str
"""Optional model (string) to generate embeddings from text or path to model or function.
"""Required. Identifier or reference to the embedding model or a custom embedding function.
Examples:
The format can vary:
- "<provider>:<model_name>" for recognized providers (e.g., "openai:text-embedding-3-large")
- "path/to/module.py:function_name" for your own local embedding function
- "my_custom_embed" if it's a known alias in your system
Examples:
- "openai:text-embedding-3-large"
- "cohere:embed-multilingual-v3.0"
- "src/app.py:embeddings
- "src/app.py:embeddings"
Note: Must return embeddings of dimension `dims`.
"""
fields: Optional[list[str]]
"""Fields to extract text from for embedding generation.
"""Optional. List of JSON fields to extract before generating embeddings.
Defaults to the root ["$"], which embeds the json object as a whole.
Defaults to ["$"], which means the entire JSON object is embedded as one piece of text.
If you provide multiple fields (e.g. ["title", "content"]), each is extracted and embedded separately,
often saving token usage if you only care about certain parts of the data.
Example:
fields=["title", "abstract", "author.biography"]
"""
class StoreConfig(TypedDict, total=False):
embed: Optional[IndexConfig]
"""Configuration for vector embeddings in store."""
"""Configuration for the built-in long-term memory store.
This store can optionally perform semantic search. If you omit `index`,
the store will just handle traditional (non-embedded) data without vector lookups.
"""
index: Optional[IndexConfig]
"""Optional. Defines the vector-based semantic search configuration.
If provided, the store will:
- Generate embeddings according to `index.embed`
- Enforce the embedding dimension given by `index.dims`
- Embed only specified JSON fields (if any) from `index.fields`
If omitted, no vector index is initialized.
"""
ttl: Optional[TTLConfig]
"""Optional. Defines the TTL (time-to-live) behavior configuration.
If provided, the store will apply TTL settings according to the configuration.
If omitted, no TTL behavior is configured.
"""
class SecurityConfig(TypedDict, total=False):
securitySchemes: dict
security: list
"""Configuration for OpenAPI security definitions and requirements.
Useful for specifying global or path-level authentication and authorization flows
(e.g., OAuth2, API key headers, etc.).
"""
securitySchemes: dict[str, dict[str, Any]]
"""Required. Dict describing each security scheme recognized by your OpenAPI spec.
Keys are scheme names (e.g. "OAuth2", "ApiKeyAuth") and values are their definitions.
Example:
{
"OAuth2": {
"type": "oauth2",
"flows": {
"password": {
"tokenUrl": "/token",
"scopes": {"read": "Read data", "write": "Write data"}
}
}
}
}
"""
security: list[dict[str, list[str]]]
"""Optional. Global security requirements across all endpoints.
Each element in the list maps a security scheme (e.g. "OAuth2") to a list of scopes (e.g. ["read", "write"]).
Example:
[
{"OAuth2": ["read", "write"]},
{"ApiKeyAuth": []}
]
"""
# path => {method => security}
paths: dict[str, dict[str, list]]
paths: dict[str, dict[str, list[dict[str, list[str]]]]]
"""Optional. Path-specific security overrides.
Keys are path templates (e.g., "/items/{item_id}"), mapping to:
- Keys that are HTTP methods (e.g., "GET", "POST"),
- Values are lists of security definitions (just like `security`) for that method.
Example:
{
"/private_data": {
"GET": [{"OAuth2": ["read"]}],
"POST": [{"OAuth2": ["write"]}]
}
}
"""
class AuthConfig(TypedDict, total=False):
path: str
"""Path to the authentication function in a Python file."""
disable_studio_auth: bool
"""Whether to disable auth when connecting from the LangSmith Studio."""
openapi: SecurityConfig
"""The schema to use for updating the openapi spec.
"""Configuration for custom authentication logic and how it integrates into the OpenAPI spec."""
Example:
path: str
"""Required. Path to an instance of the Auth() class that implements custom authentication.
Format: "path/to/file.py:my_auth"
"""
disable_studio_auth: bool
"""Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.
Defaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header
value is a valid API key for the deployment's workspace. If True, all requests will go through your custom
authentication logic, regardless of origin of the request.
"""
openapi: SecurityConfig
"""Required. Detailed security configuration that merges into your deployment's OpenAPI spec.
Example (OAuth2):
{
"securitySchemes": {
"OAuth2": {
@@ -71,88 +183,185 @@ class AuthConfig(TypedDict, total=False):
"flows": {
"password": {
"tokenUrl": "/token",
"scopes": {
"me": "Read information about the current user",
"items": "Access to create and manage items"
}
"scopes": {"me": "Read user info", "items": "Manage items"}
}
}
}
},
"security": [
{"OAuth2": ["me"]} # Default security requirement for all endpoints
{"OAuth2": ["me"]}
]
}
"""
class CorsConfig(TypedDict, total=False):
"""Specifies Cross-Origin Resource Sharing (CORS) rules for your server.
If omitted, defaults are typically very restrictive (often no cross-origin requests).
Configure carefully if you want to allow usage from browsers hosted on other domains.
"""
allow_origins: list[str]
"""Optional. List of allowed origins (e.g., "https://example.com").
Default is often an empty list (no external origins).
Use "*" only if you trust all origins, as that bypasses most restrictions.
"""
allow_methods: list[str]
"""Optional. HTTP methods permitted for cross-origin requests (e.g. ["GET", "POST"]).
Default might be ["GET", "POST", "OPTIONS"] depending on your server framework.
"""
allow_headers: list[str]
"""Optional. HTTP headers that can be used in cross-origin requests (e.g. ["Content-Type", "Authorization"])."""
allow_credentials: bool
"""Optional. If True, cross-origin requests can include credentials (cookies, auth headers).
Default False to avoid accidentally exposing secured endpoints to untrusted sites.
"""
allow_origin_regex: str
"""Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.
Example: "^https://.*\.mycompany\.com$"
"""
expose_headers: list[str]
"""Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."""
max_age: int
"""Optional. How many seconds the browser may cache preflight responses.
Default might be 600 (10 minutes). Larger values reduce preflight requests but can cause stale configurations.
"""
class HttpConfig(TypedDict, total=False):
"""Configuration for the built-in HTTP server that powers your deployment's routes and endpoints."""
app: str
"""Import path for a custom Starlette/FastAPI app to mount"""
"""Optional. Import path to a custom Starlette/FastAPI application to mount.
Format: "path/to/module.py:app_var"
If provided, it can override or extend the default routes.
"""
disable_assistants: bool
"""Disable /assistants routes"""
"""Optional. If True, /assistants routes are removed from the server.
Default is False (meaning /assistants is enabled).
"""
disable_threads: bool
"""Disable /threads routes"""
"""Optional. If True, /threads routes are removed.
Default is False.
"""
disable_runs: bool
"""Disable /runs routes"""
"""Optional. If True, /runs routes are removed.
Default is False.
"""
disable_store: bool
"""Disable /store routes"""
"""Optional. If True, /store routes are removed, disabling direct store interactions via HTTP.
Default is False.
"""
disable_meta: bool
"""Disable /ok, /info, /metrics, and /docs routes"""
"""Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.
Default is False.
"""
cors: Optional[CorsConfig]
"""Cross-Origin Resource Sharing (CORS) configuration"""
"""Optional. Defines CORS restrictions. If omitted, no special rules are set and
cross-origin behavior depends on default server settings.
"""
class Config(TypedDict, total=False):
"""Configuration for langgraph-cli."""
"""Top-level config for langgraph-cli or similar deployment tooling."""
python_version: str
"""Python version to use."""
"""Optional. Python version in 'major.minor' format (e.g. '3.11').
Must be at least 3.11 or greater for this deployment to function properly.
"""
node_version: Optional[str]
"""Node.js version to use."""
"""Optional. Node.js version as a major version (e.g. '20'), if your deployment needs Node.
Must be >= 20 if provided.
"""
pip_config_file: Optional[str]
"""Path to a pip configuration file."""
"""Optional. Path to a pip config file (e.g., "/etc/pip.conf" or "pip.ini") for controlling
package installation (custom indices, credentials, etc.).
Only relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.
"""
dockerfile_lines: list[str]
"""Additional lines to add to the Dockerfile."""
"""Optional. Additional Docker instructions that will be appended to your base Dockerfile.
Useful for installing OS packages, setting environment variables, etc.
Example:
dockerfile_lines=[
"RUN apt-get update && apt-get install -y libmagic-dev",
"ENV MY_CUSTOM_VAR=hello_world"
]
"""
dependencies: list[str]
"""Additional Python dependencies to install."""
"""List of Python dependencies to install, either from PyPI or local paths.
Examples:
- "." or "./src" if you have a local Python package
- str (aka "anthropic") for a PyPI package
- "git+https://github.com/org/repo.git@main" for a Git-based package
Defaults to an empty list, meaning no additional packages installed beyond your base environment.
"""
graphs: dict[str, str]
"""Mapping of graph names to their definitions."""
"""Optional. Named definitions of graphs, each pointing to a Python object.
Graphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context
managers that accept a single configuration argument (of type RunnableConfig) and return a pregel object
(instance of Stategraph, etc.).
Keys are graph names, values are "path/to/file.py:object_name".
Example:
{
"mygraph": "graphs/my_graph.py:graph_definition",
"anothergraph": "graphs/another.py:get_graph"
}
"""
env: Union[dict[str, str], str]
"""Environment variables to set.
If a dictionary is provided, the keys are environment variable names
and the values are the corresponding environment variable values.
If a string is provided, it is interpreted as a path to a file containing
environment variables in the format KEY=VALUE, with one environment variable
per line.
"""Optional. Environment variables to set for your deployment.
- If given as a dict, keys are variable names and values are their values.
- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.
Example as a dict:
env={"API_TOKEN": "abc123", "DEBUG": "true"}
Example as a file path:
env=".env"
"""
store: Optional[StoreConfig]
"""Configuration for vector embeddings in store."""
"""Optional. Configuration for the built-in long-term memory store, including semantic search indexing.
If omitted, no vector index is set up (the object store will still be present, however).
"""
auth: Optional[AuthConfig]
"""Configuration for authentication."""
"""Optional. Custom authentication config, including the path to your Python auth logic and
the OpenAPI security definitions it uses.
"""
http: Optional[HttpConfig]
"""Configuration for HTTP server."""
"""Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed
and how cross-origin requests are handled.
"""
ui: Optional[dict[str, str]]
"""Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.
"""
def _parse_version(version_str: str) -> tuple[int, int]:
@@ -189,6 +398,7 @@ def validate_config(config: Config) -> Config:
"store": config.get("store"),
"auth": config.get("auth"),
"http": config.get("http"),
"ui": config.get("ui"),
}
if config.get("node_version")
else {
@@ -201,6 +411,7 @@ def validate_config(config: Config) -> Config:
"store": config.get("store"),
"auth": config.get("auth"),
"http": config.get("http"),
"ui": config.get("ui"),
}
)
@@ -687,9 +898,11 @@ def python_config_to_docker(
pip_pkgs_str = f"RUN {pip_install} {' '.join(pypi_deps)}" if pypi_deps else ""
if local_deps.pip_reqs:
pip_reqs_str = os.linesep.join(
f"COPY --from=__outer_{reqpath.name} requirements.txt {destpath}"
if reqpath.parent in local_deps.additional_contexts
else f"ADD {reqpath.relative_to(config_path.parent)} {destpath}"
(
f"COPY --from=__outer_{reqpath.name} requirements.txt {destpath}"
if reqpath.parent in local_deps.additional_contexts
else f"ADD {reqpath.relative_to(config_path.parent)} {destpath}"
)
for reqpath, destpath in local_deps.pip_reqs
)
pip_reqs_str += f'{os.linesep}RUN {pip_install} {" ".join("-r " + r for _,r in local_deps.pip_reqs)}'
@@ -724,13 +937,15 @@ RUN set -ex && \\
)
local_pkgs_str = os.linesep.join(
f"""# -- Adding local package {relpath} --
(
f"""# -- Adding local package {relpath} --
COPY --from={name} . /deps/{name}
# -- End of local package {relpath} --"""
if fullpath in local_deps.additional_contexts
else f"""# -- Adding local package {relpath} --
if fullpath in local_deps.additional_contexts
else f"""# -- Adding local package {relpath} --
ADD {relpath} /deps/{name}
# -- End of local package {relpath} --"""
)
for fullpath, (relpath, name) in local_deps.real_pkgs.items()
)
@@ -845,6 +1060,7 @@ ADD . {faux_path}
RUN cd {faux_path} && {install_cmd}
{env_additional_config}
ENV LANGSERVE_GRAPHS='{json.dumps(config["graphs"])}'
{f"ENV LANGGRAPH_UI='{json.dumps(config['ui'])}'" if config.get("ui") else ""}
WORKDIR {faux_path}
libs/cli/poetry.lock
Generated
+134 -72
View File
@@ -446,54 +446,47 @@ files = [
[[package]]
name = "jsonschema-rs"
version = "0.25.1"
version = "0.20.0"
description = "A high-performance JSON Schema validator for Python"
optional = true
python-versions = ">=3.8"
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dev = ["attrs", "coverage", "eval-type-backport", "furo", "ipython", "msgpack", "mypy", "pre-commit", "pyright", "pytest", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "tomli", "tomli_w"]
doc = ["furo", "ipython", "sphinx", "sphinx-copybutton", "sphinx-design"]
test = ["attrs", "eval-type-backport", "msgpack", "pytest", "pyyaml", "tomli", "tomli_w"]
toml = ["tomli", "tomli_w"]
yaml = ["pyyaml"]
[[package]]
name = "mypy"
version = "1.15.0"
@@ -1278,13 +1340,13 @@ examples = ["fastapi"]
[[package]]
name = "starlette"
version = "0.45.3"
version = "0.46.0"
description = "The little ASGI library that shines."
optional = true
python-versions = ">=3.9"
files = [
{file = "starlette-0.45.3-py3-none-any.whl", hash = "sha256:dfb6d332576f136ec740296c7e8bb8c8a7125044e7c6da30744718880cdd059d"},
{file = "starlette-0.45.3.tar.gz", hash = "sha256:2cbcba2a75806f8a41c722141486f37c28e30a0921c5f6fe4346cb0dcee1302f"},
{file = "starlette-0.46.0-py3-none-any.whl", hash = "sha256:913f0798bd90ba90a9156383bcf1350a17d6259451d0d8ee27fc0cf2db609038"},
{file = "starlette-0.46.0.tar.gz", hash = "sha256:b359e4567456b28d473d0193f34c0de0ed49710d75ef183a74a5ce0499324f50"},
]
[package.dependencies]
@@ -1295,18 +1357,18 @@ full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart
[[package]]
name = "structlog"
version = "24.4.0"
version = "23.3.0"
description = "Structured Logging for Python"
optional = true
python-versions = ">=3.8"
files = [
{file = "structlog-24.4.0-py3-none-any.whl", hash = "sha256:597f61e80a91cc0749a9fd2a098ed76715a1c8a01f73e336b746504d1aad7610"},
{file = "structlog-24.4.0.tar.gz", hash = "sha256:b27bfecede327a6d2da5fbc96bd859f114ecc398a6389d664f62085ee7ae6fc4"},
{file = "structlog-23.3.0-py3-none-any.whl", hash = "sha256:d6922a88ceabef5b13b9eda9c4043624924f60edbb00397f4d193bd754cde60a"},
{file = "structlog-23.3.0.tar.gz", hash = "sha256:24b42b914ac6bc4a4e6f716e82ac70d7fb1e8c3b1035a765591953bfc37101a5"},
]
[package.extras]
dev = ["freezegun (>=0.2.8)", "mypy (>=1.4)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "rich", "simplejson", "twisted"]
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-mermaid", "sphinxext-opengraph", "twisted"]
dev = ["structlog[tests,typing]"]
docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-mermaid", "sphinxext-opengraph", "twisted"]
tests = ["freezegun (>=0.2.8)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "simplejson"]
typing = ["mypy (>=1.4)", "rich", "twisted"]
@@ -1655,4 +1717,4 @@ inmem = ["langgraph-api", "python-dotenv"]
[metadata]
lock-version = "2.0"
python-versions = "^3.9.0,<4.0"
content-hash = "48e374a559e6d8339c82b5271dea910f8ddfb6baf8436153ca54faefb8b2b220"
content-hash = "d0e2bdcb600ad031867413025fcc58bb162609209359d63ca99a77060cf8cbb4"
libs/cli/pyproject.toml
+3 -2
View File
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langgraph-cli"
version = "0.1.73"
version = "0.1.76"
description = "CLI for interacting with LangGraph API"
authors = []
license = "MIT"
@@ -14,7 +14,7 @@ langgraph = "langgraph_cli.cli:cli"
[tool.poetry.dependencies]
python = "^3.9.0,<4.0"
click = "^8.1.7"
langgraph-api = { version = ">=0.0.26,<0.1.0", optional = true, python = ">=3.11,<4.0" }
langgraph-api = { version = ">=0.0.27,<0.1.0", optional = true, python = ">=3.11,<4.0" }
python-dotenv = { version = ">=0.8.0", optional = true }
[tool.poetry.group.dev.dependencies]
@@ -25,6 +25,7 @@ pytest-asyncio = "^0.21.1"
pytest-mock = "^3.11.1"
pytest-watch = "^4.2.0"
mypy = "^1.10.0"
msgspec = "^0.19.0"
[tool.poetry.extras]
inmem = ["langgraph-api", "python-dotenv"]
libs/cli/schemas/schema.json
+470
View File
@@ -0,0 +1,470 @@
{
"$ref": "#/$defs/Config",
"$defs": {
"Config": {
"title": "Config",
"description": "Top-level config for langgraph-cli or similar deployment tooling.",
"type": "object",
"required": [],
"oneOf": [
{
"type": "object",
"properties": {
"python_version": {
"type": "string",
"description": "Optional. Python version in 'major.minor' format (e.g. '3.11').\nMust be at least 3.11 or greater for this deployment to function properly.\n",
"enum": [
"3.11",
"3.12"
]
},
"pip_config_file": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Path to a pip config file (e.g., \"/etc/pip.conf\" or \"pip.ini\") for controlling\npackage installation (custom indices, credentials, etc.).\n\nOnly relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.\n"
},
"auth": {
"anyOf": [
{
"$ref": "#/$defs/AuthConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
},
"dependencies": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of Python dependencies to install, either from PyPI or local paths.\n"
},
"dockerfile_lines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
},
"env": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "string"
}
],
"description": "Optional. Environment variables to set for your deployment.\n\n- If given as a dict, keys are variable names and values are their values.\n- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.\n\nenv=\".env\n"
},
"graphs": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "Optional. Named definitions of graphs, each pointing to a Python object.\n\n\nGraphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context\nmanagers that accept a single configuration argument (of type RunnableConfig) and return a pregel object\n(instance of Stategraph, etc.).\n"
},
"http": {
"anyOf": [
{
"$ref": "#/$defs/HttpConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed\nand how cross-origin requests are handled.\n"
},
"store": {
"anyOf": [
{
"$ref": "#/$defs/StoreConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in long-term memory store, including semantic search indexing.\n\nIf omitted, no vector index is set up (the object store will still be present, however).\n"
},
"ui": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
}
},
"required": [
"dependencies",
"graphs"
]
},
{
"type": "object",
"properties": {
"node_version": {
"anyOf": [
{
"type": "string",
"enum": [
"20"
]
},
{
"type": "null"
}
],
"description": "Optional. Node.js version as a major version (e.g. '20'), if your deployment needs Node.\nMust be >= 20 if provided.\n"
},
"auth": {
"anyOf": [
{
"$ref": "#/$defs/AuthConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
},
"dependencies": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of Python dependencies to install, either from PyPI or local paths.\n"
},
"dockerfile_lines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
},
"env": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "string"
}
],
"description": "Optional. Environment variables to set for your deployment.\n\n- If given as a dict, keys are variable names and values are their values.\n- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.\n\nenv=\".env\n"
},
"graphs": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "Optional. Named definitions of graphs, each pointing to a Python object.\n\n\nGraphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context\nmanagers that accept a single configuration argument (of type RunnableConfig) and return a pregel object\n(instance of Stategraph, etc.).\n"
},
"http": {
"anyOf": [
{
"$ref": "#/$defs/HttpConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed\nand how cross-origin requests are handled.\n"
},
"store": {
"anyOf": [
{
"$ref": "#/$defs/StoreConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in long-term memory store, including semantic search indexing.\n\nIf omitted, no vector index is set up (the object store will still be present, however).\n"
},
"ui": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
}
},
"required": [
"node_version",
"graphs"
]
}
]
},
"AuthConfig": {
"title": "AuthConfig",
"description": "Configuration for custom authentication logic and how it integrates into the OpenAPI spec.",
"type": "object",
"properties": {
"disable_studio_auth": {
"type": "boolean",
"description": "Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.\n\nDefaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header\nvalue is a valid API key for the deployment's workspace. If True, all requests will go through your custom\nauthentication logic, regardless of origin of the request.\n"
},
"openapi": {
"$ref": "#/$defs/SecurityConfig",
"description": "Required. Detailed security configuration that merges into your deployment's OpenAPI spec.\n\n{\n}\n}\n}\n},\n]\n}\n"
},
"path": {
"type": "string",
"description": "Required. Path to an instance of the Auth() class that implements custom authentication.\n"
}
},
"required": []
},
"SecurityConfig": {
"title": "SecurityConfig",
"description": "Configuration for OpenAPI security definitions and requirements.\n\nUseful for specifying global or path-level authentication and authorization flows\n(e.g., OAuth2, API key headers, etc.).",
"type": "object",
"properties": {
"paths": {
"type": "object",
"additionalProperties": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "string"
}
}
}
}
},
"description": "Optional. Path-specific security overrides.\n\n- Keys that are HTTP methods (e.g., \"GET\", \"POST\"),\n- Values are lists of security definitions (just like `security`) for that method.\n"
},
"security": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "string"
}
}
},
"description": "Optional. Global security requirements across all endpoints.\n\nEach element in the list maps a security scheme (e.g. \"OAuth2\") to a list of scopes (e.g. [\"read\", \"write\"])."
},
"securitySchemes": {
"type": "object",
"additionalProperties": {
"type": "object"
},
"description": "Required. Dict describing each security scheme recognized by your OpenAPI spec.\n\nKeys are scheme names (e.g. \"OAuth2\", \"ApiKeyAuth\") and values are their definitions."
}
},
"required": []
},
"HttpConfig": {
"title": "HttpConfig",
"description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
"type": "object",
"properties": {
"app": {
"type": "string",
"description": "Optional. Import path to a custom Starlette/FastAPI application to mount.\n"
},
"cors": {
"anyOf": [
{
"$ref": "#/$defs/CorsConfig"
},
{
"type": "null"
}
],
"description": "Optional. Defines CORS restrictions. If omitted, no special rules are set and\ncross-origin behavior depends on default server settings.\n"
},
"disable_assistants": {
"type": "boolean",
"description": "Optional. If True, /assistants routes are removed from the server.\n\nDefault is False (meaning /assistants is enabled).\n"
},
"disable_meta": {
"type": "boolean",
"description": "Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n"
},
"disable_runs": {
"type": "boolean",
"description": "Optional. If True, /runs routes are removed.\n\nDefault is False.\n"
},
"disable_store": {
"type": "boolean",
"description": "Optional. If True, /store routes are removed, disabling direct store interactions via HTTP.\n\nDefault is False.\n"
},
"disable_threads": {
"type": "boolean",
"description": "Optional. If True, /threads routes are removed.\n\nDefault is False.\n"
}
},
"required": []
},
"CorsConfig": {
"title": "CorsConfig",
"description": "Specifies Cross-Origin Resource Sharing (CORS) rules for your server.\n\nIf omitted, defaults are typically very restrictive (often no cross-origin requests).\nConfigure carefully if you want to allow usage from browsers hosted on other domains.",
"type": "object",
"properties": {
"allow_credentials": {
"type": "boolean",
"description": "Optional. If True, cross-origin requests can include credentials (cookies, auth headers).\n\nDefault False to avoid accidentally exposing secured endpoints to untrusted sites.\n"
},
"allow_headers": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. HTTP headers that can be used in cross-origin requests (e.g. [\"Content-Type\", \"Authorization\"])."
},
"allow_methods": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. HTTP methods permitted for cross-origin requests (e.g. [\"GET\", \"POST\"]).\n\nDefault might be [\"GET\", \"POST\", \"OPTIONS\"] depending on your server framework.\n"
},
"allow_origin_regex": {
"type": "string",
"description": "Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.\n"
},
"allow_origins": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. List of allowed origins (e.g., \"https://example.com\").\n\nDefault is often an empty list (no external origins).\nUse \"*\" only if you trust all origins, as that bypasses most restrictions.\n"
},
"expose_headers": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."
},
"max_age": {
"type": "integer",
"description": "Optional. How many seconds the browser may cache preflight responses.\n\nDefault might be 600 (10 minutes). Larger values reduce preflight requests but can cause stale configurations.\n"
}
},
"required": []
},
"StoreConfig": {
"title": "StoreConfig",
"description": "Configuration for the built-in long-term memory store.\n\nThis store can optionally perform semantic search. If you omit `index`,\nthe store will just handle traditional (non-embedded) data without vector lookups.",
"type": "object",
"properties": {
"index": {
"anyOf": [
{
"$ref": "#/$defs/IndexConfig"
},
{
"type": "null"
}
],
"description": "Optional. Defines the vector-based semantic search configuration.\n\n- Generate embeddings according to `index.embed`\n- Enforce the embedding dimension given by `index.dims`\n- Embed only specified JSON fields (if any) from `index.fields`\n\nIf omitted, no vector index is initialized.\n"
},
"ttl": {
"anyOf": [
{
"$ref": "#/$defs/TTLConfig"
},
{
"type": "null"
}
],
"description": "Optional. Defines the TTL (time-to-live) behavior configuration.\n\nIf provided, the store will apply TTL settings according to the configuration.\nIf omitted, no TTL behavior is configured.\n"
}
},
"required": []
},
"IndexConfig": {
"title": "IndexConfig",
"description": "Configuration for indexing documents for semantic search in the store.\n\nThis governs how text is converted into embeddings and stored for vector-based lookups.",
"type": "object",
"properties": {
"dims": {
"type": "integer",
"description": "Required. Dimensionality of the embedding vectors you will store.\n\nMust match the output dimension of your selected embedding model or custom embed function.\nIf mismatched, you will likely encounter shape/size errors when inserting or querying vectors.\n\n"
},
"embed": {
"type": "string",
"description": "Required. Identifier or reference to the embedding model or a custom embedding function.\n\n- \"my_custom_embed\" if it's a known alias in your system\n"
},
"fields": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. List of JSON fields to extract before generating embeddings.\n\nDefaults to [\"$\"], which means the entire JSON object is embedded as one piece of text.\nIf you provide multiple fields (e.g. [\"title\", \"content\"]), each is extracted and embedded separately,\noften saving token usage if you only care about certain parts of the data.\n"
}
},
"required": []
},
"TTLConfig": {
"title": "TTLConfig",
"description": "Configuration for TTL (time-to-live) behavior in the store.",
"type": "object",
"properties": {
"default_ttl": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
]
},
"refresh_on_read": {
"type": "boolean"
}
},
"required": []
}
},
"title": "LangGraph CLI Configuration",
"description": "Configuration schema for langgraph-cli",
"version": "v0"
}
libs/cli/schemas/schema.v0.json
+470
View File
@@ -0,0 +1,470 @@
{
"$ref": "#/$defs/Config",
"$defs": {
"Config": {
"title": "Config",
"description": "Top-level config for langgraph-cli or similar deployment tooling.",
"type": "object",
"required": [],
"oneOf": [
{
"type": "object",
"properties": {
"python_version": {
"type": "string",
"description": "Optional. Python version in 'major.minor' format (e.g. '3.11').\nMust be at least 3.11 or greater for this deployment to function properly.\n",
"enum": [
"3.11",
"3.12"
]
},
"pip_config_file": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Path to a pip config file (e.g., \"/etc/pip.conf\" or \"pip.ini\") for controlling\npackage installation (custom indices, credentials, etc.).\n\nOnly relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.\n"
},
"auth": {
"anyOf": [
{
"$ref": "#/$defs/AuthConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
},
"dependencies": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of Python dependencies to install, either from PyPI or local paths.\n"
},
"dockerfile_lines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
},
"env": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "string"
}
],
"description": "Optional. Environment variables to set for your deployment.\n\n- If given as a dict, keys are variable names and values are their values.\n- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.\n\nenv=\".env\n"
},
"graphs": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "Optional. Named definitions of graphs, each pointing to a Python object.\n\n\nGraphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context\nmanagers that accept a single configuration argument (of type RunnableConfig) and return a pregel object\n(instance of Stategraph, etc.).\n"
},
"http": {
"anyOf": [
{
"$ref": "#/$defs/HttpConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed\nand how cross-origin requests are handled.\n"
},
"store": {
"anyOf": [
{
"$ref": "#/$defs/StoreConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in long-term memory store, including semantic search indexing.\n\nIf omitted, no vector index is set up (the object store will still be present, however).\n"
},
"ui": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
}
},
"required": [
"dependencies",
"graphs"
]
},
{
"type": "object",
"properties": {
"node_version": {
"anyOf": [
{
"type": "string",
"enum": [
"20"
]
},
{
"type": "null"
}
],
"description": "Optional. Node.js version as a major version (e.g. '20'), if your deployment needs Node.\nMust be >= 20 if provided.\n"
},
"auth": {
"anyOf": [
{
"$ref": "#/$defs/AuthConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
},
"dependencies": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of Python dependencies to install, either from PyPI or local paths.\n"
},
"dockerfile_lines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
},
"env": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "string"
}
],
"description": "Optional. Environment variables to set for your deployment.\n\n- If given as a dict, keys are variable names and values are their values.\n- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.\n\nenv=\".env\n"
},
"graphs": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "Optional. Named definitions of graphs, each pointing to a Python object.\n\n\nGraphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context\nmanagers that accept a single configuration argument (of type RunnableConfig) and return a pregel object\n(instance of Stategraph, etc.).\n"
},
"http": {
"anyOf": [
{
"$ref": "#/$defs/HttpConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed\nand how cross-origin requests are handled.\n"
},
"store": {
"anyOf": [
{
"$ref": "#/$defs/StoreConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in long-term memory store, including semantic search indexing.\n\nIf omitted, no vector index is set up (the object store will still be present, however).\n"
},
"ui": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
}
},
"required": [
"node_version",
"graphs"
]
}
]
},
"AuthConfig": {
"title": "AuthConfig",
"description": "Configuration for custom authentication logic and how it integrates into the OpenAPI spec.",
"type": "object",
"properties": {
"disable_studio_auth": {
"type": "boolean",
"description": "Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.\n\nDefaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header\nvalue is a valid API key for the deployment's workspace. If True, all requests will go through your custom\nauthentication logic, regardless of origin of the request.\n"
},
"openapi": {
"$ref": "#/$defs/SecurityConfig",
"description": "Required. Detailed security configuration that merges into your deployment's OpenAPI spec.\n\n{\n}\n}\n}\n},\n]\n}\n"
},
"path": {
"type": "string",
"description": "Required. Path to an instance of the Auth() class that implements custom authentication.\n"
}
},
"required": []
},
"SecurityConfig": {
"title": "SecurityConfig",
"description": "Configuration for OpenAPI security definitions and requirements.\n\nUseful for specifying global or path-level authentication and authorization flows\n(e.g., OAuth2, API key headers, etc.).",
"type": "object",
"properties": {
"paths": {
"type": "object",
"additionalProperties": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "string"
}
}
}
}
},
"description": "Optional. Path-specific security overrides.\n\n- Keys that are HTTP methods (e.g., \"GET\", \"POST\"),\n- Values are lists of security definitions (just like `security`) for that method.\n"
},
"security": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "string"
}
}
},
"description": "Optional. Global security requirements across all endpoints.\n\nEach element in the list maps a security scheme (e.g. \"OAuth2\") to a list of scopes (e.g. [\"read\", \"write\"])."
},
"securitySchemes": {
"type": "object",
"additionalProperties": {
"type": "object"
},
"description": "Required. Dict describing each security scheme recognized by your OpenAPI spec.\n\nKeys are scheme names (e.g. \"OAuth2\", \"ApiKeyAuth\") and values are their definitions."
}
},
"required": []
},
"HttpConfig": {
"title": "HttpConfig",
"description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
"type": "object",
"properties": {
"app": {
"type": "string",
"description": "Optional. Import path to a custom Starlette/FastAPI application to mount.\n"
},
"cors": {
"anyOf": [
{
"$ref": "#/$defs/CorsConfig"
},
{
"type": "null"
}
],
"description": "Optional. Defines CORS restrictions. If omitted, no special rules are set and\ncross-origin behavior depends on default server settings.\n"
},
"disable_assistants": {
"type": "boolean",
"description": "Optional. If True, /assistants routes are removed from the server.\n\nDefault is False (meaning /assistants is enabled).\n"
},
"disable_meta": {
"type": "boolean",
"description": "Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n"
},
"disable_runs": {
"type": "boolean",
"description": "Optional. If True, /runs routes are removed.\n\nDefault is False.\n"
},
"disable_store": {
"type": "boolean",
"description": "Optional. If True, /store routes are removed, disabling direct store interactions via HTTP.\n\nDefault is False.\n"
},
"disable_threads": {
"type": "boolean",
"description": "Optional. If True, /threads routes are removed.\n\nDefault is False.\n"
}
},
"required": []
},
"CorsConfig": {
"title": "CorsConfig",
"description": "Specifies Cross-Origin Resource Sharing (CORS) rules for your server.\n\nIf omitted, defaults are typically very restrictive (often no cross-origin requests).\nConfigure carefully if you want to allow usage from browsers hosted on other domains.",
"type": "object",
"properties": {
"allow_credentials": {
"type": "boolean",
"description": "Optional. If True, cross-origin requests can include credentials (cookies, auth headers).\n\nDefault False to avoid accidentally exposing secured endpoints to untrusted sites.\n"
},
"allow_headers": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. HTTP headers that can be used in cross-origin requests (e.g. [\"Content-Type\", \"Authorization\"])."
},
"allow_methods": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. HTTP methods permitted for cross-origin requests (e.g. [\"GET\", \"POST\"]).\n\nDefault might be [\"GET\", \"POST\", \"OPTIONS\"] depending on your server framework.\n"
},
"allow_origin_regex": {
"type": "string",
"description": "Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.\n"
},
"allow_origins": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. List of allowed origins (e.g., \"https://example.com\").\n\nDefault is often an empty list (no external origins).\nUse \"*\" only if you trust all origins, as that bypasses most restrictions.\n"
},
"expose_headers": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."
},
"max_age": {
"type": "integer",
"description": "Optional. How many seconds the browser may cache preflight responses.\n\nDefault might be 600 (10 minutes). Larger values reduce preflight requests but can cause stale configurations.\n"
}
},
"required": []
},
"StoreConfig": {
"title": "StoreConfig",
"description": "Configuration for the built-in long-term memory store.\n\nThis store can optionally perform semantic search. If you omit `index`,\nthe store will just handle traditional (non-embedded) data without vector lookups.",
"type": "object",
"properties": {
"index": {
"anyOf": [
{
"$ref": "#/$defs/IndexConfig"
},
{
"type": "null"
}
],
"description": "Optional. Defines the vector-based semantic search configuration.\n\n- Generate embeddings according to `index.embed`\n- Enforce the embedding dimension given by `index.dims`\n- Embed only specified JSON fields (if any) from `index.fields`\n\nIf omitted, no vector index is initialized.\n"
},
"ttl": {
"anyOf": [
{
"$ref": "#/$defs/TTLConfig"
},
{
"type": "null"
}
],
"description": "Optional. Defines the TTL (time-to-live) behavior configuration.\n\nIf provided, the store will apply TTL settings according to the configuration.\nIf omitted, no TTL behavior is configured.\n"
}
},
"required": []
},
"IndexConfig": {
"title": "IndexConfig",
"description": "Configuration for indexing documents for semantic search in the store.\n\nThis governs how text is converted into embeddings and stored for vector-based lookups.",
"type": "object",
"properties": {
"dims": {
"type": "integer",
"description": "Required. Dimensionality of the embedding vectors you will store.\n\nMust match the output dimension of your selected embedding model or custom embed function.\nIf mismatched, you will likely encounter shape/size errors when inserting or querying vectors.\n\n"
},
"embed": {
"type": "string",
"description": "Required. Identifier or reference to the embedding model or a custom embedding function.\n\n- \"my_custom_embed\" if it's a known alias in your system\n"
},
"fields": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. List of JSON fields to extract before generating embeddings.\n\nDefaults to [\"$\"], which means the entire JSON object is embedded as one piece of text.\nIf you provide multiple fields (e.g. [\"title\", \"content\"]), each is extracted and embedded separately,\noften saving token usage if you only care about certain parts of the data.\n"
}
},
"required": []
},
"TTLConfig": {
"title": "TTLConfig",
"description": "Configuration for TTL (time-to-live) behavior in the store.",
"type": "object",
"properties": {
"default_ttl": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
]
},
"refresh_on_read": {
"type": "boolean"
}
},
"required": []
}
},
"title": "LangGraph CLI Configuration",
"description": "Configuration schema for langgraph-cli",
"version": "v0"
}
libs/cli/tests/unit_tests/test_config.py
+4
View File
@@ -33,6 +33,7 @@ def test_validate_config():
"store": None,
"auth": None,
"http": None,
"ui": None,
**expected_config,
}
actual_config = validate_config(expected_config)
@@ -52,6 +53,7 @@ def test_validate_config():
"store": None,
"auth": None,
"http": None,
"ui": None,
}
actual_config = validate_config(expected_config)
assert actual_config == expected_config
@@ -467,6 +469,7 @@ def test_config_to_docker_nodejs():
"node_version": "20",
"graphs": graphs,
"dockerfile_lines": ["ARG meow", "ARG foo"],
"ui": {"agent": "./graphs/agent.ui.jsx"},
}
),
"langchain/langgraphjs-api",
@@ -477,6 +480,7 @@ ARG foo
ADD . /deps/unit_tests
RUN cd /deps/unit_tests && npm i
ENV LANGSERVE_GRAPHS='{"agent": "./graphs/agent.js:graph"}'
ENV LANGGRAPH_UI='{"agent": "./graphs/agent.ui.jsx"}'
WORKDIR /deps/unit_tests
RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts"""
libs/langgraph/README.md
+39 -297
View File
@@ -1,339 +1,81 @@
# 🦜🕸️LangGraph
![Version](https://img.shields.io/pypi/v/langgraph)
[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
[![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
⚡ Building language agents as graphs ⚡
> [!NOTE]
> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
## Overview
LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
stateful, multi-actor applications with LLMs, used to create agent and multi-agent
workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
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.
### Why use LangGraph?
LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
- **Memory**: LangGraph persists arbitrary aspects of your application's state,
supporting memory of conversations and other updates within and across user
interactions;
- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
and resumed, allowing for decisions, validation, and corrections at key stages via
human input.
Standardizing these components allows individuals and teams to focus on the behavior
of their agent, instead of its supporting infrastructure.
Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
the development, deployment, debugging, and monitoring of your applications.
LangGraph integrates seamlessly with
[LangChain](https://python.langchain.com/docs/introduction/) and
[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
To learn more about LangGraph, check out our first LangChain Academy
course, *Introduction to LangGraph*, available for free
[here](https://academy.langchain.com/courses/intro-to-langgraph).
### LangGraph Platform
[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
(includes a free tier).
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
- **Background runs**: Runs agents asynchronously in the background
- **Support for long running agents**: Infrastructure that can handle long running processes
- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
## Installation
```shell
```bash
pip install -U langgraph
```
## Example
Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
```shell
pip install langchain-anthropic
```
```shell
export ANTHROPIC_API_KEY=sk-...
```
Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
```shell
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=lsv2_sk_...
```
The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
<details open>
<summary>High-level implementation</summary>
To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
```python
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
model = ChatAnthropic(model=“claude-3-7-sonnet-latest”)
tools = [search]
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
app = create_react_agent(model, tools, checkpointer=checkpointer)
# Use the agent
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
agent = create_react_agent(model, tools)
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
## Why use LangGraph?
```python
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what about ny"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
```
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
```
</details>
- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
> [!TIP]
> LangGraph is a **low-level** framework that allows you to implement any custom agent
architectures. Click on the low-level implementation below to see how to implement a
tool-calling agent from scratch.
LangGraph is trusted in production and powering agents for companies like:
<details>
<summary>Low-level implementation</summary>
- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
```python
from typing import Literal
## LangGraph’s ecosystem
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
## Pairing with LangGraph Platform
While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
tools = [search]
LangGraph Platform can help engineering teams:
tool_node = ToolNode(tools)
- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
## Additional resources
# Define the function that determines whether to continue or not
def should_continue(state: MessagesState) -> Literal["tools", END]:
messages = state['messages']
last_message = messages[-1]
# If the LLM makes a tool call, then we route to the "tools" node
if last_message.tool_calls:
return "tools"
# Otherwise, we stop (reply to the user)
return END
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
- [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.
- [How-to 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.).
- [API 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.
- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
## Acknowledgements
# Define the function that calls the model
def call_model(state: MessagesState):
messages = state['messages']
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("tools", 'agent')
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable.
# Note that we're (optionally) passing the memory when compiling the graph
app = workflow.compile(checkpointer=checkpointer)
# Use the agent
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
<b>Step-by-step Breakdown</b>:
<details>
<summary>Initialize the model and tools.</summary>
<ul>
<li>
We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
</li>
<li>
We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
</li>
</ul>
</details>
<details>
<summary>Initialize graph with state.</summary>
<ul>
<li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
<li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
</ul>
</details>
<details>
<summary>Define graph nodes.</summary>
There are two main nodes we need:
<ul>
<li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
<li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
</ul>
</details>
<details>
<summary>Define entry point and graph edges.</summary>
First, we need to set the entry point for graph execution - <code>agent</code> node.
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
<ul>
<li>Conditional edge: after the agent is called, we should either:
<ul>
<li>a. Run tools if the agent said to take an action, OR</li>
<li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
</ul>
</li>
<li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
</ul>
</details>
<details>
<summary>Compile the graph.</summary>
<ul>
<li>
When we compile the graph, we turn it into a LangChain
<a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>,
which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code>
with your inputs
</li>
<li>
We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory,
human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> -
a simple in-memory checkpointer
</li>
</ul>
</details>
<details>
<summary>Execute the graph.</summary>
<ol>
<li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
<li>The <code>"agent"</code> node executes, invoking the chat model.</li>
<li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
<li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
<ul>
<li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
<li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
</ul>
</li>
<li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
</ol>
</details>
</details>
## Documentation
* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
## Resources
* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
## Contributing
For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
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.
libs/langgraph/langgraph/channels/py.typed
View File
libs/langgraph/langgraph/func/__init__.py
+14 -6
View File
@@ -36,23 +36,31 @@ from langgraph.types import _DC_KWARGS, RetryPolicy, StreamMode
@overload
def task(
*, name: Optional[str] = None, retry: Optional[RetryPolicy] = None
) -> Callable[[Callable[P, T]], Callable[P, SyncAsyncFuture[T]]]: ...
*,
name: Optional[str] = None,
retry: Optional[RetryPolicy] = None,
) -> Callable[
[Union[Callable[P, Awaitable[T]], Callable[P, T]]],
Callable[P, SyncAsyncFuture[T]],
]: ...
@overload
def task(
__func_or_none__: Callable[P, T],
__func_or_none__: Union[Callable[P, Awaitable[T]], Callable[P, T]],
) -> Callable[P, SyncAsyncFuture[T]]: ...
def task(
__func_or_none__: Optional[Union[Callable[P, T], Callable[P, Awaitable[T]]]] = None,
__func_or_none__: Optional[Union[Callable[P, Awaitable[T]], Callable[P, T]]] = None,
*,
name: Optional[str] = None,
retry: Optional[RetryPolicy] = None,
) -> Union[
Callable[[Callable[P, T]], Callable[P, SyncAsyncFuture[T]]],
Callable[
[Union[Callable[P, Awaitable[T]], Callable[P, T]]],
Callable[P, SyncAsyncFuture[T]],
],
Callable[P, SyncAsyncFuture[T]],
]:
"""Define a LangGraph task using the `task` decorator.
@@ -345,7 +353,7 @@ class entrypoint:
value: R
"""Value to return. A value will always be returned even if it is None."""
save: S
"""The value for the state for the next checkpoint.
"""The value for the state for the next checkpoint.
A value will always be saved even if it is None.
"""
libs/langgraph/langgraph/func/py.typed
View File
libs/langgraph/langgraph/graph/branch.py
+215
View File
@@ -0,0 +1,215 @@
import asyncio
from inspect import (
isfunction,
ismethod,
signature,
)
from types import FunctionType
from typing import (
Any,
Awaitable,
Callable,
Hashable,
Literal,
NamedTuple,
Optional,
Sequence,
Type,
Union,
cast,
get_args,
get_origin,
get_type_hints,
)
from langchain_core.runnables import (
Runnable,
RunnableConfig,
RunnableLambda,
)
from langgraph.constants import END, START
from langgraph.errors import InvalidUpdateError
from langgraph.pregel.write import ChannelWrite
from langgraph.types import Send
from langgraph.utils.runnable import (
RunnableCallable,
)
def _get_branch_path_input_schema(
path: Union[
Callable[..., Union[Hashable, list[Hashable]]],
Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
Runnable[Any, Union[Hashable, list[Hashable]]],
],
) -> Optional[Type[Any]]:
input = None
# detect input schema annotation in the branch callable
try:
callable_: Optional[
Union[
Callable[..., Union[Hashable, list[Hashable]]],
Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
]
] = None
if isinstance(path, (RunnableCallable, RunnableLambda)):
if isfunction(path.func) or ismethod(path.func):
callable_ = path.func
elif (callable_method := getattr(path.func, "__call__", None)) and ismethod(
callable_method
):
callable_ = callable_method
elif isfunction(path.afunc) or ismethod(path.afunc):
callable_ = path.afunc
elif (
callable_method := getattr(path.afunc, "__call__", None)
) and ismethod(callable_method):
callable_ = callable_method
elif callable(path):
callable_ = path
if callable_ is not None and (hints := get_type_hints(callable_)):
first_parameter_name = next(
iter(signature(cast(FunctionType, callable_)).parameters.keys())
)
if input_hint := hints.get(first_parameter_name):
if isinstance(input_hint, type) and get_type_hints(input_hint):
input = input_hint
except (TypeError, StopIteration):
pass
return input
class Branch(NamedTuple):
path: Runnable[Any, Union[Hashable, list[Hashable]]]
ends: Optional[dict[Hashable, str]]
then: Optional[str] = None
input_schema: Optional[Type[Any]] = None
@classmethod
def from_path(
cls,
path: Runnable[Any, Union[Hashable, list[Hashable]]],
path_map: Optional[Union[dict[Hashable, str], list[str]]],
then: Optional[str] = None,
infer_schema: bool = False,
) -> "Branch":
# coerce path_map to a dictionary
path_map_: Optional[dict[Hashable, str]] = None
try:
if isinstance(path_map, dict):
path_map_ = path_map.copy()
elif isinstance(path_map, list):
path_map_ = {name: name for name in path_map}
else:
# find func
func: Optional[Callable] = None
if isinstance(path, (RunnableCallable, RunnableLambda)):
func = path.func or path.afunc
if func is not None:
# find callable method
if (cal := getattr(path, "__call__", None)) and ismethod(cal):
func = cal
# get the return type
if rtn_type := get_type_hints(func).get("return"):
if get_origin(rtn_type) is Literal:
path_map_ = {name: name for name in get_args(rtn_type)}
except Exception:
pass
# infer input schema
input_schema = _get_branch_path_input_schema(path) if infer_schema else None
# create branch
return cls(path=path, ends=path_map_, then=then, input_schema=input_schema)
def run(
self,
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
reader: Optional[Callable[[RunnableConfig], Any]] = None,
) -> RunnableCallable:
return ChannelWrite.register_writer(
RunnableCallable(
func=self._route,
afunc=self._aroute,
writer=writer,
reader=reader,
name=None,
trace=False,
)
)
def _route(
self,
input: Any,
config: RunnableConfig,
*,
reader: Optional[Callable[[RunnableConfig], Any]],
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
) -> Runnable:
if reader:
value = reader(config)
# passthrough additional keys from node to branch
# only doable when using dict states
if (
isinstance(value, dict)
and isinstance(input, dict)
and self.input_schema is None
):
value = {**input, **value}
else:
value = input
result = self.path.invoke(value, config)
return self._finish(writer, input, result, config)
async def _aroute(
self,
input: Any,
config: RunnableConfig,
*,
reader: Optional[Callable[[RunnableConfig], Any]],
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
) -> Runnable:
if reader:
value = await asyncio.to_thread(reader, config)
# passthrough additional keys from node to branch
# only doable when using dict states
if (
isinstance(value, dict)
and isinstance(input, dict)
and self.input_schema is None
):
value = {**input, **value}
else:
value = input
result = await self.path.ainvoke(value, config)
return self._finish(writer, input, result, config)
def _finish(
self,
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
input: Any,
result: Any,
config: RunnableConfig,
) -> Union[Runnable, Any]:
if not isinstance(result, (list, tuple)):
result = [result]
if self.ends:
destinations: Sequence[Union[Send, str]] = [
r if isinstance(r, Send) else self.ends[r] for r in result
]
else:
destinations = cast(Sequence[Union[Send, str]], result)
if any(dest is None or dest == START for dest in destinations):
raise ValueError("Branch did not return a valid destination")
if any(p.node == END for p in destinations if isinstance(p, Send)):
raise InvalidUpdateError("Cannot send a packet to the END node")
return writer(destinations, config) or input
libs/langgraph/langgraph/graph/graph.py
+5 -117
View File
@@ -1,4 +1,3 @@
import asyncio
import logging
from collections import defaultdict
from typing import (
@@ -6,15 +5,11 @@ from typing import (
Awaitable,
Callable,
Hashable,
Literal,
NamedTuple,
Optional,
Sequence,
Union,
cast,
get_args,
get_origin,
get_type_hints,
overload,
)
@@ -34,12 +29,12 @@ from langgraph.constants import (
TAG_HIDDEN,
Send,
)
from langgraph.errors import InvalidUpdateError
from langgraph.graph.branch import Branch
from langgraph.pregel import Channel, Pregel
from langgraph.pregel.read import PregelNode
from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
from langgraph.types import All, Checkpointer
from langgraph.utils.runnable import RunnableCallable, RunnableLike, coerce_to_runnable
from langgraph.utils.runnable import RunnableLike, coerce_to_runnable
logger = logging.getLogger(__name__)
@@ -50,95 +45,6 @@ class NodeSpec(NamedTuple):
ends: Optional[Union[tuple[str, ...], dict[str, str]]] = EMPTY_SEQ
class Branch(NamedTuple):
path: Runnable[Any, Union[Hashable, list[Hashable]]]
ends: Optional[dict[Hashable, str]]
then: Optional[str] = None
def run(
self,
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
reader: Optional[Callable[[RunnableConfig], Any]] = None,
) -> RunnableCallable:
return ChannelWrite.register_writer(
RunnableCallable(
func=self._route,
afunc=self._aroute,
writer=writer,
reader=reader,
name=None,
trace=False,
)
)
def _route(
self,
input: Any,
config: RunnableConfig,
*,
reader: Optional[Callable[[RunnableConfig], Any]],
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
) -> Runnable:
if reader:
value = reader(config)
# passthrough additional keys from node to branch
# only doable when using dict states
if isinstance(value, dict) and isinstance(input, dict):
value = {**input, **value}
else:
value = input
result = self.path.invoke(value, config)
return self._finish(writer, input, result, config)
async def _aroute(
self,
input: Any,
config: RunnableConfig,
*,
reader: Optional[Callable[[RunnableConfig], Any]],
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
) -> Runnable:
if reader:
value = await asyncio.to_thread(reader, config)
# passthrough additional keys from node to branch
# only doable when using dict states
if isinstance(value, dict) and isinstance(input, dict):
value = {**input, **value}
else:
value = input
result = await self.path.ainvoke(value, config)
return self._finish(writer, input, result, config)
def _finish(
self,
writer: Callable[
[Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
],
input: Any,
result: Any,
config: RunnableConfig,
) -> Union[Runnable, Any]:
if not isinstance(result, (list, tuple)):
result = [result]
if self.ends:
destinations: Sequence[Union[Send, str]] = [
r if isinstance(r, Send) else self.ends[r] for r in result
]
else:
destinations = cast(Sequence[Union[Send, str]], result)
if any(dest is None or dest == START for dest in destinations):
raise ValueError("Branch did not return a valid destination")
if any(p.node == END for p in destinations if isinstance(p, Send)):
raise InvalidUpdateError("Cannot send a packet to the END node")
return writer(destinations, config) or input
class Graph:
def __init__(self) -> None:
self.nodes: dict[str, NodeSpec] = {}
@@ -267,25 +173,7 @@ class Graph:
"Adding an edge to a graph that has already been compiled. This will "
"not be reflected in the compiled graph."
)
# coerce path_map to a dictionary
try:
if isinstance(path_map, dict):
path_map_ = path_map.copy()
elif isinstance(path_map, list):
path_map_ = {name: name for name in path_map}
elif isinstance(path, Runnable):
path_map_ = None
elif rtn_type := get_type_hints(path.__call__).get( # type: ignore[operator]
"return"
) or get_type_hints(path).get("return"):
if get_origin(rtn_type) is Literal:
path_map_ = {name: name for name in get_args(rtn_type)}
else:
path_map_ = None
else:
path_map_ = None
except Exception:
path_map_ = None
# find a name for the condition
path = coerce_to_runnable(path, name=None, trace=True)
name = path.name or "condition"
@@ -295,7 +183,7 @@ class Graph:
f"Branch with name `{path.name}` already exists for node " f"`{source}`"
)
# save it
self.branches[source][name] = Branch(path, path_map_, then)
self.branches[source][name] = Branch.from_path(path, path_map, then, False)
return self
def set_entry_point(self, key: str) -> Self:
@@ -584,7 +472,7 @@ class CompiledGraph(Pregel):
)
subgraph.trim_first_node()
subgraph.trim_last_node()
if len(subgraph.nodes) > 1:
if len(subgraph.nodes) >= 1:
e, s = graph.extend(subgraph, prefix=key)
if e is None:
raise ValueError(
libs/langgraph/langgraph/graph/py.typed
View File
libs/langgraph/langgraph/graph/state.py
+106 -26
View File
@@ -7,7 +7,9 @@ from inspect import isclass, isfunction, ismethod, signature
from types import FunctionType
from typing import (
Any,
Awaitable,
Callable,
Hashable,
Literal,
NamedTuple,
Optional,
@@ -40,7 +42,14 @@ from langgraph.errors import (
ParentCommand,
create_error_message,
)
from langgraph.graph.graph import END, START, Branch, CompiledGraph, Graph, Send
from langgraph.graph.branch import Branch
from langgraph.graph.graph import (
END,
START,
CompiledGraph,
Graph,
Send,
)
from langgraph.managed.base import (
ChannelKeyPlaceholder,
ChannelTypePlaceholder,
@@ -461,6 +470,57 @@ class StateGraph(Graph):
self.waiting_edges.add((tuple(start_key), end_key))
return self
def add_conditional_edges(
self,
source: str,
path: Union[
Callable[..., Union[Hashable, list[Hashable]]],
Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
Runnable[Any, Union[Hashable, list[Hashable]]],
],
path_map: Optional[Union[dict[Hashable, str], list[str]]] = None,
then: Optional[str] = None,
) -> Self:
"""Add a conditional edge from the starting node to any number of destination nodes.
Args:
source (str): The starting node. This conditional edge will run when
exiting this node.
path (Union[Callable, Runnable]): The callable that determines the next
node or nodes. If not specifying `path_map` it should return one or
more nodes. If it returns END, the graph will stop execution.
path_map (Optional[dict[Hashable, str]]): Optional mapping of paths to node
names. If omitted the paths returned by `path` should be node names.
then (Optional[str]): The name of a node to execute after the nodes
selected by `path`.
Returns:
Self: The instance of the graph, allowing for method chaining.
Note: Without typehints on the `path` function's return value (e.g., `-> Literal["foo", "__end__"]:`)
or a path_map, the graph visualization assumes the edge could transition to any node in the graph.
""" # noqa: E501
if self.compiled:
logger.warning(
"Adding an edge to a graph that has already been compiled. This will "
"not be reflected in the compiled graph."
)
# find a name for the condition
path = coerce_to_runnable(path, name=None, trace=True)
name = path.name or "condition"
# validate the condition
if name in self.branches[source]:
raise ValueError(
f"Branch with name `{path.name}` already exists for node " f"`{source}`"
)
# save it
self.branches[source][name] = Branch.from_path(path, path_map, then, True)
if schema := self.branches[source][name].input_schema:
self._add_schema(schema)
return self
def add_sequence(
self,
nodes: Sequence[Union[RunnableLike, tuple[str, RunnableLike]]],
@@ -566,6 +626,11 @@ class StateGraph(Graph):
compiled = CompiledStateGraph(
builder=self,
config_type=self.config_schema,
input_model=self.input
if len(self.channels) > 1
and isclass(self.input)
and issubclass(self.input, (BaseModel, BaseModelV1))
else None,
nodes={},
channels={
**self.channels,
@@ -695,23 +760,22 @@ class CompiledStateGraph(CompiledGraph):
updates.extend(_get_updates(i) or ())
return updates
elif get_type_hints(type(input)):
# if input is a Pydantic model, only update values
# for the keys that have been explicitly set by the users
# (this is needed to avoid sending updates for fields with None defaults)
output_keys_ = output_keys
# Pydantic v2
if hasattr(input, "model_fields_set"):
output_keys_ = [
k for k in output_keys if k in input.model_fields_set
]
if hasattr(input, "model_fields"):
defaults = {k: v.default for k, v in input.model_fields.items()}
# Pydantic v1
elif hasattr(input, "__fields_set__"):
output_keys_ = [k for k in output_keys if k in input.__fields_set__]
elif hasattr(input, "__fields__"):
defaults = {k: v.default for k, v in input.__fields__.items()}
else:
defaults = {}
# if input is a Pydantic model, only update values
# that are different from the default values
return [
(k, getattr(input, k))
for k in output_keys_
if getattr(input, k, MISSING) is not MISSING
(k, value)
for k in output_keys
if (value := getattr(input, k, MISSING)) is not MISSING
and value != defaults.get(k)
]
else:
msg = create_error_message(
@@ -752,11 +816,7 @@ class CompiledStateGraph(CompiledGraph):
# read state keys and managed values
channels=(list(input_values) if is_single_input else input_values),
# coerce state dict to schema class (eg. pydantic model)
mapper=(
None
if is_single_input or issubclass(input_schema, dict)
else partial(_coerce_state, input_schema)
),
mapper=_pick_mapper(list(input_values), input_schema),
writers=[
# publish to this channel and state keys
ChannelWrite(
@@ -826,12 +886,12 @@ class CompiledStateGraph(CompiledGraph):
config, cast(Sequence[Union[Send, ChannelWriteEntry]], writes)
)
# attach branch publisher
schema = (
schema = branch.input_schema or (
self.builder.nodes[start].input
if start in self.builder.nodes
else self.builder.schema
)
# attach branch publisher
self.nodes[start] |= branch.run(
branch_writer,
_get_state_reader(self.builder, schema) if with_reader else None,
@@ -871,14 +931,34 @@ def _get_state_reader(
select=select[0] if select == ["__root__"] else select,
fresh=True,
# coerce state dict to schema class (eg. pydantic model)
mapper=(
None
if state_keys == ["__root__"] or issubclass(schema, dict)
else partial(_coerce_state, schema)
),
mapper=_pick_mapper(state_keys, schema),
)
def _pick_mapper(
state_keys: Sequence[str], schema: Type[Any]
) -> Optional[Callable[[Any], Any]]:
if state_keys == ["__root__"]:
return None
if issubclass(schema, dict):
return None
if issubclass(schema, BaseModel):
return partial(_coerce_state_pydantic, schema)
if issubclass(schema, BaseModelV1):
return partial(_coerce_state_pydantic_v1, schema)
return partial(_coerce_state, schema)
def _coerce_state_pydantic(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
return schema.model_construct(**input)
def _coerce_state_pydantic_v1(
schema: Type[Any], input: dict[str, Any]
) -> dict[str, Any]:
return schema.construct(**input)
def _coerce_state(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
return schema(**input)
libs/langgraph/langgraph/managed/py.typed
View File
libs/langgraph/langgraph/prebuilt/tool_executor.py
-134
View File
@@ -1,134 +0,0 @@
from typing import Any, Callable, Sequence, Union
from langchain_core.load.serializable import Serializable
from langchain_core.runnables import RunnableConfig
from langchain_core.tools import BaseTool
from langchain_core.tools import tool as create_tool
from langgraph._api.deprecation import deprecated
from langgraph.utils.runnable import RunnableCallable
INVALID_TOOL_MSG_TEMPLATE = (
"{requested_tool_name} is not a valid tool, "
"try one of [{available_tool_names_str}]."
)
@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
class ToolInvocationInterface:
"""Interface for invoking a tool.
Attributes:
tool (str): The name of the tool to invoke.
tool_input (Union[str, dict]): The input to pass to the tool.
"""
tool: str
tool_input: Union[str, dict]
@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
class ToolInvocation(Serializable):
"""Information about how to invoke a tool.
Attributes:
tool (str): The name of the Tool to execute.
tool_input (Union[str, dict]): The input to pass in to the Tool.
Examples:
Basic usage:
```pycon
>>> invocation = ToolInvocation(
... tool="search",
... tool_input="What is the capital of France?"
... )
```
"""
tool: str
tool_input: Union[str, dict]
@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
class ToolExecutor(RunnableCallable):
"""Executes a tool invocation.
Args:
tools (Sequence[BaseTool]): A sequence of tools that can be invoked.
invalid_tool_msg_template (str, optional): The template for the error message
when an invalid tool is requested. Defaults to INVALID_TOOL_MSG_TEMPLATE.
Examples:
Basic usage:
```pycon
>>> from langchain_core.tools import tool
>>> from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
...
...
>>> @tool
... def search(query: str) -> str:
... \"\"\"Search engine.\"\"\"
... return f"Searching for: {query}"
...
...
>>> tools = [search]
>>> executor = ToolExecutor(tools)
...
>>> invocation = ToolInvocation(tool="search", tool_input="What is the capital of France?")
>>> result = executor.invoke(invocation)
>>> print(result)
"Searching for: What is the capital of France?"
```
Handling invalid tool:
```pycon
>>> invocation = ToolInvocation(
... tool="nonexistent", tool_input="What is the capital of France?"
... )
>>> result = executor.invoke(invocation)
>>> print(result)
"nonexistent is not a valid tool, try one of [search]."
```
"""
def __init__(
self,
tools: Sequence[Union[BaseTool, Callable]],
*,
invalid_tool_msg_template: str = INVALID_TOOL_MSG_TEMPLATE,
) -> None:
super().__init__(self._execute, afunc=self._aexecute, trace=False)
tools_ = [
tool if isinstance(tool, BaseTool) else create_tool(tool) for tool in tools
]
self.tools = tools_
self.tool_map = {t.name: t for t in tools_}
self.invalid_tool_msg_template = invalid_tool_msg_template
def _execute(
self, tool_invocation: ToolInvocationInterface, config: RunnableConfig
) -> Any:
if tool_invocation.tool not in self.tool_map:
return self.invalid_tool_msg_template.format(
requested_tool_name=tool_invocation.tool,
available_tool_names_str=", ".join([t.name for t in self.tools]),
)
else:
tool = self.tool_map[tool_invocation.tool]
output = tool.invoke(tool_invocation.tool_input, config)
return output
async def _aexecute(
self, tool_invocation: ToolInvocationInterface, config: RunnableConfig
) -> Any:
if tool_invocation.tool not in self.tool_map:
return self.invalid_tool_msg_template.format(
requested_tool_name=tool_invocation.tool,
available_tool_names_str=", ".join([t.name for t in self.tools]),
)
else:
tool = self.tool_map[tool_invocation.tool]
output = await tool.ainvoke(tool_invocation.tool_input, config)
return output
libs/langgraph/langgraph/pregel/__init__.py
+56 -9
View File
@@ -18,6 +18,7 @@ from typing import (
Type,
Union,
cast,
get_type_hints,
overload,
)
from uuid import UUID, uuid5
@@ -119,7 +120,7 @@ from langgraph.utils.config import (
recast_checkpoint_ns,
)
from langgraph.utils.fields import get_enhanced_type_hints
from langgraph.utils.pydantic import create_model
from langgraph.utils.pydantic import create_model, is_supported_by_pydantic
from langgraph.utils.queue import AsyncQueue, SyncQueue # type: ignore[attr-defined]
WriteValue = Union[Callable[[Input], Output], Any]
@@ -495,6 +496,8 @@ class Pregel(PregelProtocol):
config_type: Optional[Type[Any]] = None
input_model: Optional[Type[BaseModel]] = None
config: Optional[RunnableConfig] = None
name: str = "LangGraph"
@@ -518,6 +521,7 @@ class Pregel(PregelProtocol):
store: Optional[BaseStore] = None,
retry_policy: Optional[RetryPolicy] = None,
config_type: Optional[Type[Any]] = None,
input_model: Optional[Type[BaseModel]] = None,
config: Optional[RunnableConfig] = None,
name: str = "LangGraph",
) -> None:
@@ -536,6 +540,7 @@ class Pregel(PregelProtocol):
self.store = store
self.retry_policy = retry_policy
self.config_type = config_type
self.input_model = input_model
self.config = config
self.name = name
if auto_validate:
@@ -609,6 +614,36 @@ class Pregel(PregelProtocol):
]
]
def config_schema(
self, *, include: Optional[Sequence[str]] = None
) -> Type[BaseModel]:
# If the config type is not set explicitly, we will try to infer it.
# If the config type is provided, but isn't directly supported by pydantic
# (e.g., vanilla python class), we will also delegate to the parent class,
# which handles cases where Pydantic doesn't support the type.
if self.config_type is None or not is_supported_by_pydantic(self.config_type):
return super().config_schema(include=include)
include = include or []
fields = {
"configurable": (self.config_type, None),
**{
field_name: (field_type, None)
for field_name, field_type in get_type_hints(RunnableConfig).items()
if field_name in [i for i in include if i != "configurable"]
},
}
return create_model(self.get_name("Config"), field_definitions=fields)
def get_config_jsonschema(
self, *, include: Optional[Sequence[str]] = None
) -> Dict[str, Any]:
schema = self.config_schema(include=include)
if hasattr(schema, "model_json_schema"):
return schema.model_json_schema()
else:
return schema.schema()
@property
def InputType(self) -> Any:
if isinstance(self.input_channels, str):
@@ -619,6 +654,8 @@ class Pregel(PregelProtocol):
def get_input_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
if self.input_model is not None:
return self.input_model
config = merge_configs(self.config, config)
if isinstance(self.input_channels, str):
return super().get_input_schema(config)
@@ -634,7 +671,7 @@ class Pregel(PregelProtocol):
def get_input_jsonschema(
self, config: Optional[RunnableConfig] = None
) -> Dict[All, Any]:
) -> Dict[str, Any]:
schema = self.get_input_schema(config)
if hasattr(schema, "model_json_schema"):
return schema.model_json_schema()
@@ -666,7 +703,7 @@ class Pregel(PregelProtocol):
def get_output_jsonschema(
self, config: Optional[RunnableConfig] = None
) -> Dict[All, Any]:
) -> Dict[str, Any]:
schema = self.get_output_schema(config)
if hasattr(schema, "model_json_schema"):
return schema.model_json_schema()
@@ -971,6 +1008,12 @@ class Pregel(PregelProtocol):
raise ValueError(f"Subgraph {recast} not found")
config = merge_configs(self.config, config) if self.config else config
if self.checkpointer is True:
ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
config = merge_configs(
config, {CONF: {CONFIG_KEY_CHECKPOINT_NS: recast_checkpoint_ns(ns)}}
)
saved = checkpointer.get_tuple(config)
return self._prepare_state_snapshot(
config,
@@ -1004,6 +1047,12 @@ class Pregel(PregelProtocol):
raise ValueError(f"Subgraph {recast} not found")
config = merge_configs(self.config, config) if self.config else config
if self.checkpointer is True:
ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
config = merge_configs(
config, {CONF: {CONFIG_KEY_CHECKPOINT_NS: recast_checkpoint_ns(ns)}}
)
saved = await checkpointer.aget_tuple(config)
return await self._aprepare_state_snapshot(
config,
@@ -1911,9 +1960,7 @@ class Pregel(PregelProtocol):
# set up subgraph checkpointing
if self.checkpointer is True:
ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
config[CONF][CONFIG_KEY_CHECKPOINT_NS] = NS_SEP.join(
part.split(NS_END)[0] for part in ns.split(NS_SEP)
)
config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
# set up messages stream mode
if "messages" in stream_modes:
run_manager.inheritable_handlers.append(
@@ -1926,6 +1973,7 @@ class Pregel(PregelProtocol):
)
with SyncPregelLoop(
input,
input_model=self.input_model,
stream=StreamProtocol(stream.put, stream_modes),
config=config,
store=store,
@@ -2201,9 +2249,7 @@ class Pregel(PregelProtocol):
# set up subgraph checkpointing
if self.checkpointer is True:
ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
config[CONF][CONFIG_KEY_CHECKPOINT_NS] = NS_SEP.join(
part.split(NS_END)[0] for part in ns.split(NS_SEP)
)
config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
# set up messages stream mode
if "messages" in stream_modes:
run_manager.inheritable_handlers.append(
@@ -2218,6 +2264,7 @@ class Pregel(PregelProtocol):
)
async with AsyncPregelLoop(
input,
input_model=self.input_model,
stream=StreamProtocol(stream.put_nowait, stream_modes),
config=config,
store=store,
libs/langgraph/langgraph/pregel/loop.py
+42 -15
View File
@@ -2,7 +2,6 @@ import asyncio
import concurrent.futures
from collections import defaultdict, deque
from contextlib import AsyncExitStack, ExitStack
from dataclasses import replace
from inspect import signature
from types import TracebackType
from typing import (
@@ -24,6 +23,7 @@ from typing import (
from langchain_core.callbacks import AsyncParentRunManager, ParentRunManager
from langchain_core.runnables import RunnableConfig
from pydantic import BaseModel
from typing_extensions import ParamSpec, Self
from langgraph.channels.base import BaseChannel
@@ -55,6 +55,7 @@ from langgraph.constants import (
ERROR,
INPUT,
INTERRUPT,
MISSING,
NS_SEP,
NULL_TASK_ID,
PUSH,
@@ -67,7 +68,6 @@ from langgraph.errors import (
EmptyInputError,
GraphDelegate,
GraphInterrupt,
ParentCommand,
)
from langgraph.managed.base import (
ManagedValueMapping,
@@ -126,6 +126,7 @@ P = ParamSpec("P")
INPUT_DONE = object()
INPUT_RESUMING = object()
INPUT_SHOULD_VALIDATE = object()
SPECIAL_CHANNELS = (ERROR, INTERRUPT, SCHEDULED)
@@ -140,6 +141,7 @@ def DuplexStream(*streams: StreamProtocol) -> StreamProtocol:
class PregelLoop(LoopProtocol):
input: Optional[Any]
input_model: Optional[Type[BaseModel]]
checkpointer: Optional[BaseCheckpointSaver]
nodes: Mapping[str, PregelNode]
specs: Mapping[str, Union[BaseChannel, ManagedValueSpec]]
@@ -203,6 +205,7 @@ class PregelLoop(LoopProtocol):
interrupt_after: Union[All, Sequence[str]] = EMPTY_SEQ,
interrupt_before: Union[All, Sequence[str]] = EMPTY_SEQ,
manager: Union[None, AsyncParentRunManager, ParentRunManager] = None,
input_model: Optional[Type[BaseModel]] = None,
debug: bool = False,
) -> None:
super().__init__(
@@ -213,6 +216,7 @@ class PregelLoop(LoopProtocol):
store=store,
)
self.input = input
self.input_model = input_model
self.checkpointer = checkpointer
self.nodes = nodes
self.specs = specs
@@ -396,13 +400,14 @@ class PregelLoop(LoopProtocol):
if self.status != "pending":
raise RuntimeError("Cannot tick when status is no longer 'pending'")
if self.input not in (INPUT_DONE, INPUT_RESUMING):
if self.input not in (INPUT_DONE, INPUT_RESUMING, INPUT_SHOULD_VALIDATE):
self._first(input_keys=input_keys)
elif self.to_interrupt:
# if we need to interrupt, do so
self.status = "interrupt_before"
raise GraphInterrupt()
elif all(task.writes for task in self.tasks.values()):
# finish superstep
writes = [w for t in self.tasks.values() for w in t.writes]
# debug flag
if self.debug:
@@ -425,6 +430,13 @@ class PregelLoop(LoopProtocol):
# apply writes to managed values
for key, values in mv_writes.items():
self._update_mv(key, values)
# validate input if requested
if self.input is INPUT_SHOULD_VALIDATE:
self.input = INPUT_DONE
# validate
cast(Type[BaseModel], self.input_model)(
**read_channels(self.channels, self.stream_keys)
)
# produce values output
self._emit(
"values", map_output_values, self.output_keys, writes, self.channels
@@ -451,6 +463,9 @@ class PregelLoop(LoopProtocol):
):
self.status = "interrupt_after"
raise GraphInterrupt()
# unset resuming flag
self.config[CONF].pop(CONFIG_KEY_RESUMING, None)
else:
return False
@@ -562,7 +577,13 @@ class PregelLoop(LoopProtocol):
is_resuming = bool(self.checkpoint["channel_versions"]) and bool(
configurable.get(
CONFIG_KEY_RESUMING,
self.input is None or isinstance(self.input, Command),
self.input is None
or isinstance(self.input, Command)
or (
not self.is_nested
and self.config.get("metadata", {}).get("run_id")
== self.checkpoint_metadata.get("run_id", MISSING)
),
)
)
@@ -613,6 +634,8 @@ class PregelLoop(LoopProtocol):
self._emit(
"values", map_output_values, self.output_keys, True, self.channels
)
# set flag
self.input = INPUT_RESUMING
# map inputs to channel updates
elif input_writes := deque(map_input(input_keys, self.input)):
# TODO shouldn't these writes be passed to put_writes too?
@@ -653,10 +676,19 @@ class PregelLoop(LoopProtocol):
assert not mv_writes, "Can't write to SharedValues in graph input"
# save input checkpoint
self._put_checkpoint({"source": "input", "writes": dict(input_writes)})
# set flag
if (
self.input_model is not None
and not isinstance(self.input, self.input_model)
and not isinstance(self.stream_keys, str)
):
self.input = INPUT_SHOULD_VALIDATE
else:
self.input = INPUT_DONE
elif CONFIG_KEY_RESUMING not in configurable:
raise EmptyInputError(f"Received no input for {input_keys}")
# done with input
self.input = INPUT_RESUMING if is_resuming else INPUT_DONE
else:
self.input = INPUT_DONE
# update config
if not self.is_nested:
self.config = patch_configurable(
@@ -738,15 +770,6 @@ class PregelLoop(LoopProtocol):
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> Optional[bool]:
# add current state to parent command
if isinstance(exc_value, ParentCommand):
cmd = exc_value.args[0]
state = (
[(self.output_keys, read_channels(self.channels, self.output_keys))]
if isinstance(self.output_keys, str)
else list(read_channels(self.channels, self.output_keys).items())
)
exc_value.args = (replace(cmd, update=[*state, *cmd._update_as_tuples()]),)
# suppress interrupt
suppress = isinstance(exc_value, GraphInterrupt) and not self.is_nested
if suppress:
@@ -840,10 +863,12 @@ class SyncPregelLoop(PregelLoop, ContextManager):
interrupt_before: Union[All, Sequence[str]] = EMPTY_SEQ,
output_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
input_model: Optional[Type[BaseModel]] = None,
debug: bool = False,
) -> None:
super().__init__(
input,
input_model=input_model,
stream=stream,
config=config,
checkpointer=checkpointer,
@@ -979,10 +1004,12 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
manager: Union[None, AsyncParentRunManager, ParentRunManager] = None,
output_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
input_model: Optional[Type[BaseModel]] = None,
debug: bool = False,
) -> None:
super().__init__(
input,
input_model=input_model,
stream=stream,
config=config,
checkpointer=checkpointer,
libs/langgraph/langgraph/pregel/messages.py
+10
View File
@@ -127,6 +127,16 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP)),
metadata,
)
if isinstance(inputs, dict):
for key, value in inputs.items():
if isinstance(value, BaseMessage):
if value.id is not None:
self.seen.add(value.id)
elif isinstance(value, Sequence) and not isinstance(value, str):
for item in value:
if isinstance(item, BaseMessage):
if item.id is not None:
self.seen.add(item.id)
def on_chain_end(
self,
libs/langgraph/langgraph/pregel/py.typed
View File
libs/langgraph/langgraph/pregel/validate.py
+17 -5
View File
@@ -32,11 +32,17 @@ def validate_graph(
for chan in subscribed_channels:
if chan not in channels:
raise ValueError(f"Subscribed channel '{chan}' not in 'channels'")
raise ValueError(
f"Subscribed channel '{chan}' not "
f"in known channels: '{repr(sorted(channels))[:100]}'"
)
if isinstance(input_channels, str):
if input_channels not in channels:
raise ValueError(f"Input channel '{input_channels}' not in 'channels'")
raise ValueError(
f"Input channel '{input_channels}' not "
f"in known channels: '{repr(sorted(channels))[:100]}'"
)
if input_channels not in subscribed_channels:
raise ValueError(
f"Input channel {input_channels} is not subscribed to by any node"
@@ -44,10 +50,13 @@ def validate_graph(
else:
for chan in input_channels:
if chan not in channels:
raise ValueError(f"Input channel '{chan}' not in 'channels'")
raise ValueError(
f"Input channel '{chan}' not in '{repr(sorted(channels))[:100]}'"
)
if all(chan not in subscribed_channels for chan in input_channels):
raise ValueError(
f"None of the input channels {input_channels} are subscribed to by any node"
f"None of the input channels {input_channels} "
f"are subscribed to by any node"
)
all_output_channels = set[str]()
@@ -62,7 +71,10 @@ def validate_graph(
for chan in all_output_channels:
if chan not in channels:
raise ValueError(f"Output channel '{chan}' not in 'channels'")
raise ValueError(
f"Output channel '{chan}' not "
f"in known channels: '{repr(sorted(channels))[:100]}'"
)
if interrupt_after_nodes != "*":
for n in interrupt_after_nodes:
libs/langgraph/langgraph/utils/config.py
+3 -1
View File
@@ -1,4 +1,5 @@
from collections import ChainMap
from os import getenv
from typing import Any, Optional, Sequence, cast
from langchain_core.callbacks import (
@@ -11,7 +12,6 @@ from langchain_core.runnables import RunnableConfig
from langchain_core.runnables.config import (
CONFIG_KEYS,
COPIABLE_KEYS,
DEFAULT_RECURSION_LIMIT,
var_child_runnable_config,
)
@@ -26,6 +26,8 @@ from langgraph.constants import (
NS_SEP,
)
DEFAULT_RECURSION_LIMIT = int(getenv("LANGGRAPH_DEFAULT_RECURSION_LIMIT", "25"))
def recast_checkpoint_ns(ns: str) -> str:
"""Remove task IDs from checkpoint namespace.
libs/langgraph/langgraph/utils/py.typed
View File
libs/langgraph/langgraph/utils/pydantic.py
+32
View File
@@ -1,5 +1,9 @@
import sys
import typing
from dataclasses import is_dataclass
from typing import Any, Dict, Optional, Union
import typing_extensions
from pydantic import BaseModel
from pydantic.v1 import BaseModel as BaseModelV1
@@ -35,3 +39,31 @@ def create_model(
v1_kwargs["__root__"] = root
return create_model(model_name, **v1_kwargs, **(field_definitions or {}))
def is_supported_by_pydantic(type_: Any) -> bool:
"""Check if a given "complex" type is supported by pydantic.
This will return False for primitive types like int, str, etc.
The check is meant for container types like dataclasses, TypedDicts, etc.
"""
if is_dataclass(type_):
return True
# Pydantic does not support mixing .v1 and root namespaces, so
# we only check for BaseModel (not pydantic.v1.BaseModel).
if isinstance(type_, type) and issubclass(type_, BaseModel):
return True
if hasattr(type_, "__orig_bases__"):
for base in type_.__orig_bases__:
if base is typing_extensions.TypedDict:
return True
elif base is typing.TypedDict: # noqa: TID251
# ignoring TID251 since it's OK to use typing.TypedDict in this case.
# Pydantic supports typing.TypedDict from Python 3.12
# For older versions, only typing_extensions.TypedDict is supported.
if sys.version_info >= (3, 12):
return True
return False
libs/langgraph/poetry.lock
Generated
+31 -13
View File
@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
[[package]]
name = "aiosqlite"
@@ -1324,19 +1324,19 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.30"
version = "0.3.44"
description = "Building applications with LLMs through composability"
optional = false
python-versions = "<4.0,>=3.9"
groups = ["main", "dev"]
files = [
{file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
{file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
{file = "langchain_core-0.3.44-py3-none-any.whl", hash = "sha256:d989ce8bd62f1d07765acd575e6ec1254aec0cf7775aaea39fe4af8102377459"},
{file = "langchain_core-0.3.44.tar.gz", hash = "sha256:7c0a01e78360f007cbca448178fe7e032404068e6431dbe8ce905f84febbdfa5"},
]
[package.dependencies]
jsonpatch = ">=1.33,<2.0"
langsmith = ">=0.1.125,<0.3"
langsmith = ">=0.1.125,<0.4"
packaging = ">=23.2,<25"
pydantic = [
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@@ -1348,7 +1348,7 @@ typing-extensions = ">=4.7"
[[package]]
name = "langgraph-checkpoint"
version = "2.0.10"
version = "2.0.18"
description = "Library with base interfaces for LangGraph checkpoint savers."
optional = false
python-versions = "^3.9.0,<4.0"
@@ -1366,7 +1366,7 @@ url = "../checkpoint"
[[package]]
name = "langgraph-checkpoint-postgres"
version = "2.0.12"
version = "2.0.16"
description = "Library with a Postgres implementation of LangGraph checkpoint saver."
optional = false
python-versions = "^3.9.0,<4.0"
@@ -1375,7 +1375,7 @@ files = []
develop = true
[package.dependencies]
langgraph-checkpoint = "^2.0.10"
langgraph-checkpoint = "^2.0.15"
orjson = ">=3.10.1"
psycopg = "^3.2.0"
psycopg-pool = "^3.2.0"
@@ -1386,7 +1386,7 @@ url = "../checkpoint-postgres"
[[package]]
name = "langgraph-checkpoint-sqlite"
version = "2.0.3"
version = "2.0.6"
description = "Library with a SQLite implementation of LangGraph checkpoint saver."
optional = false
python-versions = "^3.9.0"
@@ -1395,16 +1395,34 @@ files = []
develop = true
[package.dependencies]
aiosqlite = "^0.20.0"
langgraph-checkpoint = "^2.0.10"
aiosqlite = ">=0.20,<0.22"
langgraph-checkpoint = "^2.0.15"
[package.source]
type = "directory"
url = "../checkpoint-sqlite"
[[package]]
name = "langgraph-prebuilt"
version = "0.1.2"
description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
optional = false
python-versions = "^3.9.0,<4.0"
groups = ["main", "dev"]
files = []
develop = true
[package.dependencies]
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
langgraph-checkpoint = "^2.0.10"
[package.source]
type = "directory"
url = "../prebuilt"
[[package]]
name = "langgraph-sdk"
version = "0.1.51"
version = "0.1.55"
description = "SDK for interacting with LangGraph API"
optional = false
python-versions = "^3.9.0,<4.0"
@@ -3491,4 +3509,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.9.0,<4.0"
content-hash = "caf943b02b6913c05d15c37fda6d216669f789e2a059b7e8e2490b2bdcd23e0e"
content-hash = "eb85f0bcc0e8a715ef38afb58cf888f7c2ee8579ea6ed94900244365f24cddd9"
libs/langgraph/pyproject.toml
+4 -2
View File
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langgraph"
version = "0.2.74"
version = "0.3.8"
description = "Building stateful, multi-actor applications with LLMs"
authors = []
license = "MIT"
@@ -9,9 +9,10 @@ repository = "https://www.github.com/langchain-ai/langgraph"
[tool.poetry.dependencies]
python = ">=3.9.0,<4.0"
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
langchain-core = ">=0.1,<0.4"
langgraph-checkpoint = "^2.0.10"
langgraph-sdk = "^0.1.42"
langgraph-prebuilt = ">=0.1.1,<0.2"
[tool.poetry.group.dev.dependencies]
pytest = "^8.3.2"
@@ -26,6 +27,7 @@ ruff = "^0.6.2"
jupyter = "^1.0.0"
pytest-xdist = {extras = ["psutil"], version = "^3.6.1"}
pytest-repeat = "^0.9.3"
langgraph-prebuilt = {path = "../prebuilt", develop = true}
langgraph-checkpoint = {path = "../checkpoint", develop = true}
langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}
libs/langgraph/tests/__snapshots__/test_large_cases.ambr
+12 -12
View File
@@ -3116,8 +3116,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -3126,6 +3124,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -3141,8 +3141,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -3151,6 +3149,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -3166,8 +3166,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -3176,6 +3174,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -3191,8 +3191,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -3201,6 +3199,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -3216,8 +3216,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -3226,6 +3224,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -3241,8 +3241,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -3251,6 +3249,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
libs/langgraph/tests/__snapshots__/test_large_cases_async.ambr
+12 -12
View File
@@ -6,8 +6,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -16,6 +14,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -31,8 +31,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -41,6 +39,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -56,8 +56,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -66,6 +64,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -81,8 +81,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -91,6 +89,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -106,8 +106,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -116,6 +114,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
@@ -131,8 +131,6 @@
__start__([<p>__start__</p>]):::first
router_node(router_node)
normal_llm_node(normal_llm_node)
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
__end__([<p>__end__</p>]):::last
__start__ --> router_node;
normal_llm_node --> __end__;
@@ -141,6 +139,8 @@
router_node -.-> weather_graph_model_node;
router_node -.-> __end__;
subgraph weather_graph
weather_graph_model_node(model_node)
weather_graph_weather_node(weather_node<hr/><small><em>__interrupt = before</em></small>)
weather_graph_model_node --> weather_graph_weather_node;
end
classDef default fill:#f2f0ff,line-height:1.2
libs/langgraph/tests/__snapshots__/test_pregel.ambr
+444 -22
View File
@@ -1217,6 +1217,426 @@
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[memory]
'''
graph TD;
__start__ --> rewrite_query;
analyzer_one --> retriever_one;
qa --> __end__;
retriever_one --> qa;
retriever_two --> qa;
rewrite_query --> analyzer_one;
rewrite_query -.-> retriever_two;
'''
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[memory].1
dict({
'$defs': dict({
'InnerObject': dict({
'properties': dict({
'yo': dict({
'title': 'Yo',
'type': 'integer',
}),
}),
'required': list([
'yo',
]),
'title': 'InnerObject',
'type': 'object',
}),
}),
'properties': dict({
'inner': dict({
'$ref': '#/$defs/InnerObject',
}),
'query': dict({
'title': 'Query',
'type': 'string',
}),
}),
'required': list([
'query',
'inner',
]),
'title': 'Input',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[memory].2
dict({
'properties': dict({
'answer': dict({
'title': 'Answer',
'type': 'string',
}),
'docs': dict({
'items': dict({
'type': 'string',
}),
'title': 'Docs',
'type': 'array',
}),
}),
'required': list([
'answer',
'docs',
]),
'title': 'Output',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres]
'''
graph TD;
__start__ --> rewrite_query;
analyzer_one --> retriever_one;
qa --> __end__;
retriever_one --> qa;
retriever_two --> qa;
rewrite_query --> analyzer_one;
rewrite_query -.-> retriever_two;
'''
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres].1
dict({
'$defs': dict({
'InnerObject': dict({
'properties': dict({
'yo': dict({
'title': 'Yo',
'type': 'integer',
}),
}),
'required': list([
'yo',
]),
'title': 'InnerObject',
'type': 'object',
}),
}),
'properties': dict({
'inner': dict({
'$ref': '#/$defs/InnerObject',
}),
'query': dict({
'title': 'Query',
'type': 'string',
}),
}),
'required': list([
'query',
'inner',
]),
'title': 'Input',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres].2
dict({
'properties': dict({
'answer': dict({
'title': 'Answer',
'type': 'string',
}),
'docs': dict({
'items': dict({
'type': 'string',
}),
'title': 'Docs',
'type': 'array',
}),
}),
'required': list([
'answer',
'docs',
]),
'title': 'Output',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_pipe]
'''
graph TD;
__start__ --> rewrite_query;
analyzer_one --> retriever_one;
qa --> __end__;
retriever_one --> qa;
retriever_two --> qa;
rewrite_query --> analyzer_one;
rewrite_query -.-> retriever_two;
'''
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_pipe].1
dict({
'$defs': dict({
'InnerObject': dict({
'properties': dict({
'yo': dict({
'title': 'Yo',
'type': 'integer',
}),
}),
'required': list([
'yo',
]),
'title': 'InnerObject',
'type': 'object',
}),
}),
'properties': dict({
'inner': dict({
'$ref': '#/$defs/InnerObject',
}),
'query': dict({
'title': 'Query',
'type': 'string',
}),
}),
'required': list([
'query',
'inner',
]),
'title': 'Input',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_pipe].2
dict({
'properties': dict({
'answer': dict({
'title': 'Answer',
'type': 'string',
}),
'docs': dict({
'items': dict({
'type': 'string',
}),
'title': 'Docs',
'type': 'array',
}),
}),
'required': list([
'answer',
'docs',
]),
'title': 'Output',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_pool]
'''
graph TD;
__start__ --> rewrite_query;
analyzer_one --> retriever_one;
qa --> __end__;
retriever_one --> qa;
retriever_two --> qa;
rewrite_query --> analyzer_one;
rewrite_query -.-> retriever_two;
'''
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_pool].1
dict({
'$defs': dict({
'InnerObject': dict({
'properties': dict({
'yo': dict({
'title': 'Yo',
'type': 'integer',
}),
}),
'required': list([
'yo',
]),
'title': 'InnerObject',
'type': 'object',
}),
}),
'properties': dict({
'inner': dict({
'$ref': '#/$defs/InnerObject',
}),
'query': dict({
'title': 'Query',
'type': 'string',
}),
}),
'required': list([
'query',
'inner',
]),
'title': 'Input',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_pool].2
dict({
'properties': dict({
'answer': dict({
'title': 'Answer',
'type': 'string',
}),
'docs': dict({
'items': dict({
'type': 'string',
}),
'title': 'Docs',
'type': 'array',
}),
}),
'required': list([
'answer',
'docs',
]),
'title': 'Output',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_shallow]
'''
graph TD;
__start__ --> rewrite_query;
analyzer_one --> retriever_one;
qa --> __end__;
retriever_one --> qa;
retriever_two --> qa;
rewrite_query --> analyzer_one;
rewrite_query -.-> retriever_two;
'''
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_shallow].1
dict({
'$defs': dict({
'InnerObject': dict({
'properties': dict({
'yo': dict({
'title': 'Yo',
'type': 'integer',
}),
}),
'required': list([
'yo',
]),
'title': 'InnerObject',
'type': 'object',
}),
}),
'properties': dict({
'inner': dict({
'$ref': '#/$defs/InnerObject',
}),
'query': dict({
'title': 'Query',
'type': 'string',
}),
}),
'required': list([
'query',
'inner',
]),
'title': 'Input',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[postgres_shallow].2
dict({
'properties': dict({
'answer': dict({
'title': 'Answer',
'type': 'string',
}),
'docs': dict({
'items': dict({
'type': 'string',
}),
'title': 'Docs',
'type': 'array',
}),
}),
'required': list([
'answer',
'docs',
]),
'title': 'Output',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[sqlite]
'''
graph TD;
__start__ --> rewrite_query;
analyzer_one --> retriever_one;
qa --> __end__;
retriever_one --> qa;
retriever_two --> qa;
rewrite_query --> analyzer_one;
rewrite_query -.-> retriever_two;
'''
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[sqlite].1
dict({
'$defs': dict({
'InnerObject': dict({
'properties': dict({
'yo': dict({
'title': 'Yo',
'type': 'integer',
}),
}),
'required': list([
'yo',
]),
'title': 'InnerObject',
'type': 'object',
}),
}),
'properties': dict({
'inner': dict({
'$ref': '#/$defs/InnerObject',
}),
'query': dict({
'title': 'Query',
'type': 'string',
}),
}),
'required': list([
'query',
'inner',
]),
'title': 'Input',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input[sqlite].2
dict({
'properties': dict({
'answer': dict({
'title': 'Answer',
'type': 'string',
}),
'docs': dict({
'items': dict({
'type': 'string',
}),
'title': 'Docs',
'type': 'array',
}),
}),
'required': list([
'answer',
'docs',
]),
'title': 'Output',
'type': 'object',
})
# ---
# name: test_in_one_fan_out_state_graph_waiting_edge_via_branch[memory]
'''
graph TD;
@@ -1302,13 +1722,13 @@
__start__([<p>__start__</p>]):::first
uno(uno)
dos(dos)
subgraph_one(one)
subgraph_two(two)
subgraph_three(three)
__start__ --> uno;
uno -.-> dos;
uno -.-> subgraph_one;
subgraph subgraph
subgraph_one(one)
subgraph_two(two)
subgraph_three(three)
subgraph_one -.-> subgraph_two;
subgraph_one -.-> subgraph_three;
end
@@ -1332,12 +1752,14 @@
%%{init: {'flowchart': {'curve': 'linear'}}}%%
graph TD;
__start__([<p>__start__</p>]):::first
inner(inner)
side(side)
__end__([<p>__end__</p>]):::last
__start__ --> inner;
inner --> side;
__start__ --> inner_up;
inner_up --> side;
side --> __end__;
subgraph inner
inner_up(up)
end
classDef default fill:#f2f0ff,line-height:1.2
classDef first fill-opacity:0
classDef last fill:#bfb6fc
@@ -1475,10 +1897,6 @@
graph TD;
__start__([<p>__start__</p>]):::first
tool_one(tool_one)
tool_two___start__(<p>__start__</p>)
tool_two_tool_two_slow(tool_two_slow)
tool_two_tool_two_fast(tool_two_fast)
tool_two___end__(<p>__end__</p>)
tool_three(tool_three)
__end__([<p>__end__</p>]):::last
__start__ -.-> tool_one;
@@ -1488,6 +1906,10 @@
__start__ -.-> tool_three;
tool_three --> __end__;
subgraph tool_two
tool_two___start__(<p>__start__</p>)
tool_two_tool_two_slow(tool_two_slow)
tool_two_tool_two_fast(tool_two_fast)
tool_two___end__(<p>__end__</p>)
tool_two___start__ -.-> tool_two_tool_two_slow;
tool_two_tool_two_slow --> tool_two___end__;
tool_two___start__ -.-> tool_two_tool_two_fast;
@@ -1528,7 +1950,7 @@
'''
# ---
# name: test_state_graph_w_config_inherited_state_keys
'{"$defs": {"Configurable": {"properties": {"tools": {"default": null, "items": {"type": "string"}, "title": "Tools", "type": "array"}}, "title": "Configurable", "type": "object"}}, "properties": {"configurable": {"$ref": "#/$defs/Configurable", "default": null}}, "title": "LangGraphConfig", "type": "object"}'
'{"$defs": {"Config": {"properties": {"tools": {"items": {"type": "string"}, "title": "Tools", "type": "array"}}, "title": "Config", "type": "object"}}, "properties": {"configurable": {"$ref": "#/$defs/Config", "default": null}}, "title": "LangGraphConfig", "type": "object"}'
# ---
# name: test_state_graph_w_config_inherited_state_keys.1
'{"$defs": {"AgentAction": {"description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"anyOf": [{"type": "string"}, {"type": "object"}], "title": "Tool Input"}, "log": {"title": "Log", "type": "string"}, "type": {"const": "AgentAction", "default": "AgentAction", "enum": ["AgentAction"], "title": "Type", "type": "string"}}, "required": ["tool", "tool_input", "log"], "title": "AgentAction", "type": "object"}, "AgentFinish": {"description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"const": "AgentFinish", "default": "AgentFinish", "enum": ["AgentFinish"], "title": "Type", "type": "string"}}, "required": ["return_values", "log"], "title": "AgentFinish", "type": "object"}}, "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"anyOf": [{"$ref": "#/$defs/AgentAction"}, {"$ref": "#/$defs/AgentFinish"}, {"type": "null"}], "default": null, "title": "Agent Outcome"}, "intermediate_steps": {"default": null, "items": {"maxItems": 2, "minItems": 2, "prefixItems": [{"$ref": "#/$defs/AgentAction"}, {"type": "string"}], "type": "array"}, "title": "Intermediate Steps", "type": "array"}}, "required": ["input"], "title": "LangGraphInput", "type": "object"}'
@@ -1542,24 +1964,24 @@
graph TD;
__start__([<p>__start__</p>]):::first
gp_one(gp_one)
gp_two___start__(<p>__start__</p>)
gp_two_p_one(p_one)
gp_two_p_two___start__(<p>__start__</p>)
gp_two_p_two_c_one(c_one)
gp_two_p_two_c_two(c_two)
gp_two_p_two___end__(<p>__end__</p>)
gp_two___end__(<p>__end__</p>)
__end__([<p>__end__</p>]):::last
__start__ --> gp_one;
gp_two___end__ --> gp_one;
gp_one -. &nbsp;0&nbsp; .-> gp_two___start__;
gp_one -. &nbsp;1&nbsp; .-> __end__;
subgraph gp_two
gp_two___start__(<p>__start__</p>)
gp_two_p_one(p_one)
gp_two___end__(<p>__end__</p>)
gp_two___start__ --> gp_two_p_one;
gp_two_p_two___end__ --> gp_two_p_one;
gp_two_p_one -. &nbsp;0&nbsp; .-> gp_two_p_two___start__;
gp_two_p_one -. &nbsp;1&nbsp; .-> gp_two___end__;
subgraph p_two
gp_two_p_two___start__(<p>__start__</p>)
gp_two_p_two_c_one(c_one)
gp_two_p_two_c_two(c_two)
gp_two_p_two___end__(<p>__end__</p>)
gp_two_p_two___start__ --> gp_two_p_two_c_one;
gp_two_p_two_c_two --> gp_two_p_two_c_one;
gp_two_p_two_c_one -. &nbsp;0&nbsp; .-> gp_two_p_two_c_two;
@@ -1578,16 +2000,16 @@
graph TD;
__start__([<p>__start__</p>]):::first
p_one(p_one)
p_two___start__(<p>__start__</p>)
p_two_c_one(c_one)
p_two_c_two(c_two)
p_two___end__(<p>__end__</p>)
__end__([<p>__end__</p>]):::last
__start__ --> p_one;
p_two___end__ --> p_one;
p_one -. &nbsp;0&nbsp; .-> p_two___start__;
p_one -. &nbsp;1&nbsp; .-> __end__;
subgraph p_two
p_two___start__(<p>__start__</p>)
p_two_c_one(c_one)
p_two_c_two(c_two)
p_two___end__(<p>__end__</p>)
p_two___start__ --> p_two_c_one;
p_two_c_two --> p_two_c_one;
p_two_c_one -. &nbsp;0&nbsp; .-> p_two_c_two;
libs/langgraph/tests/test_large_cases.py
+1 -1
View File
@@ -2827,7 +2827,7 @@ def test_state_graph_packets(
}
# Define decision-making logic
def should_continue(data: AgentState) -> str:
def should_continue(data: dict) -> str:
assert isinstance(data["session"], httpx.Client)
assert (
data["something_extra"] == "hi there"
libs/langgraph/tests/test_pregel.py
+487 -163
View File
@@ -10,7 +10,7 @@ import warnings
from collections import Counter, deque
from concurrent.futures import ThreadPoolExecutor
from contextlib import contextmanager
from dataclasses import dataclass
from dataclasses import dataclass, field
from random import randrange
from typing import (
Annotated,
@@ -275,6 +275,61 @@ def test_checkpoint_errors() -> None:
graph.invoke("", {"configurable": {"thread_id": "thread-1"}})
def test_config_json_schema() -> None:
"""Test that config json schema is generated properly."""
chain = Channel.subscribe_to("input") | Channel.write_to("output")
@dataclass
class Foo:
x: int
y: str = field(default="foo")
app = Pregel(
nodes={
"one": chain,
},
channels={
"ephemeral": EphemeralValue(Any),
"input": LastValue(int),
"output": LastValue(int),
},
input_channels=["input", "ephemeral"],
output_channels="output",
config_type=Foo,
)
assert app.get_config_jsonschema() == {
"$defs": {
"Foo": {
"properties": {
"x": {
"title": "X",
"type": "integer",
},
"y": {
"default": "foo",
"title": "Y",
"type": "string",
},
},
"required": [
"x",
],
"title": "Foo",
"type": "object",
},
},
"properties": {
"configurable": {
"$ref": "#/$defs/Foo",
"default": None,
},
},
"title": "LangGraphConfig",
"type": "object",
}
def test_node_schemas_custom_output() -> None:
class State(TypedDict):
hello: str
@@ -1444,7 +1499,7 @@ def test_imp_task(request: pytest.FixtureRequest, checkpointer_name: str) -> Non
checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
mapper_calls = 0
class Config:
class Configurable:
model: str
@task()
@@ -1454,7 +1509,7 @@ def test_imp_task(request: pytest.FixtureRequest, checkpointer_name: str) -> Non
time.sleep(input / 100)
return str(input) * 2
@entrypoint(checkpointer=checkpointer, config_schema=Config)
@entrypoint(checkpointer=checkpointer, config_schema=Configurable)
def graph(input: list[int]) -> list[str]:
futures = [mapper(i) for i in input]
mapped = [f.result() for f in futures]
@@ -2839,6 +2894,139 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2(
}
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_input(
snapshot: SnapshotAssertion,
mocker: MockerFixture,
request: pytest.FixtureRequest,
checkpointer_name: str,
) -> None:
from pydantic import BaseModel
checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
def sorted_add(
x: list[str], y: Union[list[str], list[tuple[str, str]]]
) -> list[str]:
if isinstance(y[0], tuple):
for rem, _ in y:
x.remove(rem)
y = [t[1] for t in y]
return sorted(operator.add(x, y))
class InnerObject(BaseModel):
yo: int
class QueryModel(BaseModel):
query: str
class State(QueryModel):
inner: InnerObject
answer: Optional[str] = None
docs: Annotated[list[str], sorted_add]
class StateUpdate(BaseModel):
query: Optional[str] = None
answer: Optional[str] = None
docs: Optional[list[str]] = None
class Input(QueryModel):
inner: InnerObject
class Output(BaseModel):
answer: str
docs: list[str]
def rewrite_query(data: State) -> State:
return {"query": f"query: {data.query}"}
def analyzer_one(data: State) -> State:
return StateUpdate(query=f"analyzed: {data.query}")
def retriever_one(data: State) -> State:
return {"docs": ["doc1", "doc2"]}
def retriever_two(data: State) -> State:
time.sleep(0.1)
return {"docs": ["doc3", "doc4"]}
def qa(data: State) -> State:
return {"answer": ",".join(data.docs)}
def decider(data: State) -> str:
assert isinstance(data, State)
return "retriever_two"
workflow = StateGraph(State, input=Input, output=Output)
workflow.add_node("rewrite_query", rewrite_query)
workflow.add_node("analyzer_one", analyzer_one)
workflow.add_node("retriever_one", retriever_one)
workflow.add_node("retriever_two", retriever_two)
workflow.add_node("qa", qa)
workflow.set_entry_point("rewrite_query")
workflow.add_edge("rewrite_query", "analyzer_one")
workflow.add_edge("analyzer_one", "retriever_one")
workflow.add_conditional_edges(
"rewrite_query", decider, {"retriever_two": "retriever_two"}
)
workflow.add_edge(["retriever_one", "retriever_two"], "qa")
workflow.set_finish_point("qa")
app = workflow.compile()
assert app.invoke(
Input(query="what is weather in sf", inner=InnerObject(yo=1))
) == {
"docs": ["doc1", "doc2", "doc3", "doc4"],
"answer": "doc1,doc2,doc3,doc4",
}
assert [
*app.stream(Input(query="what is weather in sf", inner=InnerObject(yo=1)))
] == [
{"rewrite_query": {"query": "query: what is weather in sf"}},
{"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
{"retriever_two": {"docs": ["doc3", "doc4"]}},
{"retriever_one": {"docs": ["doc1", "doc2"]}},
{"qa": {"answer": "doc1,doc2,doc3,doc4"}},
]
app_w_interrupt = workflow.compile(
checkpointer=checkpointer,
interrupt_after=["retriever_one"],
)
config = {"configurable": {"thread_id": "1"}}
assert [
c
for c in app_w_interrupt.stream(
Input(query="what is weather in sf", inner=InnerObject(yo=1)), config
)
] == [
{"rewrite_query": {"query": "query: what is weather in sf"}},
{"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
{"retriever_two": {"docs": ["doc3", "doc4"]}},
{"retriever_one": {"docs": ["doc1", "doc2"]}},
{"__interrupt__": ()},
]
assert [c for c in app_w_interrupt.stream(None, config)] == [
{"qa": {"answer": "doc1,doc2,doc3,doc4"}},
]
assert app_w_interrupt.update_state(
config, {"docs": ["doc5"]}, as_node="rewrite_query"
) == {
"configurable": {
"thread_id": "1",
"checkpoint_id": AnyStr(),
"checkpoint_ns": "",
}
}
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
def test_in_one_fan_out_state_graph_waiting_edge_plus_regular(
request: pytest.FixtureRequest, checkpointer_name: str
@@ -2905,14 +3093,24 @@ def test_in_one_fan_out_state_graph_waiting_edge_plus_regular(
"answer": "doc1,doc2,doc3,doc4",
}
assert [*app.stream({"query": "what is weather in sf"})] == [
{"rewrite_query": {"query": "query: what is weather in sf"}},
{"qa": {"answer": ""}},
{"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
{"retriever_two": {"docs": ["doc3", "doc4"]}},
{"retriever_one": {"docs": ["doc1", "doc2"]}},
{"qa": {"answer": "doc1,doc2,doc3,doc4"}},
]
assert [*app.stream({"query": "what is weather in sf"})] in (
[
{"rewrite_query": {"query": "query: what is weather in sf"}},
{"qa": {"answer": ""}},
{"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
{"retriever_two": {"docs": ["doc3", "doc4"]}},
{"retriever_one": {"docs": ["doc1", "doc2"]}},
{"qa": {"answer": "doc1,doc2,doc3,doc4"}},
],
[
{"rewrite_query": {"query": "query: what is weather in sf"}},
{"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
{"qa": {"answer": ""}},
{"retriever_two": {"docs": ["doc3", "doc4"]}},
{"retriever_one": {"docs": ["doc1", "doc2"]}},
{"qa": {"answer": "doc1,doc2,doc3,doc4"}},
],
)
app_w_interrupt = workflow.compile(
checkpointer=checkpointer,
@@ -3371,6 +3569,59 @@ def test_subgraph_checkpoint_true(
]
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
def test_subgraph_checkpoint_true_interrupt(
request: pytest.FixtureRequest, checkpointer_name: str
) -> None:
checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name)
# Define subgraph
class SubgraphState(TypedDict):
# note that none of these keys are shared with the parent graph state
bar: str
baz: str
def subgraph_node_1(state: SubgraphState):
baz_value = interrupt("Provide baz value")
return {"baz": baz_value}
def subgraph_node_2(state: SubgraphState):
return {"bar": state["bar"] + state["baz"]}
subgraph_builder = StateGraph(SubgraphState)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_node(subgraph_node_2)
subgraph_builder.add_edge(START, "subgraph_node_1")
subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
subgraph = subgraph_builder.compile(checkpointer=True)
class ParentState(TypedDict):
foo: str
def node_1(state: ParentState):
return {"foo": "hi! " + state["foo"]}
def node_2(state: ParentState):
response = subgraph.invoke({"bar": state["foo"]})
return {"foo": response["bar"]}
builder = StateGraph(ParentState)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
assert graph.invoke({"foo": "foo"}, config) == {"foo": "hi! foo"}
assert graph.get_state(config, subgraphs=True).tasks[0].state.values == {
"bar": "hi! foo"
}
assert graph.invoke(Command(resume="baz"), config) == {"foo": "hi! foobaz"}
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
def test_stream_subgraphs_during_execution(
request: pytest.FixtureRequest, checkpointer_name: str
@@ -4830,13 +5081,6 @@ def test_parent_command(request: pytest.FixtureRequest, checkpointer_name: str)
"source": "loop",
"writes": {
"alice": {
"messages": [
_AnyIdHumanMessage(
content="get user name",
additional_kwargs={},
response_metadata={},
),
],
"user_name": "Meow",
}
},
@@ -6176,151 +6420,6 @@ def test_multiple_subgraphs_checkpointer(
]
def test_merging_updates_command_parent():
# simple reducer
def append_unique(left, right):
combined = list(left)
for item in right:
if item in combined:
continue
else:
combined.append(item)
return combined
class State(TypedDict):
foo: str
bar: Annotated[list[str], append_unique]
# Define subgraph
def subgraph_node_1(state: State):
return Command(
goto="subgraph_node_2",
update={
"foo": "foo",
"bar": ["subgraph_node_1"],
},
)
def subgraph_node_2(state: State):
return Command(
goto="node_3",
update={"bar": ["subgraph_node_2"]},
graph=Command.PARENT,
)
subgraph_builder = StateGraph(State)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_node(subgraph_node_2)
subgraph_builder.add_edge(START, "subgraph_node_1")
# Define main graph
def node_1(state: State):
return Command(
goto="node_2",
update={"bar": ["node_1"]},
)
def node_3(state: State, store):
return Command(
update={"bar": ["node_3"]},
)
main_builder = StateGraph(State)
main_builder.add_node("node_1", node_1)
main_builder.add_node("node_2", subgraph_builder.compile())
main_builder.add_node("node_3", node_3)
main_builder.add_edge(START, "node_1")
main_builder.add_edge("node_2", "node_3")
main_graph = main_builder.compile()
assert main_graph.invoke({"foo": ""}) == {
"foo": "foo",
"bar": ["node_1", "subgraph_node_1", "subgraph_node_2", "node_3"],
}
assert list(
main_graph.stream({"foo": ""}, stream_mode="updates", subgraphs=True)
) == [
((), {"node_1": {"bar": ["node_1"]}}),
(
(AnyStr("node_2:"),),
{"subgraph_node_1": {"foo": "foo", "bar": ["subgraph_node_1"]}},
),
(
(),
{
"node_2": [
{"foo": "foo"},
{"bar": ["node_1", "subgraph_node_1"]},
{"bar": ["subgraph_node_2"]},
]
},
),
((), {"node_3": {"bar": ["node_3"]}}),
]
def test_merging_non_overlapping_updates_command_parent():
# simple reducer
def append_unique(left, right):
combined = list(left)
for item in right:
if item in combined:
continue
else:
combined.append(item)
return combined
class State(TypedDict):
foo: Annotated[list, append_unique]
# Define subgraph
def subgraph_node_1(state: State):
return Command(
goto="subgraph_node_2",
update={
"foo": ["bar"],
"bar": ["subgraph_node_1"],
},
)
def subgraph_node_2(state: State):
return Command(
goto="node_3",
update={"bar": ["subgraph_node_2"]},
graph=Command.PARENT,
)
subgraph_builder = StateGraph(State)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_node(subgraph_node_2)
subgraph_builder.add_edge(START, "subgraph_node_1")
# Define main graph
def node_1(state: State):
return Command(
goto="node_2",
update={"foo": ["foo"]},
)
def node_3(state: State, store):
return Command(
update={"foo": ["baz"]},
)
main_builder = StateGraph(State)
main_builder.add_node("node_1", node_1)
main_builder.add_node("node_2", subgraph_builder.compile())
main_builder.add_node("node_3", node_3)
main_builder.add_edge(START, "node_1")
main_builder.add_edge("node_2", "node_3")
main_graph = main_builder.compile()
assert main_graph.invoke({"foo": []}) == {
"foo": ["foo", "bar", "baz"],
}
def test_entrypoint_output_schema_with_return_and_save() -> None:
"""Test output schema inference with entrypoint.final."""
@@ -6559,6 +6658,40 @@ def test_pydantic_none_state_update() -> None:
assert graph.invoke({"foo": ""}) == {"foo": None}
def test_pydantic_state_mutation() -> None:
from pydantic import BaseModel, Field
class Inner(BaseModel):
a: int = 0
class State(BaseModel):
inner: Inner = Inner()
outer: int = 0
def my_node(state: State) -> State:
state.inner.a = 5
state.outer = 10
return state
graph = StateGraph(State).add_node(my_node).add_edge(START, "my_node").compile()
assert graph.invoke({"outer": 1}) == {"outer": 10, "inner": Inner(a=5)}
# test w/ default_factory
class State(BaseModel):
inner: Inner = Field(default_factory=Inner)
outer: int = 0
def my_node(state: State) -> State:
state.inner.a = 5
state.outer = 10
return state
graph = StateGraph(State).add_node(my_node).add_edge(START, "my_node").compile()
assert graph.invoke({"outer": 1}) == {"outer": 10, "inner": Inner(a=5)}
def test_get_stream_writer() -> None:
class State(TypedDict):
foo: str
@@ -6588,3 +6721,194 @@ def test_get_stream_writer() -> None:
},
),
]
def test_stream_messages_dedupe_inputs() -> None:
from langchain_core.messages import AIMessage
def call_model(state):
return {"messages": AIMessage("hi", id="1")}
def route(state):
return Command(goto="node_2", graph=Command.PARENT)
subgraph = (
StateGraph(MessagesState)
.add_node(call_model)
.add_node(route)
.add_edge(START, "call_model")
.add_edge("call_model", "route")
.compile()
)
graph = (
StateGraph(MessagesState)
.add_node("node_1", subgraph)
.add_node("node_2", lambda state: state)
.add_edge(START, "node_1")
.compile()
)
chunks = [
chunk
for ns, chunk in graph.stream(
{"messages": "hi"}, stream_mode="messages", subgraphs=True
)
]
assert len(chunks) == 1
assert chunks[0][0] == AIMessage("hi", id="1")
assert chunks[0][1]["langgraph_node"] == "call_model"
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
def test_stream_messages_dedupe_state(
request: pytest.FixtureRequest, checkpointer_name: str
) -> None:
from langchain_core.messages import AIMessage
checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
to_emit = [AIMessage("bye", id="1"), AIMessage("bye again", id="2")]
def call_model(state):
return {"messages": to_emit.pop(0)}
def route(state):
return Command(goto="node_2", graph=Command.PARENT)
subgraph = (
StateGraph(MessagesState)
.add_node(call_model)
.add_node(route)
.add_edge(START, "call_model")
.add_edge("call_model", "route")
.compile()
)
graph = (
StateGraph(MessagesState)
.add_node("node_1", subgraph)
.add_node("node_2", lambda state: state)
.add_edge(START, "node_1")
.compile(checkpointer=checkpointer)
)
thread1 = {"configurable": {"thread_id": "1"}}
chunks = [
chunk
for ns, chunk in graph.stream(
{"messages": "hi"}, thread1, stream_mode="messages", subgraphs=True
)
]
assert len(chunks) == 1
assert chunks[0][0] == AIMessage("bye", id="1")
assert chunks[0][1]["langgraph_node"] == "call_model"
chunks = [
chunk
for ns, chunk in graph.stream(
{"messages": "hi again"},
thread1,
stream_mode="messages",
subgraphs=True,
)
]
assert len(chunks) == 1
assert chunks[0][0] == AIMessage("bye again", id="2")
assert chunks[0][1]["langgraph_node"] == "call_model"
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
def test_interrupt_subgraph_reenter_checkpointer_true(
request: pytest.FixtureRequest, checkpointer_name: str
) -> None:
checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
class SubgraphState(TypedDict):
foo: str
bar: str
class ParentState(TypedDict):
foo: str
counter: int
called = []
bar_values = []
def subnode_1(state: SubgraphState):
called.append("subnode_1")
bar_values.append(state.get("bar"))
return {"foo": "subgraph_1"}
def subnode_2(state: SubgraphState):
called.append("subnode_2")
value = interrupt("Provide value")
value += "baz"
return {"foo": "subgraph_2", "bar": value}
subgraph = (
StateGraph(SubgraphState)
.add_node(subnode_1)
.add_node(subnode_2)
.add_edge(START, "subnode_1")
.add_edge("subnode_1", "subnode_2")
.compile(checkpointer=True)
)
def call_subgraph(state: ParentState):
called.append("call_subgraph")
return subgraph.invoke(state)
def node(state: ParentState):
called.append("parent")
if state["counter"] < 1:
return Command(
goto="call_subgraph", update={"counter": state["counter"] + 1}
)
return {"foo": state["foo"] + "|" + "parent"}
parent = (
StateGraph(ParentState)
.add_node(call_subgraph)
.add_node(node)
.add_edge(START, "call_subgraph")
.add_edge("call_subgraph", "node")
.compile(checkpointer=checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
assert parent.invoke({"foo": "", "counter": 0}, config) == {"foo": "", "counter": 0}
assert parent.invoke(Command(resume="bar"), config) == {
"foo": "subgraph_2",
"counter": 1,
}
assert parent.invoke(Command(resume="qux"), config) == {
"foo": "subgraph_2|parent",
"counter": 1,
}
assert called == [
"call_subgraph",
"subnode_1",
"subnode_2",
"call_subgraph",
"subnode_2",
"parent",
"call_subgraph",
"subnode_1",
"subnode_2",
"call_subgraph",
"subnode_2",
"parent",
]
# invoke parent again (new turn)
assert parent.invoke({"foo": "meow", "counter": 0}, config) == {
"foo": "meow",
"counter": 0,
}
# confirm that we preserve the state values from the previous invocation
assert bar_values == [None, "barbaz", "quxbaz"]
libs/langgraph/tests/test_pregel_async.py
+247 -10
View File
@@ -6148,13 +6148,6 @@ async def test_parent_command(checkpointer_name: str) -> None:
"source": "loop",
"writes": {
"alice": {
"messages": [
_AnyIdHumanMessage(
content="get user name",
additional_kwargs={},
response_metadata={},
),
],
"user_name": "Meow",
}
},
@@ -7282,9 +7275,7 @@ async def test_multiple_subgraphs_mixed_state_graph(
@NEEDS_CONTEXTVARS
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
async def test_multiple_subgraphs_checkpointer(
request: pytest.FixtureRequest, checkpointer_name: str
) -> None:
async def test_multiple_subgraphs_checkpointer(checkpointer_name: str) -> None:
async with awith_checkpointer(checkpointer_name) as checkpointer:
class SubgraphState(TypedDict):
@@ -7513,3 +7504,249 @@ async def test_tags_stream_mode_messages() -> None:
},
)
]
async def test_stream_messages_dedupe_inputs() -> None:
from langchain_core.messages import AIMessage
async def call_model(state):
return {"messages": AIMessage("hi", id="1")}
async def route(state):
return Command(goto="node_2", graph=Command.PARENT)
subgraph = (
StateGraph(MessagesState)
.add_node(call_model)
.add_node(route)
.add_edge(START, "call_model")
.add_edge("call_model", "route")
.compile()
)
graph = (
StateGraph(MessagesState)
.add_node("node_1", subgraph)
.add_node("node_2", lambda state: state)
.add_edge(START, "node_1")
.compile()
)
chunks = [
chunk
async for ns, chunk in graph.astream(
{"messages": "hi"}, stream_mode="messages", subgraphs=True
)
]
assert len(chunks) == 1
assert chunks[0][0] == AIMessage("hi", id="1")
assert chunks[0][1]["langgraph_node"] == "call_model"
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
async def test_stream_messages_dedupe_state(checkpointer_name: str) -> None:
async with awith_checkpointer(checkpointer_name) as checkpointer:
from langchain_core.messages import AIMessage
to_emit = [AIMessage("bye", id="1"), AIMessage("bye again", id="2")]
async def call_model(state):
return {"messages": to_emit.pop(0)}
async def route(state):
return Command(goto="node_2", graph=Command.PARENT)
subgraph = (
StateGraph(MessagesState)
.add_node(call_model)
.add_node(route)
.add_edge(START, "call_model")
.add_edge("call_model", "route")
.compile()
)
graph = (
StateGraph(MessagesState)
.add_node("node_1", subgraph)
.add_node("node_2", lambda state: state)
.add_edge(START, "node_1")
.compile(checkpointer=checkpointer)
)
thread1 = {"configurable": {"thread_id": "1"}}
chunks = [
chunk
async for ns, chunk in graph.astream(
{"messages": "hi"}, thread1, stream_mode="messages", subgraphs=True
)
]
assert len(chunks) == 1
assert chunks[0][0] == AIMessage("bye", id="1")
assert chunks[0][1]["langgraph_node"] == "call_model"
chunks = [
chunk
async for ns, chunk in graph.astream(
{"messages": "hi again"},
thread1,
stream_mode="messages",
subgraphs=True,
)
]
assert len(chunks) == 1
assert chunks[0][0] == AIMessage("bye again", id="2")
assert chunks[0][1]["langgraph_node"] == "call_model"
@NEEDS_CONTEXTVARS
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
async def test_interrupt_subgraph_reenter_checkpointer_true(
checkpointer_name: str,
) -> None:
async with awith_checkpointer(checkpointer_name) as checkpointer:
class SubgraphState(TypedDict):
foo: str
bar: str
class ParentState(TypedDict):
foo: str
counter: int
called = []
bar_values = []
async def subnode_1(state: SubgraphState):
called.append("subnode_1")
bar_values.append(state.get("bar"))
return {"foo": "subgraph_1"}
async def subnode_2(state: SubgraphState):
called.append("subnode_2")
value = interrupt("Provide value")
value += "baz"
return {"foo": "subgraph_2", "bar": value}
subgraph = (
StateGraph(SubgraphState)
.add_node(subnode_1)
.add_node(subnode_2)
.add_edge(START, "subnode_1")
.add_edge("subnode_1", "subnode_2")
.compile(checkpointer=True)
)
async def call_subgraph(state: ParentState):
called.append("call_subgraph")
return await subgraph.ainvoke(state)
async def node(state: ParentState):
called.append("parent")
if state["counter"] < 1:
return Command(
goto="call_subgraph", update={"counter": state["counter"] + 1}
)
return {"foo": state["foo"] + "|" + "parent"}
parent = (
StateGraph(ParentState)
.add_node(call_subgraph)
.add_node(node)
.add_edge(START, "call_subgraph")
.add_edge("call_subgraph", "node")
.compile(checkpointer=checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
assert await parent.ainvoke({"foo": "", "counter": 0}, config) == {
"foo": "",
"counter": 0,
}
assert await parent.ainvoke(Command(resume="bar"), config) == {
"foo": "subgraph_2",
"counter": 1,
}
assert await parent.ainvoke(Command(resume="qux"), config) == {
"foo": "subgraph_2|parent",
"counter": 1,
}
assert called == [
"call_subgraph",
"subnode_1",
"subnode_2",
"call_subgraph",
"subnode_2",
"parent",
"call_subgraph",
"subnode_1",
"subnode_2",
"call_subgraph",
"subnode_2",
"parent",
]
# invoke parent again (new turn)
assert await parent.ainvoke({"foo": "meow", "counter": 0}, config) == {
"foo": "meow",
"counter": 0,
}
# confirm that we preserve the state values from the previous invocation
assert bar_values == [None, "barbaz", "quxbaz"]
@NEEDS_CONTEXTVARS
async def test_handles_multiple_interrupts_from_tasks() -> None:
@task
async def add_participant(name: str) -> str:
feedback = interrupt(f"Hey do you want to add {name}?")
if feedback is False:
return f"The user changed their mind and doesn't want to add {name}!"
if feedback is True:
return f"Added {name}!"
raise ValueError("Invalid feedback")
@entrypoint(checkpointer=MemorySaver())
async def program(_state: Any) -> list[str]:
first = await add_participant("James")
second = await add_participant("Will")
return [first, second]
config = {"configurable": {"thread_id": "1"}}
result = await program.ainvoke("this is ignored", config=config)
assert result is None
state = await program.aget_state(config=config)
assert len(state.tasks[0].interrupts) == 1
task_interrupt = state.tasks[0].interrupts[0]
assert task_interrupt.resumable is True
assert len(task_interrupt.ns) == 2
assert task_interrupt.ns[0].startswith("program:")
assert task_interrupt.ns[1].startswith("add_participant:")
assert task_interrupt.value == "Hey do you want to add James?"
result = await program.ainvoke(Command(resume=True), config=config)
assert result is None
state = await program.aget_state(config=config)
assert len(state.tasks[0].interrupts) == 1
task_interrupt = state.tasks[0].interrupts[0]
assert task_interrupt.resumable is True
assert len(task_interrupt.ns) == 2
assert task_interrupt.ns[0].startswith("program:")
assert task_interrupt.ns[1].startswith("add_participant:")
assert task_interrupt.value == "Hey do you want to add Will?"
result = await program.ainvoke(Command(resume=True), config=config)
assert result is not None
assert len(result) == 2
assert result[0] == "Added James!"
assert result[1] == "Added Will!"
libs/langgraph/tests/test_pydantic.py
+43
View File
@@ -0,0 +1,43 @@
import sys
import typing
import pydantic
import typing_extensions
from langgraph.utils.pydantic import is_supported_by_pydantic
def test_is_supported_by_pydantic() -> None:
"""Test if types are supported by pydantic."""
class TypedDictExtensions(typing_extensions.TypedDict):
x: int
assert is_supported_by_pydantic(TypedDictExtensions) is True
class VanillaClass:
x: int
assert is_supported_by_pydantic(VanillaClass) is False
class BuiltinTypedDict(typing.TypedDict): # noqa: TID251
x: int
if sys.version_info >= (3, 12):
assert is_supported_by_pydantic(BuiltinTypedDict) is True
else:
assert is_supported_by_pydantic(BuiltinTypedDict) is False
class PydanticModel(pydantic.BaseModel):
x: int
assert is_supported_by_pydantic(PydanticModel) is True
if hasattr(pydantic, "v1"):
class PydanticModelV1(pydantic.v1.BaseModel):
x: int
assert is_supported_by_pydantic(PydanticModelV1) is False
assert is_supported_by_pydantic(int) is False
libs/langgraph/tests/test_state.py
+72
View File
@@ -1,4 +1,5 @@
import inspect
import operator
import warnings
from dataclasses import dataclass, field
from typing import Annotated as Annotated2
@@ -328,3 +329,74 @@ def test__get_node_name() -> None:
# class method
assert _get_node_name(MyClass().class_method) == "class_method"
def test_input_schema_conditional_edge():
class OverallState(TypedDict):
foo: Annotated[int, operator.add]
bar: str
class PrivateState(TypedDict):
baz: str
builder = StateGraph(OverallState)
def node_1(state: OverallState):
return {"foo": 1, "baz": "bar"}
def node_2(state: PrivateState):
return {"foo": 1, "bar": state["baz"], "something_else": "meow"}
def node_3(state: OverallState):
return {"foo": 1}
def router(state: OverallState):
assert state == {"foo": 2, "bar": "bar"}
if state["foo"] == 2:
return "node_3"
else:
return "__end__"
builder.add_node(node_1)
builder.add_node(node_2)
builder.add_node(node_3)
builder.add_conditional_edges("node_2", router)
builder.add_edge("__start__", "node_1")
builder.add_edge("node_1", "node_2")
graph = builder.compile()
assert graph.invoke({"foo": 0}) == {"foo": 3, "bar": "bar"}
def test_private_input_schema_conditional_edge():
class OverallState(TypedDict):
foo: Annotated[int, operator.add]
bar: str
class RouterState(TypedDict):
baz: str
class Node2State(TypedDict):
foo: Annotated[int, operator.add]
baz: str
builder = StateGraph(OverallState)
def node_1(state: OverallState):
return {"foo": 1, "baz": "meow"}
def node_2(state: Node2State):
return {"foo": 1, "bar": state["baz"]}
def router(state: RouterState):
assert state == {"baz": "meow"}
if state["baz"] == "meow":
return "node_2"
else:
return "__end__"
builder.add_node(node_1)
builder.add_node(node_2)
builder.add_conditional_edges("node_1", router)
builder.add_edge("__start__", "node_1")
graph = builder.compile()
assert graph.invoke({"foo": 0}) == {"foo": 2, "bar": "meow"}
libs/prebuilt/LICENSE
+21
View File
@@ -0,0 +1,21 @@
MIT License
Copyright (c) 2024 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
libs/prebuilt/Makefile
+78
View File
@@ -0,0 +1,78 @@
.PHONY: all format lint test test_watch integration_tests spell_check spell_fix benchmark profile
# Default target executed when no arguments are given to make.
all: help
######################
# TESTING AND COVERAGE
######################
start-postgres:
docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait --remove-orphans
stop-postgres:
docker compose -f tests/compose-postgres.yml down -v
TEST ?= .
test:
make start-postgres && poetry run pytest $(TEST); \
EXIT_CODE=$$?; \
make stop-postgres; \
exit $$EXIT_CODE
test_watch:
make start-postgres && poetry run ptw $(TEST); \
EXIT_CODE=$$?; \
make stop-postgres; \
exit $$EXIT_CODE
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langgraph
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
poetry run ruff check .
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE)
[ "$(PYTHON_FILES)" = "" ] || poetry run mypy langgraph --cache-dir $(MYPY_CACHE)
format format_diff:
poetry run ruff format $(PYTHON_FILES)
poetry run ruff check --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
######################
# HELP
######################
help:
@echo '===================='
@echo '-- DOCUMENTATION --'
@echo '-- LINTING --'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'spell_check - run codespell on the project'
@echo 'spell_fix - run codespell on the project and fix the errors'
@echo '-- TESTS --'
@echo 'coverage - run unit tests and generate coverage report'
@echo 'test - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
@echo 'test_watch - run unit tests in watch mode'
libs/prebuilt/README.md
+117
View File
@@ -0,0 +1,117 @@
# LangGraph Prebuilt
This library defines high-level APIs for creating and executing LangGraph agents and tools.
> [!IMPORTANT]
> This library is meant to be bundled with `langgraph`, don't install it directly
## Agents
`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) of a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent - `create_react_agent`:
```bash
pip install langchain-anthropic
```
```python
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
# Define the tools for the agent to use
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tools = [search]
model = ChatAnthropic(model="claude-3-7-sonnet-latest")
app = create_react_agent(model, tools)
# run the agent
app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
)
```
## Tools
### ToolNode
`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode) of a node that executes tool calls - `ToolNode`:
```python
from langgraph.prebuilt import ToolNode
from langchain_core.messages import AIMessage
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tool_node = ToolNode([search])
tool_calls = [{"name": "search", "args": {"query": "what is the weather in sf"}, "id": "1"}]
ai_message = AIMessage(content="", tool_calls=tool_calls)
# execute tool call
tool_node.invoke({"messages": [ai_message]})
```
### ValidationNode
`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_validator.ValidationNode) of a node that validates tool calls against a pydantic schema - `ValidationNode`:
```python
from pydantic import BaseModel, field_validator
from langgraph.prebuilt import ValidationNode
from langchain_core.messages import AIMessage
class SelectNumber(BaseModel):
a: int
@field_validator("a")
def a_must_be_meaningful(cls, v):
if v != 37:
raise ValueError("Only 37 is allowed")
return v
validation_node = ValidationNode([SelectNumber])
validation_node.invoke({
"messages": [AIMessage("", tool_calls=[{"name": "SelectNumber", "args": {"a": 42}, "id": "1"}])]
})
```
## Agent Inbox
The library contains schemas for using the [Agent Inbox](https://github.com/langchain-ai/agent-inbox) with LangGraph agents. Learn more about how to use Agent Inbox [here](https://github.com/langchain-ai/agent-inbox#interrupts).
```python
from langgraph.types import interrupt
from langgraph.prebuilt.interrupt import HumanInterrupt, HumanResponse
def my_graph_function():
# Extract the last tool call from the `messages` field in the state
tool_call = state["messages"][-1].tool_calls[0]
# Create an interrupt
request: HumanInterrupt = {
"action_request": {
"action": tool_call['name'],
"args": tool_call['args']
},
"config": {
"allow_ignore": True,
"allow_respond": True,
"allow_edit": False,
"allow_accept": False
},
"description": _generate_email_markdown(state) # Generate a detailed markdown description.
}
# Send the interrupt request inside a list, and extract the first response
response = interrupt([request])[0]
if response['type'] == "response":
# Do something with the response
...
```
libs/langgraph/langgraph/prebuilt/__init__.py → libs/prebuilt/langgraph/prebuilt/__init__.py
-3
View File
@@ -1,7 +1,6 @@
"""langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""
from langgraph.prebuilt.chat_agent_executor import create_react_agent
from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
from langgraph.prebuilt.tool_node import (
InjectedState,
InjectedStore,
@@ -12,8 +11,6 @@ from langgraph.prebuilt.tool_validator import ValidationNode
__all__ = [
"create_react_agent",
"ToolExecutor",
"ToolInvocation",
"ToolNode",
"tools_condition",
"ValidationNode",
libs/langgraph/langgraph/prebuilt/chat_agent_executor.py → libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py
+93 -103
View File
@@ -10,6 +10,7 @@ from typing import (
TypeVar,
Union,
cast,
get_type_hints,
)
from langchain_core.language_models import (
@@ -22,6 +23,7 @@ from langchain_core.runnables import (
Runnable,
RunnableBinding,
RunnableConfig,
RunnableSequence,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel
@@ -32,7 +34,6 @@ from langgraph.graph import END, StateGraph
from langgraph.graph.graph import CompiledGraph
from langgraph.graph.message import add_messages
from langgraph.managed import IsLastStep, RemainingSteps
from langgraph.prebuilt.tool_executor import ToolExecutor
from langgraph.prebuilt.tool_node import ToolNode
from langgraph.store.base import BaseStore
from langgraph.types import Checkpointer, Send
@@ -57,24 +58,31 @@ class AgentState(TypedDict):
remaining_steps: RemainingSteps
class AgentStatePydantic(BaseModel):
"""The state of the agent."""
messages: Annotated[Sequence[BaseMessage], add_messages]
remaining_steps: RemainingSteps = 25
class AgentStateWithStructuredResponse(AgentState):
"""The state of the agent with a structured response."""
structured_response: StructuredResponse
StateSchema = TypeVar("StateSchema", bound=AgentState)
class AgentStateWithStructuredResponsePydantic(AgentStatePydantic):
"""The state of the agent with a structured response."""
structured_response: StructuredResponse
StateSchema = TypeVar("StateSchema", bound=Union[AgentState, AgentStatePydantic])
StateSchemaType = Type[StateSchema]
PROMPT_RUNNABLE_NAME = "Prompt"
MessagesModifier = Union[
SystemMessage,
str,
Callable[[Sequence[BaseMessage]], LanguageModelInput],
Runnable[Sequence[BaseMessage], LanguageModelInput],
]
Prompt = Union[
SystemMessage,
str,
@@ -83,21 +91,29 @@ Prompt = Union[
]
def _get_state_value(state: StateSchema, key: str, default: Any = None) -> Any:
return (
state.get(key, default)
if isinstance(state, dict)
else getattr(state, key, default)
)
def _get_prompt_runnable(prompt: Optional[Prompt]) -> Runnable:
prompt_runnable: Runnable
if prompt is None:
prompt_runnable = RunnableCallable(
lambda state: state["messages"], name=PROMPT_RUNNABLE_NAME
lambda state: _get_state_value(state, "messages"), name=PROMPT_RUNNABLE_NAME
)
elif isinstance(prompt, str):
_system_message: BaseMessage = SystemMessage(content=prompt)
prompt_runnable = RunnableCallable(
lambda state: [_system_message] + state["messages"],
lambda state: [_system_message] + _get_state_value(state, "messages"),
name=PROMPT_RUNNABLE_NAME,
)
elif isinstance(prompt, SystemMessage):
prompt_runnable = RunnableCallable(
lambda state: [prompt] + state["messages"],
lambda state: [prompt] + _get_state_value(state, "messages"),
name=PROMPT_RUNNABLE_NAME,
)
elif inspect.iscoroutinefunction(prompt):
@@ -119,43 +135,20 @@ def _get_prompt_runnable(prompt: Optional[Prompt]) -> Runnable:
return prompt_runnable
def _convert_messages_modifier_to_prompt(
messages_modifier: MessagesModifier,
) -> Prompt:
prompt: Prompt
if isinstance(messages_modifier, (str, SystemMessage)):
return messages_modifier
elif callable(messages_modifier):
def prompt(state: AgentState) -> Sequence[BaseMessage]:
return messages_modifier(state["messages"])
return prompt
elif isinstance(messages_modifier, Runnable):
prompt = (lambda state: state["messages"]) | messages_modifier
return prompt
raise ValueError(
f"Got unexpected type for `messages_modifier`: {type(messages_modifier)}"
)
def _convert_modifier_to_prompt(func: F) -> F:
"""Decorator that converts state_modifier/messages_modifier kwargs to prompt kwarg."""
"""Decorator that converts state_modifier kwarg to prompt kwarg."""
@functools.wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
prompt = kwargs.get("prompt")
state_modifier = kwargs.pop("state_modifier", None)
messages_modifier = kwargs.pop("messages_modifier", None)
if sum(p is not None for p in (prompt, state_modifier, messages_modifier)) > 1:
if sum(p is not None for p in (prompt, state_modifier)) > 1:
raise ValueError(
"Expected only one of prompt, state_modifier, or messages_modifier, got multiple values"
"Expected only one of (prompt, state_modifier), got multiple values"
)
if state_modifier is not None:
prompt = state_modifier
elif messages_modifier is not None:
prompt = _convert_messages_modifier_to_prompt(messages_modifier)
kwargs["prompt"] = prompt
return func(*args, **kwargs)
@@ -164,6 +157,16 @@ def _convert_modifier_to_prompt(func: F) -> F:
def _should_bind_tools(model: LanguageModelLike, tools: Sequence[BaseTool]) -> bool:
if isinstance(model, RunnableSequence):
model = next(
(
step
for step in model.steps
if isinstance(step, (RunnableBinding, BaseChatModel))
),
model,
)
if not isinstance(model, RunnableBinding):
return True
@@ -199,6 +202,16 @@ def _should_bind_tools(model: LanguageModelLike, tools: Sequence[BaseTool]) -> b
def _get_model(model: LanguageModelLike) -> BaseChatModel:
"""Get the underlying model from a RunnableBinding or return the model itself."""
if isinstance(model, RunnableSequence):
model = next(
(
step
for step in model.steps
if isinstance(step, (RunnableBinding, BaseChatModel))
),
model,
)
if isinstance(model, RunnableBinding):
model = model.bound
@@ -244,7 +257,7 @@ def _validate_chat_history(
@_convert_modifier_to_prompt
def create_react_agent(
model: Union[str, LanguageModelLike],
tools: Union[ToolExecutor, Sequence[BaseTool], ToolNode],
tools: Union[Sequence[BaseTool], ToolNode],
*,
prompt: Optional[Prompt] = None,
response_format: Optional[
@@ -264,7 +277,7 @@ def create_react_agent(
Args:
model: The `LangChain` chat model that supports tool calling.
tools: A list of tools, a ToolExecutor, or a ToolNode instance.
tools: A list of tools or a ToolNode instance.
If an empty list is provided, the agent will consist of a single LLM node without tool calling.
prompt: An optional prompt for the LLM. Can take a few different forms:
@@ -273,8 +286,6 @@ def create_react_agent(
- Callable: This function should take in full graph state and the output is then passed to the language model.
- Runnable: This runnable should take in full graph state and the output is then passed to the language model.
!!! Note
Prior to `v0.2.68`, the prompt was set using `state_modifier` / `messages_modifier` parameters.
response_format: An optional schema for the final agent output.
If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key.
@@ -295,7 +306,7 @@ def create_react_agent(
The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished.
This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/).
state_schema: An optional state schema that defines graph state.
Must have `messages` and `is_last_step` keys.
Must have `messages` and `remaining_steps` keys.
Defaults to `AgentState` that defines those two keys.
config_schema: An optional schema for configuration.
Use this to expose configurable parameters via agent.config_specs.
@@ -607,7 +618,8 @@ def create_react_agent(
if response_format is not None:
required_keys.add("structured_response")
if missing_keys := required_keys - set(state_schema.__annotations__):
schema_keys = set(get_type_hints(state_schema))
if missing_keys := required_keys - set(schema_keys):
raise ValueError(f"Missing required key(s) {missing_keys} in state_schema")
if state_schema is None:
@@ -617,10 +629,7 @@ def create_react_agent(
else AgentState
)
if isinstance(tools, ToolExecutor):
tool_classes: Sequence[BaseTool] = tools.tools
tool_node = ToolNode(tool_classes)
elif isinstance(tools, ToolNode):
if isinstance(tools, ToolNode):
tool_classes = list(tools.tools_by_name.values())
tool_node = tools
else:
@@ -651,35 +660,34 @@ def create_react_agent(
# our graph needs to check if these were called
should_return_direct = {t.name for t in tool_classes if t.return_direct}
# Define the function that calls the model
def call_model(state: AgentState, config: RunnableConfig) -> AgentState:
_validate_chat_history(state["messages"])
response = cast(AIMessage, model_runnable.invoke(state, config))
# add agent name to the AIMessage
response.name = name
def _are_more_steps_needed(state: StateSchema, response: BaseMessage) -> bool:
has_tool_calls = isinstance(response, AIMessage) and response.tool_calls
all_tools_return_direct = (
all(call["name"] in should_return_direct for call in response.tool_calls)
if isinstance(response, AIMessage)
else False
)
if (
(
"remaining_steps" not in state
and state.get("is_last_step", False)
and has_tool_calls
)
remaining_steps = _get_state_value(state, "remaining_steps", None)
is_last_step = _get_state_value(state, "is_last_step", False)
return (
(remaining_steps is None and is_last_step and has_tool_calls)
or (
"remaining_steps" in state
and state["remaining_steps"] < 1
remaining_steps is not None
and remaining_steps < 1
and all_tools_return_direct
)
or (
"remaining_steps" in state
and state["remaining_steps"] < 2
and has_tool_calls
)
):
or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)
)
# Define the function that calls the model
def call_model(state: StateSchema, config: RunnableConfig) -> StateSchema:
messages = _get_state_value(state, "messages")
_validate_chat_history(messages)
response = cast(AIMessage, model_runnable.invoke(state, config))
# add agent name to the AIMessage
response.name = name
if _are_more_steps_needed(state, response):
return {
"messages": [
AIMessage(
@@ -691,34 +699,13 @@ def create_react_agent(
# We return a list, because this will get added to the existing list
return {"messages": [response]}
async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
_validate_chat_history(state["messages"])
async def acall_model(state: StateSchema, config: RunnableConfig) -> StateSchema:
messages = _get_state_value(state, "messages")
_validate_chat_history(messages)
response = cast(AIMessage, await model_runnable.ainvoke(state, config))
# add agent name to the AIMessage
response.name = name
has_tool_calls = isinstance(response, AIMessage) and response.tool_calls
all_tools_return_direct = (
all(call["name"] in should_return_direct for call in response.tool_calls)
if isinstance(response, AIMessage)
else False
)
if (
(
"remaining_steps" not in state
and state.get("is_last_step", False)
and has_tool_calls
)
or (
"remaining_steps" in state
and state["remaining_steps"] < 1
and all_tools_return_direct
)
or (
"remaining_steps" in state
and state["remaining_steps"] < 2
and has_tool_calls
)
):
if _are_more_steps_needed(state, response):
return {
"messages": [
AIMessage(
@@ -731,11 +718,11 @@ def create_react_agent(
return {"messages": [response]}
def generate_structured_response(
state: AgentState, config: RunnableConfig
) -> AgentState:
state: StateSchema, config: RunnableConfig
) -> StateSchema:
# NOTE: we exclude the last message because there is enough information
# for the LLM to generate the structured response
messages = state["messages"][:-1]
messages = _get_state_value(state, "messages")[:-1]
structured_response_schema = response_format
if isinstance(response_format, tuple):
system_prompt, structured_response_schema = response_format
@@ -748,11 +735,11 @@ def create_react_agent(
return {"structured_response": response}
async def agenerate_structured_response(
state: AgentState, config: RunnableConfig
) -> AgentState:
state: StateSchema, config: RunnableConfig
) -> StateSchema:
# NOTE: we exclude the last message because there is enough information
# for the LLM to generate the structured response
messages = state["messages"][:-1]
messages = _get_state_value(state, "messages")[:-1]
structured_response_schema = response_format
if isinstance(response_format, tuple):
system_prompt, structured_response_schema = response_format
@@ -788,8 +775,8 @@ def create_react_agent(
)
# Define the function that determines whether to continue or not
def should_continue(state: AgentState) -> Union[str, list]:
messages = state["messages"]
def should_continue(state: StateSchema) -> Union[str, list]:
messages = _get_state_value(state, "messages")
last_message = messages[-1]
# If there is no function call, then we finish
if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
@@ -839,8 +826,8 @@ def create_react_agent(
path_map=should_continue_destinations,
)
def route_tool_responses(state: AgentState) -> Literal["agent", "__end__"]:
for m in reversed(state["messages"]):
def route_tool_responses(state: StateSchema) -> Literal["agent", "__end__"]:
for m in reversed(_get_state_value(state, "messages")):
if not isinstance(m, ToolMessage):
break
if m.name in should_return_direct:
@@ -872,4 +859,7 @@ __all__ = [
"create_react_agent",
"create_tool_calling_executor",
"AgentState",
"AgentStatePydantic",
"AgentStateWithStructuredResponse",
"AgentStateWithStructuredResponsePydantic",
]
libs/langgraph/langgraph/prebuilt/interrupt.py → libs/prebuilt/langgraph/prebuilt/interrupt.py
View File
libs/prebuilt/langgraph/prebuilt/py.typed
View File
libs/langgraph/langgraph/prebuilt/tool_node.py → libs/prebuilt/langgraph/prebuilt/tool_node.py
View File
libs/langgraph/langgraph/prebuilt/tool_validator.py → libs/prebuilt/langgraph/prebuilt/tool_validator.py
+12 -7
View File
@@ -29,6 +29,7 @@ from langchain_core.runnables import (
)
from langchain_core.runnables.config import get_executor_for_config
from langchain_core.tools import BaseTool, create_schema_from_function
from langchain_core.utils.pydantic import is_basemodel_subclass
from pydantic import BaseModel, ValidationError
from pydantic.v1 import BaseModel as BaseModelV1
from pydantic.v1 import ValidationError as ValidationErrorV1
@@ -78,7 +79,7 @@ class ValidationNode(RunnableCallable):
>>> from typing_extensions import TypedDict
...
>>> from langchain_anthropic import ChatAnthropic
>>> from pydantic import BaseModel, validator
>>> from pydantic import BaseModel, field_validator
...
>>> from langgraph.graph import END, START, StateGraph
>>> from langgraph.prebuilt import ValidationNode
@@ -88,18 +89,15 @@ class ValidationNode(RunnableCallable):
>>> class SelectNumber(BaseModel):
... a: int
...
... @validator("a")
... @field_validator("a")
... def a_must_be_meaningful(cls, v):
... if v != 37:
... raise ValueError("Only 37 is allowed")
... return v
...
...
>>> class State(TypedDict):
... messages: Annotated[list, add_messages]
...
>>> builder = StateGraph(State)
>>> llm = ChatAnthropic(model="claude-3-haiku-20240307").bind_tools([SelectNumber])
>>> builder = StateGraph(Annotated[list, add_messages])
>>> llm = ChatAnthropic(model="claude-3-5-haiku-latest").bind_tools([SelectNumber])
>>> builder.add_node("model", llm)
>>> builder.add_node("validation", ValidationNode([SelectNumber]))
>>> builder.add_edge(START, "model")
@@ -177,6 +175,13 @@ class ValidationNode(RunnableCallable):
raise ValueError(
f"Tool {schema.name} does not have an args_schema defined."
)
elif not isinstance(
schema.args_schema, type
) or not is_basemodel_subclass(schema.args_schema):
raise ValueError(
"Validation node only works with tools that have a pydantic BaseModel args_schema. "
f"Got {schema.name} with args_schema: {schema.args_schema}."
)
self.schemas_by_name[schema.name] = schema.args_schema
elif isinstance(schema, type) and issubclass(
schema, (BaseModel, BaseModelV1)
libs/prebuilt/poetry.lock
Generated
+1476
View File
File diff suppressed because it is too large Load Diff
libs/prebuilt/pyproject.toml
+62
View File
@@ -0,0 +1,62 @@
[tool.poetry]
name = "langgraph-prebuilt"
version = "0.1.2"
description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
authors = []
license = "MIT"
readme = "README.md"
repository = "https://www.github.com/langchain-ai/langgraph"
packages = [{ include = "langgraph" }]
[tool.poetry.dependencies]
python = "^3.9.0,<4.0"
langgraph-checkpoint = "^2.0.10"
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
[tool.poetry.group.dev.dependencies]
ruff = "^0.6.2"
codespell = "^2.2.0"
pytest = "^7.2.1"
pytest-asyncio = "^0.21.1"
pytest-mock = "^3.11.1"
pytest-watcher = "^0.4.1"
mypy = "^1.10.0"
langgraph = {path = "../langgraph", develop = true}
langgraph-checkpoint = {path = "../checkpoint", develop = true}
langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}
[tool.pytest.ini_options]
# --strict-markers will raise errors on unknown marks.
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
#
# https://docs.pytest.org/en/7.1.x/reference/reference.html
# --strict-config any warnings encountered while parsing the `pytest`
# section of the configuration file raise errors.
addopts = "--strict-markers --strict-config --durations=5 -vv"
asyncio_mode = "auto"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.ruff]
lint.select = [ "E", "F", "I", "TID251" ]
lint.ignore = [ "E501" ]
[tool.pytest-watcher]
now = true
delay = 0.1
runner_args = ["--ff", "-v", "--tb", "short"]
patterns = ["*.py"]
[tool.mypy]
# https://mypy.readthedocs.io/en/stable/config_file.html
disallow_untyped_defs = "True"
explicit_package_bases = "True"
warn_no_return = "False"
warn_unused_ignores = "True"
warn_redundant_casts = "True"
allow_redefinition = "True"
disable_error_code = "typeddict-item, return-value"
libs/prebuilt/tests/__init__.py
View File
libs/prebuilt/tests/any_str.py
+86
View File
@@ -0,0 +1,86 @@
import re
from typing import Any, Sequence, Union
from typing_extensions import Self
class FloatBetween(float):
def __new__(cls, min_value: float, max_value: float) -> Self:
return super().__new__(cls, min_value)
def __init__(self, min_value: float, max_value: float) -> None:
super().__init__()
self.min_value = min_value
self.max_value = max_value
def __eq__(self, other: object) -> bool:
return (
isinstance(other, float)
and other >= self.min_value
and other <= self.max_value
)
def __hash__(self) -> int:
return hash((float(self), self.min_value, self.max_value))
class AnyStr(str):
def __init__(self, prefix: Union[str, re.Pattern] = "") -> None:
super().__init__()
self.prefix = prefix
def __eq__(self, other: object) -> bool:
return isinstance(other, str) and (
other.startswith(self.prefix)
if isinstance(self.prefix, str)
else self.prefix.match(other)
)
def __hash__(self) -> int:
return hash((str(self), self.prefix))
class AnyDict(dict):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
def __eq__(self, other: object) -> bool:
if not isinstance(other, dict) or len(self) != len(other):
return False
for k, v in self.items():
if kk := next((kk for kk in other if kk == k), None):
if v == other[kk]:
continue
else:
return False
else:
return True
class AnyVersion:
def __init__(self) -> None:
super().__init__()
def __eq__(self, other: object) -> bool:
return isinstance(other, (str, int, float))
def __hash__(self) -> int:
return hash(str(self))
class UnsortedSequence:
def __init__(self, *values: Any) -> None:
self.seq = values
def __eq__(self, value: object) -> bool:
return (
isinstance(value, Sequence)
and len(self.seq) == len(value)
and all(a in value for a in self.seq)
)
def __hash__(self) -> int:
return hash(frozenset(self.seq))
def __repr__(self) -> str:
return repr(self.seq)
libs/prebuilt/tests/compose-postgres.yml
+17
View File
@@ -0,0 +1,17 @@
name: langgraph-tests
services:
postgres-test:
image: postgres:16
ports:
- "5442:5432"
environment:
POSTGRES_DB: postgres
POSTGRES_USER: postgres
POSTGRES_PASSWORD: postgres
healthcheck:
test: pg_isready -U postgres
start_period: 10s
timeout: 1s
retries: 5
interval: 60s
start_interval: 1s
libs/prebuilt/tests/conftest.py
+448
View File
@@ -0,0 +1,448 @@
import sys
from contextlib import asynccontextmanager
from typing import AsyncIterator, Optional
from uuid import UUID, uuid4
import pytest
from langchain_core import __version__ as core_version
from packaging import version
from psycopg import AsyncConnection, Connection
from psycopg_pool import AsyncConnectionPool, ConnectionPool
from pytest_mock import MockerFixture
from langgraph.checkpoint.base import BaseCheckpointSaver
from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
from langgraph.checkpoint.postgres.aio import (
AsyncPostgresSaver,
AsyncShallowPostgresSaver,
)
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
from langgraph.store.base import BaseStore
from langgraph.store.memory import InMemoryStore
from langgraph.store.postgres import AsyncPostgresStore, PostgresStore
pytest.register_assert_rewrite("tests.memory_assert")
DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5442/"
# TODO: fix this once core is released
IS_LANGCHAIN_CORE_030_OR_GREATER = version.parse(core_version) >= version.parse(
"0.3.0.dev0"
)
@pytest.fixture
def anyio_backend():
return "asyncio"
@pytest.fixture()
def deterministic_uuids(mocker: MockerFixture) -> MockerFixture:
side_effect = (
UUID(f"00000000-0000-4000-8000-{i:012}", version=4) for i in range(10000)
)
return mocker.patch("uuid.uuid4", side_effect=side_effect)
# checkpointer fixtures
@pytest.fixture(scope="function")
def checkpointer_memory():
from tests.memory_assert import MemorySaverAssertImmutable
yield MemorySaverAssertImmutable()
@pytest.fixture(scope="function")
def checkpointer_sqlite():
with SqliteSaver.from_conn_string(":memory:") as checkpointer:
yield checkpointer
@asynccontextmanager
async def _checkpointer_sqlite_aio():
async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
yield checkpointer
@pytest.fixture(scope="function")
def checkpointer_postgres():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
with PostgresSaver.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as checkpointer:
checkpointer.setup()
yield checkpointer
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def checkpointer_postgres_shallow():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
with ShallowPostgresSaver.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as checkpointer:
checkpointer.setup()
yield checkpointer
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def checkpointer_postgres_pipe():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
with PostgresSaver.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as checkpointer:
checkpointer.setup()
# setup can't run inside pipeline because of implicit transaction
with checkpointer.conn.pipeline() as pipe:
checkpointer.pipe = pipe
yield checkpointer
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def checkpointer_postgres_pool():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
with ConnectionPool(
DEFAULT_POSTGRES_URI + database, max_size=10, kwargs={"autocommit": True}
) as pool:
checkpointer = PostgresSaver(pool)
checkpointer.setup()
yield checkpointer
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def _checkpointer_postgres_aio():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
# create unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
async with AsyncPostgresSaver.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as checkpointer:
await checkpointer.setup()
yield checkpointer
finally:
# drop unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def _checkpointer_postgres_aio_shallow():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
# create unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
async with AsyncShallowPostgresSaver.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as checkpointer:
await checkpointer.setup()
yield checkpointer
finally:
# drop unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def _checkpointer_postgres_aio_pipe():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
# create unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
async with AsyncPostgresSaver.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as checkpointer:
await checkpointer.setup()
# setup can't run inside pipeline because of implicit transaction
async with checkpointer.conn.pipeline() as pipe:
checkpointer.pipe = pipe
yield checkpointer
finally:
# drop unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def _checkpointer_postgres_aio_pool():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
# create unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
# yield checkpointer
async with AsyncConnectionPool(
DEFAULT_POSTGRES_URI + database, max_size=10, kwargs={"autocommit": True}
) as pool:
checkpointer = AsyncPostgresSaver(pool)
await checkpointer.setup()
yield checkpointer
finally:
# drop unique db
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def awith_checkpointer(
checkpointer_name: Optional[str],
) -> AsyncIterator[BaseCheckpointSaver]:
if checkpointer_name is None:
yield None
elif checkpointer_name == "memory":
from tests.memory_assert import MemorySaverAssertImmutable
yield MemorySaverAssertImmutable()
elif checkpointer_name == "sqlite_aio":
async with _checkpointer_sqlite_aio() as checkpointer:
yield checkpointer
elif checkpointer_name == "postgres_aio":
async with _checkpointer_postgres_aio() as checkpointer:
yield checkpointer
elif checkpointer_name == "postgres_aio_shallow":
async with _checkpointer_postgres_aio_shallow() as checkpointer:
yield checkpointer
elif checkpointer_name == "postgres_aio_pipe":
async with _checkpointer_postgres_aio_pipe() as checkpointer:
yield checkpointer
elif checkpointer_name == "postgres_aio_pool":
async with _checkpointer_postgres_aio_pool() as checkpointer:
yield checkpointer
else:
raise NotImplementedError(f"Unknown checkpointer: {checkpointer_name}")
@asynccontextmanager
async def _store_postgres_aio():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
async with AsyncPostgresStore.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as store:
await store.setup()
yield store
finally:
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def _store_postgres_aio_pipe():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
async with AsyncPostgresStore.from_conn_string(
DEFAULT_POSTGRES_URI + database
) as store:
await store.setup() # Run in its own transaction
async with AsyncPostgresStore.from_conn_string(
DEFAULT_POSTGRES_URI + database, pipeline=True
) as store:
yield store
finally:
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@asynccontextmanager
async def _store_postgres_aio_pool():
if sys.version_info < (3, 10):
pytest.skip("Async Postgres tests require Python 3.10+")
database = f"test_{uuid4().hex[:16]}"
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"CREATE DATABASE {database}")
try:
async with AsyncPostgresStore.from_conn_string(
DEFAULT_POSTGRES_URI + database,
pool_config={"max_size": 10},
) as store:
await store.setup()
yield store
finally:
async with await AsyncConnection.connect(
DEFAULT_POSTGRES_URI, autocommit=True
) as conn:
await conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def store_postgres():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield store
with PostgresStore.from_conn_string(DEFAULT_POSTGRES_URI + database) as store:
store.setup()
yield store
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def store_postgres_pipe():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield store
with PostgresStore.from_conn_string(DEFAULT_POSTGRES_URI + database) as store:
store.setup() # Run in its own transaction
with PostgresStore.from_conn_string(
DEFAULT_POSTGRES_URI + database, pipeline=True
) as store:
yield store
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def store_postgres_pool():
database = f"test_{uuid4().hex[:16]}"
# create unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"CREATE DATABASE {database}")
try:
# yield store
with PostgresStore.from_conn_string(
DEFAULT_POSTGRES_URI + database, pool_config={"max_size": 10}
) as store:
store.setup()
yield store
finally:
# drop unique db
with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
conn.execute(f"DROP DATABASE {database}")
@pytest.fixture(scope="function")
def store_in_memory():
yield InMemoryStore()
@asynccontextmanager
async def awith_store(store_name: Optional[str]) -> AsyncIterator[BaseStore]:
if store_name is None:
yield None
elif store_name == "in_memory":
yield InMemoryStore()
elif store_name == "postgres_aio":
async with _store_postgres_aio() as store:
yield store
elif store_name == "postgres_aio_pipe":
async with _store_postgres_aio_pipe() as store:
yield store
elif store_name == "postgres_aio_pool":
async with _store_postgres_aio_pool() as store:
yield store
else:
raise NotImplementedError(f"Unknown store {store_name}")
ALL_CHECKPOINTERS_SYNC = [
"memory",
"sqlite",
"postgres",
"postgres_pipe",
"postgres_pool",
"postgres_shallow",
]
ALL_CHECKPOINTERS_ASYNC = [
"memory",
"sqlite_aio",
"postgres_aio",
"postgres_aio_pipe",
"postgres_aio_pool",
"postgres_aio_shallow",
]
libs/prebuilt/tests/memory_assert.py
+134
View File
@@ -0,0 +1,134 @@
import asyncio
import os
import tempfile
from collections import defaultdict
from functools import partial
from typing import Any, Optional
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
SerializerProtocol,
copy_checkpoint,
)
from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
class NoopSerializer(SerializerProtocol):
def loads_typed(self, data: tuple[str, bytes]) -> Any:
return data[1]
def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
return "type", obj
class MemorySaverAssertImmutable(InMemorySaver):
storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]
def __init__(
self,
*,
serde: Optional[SerializerProtocol] = None,
put_sleep: Optional[float] = None,
) -> None:
_, filename = tempfile.mkstemp()
super().__init__(
serde=serde, factory=partial(PersistentDict, filename=filename)
)
self.storage_for_copies = defaultdict(lambda: defaultdict(dict))
self.put_sleep = put_sleep
self.stack.callback(os.remove, filename)
def put(
self,
config: dict,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> None:
if self.put_sleep:
import time
time.sleep(self.put_sleep)
# assert checkpoint hasn't been modified since last written
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
if saved := super().get(config):
assert (
self.serde.loads_typed(
self.storage_for_copies[thread_id][checkpoint_ns][saved["id"]]
)
== saved
)
self.storage_for_copies[thread_id][checkpoint_ns][checkpoint["id"]] = (
self.serde.dumps_typed(copy_checkpoint(checkpoint))
)
# call super to write checkpoint
return super().put(config, checkpoint, metadata, new_versions)
class MemorySaverAssertCheckpointMetadata(InMemorySaver):
"""This custom checkpointer is for verifying that a run's configurable
fields are merged with the previous checkpoint config for each step in
the run. This is the desired behavior. Because the checkpointer's (a)put()
method is called for each step, the implementation of this checkpointer
should produce a side effect that can be asserted.
"""
def put(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> None:
"""The implementation of put() merges config["configurable"] (a run's
configurable fields) with the metadata field. The state of the
checkpoint metadata can be asserted to confirm that the run's
configurable fields were merged with the previous checkpoint config.
"""
configurable = config["configurable"].copy()
# remove checkpoint_id to make testing simpler
checkpoint_id = configurable.pop("checkpoint_id", None)
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
self.storage[thread_id][checkpoint_ns].update(
{
checkpoint["id"]: (
self.serde.dumps_typed(checkpoint),
# merge configurable fields and metadata
self.serde.dumps_typed({**configurable, **metadata}),
checkpoint_id,
)
}
)
return {
"configurable": {
"thread_id": config["configurable"]["thread_id"],
"checkpoint_id": checkpoint["id"],
}
}
async def aput(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
return await asyncio.get_running_loop().run_in_executor(
None, self.put, config, checkpoint, metadata, new_versions
)
class MemorySaverNoPending(InMemorySaver):
def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
result = super().get_tuple(config)
if result:
return CheckpointTuple(result.config, result.checkpoint, result.metadata)
return result
libs/prebuilt/tests/messages.py
+50
View File
@@ -0,0 +1,50 @@
"""Redefined messages as a work-around for pydantic issue with AnyStr.
The code below creates version of pydantic models
that will work in unit tests with AnyStr as id field
Please note that the `id` field is assigned AFTER the model is created
to workaround an issue with pydantic ignoring the __eq__ method on
subclassed strings.
"""
from typing import Any
from langchain_core.documents import Document
from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
from tests.any_str import AnyStr
def _AnyIdDocument(**kwargs: Any) -> Document:
"""Create a document with an id field."""
message = Document(**kwargs)
message.id = AnyStr()
return message
def _AnyIdAIMessage(**kwargs: Any) -> AIMessage:
"""Create ai message with an any id field."""
message = AIMessage(**kwargs)
message.id = AnyStr()
return message
def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk:
"""Create ai message with an any id field."""
message = AIMessageChunk(**kwargs)
message.id = AnyStr()
return message
def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage:
"""Create a human message with an any id field."""
message = HumanMessage(**kwargs)
message.id = AnyStr()
return message
def _AnyIdToolMessage(**kwargs: Any) -> ToolMessage:
"""Create a tool message with an any id field."""
message = ToolMessage(**kwargs)
message.id = AnyStr()
return message
libs/prebuilt/tests/model.py
+98
View File
@@ -0,0 +1,98 @@
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Type,
Union,
)
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import BaseChatModel, LanguageModelInput
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolCall,
)
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.runnables import Runnable, RunnableLambda
from langchain_core.tools import BaseTool
from pydantic import BaseModel
from langgraph.prebuilt.chat_agent_executor import StructuredResponse
class FakeToolCallingModel(BaseChatModel):
tool_calls: Optional[list[list[ToolCall]]] = None
structured_response: Optional[StructuredResponse] = None
index: int = 0
tool_style: Literal["openai", "anthropic"] = "openai"
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Top Level call"""
messages_string = "-".join([m.content for m in messages])
tool_calls = (
self.tool_calls[self.index % len(self.tool_calls)]
if self.tool_calls
else []
)
message = AIMessage(
content=messages_string, id=str(self.index), tool_calls=tool_calls.copy()
)
self.index += 1
return ChatResult(generations=[ChatGeneration(message=message)])
@property
def _llm_type(self) -> str:
return "fake-tool-call-model"
def with_structured_output(
self, schema: Type[BaseModel]
) -> Runnable[LanguageModelInput, StructuredResponse]:
if self.structured_response is None:
raise ValueError("Structured response is not set")
return RunnableLambda(lambda x: self.structured_response)
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
if len(tools) == 0:
raise ValueError("Must provide at least one tool")
tool_dicts = []
for tool in tools:
if not isinstance(tool, BaseTool):
raise TypeError(
"Only BaseTool is supported by FakeToolCallingModel.bind_tools"
)
# NOTE: this is a simplified tool spec for testing purposes only
if self.tool_style == "openai":
tool_dicts.append(
{
"type": "function",
"function": {
"name": tool.name,
},
}
)
elif self.tool_style == "anthropic":
tool_dicts.append(
{
"name": tool.name,
}
)
return self.bind(tools=tool_dicts)
libs/langgraph/tests/test_prebuilt.py → libs/prebuilt/tests/test_react_agent.py
+248 -1297
View File
File diff suppressed because it is too large Load Diff
libs/prebuilt/tests/test_tool_node.py
+1053
View File
File diff suppressed because it is too large Load Diff
libs/prebuilt/tests/test_validation_node.py
+81
View File
@@ -0,0 +1,81 @@
from typing import Any
import pytest
from langchain_core.messages import AIMessage
from langchain_core.tools import tool as dec_tool
from pydantic import BaseModel
from pydantic.v1 import BaseModel as BaseModelV1
from langgraph.prebuilt import ValidationNode
pytestmark = pytest.mark.anyio
def my_function(some_val: int, some_other_val: str) -> str:
return f"{some_val} - {some_other_val}"
class MyModel(BaseModel):
some_val: int
some_other_val: str
class MyModelV1(BaseModelV1):
some_val: int
some_other_val: str
@dec_tool
def my_tool(some_val: int, some_other_val: str) -> str:
"""Cool."""
return f"{some_val} - {some_other_val}"
@pytest.mark.parametrize(
"tool_schema",
[
my_function,
MyModel,
MyModelV1,
my_tool,
],
)
@pytest.mark.parametrize("use_message_key", [True, False])
async def test_validation_node(tool_schema: Any, use_message_key: bool):
validation_node = ValidationNode([tool_schema])
tool_name = getattr(tool_schema, "name", getattr(tool_schema, "__name__", None))
inputs = [
AIMessage(
"hi?",
tool_calls=[
{
"name": tool_name,
"args": {"some_val": 1, "some_other_val": "foo"},
"id": "some 0",
},
{
"name": tool_name,
# Wrong type for some_val
"args": {"some_val": "bar", "some_other_val": "foo"},
"id": "some 1",
},
],
),
]
if use_message_key:
inputs = {"messages": inputs}
result = await validation_node.ainvoke(inputs)
if use_message_key:
result = result["messages"]
def check_results(messages: list):
assert len(messages) == 2
assert all(m.type == "tool" for m in messages)
assert not messages[0].additional_kwargs.get("is_error")
assert messages[1].additional_kwargs.get("is_error")
check_results(result)
result_sync = validation_node.invoke(inputs)
if use_message_key:
result_sync = result_sync["messages"]
check_results(result_sync)
libs/sdk-js/.gitignore
+8
View File
@@ -10,6 +10,14 @@ react.cjs
react.js
react.d.ts
react.d.cts
react-ui.cjs
react-ui.js
react-ui.d.ts
react-ui.d.cts
react-ui/server.cjs
react-ui/server.js
react-ui/server.d.ts
react-ui/server.d.cts
node_modules
dist
.yarn
libs/sdk-js/langchain.config.js
+7 -1
View File
@@ -11,7 +11,13 @@ function abs(relativePath) {
export const config = {
internals: [/react/],
entrypoints: { index: "index", client: "client", react: "react/index" },
entrypoints: {
index: "index",
client: "client",
react: "react/index",
"react-ui": "react-ui/index",
"react-ui/server": "react-ui/server/index",
},
tsConfigPath: resolve("./tsconfig.json"),
cjsSource: "./dist-cjs",
cjsDestination: "./dist",

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