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Merge branch 'main' into wfh/update_doc

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William Fu-Hinthorn
2025-07-08 12:45:23 -07:00
parent c7badc06ae 0d8a8c5847
commit bc929cbf29
87 changed files with 4334 additions and 6514 deletions
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.github/workflows/uv_lock_ugprade.yml
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@@ -30,11 +30,11 @@ jobs:
run: make lock-upgrade
- name: Create Pull Request
uses: peter-evans/create-pull-request@v6
uses: peter-evans/create-pull-request@v7
with:
token: ${{ secrets.GITHUB_TOKEN }}
commit-message: "chore: upgrade dependencies with `uv lock --upgrade`"
title: "chore: upgrade dependencies with `uv lock --upgrade`"
commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
title: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
body: |
This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
CONTRIBUTING.md
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@@ -60,7 +60,7 @@ In LangGraph, these are often higher level guides that show off end-to-end use c
Some examples include:
- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
Here are some high-level tips on writing a good tutorial:
docs/_scripts/generate_llms_text.py
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@@ -14,10 +14,7 @@ from mkdocs.structure.pages import Page
from pydantic import BaseModel, Field
from yaml import SafeLoader
from _scripts.notebook_hooks import (
_on_page_markdown_with_config,
_apply_conditional_rendering,
)
from _scripts.notebook_hooks import _on_page_markdown_with_config
HERE = os.path.dirname(os.path.abspath(__file__))
# Get source directory (parent of HERE / docs)
docs/_scripts/notebook_hooks.py
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@@ -72,6 +72,7 @@ REDIRECT_MAP = {
"how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
"how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
"how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
"agents/tools.md": "how-tos/tool-calling.md",
# multi-agent how-tos
"how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs",
"how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system",
docs/docs/additional-resources/index.md
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@@ -0,0 +1,11 @@
# Additional resources
This section contains additional resources for LangGraph.
- [Community agents](../agents/prebuilt.md): A collection of prebuilt libraries that you can use in your LangGraph applications.
- [LangGraph Academy](https://academy.langchain.com/courses/intro-to-langgraph): A collection of courses that teach you how to use LangGraph.
- [Case studies](../adopters.md): A collection of case studies that show how LangGraph is used in production.
- [FAQ](../concepts/faq.md): A collection of frequently asked questions about LangGraph.
- [llms.txt](../llms-txt-overview.md): A list of documentation files in the `llms.txt` format that allow LLMs and agents to access our documentation.
- [LangChain Forum](https://forum.langchain.com/): A place to ask questions and get help from other LangGraph users.
- [Troubleshooting](../troubleshooting/errors/index.md.md): A collection of troubleshooting guides for common issues.
docs/docs/adopters.md
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@@ -8,24 +8,41 @@ This list of companies using LangGraph and their success stories is compiled fro
| [AirTop](https://www.airtop.ai/) | Software & Technology (GenAI Native) | Browser automation for AI agents | [Case study, 2024](https://blog.langchain.dev/customers-airtop/) |
| [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/) |
| [BlackRock](https://www.blackrock.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/oyqeCHFM5U4?feature=shared) |
| [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) |
| [Cisco CX](https://www.cisco.com/site/us/en/services/modern-data-center/index.html?CCID=cc005911&DTID=eivtotr001480&OID=srwsas032775) | Software & Technology | Customer support | [Interrupt Talk, 2025](https://youtu.be/gPhyPRtIMn0?feature=shared) |
| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Video story, 2025](https://www.youtube.com/watch?v=htcb-vGR_x0); [Case study, 2025](https://blog.langchain.com/cisco-outshift/); [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
| [Cisco TAC](https://www.cisco.com/c/en/us/support/index.html) | Software & Technology | Customer support | [Video story, 2025](https://youtu.be/EAj0HBDGqaE?feature=shared) |
| [City of Hope](https://www.cityofhope.org/) | Non-profit | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/9ABwtK2gIZU?feature=shared) |
| [C.H. Robinson](https://www.chrobinson.com/en-us/) | Logistics | Automation | [Case study, 2025](https://blog.langchain.dev/customers-chrobinson/) |
| [Definely](https://www.definely.com/) | Legal | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.com/customers-definely/) |
| [Docent Pro](https://docentpro.com/) | Travel | GenAI embedded product experiences | [Case study, 2025](https://blog.langchain.com/customers-docentpro/) |
| [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) |
| [Exa](https://exa.ai/) | Software & Technology (GenAI Native) | Search | [Case study, 2025](https://blog.langchain.com/exa/) |
| [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
| [Harmonic](https://harmonic.ai/) | Software & Technology | Search | [Case study, 2025](https://blog.langchain.com/customers-harmonic/) |
| [Inconvo](https://inconvo.ai/?ref=blog.langchain.dev) | Software & Technology | Code generation | [Case study, 2025](https://blog.langchain.dev/customers-inconvo/) |
| [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
| [J.P. Morgan](https://www.jpmorganchase.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/yMalr0jiOAc?feature=shared) |
| [Klarna](https://www.klarna.com/) | Fintech | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/customers-klarna/) |
| [Komodo Health](https://www.komodohealth.com/) | Healthcare | Copilot for domain-specific task | [Blog post](https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise/) |
| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Interrupt talk, 2025](https://youtu.be/NmblVxyBhi8?feature=shared); [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
| [Minimal](https://gominimal.ai/) | E-commerce | Customer support | [Case study, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
| [Modern Treasury](https://www.moderntreasury.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/AwAiffXqaCU?feature=shared) |
| [Monday](https://monday.com/) | Software & Technology | GenAI embedded product experiences | [Interrupt talk, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
| [Morningstar](https://www.morningstar.com/) | Financial Services | Research & summarization | [Video story, 2025](https://youtu.be/6LidoFXCJPs?feature=shared) |
| [OpenRecovery](https://www.openrecovery.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-openrecovery/) |
| [Pigment](https://www.pigment.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/5JVSO2KYOmE?feature=shared) |
| [Prosper](https://www.prosper.com/) | Fintech | Customer support | [Video story, 2025](https://youtu.be/9RFNOYtkwsc?feature=shared) |
| [Qodo](https://www.qodo.ai/) | Software & Technology (GenAI Native) | Code generation | [Blog post, 2025](https://www.qodo.ai/blog/why-we-chose-langgraph-to-build-our-coding-agent/) |
| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/gD1LIjCkuA8?feature=shared); [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
| [Replit](https://replit.com/) | Software & Technology | Code generation | [Blog post, 2024](https://blog.langchain.dev/customers-replit/); [Breakout agent story, 2024](https://www.langchain.com/breakoutagents/replit); [Fireside chat video, 2024](https://www.youtube.com/watch?v=ViykMqljjxU) |
| [Rexera](https://www.rexera.com/) | Real Estate (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-rexera/) |
| [Abu Dhabi Government](https://www.tamm.abudhabi/) | Government | Search | [Case study, 2025](https://blog.langchain.com/customers-abu-dhabi-government/) |
| [Tradestack](https://www.tradestack.uk/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-tradestack/) |
| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Interrupt talk, 2025](https://youtu.be/Bugs0dVcNI8?feature=shared); [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/pKk-LfhujwI?feature=shared); [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Video story, 2025](https://www.youtube.com/watch?v=vrjJ6NuyTWA); [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
| [Vodafone](https://www.vodafone.com/) | Telecommunications | Code generation; internal search | [Case study, 2025](https://blog.langchain.dev/customers-vodafone/) |
| [WebToon](https://www.webtoons.com/en/) | Media & Entertainment | Data extraction | [Case study, 2025](https://blog.langchain.com/customers-webtoon/) |
| [11x](https://www.11x.ai/) | Software & Technology (GenAI Native) | Research & outreach | [Interrupt talk, 2025](https://youtu.be/fegwPmaAPQk?feature=shared) |
docs/docs/agents/agents.md
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@@ -52,7 +52,7 @@ agent.invoke(
)
```
1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page.
1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](../how-tos/tool-calling.md) page.
2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
3. Provide a list of tools for the model to use.
4. Provide a system prompt (instructions) to the language model used by the agent.
docs/docs/agents/overview.md
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@@ -60,7 +60,7 @@ Use the following tool to visualize the graph generated by
and to view an outline of the corresponding code.
It allows you to explore the infrastructure of the agent as defined by the presence of:
* [`tools`](../agents/tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
* [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
* [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
* `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
* [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
docs/docs/agents/tools.md
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@@ -1,310 +0,0 @@
---
search:
boost: 2
tags:
- agent
hide:
- tags
---
# Tools
[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
## Define simple tools
You can pass a vanilla function to `create_react_agent` to use as a tool:
```python
from langgraph.prebuilt import create_react_agent
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b
create_react_agent(
model="anthropic:claude-3-7-sonnet",
tools=[multiply]
)
```
`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
## Customize tools
For more control over tool behavior, use the `@tool` decorator:
```python
# highlight-next-line
from langchain_core.tools import tool
# highlight-next-line
@tool("multiply_tool", parse_docstring=True)
def multiply(a: int, b: int) -> int:
"""Multiply two numbers.
Args:
a: First operand
b: Second operand
"""
return a * b
```
You can also define a custom input schema using Pydantic:
```python
from pydantic import BaseModel, Field
class MultiplyInputSchema(BaseModel):
"""Multiply two numbers"""
a: int = Field(description="First operand")
b: int = Field(description="Second operand")
# highlight-next-line
@tool("multiply_tool", args_schema=MultiplyInputSchema)
def multiply(a: int, b: int) -> int:
return a * b
```
For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
## Hide arguments from the model
Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
You can put these arguments in the `state` or `config` of the agent, and access
this information inside the tool:
```python
from langgraph.prebuilt import InjectedState
from langgraph.prebuilt.chat_agent_executor import AgentState
from langchain_core.runnables import RunnableConfig
def my_tool(
# This will be populated by an LLM
tool_arg: str,
# access information that's dynamically updated inside the agent
# highlight-next-line
state: Annotated[AgentState, InjectedState],
# access static data that is passed at agent invocation
# highlight-next-line
config: RunnableConfig,
) -> str:
"""My tool."""
do_something_with_state(state["messages"])
do_something_with_config(config)
...
```
## Disable parallel tool calling
Some model providers support executing multiple tools in parallel, but
allow users to disable this feature.
For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
```python
from langchain.chat_models import init_chat_model
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b
model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
tools = [add, multiply]
agent = create_react_agent(
# disable parallel tool calls
# highlight-next-line
model=model.bind_tools(tools, parallel_tool_calls=False),
tools=tools
)
agent.invoke(
{"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
)
```
## Return tool results directly
Use `return_direct=True` to return tool results immediately and stop the agent loop:
```python
from langchain_core.tools import tool
# highlight-next-line
@tool(return_direct=True)
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[add]
)
agent.invoke(
{"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
)
```
## Force tool use
To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
```python
from langchain_core.tools import tool
# highlight-next-line
@tool(return_direct=True)
def greet(user_name: str) -> int:
"""Greet user."""
return f"Hello {user_name}!"
tools = [greet]
agent = create_react_agent(
# highlight-next-line
model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
tools=tools
)
agent.invoke(
{"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
)
```
!!! Warning "Avoid infinite loops"
Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
- Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
- Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
## Handle tool errors
By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
=== "Enable error handling (default)"
```python
from langgraph.prebuilt import create_react_agent
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
if a == 42:
raise ValueError("The ultimate error")
return a * b
# Run with error handling (default)
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[multiply]
)
agent.invoke(
{"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
)
```
=== "Disable error handling"
```python
from langgraph.prebuilt import create_react_agent, ToolNode
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
if a == 42:
raise ValueError("The ultimate error")
return a * b
# highlight-next-line
tool_node = ToolNode(
[multiply],
# highlight-next-line
handle_tool_errors=False # (1)!
)
agent_no_error_handling = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=tool_node
)
agent_no_error_handling.invoke(
{"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
)
```
1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
=== "Custom error handling"
```python
from langgraph.prebuilt import create_react_agent, ToolNode
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
if a == 42:
raise ValueError("The ultimate error")
return a * b
# highlight-next-line
tool_node = ToolNode(
[multiply],
# highlight-next-line
handle_tool_errors=(
"Can't use 42 as a first operand, you must switch operands!" # (1)!
)
)
agent_custom_error_handling = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=tool_node
)
agent_custom_error_handling.invoke(
{"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
)
```
1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
## Working with memory
LangGraph allows access to short-term and long-term memory from tools. See [Memory](../how-tos/memory/add-memory.md) guide for more information on:
* how to [read](../how-tos/memory/add-memory.md#read-short-term) from and [write](../how-tos/memory/add-memory.md#write-short-term) to **short-term** memory
* how to [read](../how-tos/memory/add-memory.md#read-long-term) from and [write](../how-tos/memory/add-memory.md#write-long-term) to **long-term** memory
## Prebuilt tools
You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
```python
from langgraph.prebuilt import create_react_agent
agent = create_react_agent(
model="openai:gpt-4o-mini",
tools=[{"type": "web_search_preview"}]
)
response = agent.invoke(
{"messages": ["What was a positive news story from today?"]}
)
```
Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
Some commonly used tool categories include:
- **Search**: Bing, SerpAPI, Tavily
- **Code interpreters**: Python REPL, Node.js REPL
- **Databases**: SQL, MongoDB, Redis
- **Web data**: Web scraping and browsing
- **APIs**: OpenWeatherMap, NewsAPI, and others
These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
docs/docs/cloud/concepts/threads.md
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@@ -1,12 +0,0 @@
# Threads
A thread contains the accumulated state of a sequence of [runs](../../concepts/assistants.md#execution). When a run is executed, the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints are persisted and can be used to restore the state of a thread at a later time.
## Learn more
* For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
* The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.
docs/docs/cloud/quick_start.md
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@@ -154,8 +154,9 @@ You can now test the API:
```bash
curl -s --request POST \
--url <DEPLOYMENT_URL> \
--url <DEPLOYMENT_URL>/runs/stream \
--header 'Content-Type: application/json' \
--header "X-Api-Key: <LANGSMITH API KEY> \
--data "{
\"assistant_id\": \"agent\",
\"input\": {
docs/docs/cloud/reference/cli.md
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@@ -51,6 +51,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
| <span style="white-space: nowrap;">`node_version`</span> | Specify `node_version: 20` to use LangGraph.js. |
| <span style="white-space: nowrap;">`pip_config_file`</span> | Path to `pip` config file. |
| <span style="white-space: nowrap;">`pip_installer`</span> | _(Added in v0.3)_ Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version&nbsp;0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour. |
| <span style="white-space: nowrap;">`keep_pkg_tools`</span> | _(Added in v0.3.4)_ Optional. Control whether to retain Python packaging tools (`pip`, `setuptools`, `wheel`) in the final image. Accepted values: <ul><li><code>true</code> : Keep all three tools (skip uninstall).</li><li><code>false</code> / omitted : Uninstall all three tools (default behaviour).</li><li><code>list[str]</code> : Names of tools <strong>to retain</strong>. Each value must be one of "pip", "setuptools", "wheel".</li></ul>. By default, all three tools are uninstalled. |
| <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image. |
| <span style="white-space: nowrap;">`checkpointer`</span> | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
| <span style="white-space: nowrap;">`http`</span> | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_mcp`: Disable `/mcp` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_ui`: Disable `/ui` routes</li><li>`disable_webhooks`: Disable webhooks calls on run completion in all routes</li><li>`mount_prefix`: Prefix for mounted routes (e.g., "/my-deployment/api")</li></ul> |
docs/docs/concepts/langgraph_control_plane.md
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@@ -46,21 +46,40 @@ For simplicity, the control plane offers two deployment types with different res
| **Deployment Type** | **CPU/Memory** | **Scaling** | **Database** |
|---------------------|-----------------|---------------------|----------------------------------------------------------------------------------|
| Development | 1 CPU, 1 GB RAM | Up to 1 container | 10 GB disk, no backups |
| Production | 2 CPU, 2 GB RAM | Up to 10 containers | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
| Development | 1 CPU, 1 GB RAM | Up to 1 replica | 10 GB disk, no backups |
| Production | 2 CPU, 2 GB RAM | Up to 10 replicas | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
CPU and memory resources are per container.
CPU and memory resources are per replica.
!!! warning "Immutable Deployment Type"
Once a deployment is created, the deployment type cannot be changed.
!!! info "Resource Customization"
For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
!!! info "Self-Hosted Deployment"
Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized. Deployment types are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
For `Development` types deployments, database disk size can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
#### Production
Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized.
`Production` type deployments are suitable for "production" workloads. For example, select `Production` for customer-facing applications in the critical path.
Resources for `Production` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
#### Development
`Development` type deployments are suitable development and testing. For example, select `Development` for internal testing environments. `Development` type deployments are not suitable for "production" workloads.
!!! danger "Preemptible Compute Infrastructure"
`Development` type deployments (API server, queue server, and database) are provisioned on preemptible compute infrastructure. This means the compute infrastructure **may be terminated at any time without notice**. This may result in intermittent...
- Redis connection timeouts/errors
- Postgres connection timeouts/errors
- Failed or retrying background runs
This behavior is expected. Preemptible compute infrastructure **significantly reduces the cost to provision a `Development` type deployment**. By design, LangGraph Server is fault-tolerant. The implementation will automatically attempt to recover from Redis/Postgres connection errors and retry failed background runs.
`Production` type deployments are provisioned on durable compute infrastructure, not preemptible compute infrastructure.
Database disk size for `Development` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
### Database Provisioning
@@ -93,6 +112,8 @@ After a deployment is ready, the control plane monitors the deployment and recor
- Number of container restarts.
- Number of replicas (this will increase with [autoscaling](../concepts/langgraph_data_plane.md#autoscaling)).
- [Postgres](../concepts/langgraph_data_plane.md#postgres) CPU, memory usage, and disk usage.
- [LangGraph Server queue](../concepts/langgraph_server.md#persistence-and-task-queue) pending/active run count.
- [LangGraph Server API](../concepts/langgraph_server.md) success response count, error response count, and latency.
These metrics are displayed as charts in the Control Plane UI.
docs/docs/concepts/langgraph_data_plane.md
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@@ -50,6 +50,15 @@ Runs in a LangGraph Server may be retried for specific failures (currently only
This section describes various features of the data plane.
### Data Region
!!! info "Only for Cloud SaaS"
Data regions are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
Deployments can be created in 2 data regions: US and EU
The data region for a deployment is implied by the data region of the LangSmith organization where the deployment is created. Deployments and the underlying database for the deployments cannot be migrated between data regions.
### Autoscaling
[`Production` type](../concepts/langgraph_control_plane.md#deployment-types) deployments automatically scale up to 10 containers. Scaling is based on 3 metrics:
docs/docs/concepts/multi_agent.md
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@@ -26,7 +26,7 @@ The primary benefits of using multi-agent systems are:
There are several ways to connect agents in a multi-agent system:
- **Network**: each agent can communicate with [every other agent](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/). Any agent can decide which other agent to call next.
- **Supervisor**: each agent communicates with a single [supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) agent. Supervisor agent makes decisions on which agent should be called next.
- **Supervisor**: each agent communicates with a single [supervisor](../tutorials/multi_agent/agent_supervisor.md) agent. Supervisor agent makes decisions on which agent should be called next.
- **Supervisor (tool-calling)**: this is a special case of supervisor architecture. Individual agents can be represented as tools. In this case, a supervisor agent uses a tool-calling LLM to decide which of the agent tools to call, as well as the arguments to pass to those agents.
- **Hierarchical**: you can define a multi-agent system with [a supervisor of supervisors](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/). This is a generalization of the supervisor architecture and allows for more complex control flows.
- **Custom multi-agent workflow**: each agent communicates with only a subset of agents. Parts of the flow are deterministic, and only some agents can decide which other agents to call next.
@@ -211,7 +211,7 @@ builder.add_edge(START, "supervisor")
supervisor = builder.compile()
```
Check out this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) for an example of supervisor multi-agent architecture.
Check out this [tutorial](../tutorials/multi_agent/agent_supervisor.md) for an example of supervisor multi-agent architecture.
### Supervisor (tool-calling)
docs/docs/concepts/persistence.md
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@@ -5,7 +5,7 @@ search:
# Persistence
LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. Below, we'll discuss each of these concepts in more detail.
LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile a graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. Below, we'll discuss each of these concepts in more detail.
![Checkpoints](img/persistence/checkpoints.jpg)
@@ -78,7 +78,7 @@ After we run the graph, we expect to see exactly 4 checkpoints:
* checkpoint with the outputs of `node_a` `{'foo': 'a', 'bar': ['a']}` and `node_b` as the next node to be executed
* checkpoint with the outputs of `node_b` `{'foo': 'b', 'bar': ['a', 'b']}` and no next nodes to be executed
Note that we `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel.
Note that the `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel.
### Get state
docs/docs/concepts/server-mcp.md
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@@ -209,7 +209,7 @@ To make user-scoped tools available to your LangGraph Platform deployment, start
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
def get_mcp_tools_node(state, config):
def mcp_tools_node(state, config):
user = config["configurable"].get("langgraph_auth_user")
# e.g., user["github_token"], user["email"], etc.
@@ -223,14 +223,19 @@ def get_mcp_tools_node(state, config):
}
})
tools = await client.get_tools() # (3)
return {"tools": tools}
# Your tool-calling logic here
tool_messages = ...
return {"messages": tool_messages}
```
1. MCP only supports adding headers to requests made to `streamable_http` and `sse` `transport` servers.
2. Your MCP server URL.
3. Get available tools from your MCP server.
_This can also be done by [rebuilding your graph at runtime](https://langchain-ai.github.io/langgraph/cloud/deployment/graph_rebuild/) to have a different configuration for a new run_
## Session behavior
The current LangGraph MCP implementation does not support sessions. Each `/mcp` request is stateless and independent.
docs/docs/concepts/template_applications.md
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@@ -64,7 +64,7 @@ To create a new app from a template, use the `langgraph new` command.
=== "JS"
```bash
npx @langchain/langgraph-cli new
npm create langgraph@latest
```
## Next Steps
docs/docs/examples/index.md
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# Examples
The pages in this section provide end-to-end examples for the following topics:
## General
- [Template Applications](../concepts/template_applications.md): Create a LangGraph application from a template.
- [Agentic RAG](./rag/langgraph_agentic_rag.md): Build a retrieval agent that can decide when to use a retriever tool.
- [Agent Supervisor](./multi_agent/agent_supervisor.md): Build a supervisor agent that can manage a team of agents.
- [SQL agent](./sql/sql-agent.md): Build a SQL agent that can execute SQL queries and return the results.
- [Prebuilt chat UI](../agents/ui.md): Use a prebuilt chat UI to interact with any LangGraph agent.
- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md): Use LangSmith to track and analyze graph runs.
## LangGraph Platform
- [Set up custom authentication](./auth/getting_started.md): Set up custom authentication for your LangGraph application.
- [Make conversations private](./auth/resource_auth.md): Make conversations private by using resource-based authentication.
- [Connect an authentication provider](./auth/add_auth_server.md): Connect an authentication provider to your LangGraph application.
- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md): Rebuild a graph at runtime.
- [Use RemoteGraph](../how-tos/use-remote-graph.md): Use RemoteGraph to deploy your LangGraph application to a remote server.
- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-integration.md): Deploy CrewAI, AutoGen, and other frameworks with LangGraph.
- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md)
- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)
docs/docs/guides/index.md
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# Guides
The pages in this section provide a conceptual overview and how-tos for the following topics:
## LangGraph APIs
- [Graph API](../concepts/low_level.md): Use the Graph API to define workflows using a graph paradigm.
- [Functional API](../concepts/functional_api.md): Use Functional API to build workflows using a functional paradigm without thinking about the graph structure.
- [Runtime](../concepts/pregel.md): Pregel implements LangGraph's runtime, managing the execution of LangGraph applications.
## Core capabilities
These capabilities are available in both LangGraph OSS and the LangGraph Platform.
- [Streaming](../concepts/streaming.md): Stream outputs from a LangGraph graph.
- [Persistence](../concepts/persistence.md): Persist the state of a LangGraph graph.
- [Durable execution](../concepts/durable_execution.md): Save progress at key points in the graph execution.
- [Memory](../concepts/memory.md): Remember information about previous interactions.
- [Context](../agents/context.md): Pass outside data to a LangGraph graph to provide context for the graph execution.
- [Models](../agents/models.md): Integrate various LLMs into your LangGraph application.
- [Tools](../concepts/tools.md): Interface directly with external systems.
- [Human-in-the-loop](../concepts/human_in_the_loop.md): Enable human intervention at any point in a workflow.
- [Breakpoints](../concepts/breakpoints.md): Pause the execution of a LangGraph graph at a specific point.
- [Time travel](../concepts/time-travel.md): Travel back in time to a specific point in the execution of a LangGraph graph.
- [Subgraphs](../concepts/subgraphs.md): Build modular graphs.
- [Multi-agent](../concepts/multi_agent.md): Break down a complex workflow into multiple agents.
- [MCP](../concepts/mcp.md): Use MCP servers in a LangGraph graph.
- [Evaluation](../agents/evals.md): Use LangSmith to evaluate your graph's performance.
## Platform-only capabilities
These capabilities are only available in [LangGraph Platform](../concepts/langgraph_platform.md).
- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a Langraph graph.
- [Assistants](../concepts/assistants.md): Build assistants that can be used to interact with a LangGraph graph.
- [Double-texting](../concepts/double_texting.md): Handle double-texting (consecutive messages before a first response is returned) in a LangGraph graph.
- [Webhooks](../cloud/concepts/webhooks.md): Send webhooks to a LangGraph graph.
- [Cron jobs](../cloud/concepts/cron_jobs.md): Schedule jobs to run at a specific time.
- [Server customization](../how-tos/http/custom_lifespan.md): Customize the server that runs a LangGraph graph.
- [Data management](../cloud/concepts/data_storage_and_privacy.md): Manage data in a LangGraph graph.
- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server.
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# How to integrate LangGraph with AutoGen, CrewAI, and other frameworks
This guide shows how to integrate AutoGen agents with LangGraph to leverage features like persistence, streaming, and memory, and then deploy the integrated solution to LangGraph Platform for scalable production use. In this guide we show how to build a LangGraph chatbot that integrates with AutoGen, but you can follow the same approach with other frameworks.
Integrating AutoGen with LangGraph provides several benefits:
- Enhanced features: Add [persistence](../concepts/persistence.md), [streaming](../concepts/streaming.md), [short and long-term memory](../concepts/memory.md) and more to your AutoGen agents.
- Multi-agent systems: Build [multi-agent systems](../concepts/multi_agent.md) where individual agents are built with different frameworks.
- Production deployment: Deploy your integrated solution to [LangGraph Platform](../concepts/langgraph_platform.md) for scalable production use.
## Prerequisites
- Python 3.9+
- Autogen: `pip install autogen`
- LangGraph: `pip install langgraph`
- OpenAI API key
## Setup
Set your your environment:
```python
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
```
## 1. Define AutoGen agent
Create an AutoGen agent that can execute code. This example is adapted from AutoGen's [official tutorials](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb):
```python
import autogen
import os
config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]
llm_config = {
"timeout": 600,
"cache_seed": 42,
"config_list": config_list,
"temperature": 0,
}
autogen_agent = autogen.AssistantAgent(
name="assistant",
llm_config=llm_config,
)
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "web",
"use_docker": False,
}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
llm_config=llm_config,
system_message="Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.",
)
```
## 2. Create the graph
We will now create a LangGraph chatbot graph that calls AutoGen agent.
```python
from langchain_core.messages import convert_to_openai_messages
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.memory import MemorySaver
def call_autogen_agent(state: MessagesState):
# Convert LangGraph messages to OpenAI format for AutoGen
messages = convert_to_openai_messages(state["messages"])
# Get the last user message
last_message = messages[-1]
# Pass previous message history as context (excluding the last message)
carryover = messages[:-1] if len(messages) > 1 else []
# Initiate chat with AutoGen
response = user_proxy.initiate_chat(
autogen_agent,
message=last_message,
carryover=carryover
)
# Extract the final response from the agent
final_content = response.chat_history[-1]["content"]
# Return the response in LangGraph format
return {"messages": {"role": "assistant", "content": final_content}}
# Create the graph with memory for persistence
checkpointer = MemorySaver()
# Build the graph
builder = StateGraph(MessagesState)
builder.add_node("autogen", call_autogen_agent)
builder.add_edge(START, "autogen")
# Compile with checkpointer for persistence
graph = builder.compile(checkpointer=checkpointer)
```
```python
from IPython.display import display, Image
display(Image(graph.get_graph().draw_mermaid_png()))
```
![Graph](./assets/autogen-output.png)
## 3. Test the graph locally
Before deploying to LangGraph Platform, you can test the graph locally:
```python
# pass the thread ID to persist agent outputs for future interactions
# highlight-next-line
config = {"configurable": {"thread_id": "1"}}
for chunk in graph.stream(
{
"messages": [
{
"role": "user",
"content": "Find numbers between 10 and 30 in fibonacci sequence",
}
]
},
# highlight-next-line
config,
):
print(chunk)
```
**Output:**
```
user_proxy (to assistant):
Find numbers between 10 and 30 in fibonacci sequence
--------------------------------------------------------------------------------
assistant (to user_proxy):
To find numbers between 10 and 30 in the Fibonacci sequence, we can generate the Fibonacci sequence and check which numbers fall within this range. Here's a plan:
1. Generate Fibonacci numbers starting from 0.
2. Continue generating until the numbers exceed 30.
3. Collect and print the numbers that are between 10 and 30.
...
```
Since we're leveraging LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) features we can now continue the conversation using the same thread ID -- LangGraph will automatically pass previous history to the AutoGen agent:
```python
for chunk in graph.stream(
{
"messages": [
{
"role": "user",
"content": "Multiply the last number by 3",
}
]
},
# highlight-next-line
config,
):
print(chunk)
```
**Output:**
```
user_proxy (to assistant):
Multiply the last number by 3
Context:
Find numbers between 10 and 30 in fibonacci sequence
The Fibonacci numbers between 10 and 30 are 13 and 21.
These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1.
The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...
As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.
TERMINATE
--------------------------------------------------------------------------------
assistant (to user_proxy):
The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:
21 * 3 = 63
TERMINATE
--------------------------------------------------------------------------------
{'call_autogen_agent': {'messages': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\n\n21 * 3 = 63\n\nTERMINATE'}}}
```
## 4. Prepare for deployment
To deploy to LangGraph Platform, create a file structure like the following:
```
my-autogen-agent/
├── agent.py # Your main agent code
├── requirements.txt # Python dependencies
└── langgraph.json # LangGraph configuration
```
=== "agent.py"
```python
import os
import autogen
from langchain_core.messages import convert_to_openai_messages
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.memory import MemorySaver
# AutoGen configuration
config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]
llm_config = {
"timeout": 600,
"cache_seed": 42,
"config_list": config_list,
"temperature": 0,
}
# Create AutoGen agents
autogen_agent = autogen.AssistantAgent(
name="assistant",
llm_config=llm_config,
)
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "/tmp/autogen_work",
"use_docker": False,
},
llm_config=llm_config,
system_message="Reply TERMINATE if the task has been solved at full satisfaction.",
)
def call_autogen_agent(state: MessagesState):
"""Node function that calls the AutoGen agent"""
messages = convert_to_openai_messages(state["messages"])
last_message = messages[-1]
carryover = messages[:-1] if len(messages) > 1 else []
response = user_proxy.initiate_chat(
autogen_agent,
message=last_message,
carryover=carryover
)
final_content = response.chat_history[-1]["content"]
return {"messages": {"role": "assistant", "content": final_content}}
# Create and compile the graph
def create_graph():
checkpointer = MemorySaver()
builder = StateGraph(MessagesState)
builder.add_node("autogen", call_autogen_agent)
builder.add_edge(START, "autogen")
return builder.compile(checkpointer=checkpointer)
# Export the graph for LangGraph Platform
graph = create_graph()
```
=== "requirements.txt"
```
langgraph>=0.1.0
pyautogen>=0.2.0
langchain-core>=0.1.0
langchain-openai>=0.0.5
```
=== "langgraph.json"
```json
{
"dependencies": ["."],
"graphs": {
"autogen_agent": "./agent.py:graph"
},
"env": ".env"
}
```
## 5. Deploy to LangGraph Platform
Deploy the graph with the LangGraph Platform CLI:
```
pip install -U langgraph-cli
```
```
langgraph deploy --config langgraph.json
```
docs/docs/how-tos/autogen-langgraph-platform.ipynb
-171
View File
@@ -1,171 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8381b6e0-29a6-48c5-b451-5d2549351249",
"metadata": {},
"source": [
"# How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks\n",
"\n",
"[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) provides infrastructure for deploying agents. This integrates seamlessly with LangGraph, but can also work with other frameworks. The way to make this work is to wrap the agent in a single LangGraph node, and have that be the entire graph.\n",
"\n",
"Doing so will allow you to deploy to LangGraph Platform, and allows you to get a lot of the [benefits](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). You get horizontally scalable infrastructure, a task queue to handle bursty operations, a persistence layer to power short term memory, and long term memory support.\n",
"\n",
"In this guide we show how to do this with an AutoGen agent, but this method should work for agents defined in other frameworks like CrewAI, LlamaIndex, and others as well."
]
},
{
"cell_type": "markdown",
"id": "1113cb16-b538-448c-924c-85731ce96ebd",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f05993fa-9d03-4f45-bc13-0a8d87260d86",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%pip install autogen langgraph"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4e0ca12-1714-4776-a30a-9527e519799b",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"\n",
"def _set_env(var: str):\n",
" if not os.environ.get(var):\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"\n",
"\n",
"_set_env(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
"metadata": {},
"source": [
"## Define autogen agent\n",
"\n",
"Here we define our AutoGen agent. From https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4a14dc7-d565-4207-8788-525f85b9fb27",
"metadata": {},
"outputs": [],
"source": [
"import autogen\n",
"import os\n",
"\n",
"config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
"\n",
"llm_config = {\n",
" \"timeout\": 600,\n",
" \"cache_seed\": 42,\n",
" \"config_list\": config_list,\n",
" \"temperature\": 0,\n",
"}\n",
"\n",
"autogen_agent = autogen.AssistantAgent(\n",
" name=\"assistant\",\n",
" llm_config=llm_config,\n",
")\n",
"\n",
"user_proxy = autogen.UserProxyAgent(\n",
" name=\"user_proxy\",\n",
" human_input_mode=\"NEVER\",\n",
" max_consecutive_auto_reply=10,\n",
" is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
" code_execution_config={\n",
" \"work_dir\": \"web\",\n",
" \"use_docker\": False,\n",
" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
" llm_config=llm_config,\n",
" system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b1170836-f23e-4e4c-ab83-ce791cd7fbd2",
"metadata": {},
"source": [
"## Wrap in LangGraph\n",
"\n",
"We now wrap the AutoGen agent in a single LangGraph node, and make that the entire graph.\n",
"The main thing this involves is defining an Input and Output schema for the node, which you would need to do if deploying this manually, so it's no extra work"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7b417c16-ff4e-4d5c-a9a9-0aaeeef6ede5",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.graph import StateGraph, MessagesState\n",
"\n",
"\n",
"def call_autogen_agent(state: MessagesState):\n",
" last_message = state[\"messages\"][-1]\n",
" response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)\n",
" # get the final response from the agent\n",
" content = response.chat_history[-1][\"content\"]\n",
" return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n",
"\n",
"\n",
"graph = StateGraph(MessagesState)\n",
"graph.add_node(call_autogen_agent)\n",
"graph.set_entry_point(\"call_autogen_agent\")\n",
"graph = graph.compile()"
]
},
{
"cell_type": "markdown",
"id": "f6a18377-ac29-478f-a76a-b213f1a3c85d",
"metadata": {},
"source": [
"## Deploy with LangGraph Platform\n",
"\n",
"You can now deploy this as you normally would with LangGraph Platform. See [these instructions](https://langchain-ai.github.io/langgraph/concepts/deployment_options/) for more details."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/docs/how-tos/graph-api.md
+3 -3
View File
@@ -163,7 +163,7 @@ Hello!
In practice, there are additional considerations for updating lists of messages:
- We may wish to update an existing message in the state.
- We may want to accept short-hands for [message formats](../concepts/low_level.md#using-messages-in-your-graph), such as [OpenAI format](https://python.langchain.com/docs/concepts/messages.md#openai-format).
- We may want to accept short-hands for [message formats](../concepts/low_level.md#using-messages-in-your-graph), such as [OpenAI format](https://python.langchain.com/docs/concepts/messages/#openai-format).
LangGraph includes a built-in reducer `add_messages` that handles these considerations:
@@ -658,7 +658,7 @@ print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}}))
There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. LangGraph lets you add retry policies to nodes.
To configure a retry policy, pass the `retry_policy` parameter to the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node:
To configure a retry policy, pass the `retry_policy` parameter to the [add_node](../reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node:
```python
from langgraph.pregel import RetryPolicy
@@ -1874,4 +1874,4 @@ except ImportError:
print(
"You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt"
)
```
```
docs/docs/how-tos/memory/add-memory.md
+5 -5
View File
@@ -1351,7 +1351,7 @@ The problem with trimming or removing messages, as shown above, is that you may
```python
from langchain_anthropic import ChatAnthropic
from langmem.short_term import SummarizationNode
from langmem.short_term import SummarizationNode, RunningSummary
from langchain_core.messages.utils import count_tokens_approximately
from langgraph.prebuilt import create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
@@ -1372,7 +1372,7 @@ The problem with trimming or removing messages, as shown above, is that you may
# NOTE: we're adding this key to keep track of previous summary information
# to make sure we're not summarizing on every LLM call
# highlight-next-line
context: dict[str, Any] # (2)!
context: dict[str, RunningSummary] # (2)!
checkpointer = InMemorySaver() # (3)!
@@ -1447,18 +1447,18 @@ The problem with trimming or removing messages, as shown above, is that you may
from langgraph.graph import StateGraph, START, MessagesState
from langgraph.checkpoint.memory import InMemorySaver
# highlight-next-line
from langmem.short_term import SummarizationNode
from langmem.short_term import SummarizationNode, RunningSummary
model = init_chat_model("anthropic:claude-3-7-sonnet-latest")
summarization_model = model.bind(max_tokens=128)
class State(MessagesState):
# highlight-next-line
context: dict[str, Any] # (1)!
context: dict[str, RunningSummary] # (1)!
class LLMInputState(TypedDict): # (2)!
summarized_messages: list[AnyMessage]
context: dict[str, Any]
context: dict[str, RunningSummary]
# highlight-next-line
summarization_node = SummarizationNode(
docs/docs/how-tos/multi_agent.ipynb
-657
View File
@@ -1,657 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "34d3d54e-9a2b-481e-bccd-74aca7a53f9a",
"metadata": {},
"source": [
"# Build multi-agent systems"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3f0b4f70-f14e-4026-82c0-874786789ee8",
"metadata": {},
"source": [
"A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../../concepts/multi_agent).\n",
"\n",
"In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent.\n",
"\n",
"This guide covers the following:\n",
"\n",
"* implementing [handoffs](#handoffs) between agents\n",
"* using handoffs and the prebuilt [agent](../../agents/agents) to [build a custom multi-agent system](#build-a-multi-agent-system)\n",
"\n",
"To get started with building multi-agent systems, check out LangGraph [prebuilt implementations](#prebuilt-implementations) of two of the most popular multi-agent architectures — [supervisor](../../agents/multi-agent#supervisor) and [swarm](../../agents/multi-agent#swarm)."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7d43e110-16fc-4899-97f1-015d5b804b87",
"metadata": {},
"source": [
"## Handoffs\n",
"\n",
"To set up communication between the agents in a multi-agent system you can use [**handoffs**](../../concepts/multi_agent#handoffs) — a pattern where one agent *hands off* control to another. Handoffs allow you to specify:\n",
"\n",
"- **destination**: target agent to navigate to (e.g., name of the LangGraph node to go to)\n",
"- **payload**: information to pass to that agent (e.g., state update)\n",
"\n",
"### Create handoffs\n",
"\n",
"To implement handoffs, you can return [`Command`](../command) objects from your agent nodes or tools:\n",
"\n",
"```python\n",
"from typing import Annotated\n",
"from langchain_core.tools import tool, InjectedToolCallId\n",
"from langgraph.prebuilt import create_react_agent, InjectedState\n",
"from langgraph.graph import StateGraph, START, MessagesState\n",
"from langgraph.types import Command\n",
"\n",
"def create_handoff_tool(*, agent_name: str, description: str | None = None):\n",
" name = f\"transfer_to_{agent_name}\"\n",
" description = description or f\"Transfer to {agent_name}\"\n",
"\n",
" @tool(name, description=description)\n",
" def handoff_tool(\n",
" # highlight-next-line\n",
" state: Annotated[MessagesState, InjectedState], # (1)!\n",
" # highlight-next-line\n",
" tool_call_id: Annotated[str, InjectedToolCallId],\n",
" ) -> Command:\n",
" tool_message = {\n",
" \"role\": \"tool\",\n",
" \"content\": f\"Successfully transferred to {agent_name}\",\n",
" \"name\": name,\n",
" \"tool_call_id\": tool_call_id,\n",
" }\n",
" return Command( # (2)!\n",
" # highlight-next-line\n",
" goto=agent_name, # (3)!\n",
" # highlight-next-line\n",
" update={\"messages\": state[\"messages\"] + [tool_message]}, # (4)!\n",
" # highlight-next-line\n",
" graph=Command.PARENT, # (5)!\n",
" )\n",
" return handoff_tool\n",
"```\n",
"\n",
"1. Access the [state](../../concepts/low_level#state) of the agent that is calling the handoff tool using the [InjectedState][langgraph.prebuilt.InjectedState] annotation. See [this guide](../tool-calling/#read-state) for more information.\n",
"2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.\n",
"3. Name of the agent or node to hand off to.\n",
"4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.\n",
"5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.\n",
"\n",
"!!! tip\n",
"\n",
" If you want to use tools that return `Command`, you can either use prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] / [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] components, or implement your own tool-executing node that collects `Command` objects returned by the tools and returns a list of them, e.g.:\n",
" \n",
" ```python\n",
" def call_tools(state):\n",
" ...\n",
" commands = [tools_by_name[tool_call[\"name\"]].invoke(tool_call) for tool_call in tool_calls]\n",
" return commands\n",
" ```\n",
"\n",
"!!! Important\n",
"\n",
" This handoff implementation assumes that:\n",
" \n",
" - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs)\n",
" - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function:\n",
"\n",
" ```python\n",
" def call_hotel_assistant(state):\n",
" # return agent's final response,\n",
" # excluding inner monologue\n",
" response = hotel_assistant.invoke(state)\n",
" # highlight-next-line\n",
" return {\"messages\": response[\"messages\"][-1]}\n",
" ```"
]
},
{
"cell_type": "markdown",
"id": "3956f12d-285a-4799-a0a5-db13def58a15",
"metadata": {},
"source": [
"### Control agent inputs\n",
"\n",
"You can use the [`Send()`][langgraph.types.Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent:\n",
"\n",
"```python\n",
"\n",
"from typing import Annotated\n",
"from langchain_core.tools import tool, InjectedToolCallId\n",
"from langgraph.prebuilt import InjectedState\n",
"from langgraph.graph import StateGraph, START, MessagesState\n",
"# highlight-next-line\n",
"from langgraph.types import Command, Send\n",
"\n",
"def create_task_description_handoff_tool(\n",
" *, agent_name: str, description: str | None = None\n",
"):\n",
" name = f\"transfer_to_{agent_name}\"\n",
" description = description or f\"Ask {agent_name} for help.\"\n",
"\n",
" @tool(name, description=description)\n",
" def handoff_tool(\n",
" # this is populated by the calling agent\n",
" task_description: Annotated[\n",
" str,\n",
" \"Description of what the next agent should do, including all of the relevant context.\",\n",
" ],\n",
" # these parameters are ignored by the LLM\n",
" state: Annotated[MessagesState, InjectedState],\n",
" ) -> Command:\n",
" task_description_message = {\"role\": \"user\", \"content\": task_description}\n",
" agent_input = {**state, \"messages\": [task_description_message]}\n",
" return Command(\n",
" # highlight-next-line\n",
" goto=[Send(agent_name, agent_input)],\n",
" graph=Command.PARENT,\n",
" )\n",
"\n",
" return handoff_tool\n",
"```\n",
"\n",
"See the multi-agent [supervisor](../tutorials/agent_supervisor.ipynb#4-create-delegation-tasks) tutorial for a full example of using [`Send()`][langgraph.types.Send] in handoffs."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "21511f57-7bf3-4223-9a17-ce9fc84c40ab",
"metadata": {},
"source": [
"## Build a multi-agent system\n",
"\n",
"You can use handoffs in any agents built with LangGraph. We recommend using the prebuilt [agent](../../agents/overview) or [`ToolNode`](../tool-calling#use-prebuilt-toolnode), as they natively support handoffs tools returning `Command`. Below is an example of how you can implement a multi-agent system for booking travel using handoffs:\n",
"\n",
"```python\n",
"from langgraph.prebuilt import create_react_agent\n",
"from langgraph.graph import StateGraph, START, MessagesState\n",
"\n",
"def create_handoff_tool(*, agent_name: str, description: str | None = None):\n",
" # same implementation as above\n",
" ...\n",
" return Command(...)\n",
"\n",
"# Handoffs\n",
"transfer_to_hotel_assistant = create_handoff_tool(agent_name=\"hotel_assistant\")\n",
"transfer_to_flight_assistant = create_handoff_tool(agent_name=\"flight_assistant\")\n",
"\n",
"# Define agents\n",
"flight_assistant = create_react_agent(\n",
" model=\"anthropic:claude-3-5-sonnet-latest\",\n",
" # highlight-next-line\n",
" tools=[..., transfer_to_hotel_assistant],\n",
" # highlight-next-line\n",
" name=\"flight_assistant\"\n",
")\n",
"hotel_assistant = create_react_agent(\n",
" model=\"anthropic:claude-3-5-sonnet-latest\",\n",
" # highlight-next-line\n",
" tools=[..., transfer_to_flight_assistant],\n",
" # highlight-next-line\n",
" name=\"hotel_assistant\"\n",
")\n",
"\n",
"# Define multi-agent graph\n",
"multi_agent_graph = (\n",
" StateGraph(MessagesState)\n",
" # highlight-next-line\n",
" .add_node(flight_assistant)\n",
" # highlight-next-line\n",
" .add_node(hotel_assistant)\n",
" .add_edge(START, \"flight_assistant\")\n",
" .compile()\n",
")\n",
"```\n",
"\n",
"??? example \"Full example: Multi-agent system for booking travel\"\n",
"\n",
" ```python\n",
" from typing import Annotated\n",
" from langchain_core.messages import convert_to_messages\n",
" from langchain_core.tools import tool, InjectedToolCallId\n",
" from langgraph.prebuilt import create_react_agent, InjectedState\n",
" from langgraph.graph import StateGraph, START, MessagesState\n",
" from langgraph.types import Command\n",
" \n",
" # We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely\n",
" \n",
" def pretty_print_message(message, indent=False):\n",
" pretty_message = message.pretty_repr(html=True)\n",
" if not indent:\n",
" print(pretty_message)\n",
" return\n",
" \n",
" indented = \"\\n\".join(\"\\t\" + c for c in pretty_message.split(\"\\n\"))\n",
" print(indented)\n",
" \n",
" \n",
" def pretty_print_messages(update, last_message=False):\n",
" is_subgraph = False\n",
" if isinstance(update, tuple):\n",
" ns, update = update\n",
" # skip parent graph updates in the printouts\n",
" if len(ns) == 0:\n",
" return\n",
" \n",
" graph_id = ns[-1].split(\":\")[0]\n",
" print(f\"Update from subgraph {graph_id}:\")\n",
" print(\"\\n\")\n",
" is_subgraph = True\n",
" \n",
" for node_name, node_update in update.items():\n",
" update_label = f\"Update from node {node_name}:\"\n",
" if is_subgraph:\n",
" update_label = \"\\t\" + update_label\n",
" \n",
" print(update_label)\n",
" print(\"\\n\")\n",
" \n",
" messages = convert_to_messages(node_update[\"messages\"])\n",
" if last_message:\n",
" messages = messages[-1:]\n",
" \n",
" for m in messages:\n",
" pretty_print_message(m, indent=is_subgraph)\n",
" print(\"\\n\")\n",
"\n",
"\n",
" def create_handoff_tool(*, agent_name: str, description: str | None = None):\n",
" name = f\"transfer_to_{agent_name}\"\n",
" description = description or f\"Transfer to {agent_name}\"\n",
" \n",
" @tool(name, description=description)\n",
" def handoff_tool(\n",
" # highlight-next-line\n",
" state: Annotated[MessagesState, InjectedState], # (1)!\n",
" # highlight-next-line\n",
" tool_call_id: Annotated[str, InjectedToolCallId],\n",
" ) -> Command:\n",
" tool_message = {\n",
" \"role\": \"tool\",\n",
" \"content\": f\"Successfully transferred to {agent_name}\",\n",
" \"name\": name,\n",
" \"tool_call_id\": tool_call_id,\n",
" }\n",
" return Command( # (2)!\n",
" # highlight-next-line\n",
" goto=agent_name, # (3)!\n",
" # highlight-next-line\n",
" update={\"messages\": state[\"messages\"] + [tool_message]}, # (4)!\n",
" # highlight-next-line\n",
" graph=Command.PARENT, # (5)!\n",
" )\n",
" return handoff_tool\n",
" \n",
" # Handoffs\n",
" transfer_to_hotel_assistant = create_handoff_tool(\n",
" agent_name=\"hotel_assistant\",\n",
" description=\"Transfer user to the hotel-booking assistant.\",\n",
" )\n",
" transfer_to_flight_assistant = create_handoff_tool(\n",
" agent_name=\"flight_assistant\",\n",
" description=\"Transfer user to the flight-booking assistant.\",\n",
" )\n",
" \n",
" # Simple agent tools\n",
" def book_hotel(hotel_name: str):\n",
" \"\"\"Book a hotel\"\"\"\n",
" return f\"Successfully booked a stay at {hotel_name}.\"\n",
" \n",
" def book_flight(from_airport: str, to_airport: str):\n",
" \"\"\"Book a flight\"\"\"\n",
" return f\"Successfully booked a flight from {from_airport} to {to_airport}.\"\n",
" \n",
" # Define agents\n",
" flight_assistant = create_react_agent(\n",
" model=\"anthropic:claude-3-5-sonnet-latest\",\n",
" # highlight-next-line\n",
" tools=[book_flight, transfer_to_hotel_assistant],\n",
" prompt=\"You are a flight booking assistant\",\n",
" # highlight-next-line\n",
" name=\"flight_assistant\"\n",
" )\n",
" hotel_assistant = create_react_agent(\n",
" model=\"anthropic:claude-3-5-sonnet-latest\",\n",
" # highlight-next-line\n",
" tools=[book_hotel, transfer_to_flight_assistant],\n",
" prompt=\"You are a hotel booking assistant\",\n",
" # highlight-next-line\n",
" name=\"hotel_assistant\"\n",
" )\n",
" \n",
" # Define multi-agent graph\n",
" multi_agent_graph = (\n",
" StateGraph(MessagesState)\n",
" .add_node(flight_assistant)\n",
" .add_node(hotel_assistant)\n",
" .add_edge(START, \"flight_assistant\")\n",
" .compile()\n",
" )\n",
" \n",
" # Run the multi-agent graph\n",
" for chunk in multi_agent_graph.stream(\n",
" {\n",
" \"messages\": [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"book a flight from BOS to JFK and a stay at McKittrick Hotel\"\n",
" }\n",
" ]\n",
" },\n",
" # highlight-next-line\n",
" subgraphs=True\n",
" ):\n",
" pretty_print_messages(chunk)\n",
" ```\n",
"\n",
" 1. Access agent's state\n",
" 2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.\n",
" 3. Name of the agent or node to hand off to.\n",
" 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.\n",
" 5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph."
]
},
{
"cell_type": "markdown",
"id": "e8314da4-9971-429b-9e70-58b40795de74",
"metadata": {},
"source": [
"## Multi-turn conversation\n",
"\n",
"Users might want to engage in a *multi-turn conversation* with one or more agents. To build a system that can handle this, you can create a node that uses an [`interrupt`][langgraph.types.interrupt] to collect user input and routes back to the **active** agent.\n",
"\n",
"The agents can then be implemented as nodes in a graph that executes agent steps and determines the next action:\n",
"\n",
"1. **Wait for user input** to continue the conversation, or \n",
"2. **Route to another agent** (or back to itself, such as in a loop) via a [handoff](#handoffs)\n",
"\n",
"```python\n",
"def human(state) -> Command[Literal[\"agent\", \"another_agent\"]]:\n",
" \"\"\"A node for collecting user input.\"\"\"\n",
" user_input = interrupt(value=\"Ready for user input.\")\n",
"\n",
" # Determine the active agent.\n",
" active_agent = ...\n",
"\n",
" ...\n",
" return Command(\n",
" update={\n",
" \"messages\": [{\n",
" \"role\": \"human\",\n",
" \"content\": user_input,\n",
" }]\n",
" },\n",
" goto=active_agent\n",
" )\n",
"\n",
"def agent(state) -> Command[Literal[\"agent\", \"another_agent\", \"human\"]]:\n",
" # The condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.\n",
" goto = get_next_agent(...) # 'agent' / 'another_agent'\n",
" if goto:\n",
" return Command(goto=goto, update={\"my_state_key\": \"my_state_value\"})\n",
" else:\n",
" return Command(goto=\"human\") # Go to human node\n",
"```\n",
"\n",
"??? example \"Full example: multi-agent system for travel recommendations\"\n",
"\n",
" In this example, we will build a team of travel assistant agents that can communicate with each other via handoffs.\n",
" \n",
" We will create 2 agents:\n",
" \n",
" * travel_advisor: can help with travel destination recommendations. Can ask hotel_advisor for help.\n",
" * hotel_advisor: can help with hotel recommendations. Can ask travel_advisor for help.\n",
"\n",
" ```python\n",
" from langchain_anthropic import ChatAnthropic\n",
" from langgraph.graph import MessagesState, StateGraph, START\n",
" from langgraph.prebuilt import create_react_agent, InjectedState\n",
" from langgraph.types import Command, interrupt\n",
" from langgraph.checkpoint.memory import MemorySaver\n",
" \n",
" \n",
" model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
"\n",
" class MultiAgentState(MessagesState):\n",
" last_active_agent: str\n",
" \n",
" \n",
" # Define travel advisor tools and ReAct agent\n",
" travel_advisor_tools = [\n",
" get_travel_recommendations,\n",
" make_handoff_tool(agent_name=\"hotel_advisor\"),\n",
" ]\n",
" travel_advisor = create_react_agent(\n",
" model,\n",
" travel_advisor_tools,\n",
" prompt=(\n",
" \"You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). \"\n",
" \"If you need hotel recommendations, ask 'hotel_advisor' for help. \"\n",
" \"You MUST include human-readable response before transferring to another agent.\"\n",
" ),\n",
" )\n",
" \n",
" \n",
" def call_travel_advisor(\n",
" state: MultiAgentState,\n",
" ) -> Command[Literal[\"hotel_advisor\", \"human\"]]:\n",
" # You can also add additional logic like changing the input to the agent / output from the agent, etc.\n",
" # NOTE: we're invoking the ReAct agent with the full history of messages in the state\n",
" response = travel_advisor.invoke(state)\n",
" update = {**response, \"last_active_agent\": \"travel_advisor\"}\n",
" return Command(update=update, goto=\"human\")\n",
" \n",
" \n",
" # Define hotel advisor tools and ReAct agent\n",
" hotel_advisor_tools = [\n",
" get_hotel_recommendations,\n",
" make_handoff_tool(agent_name=\"travel_advisor\"),\n",
" ]\n",
" hotel_advisor = create_react_agent(\n",
" model,\n",
" hotel_advisor_tools,\n",
" prompt=(\n",
" \"You are a hotel expert that can provide hotel recommendations for a given destination. \"\n",
" \"If you need help picking travel destinations, ask 'travel_advisor' for help.\"\n",
" \"You MUST include human-readable response before transferring to another agent.\"\n",
" ),\n",
" )\n",
" \n",
" \n",
" def call_hotel_advisor(\n",
" state: MultiAgentState,\n",
" ) -> Command[Literal[\"travel_advisor\", \"human\"]]:\n",
" response = hotel_advisor.invoke(state)\n",
" update = {**response, \"last_active_agent\": \"hotel_advisor\"}\n",
" return Command(update=update, goto=\"human\")\n",
" \n",
" \n",
" def human_node(\n",
" state: MultiAgentState, config\n",
" ) -> Command[Literal[\"hotel_advisor\", \"travel_advisor\", \"human\"]]:\n",
" \"\"\"A node for collecting user input.\"\"\"\n",
" \n",
" user_input = interrupt(value=\"Ready for user input.\")\n",
" active_agent = state[\"last_active_agent\"]\n",
" \n",
" return Command(\n",
" update={\n",
" \"messages\": [\n",
" {\n",
" \"role\": \"human\",\n",
" \"content\": user_input,\n",
" }\n",
" ]\n",
" },\n",
" goto=active_agent,\n",
" )\n",
" \n",
" \n",
" builder = StateGraph(MultiAgentState)\n",
" builder.add_node(\"travel_advisor\", call_travel_advisor)\n",
" builder.add_node(\"hotel_advisor\", call_hotel_advisor)\n",
" \n",
" # This adds a node to collect human input, which will route\n",
" # back to the active agent.\n",
" builder.add_node(\"human\", human_node)\n",
" \n",
" # We'll always start with a general travel advisor.\n",
" builder.add_edge(START, \"travel_advisor\")\n",
" \n",
" \n",
" checkpointer = MemorySaver()\n",
" graph = builder.compile(checkpointer=checkpointer)\n",
" ```\n",
" \n",
" Let's test a multi turn conversation with this application.\n",
"\n",
" ```python\n",
" import uuid\n",
" \n",
" thread_config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n",
" \n",
" inputs = [\n",
" # 1st round of conversation,\n",
" {\n",
" \"messages\": [\n",
" {\"role\": \"user\", \"content\": \"i wanna go somewhere warm in the caribbean\"}\n",
" ]\n",
" },\n",
" # Since we're using `interrupt`, we'll need to resume using the Command primitive.\n",
" # 2nd round of conversation,\n",
" Command(\n",
" resume=\"could you recommend a nice hotel in one of the areas and tell me which area it is.\"\n",
" ),\n",
" # 3rd round of conversation,\n",
" Command(\n",
" resume=\"i like the first one. could you recommend something to do near the hotel?\"\n",
" ),\n",
" ]\n",
" \n",
" for idx, user_input in enumerate(inputs):\n",
" print()\n",
" print(f\"--- Conversation Turn {idx + 1} ---\")\n",
" print()\n",
" print(f\"User: {user_input}\")\n",
" print()\n",
" for update in graph.stream(\n",
" user_input,\n",
" config=thread_config,\n",
" stream_mode=\"updates\",\n",
" ):\n",
" for node_id, value in update.items():\n",
" if isinstance(value, dict) and value.get(\"messages\", []):\n",
" last_message = value[\"messages\"][-1]\n",
" if isinstance(last_message, dict) or last_message.type != \"ai\":\n",
" continue\n",
" print(f\"{node_id}: {last_message.content}\")\n",
" ```\n",
" \n",
" ```\n",
" --- Conversation Turn 1 ---\n",
" \n",
" User: {'messages': [{'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean'}]}\n",
" \n",
" travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known as \"One Happy Island\" and offers:\n",
" - Year-round warm weather with consistent temperatures around 82°F (28°C)\n",
" - Beautiful white sand beaches like Eagle Beach and Palm Beach\n",
" - Clear turquoise waters perfect for swimming and snorkeling\n",
" - Minimal rainfall and location outside the hurricane belt\n",
" - A blend of Caribbean and Dutch culture\n",
" - Great dining options and nightlife\n",
" - Various water sports and activities\n",
" \n",
" Would you like me to get some specific hotel recommendations in Aruba for your stay? I can transfer you to our hotel advisor who can help with accommodations.\n",
" \n",
" --- Conversation Turn 2 ---\n",
" \n",
" User: Command(resume='could you recommend a nice hotel in one of the areas and tell me which area it is.')\n",
" \n",
" hotel_advisor: Based on the recommendations, I can suggest two excellent options:\n",
" \n",
" 1. The Ritz-Carlton, Aruba - Located in Palm Beach\n",
" - This luxury resort is situated in the vibrant Palm Beach area\n",
" - Known for its exceptional service and amenities\n",
" - Perfect if you want to be close to dining, shopping, and entertainment\n",
" - Features multiple restaurants, a casino, and a world-class spa\n",
" - Located on a pristine stretch of Palm Beach\n",
" \n",
" 2. Bucuti & Tara Beach Resort - Located in Eagle Beach\n",
" - An adults-only boutique resort on Eagle Beach\n",
" - Known for being more intimate and peaceful\n",
" - Award-winning for its sustainability practices\n",
" - Perfect for a romantic getaway or peaceful vacation\n",
" - Located on one of the most beautiful beaches in the Caribbean\n",
" \n",
" Would you like more specific information about either of these properties or their locations?\n",
" \n",
" --- Conversation Turn 3 ---\n",
" \n",
" User: Command(resume='i like the first one. could you recommend something to do near the hotel?')\n",
" \n",
" travel_advisor: Near the Ritz-Carlton in Palm Beach, here are some highly recommended activities:\n",
" \n",
" 1. Visit the Palm Beach Plaza Mall - Just a short walk from the hotel, featuring shopping, dining, and entertainment\n",
" 2. Try your luck at the Stellaris Casino - It's right in the Ritz-Carlton\n",
" 3. Take a sunset sailing cruise - Many depart from the nearby pier\n",
" 4. Visit the California Lighthouse - A scenic landmark just north of Palm Beach\n",
" 5. Enjoy water sports at Palm Beach:\n",
" - Jet skiing\n",
" - Parasailing\n",
" - Snorkeling\n",
" - Stand-up paddleboarding\n",
" \n",
" Would you like more specific information about any of these activities or would you like to know about other options in the area?\n",
" ```"
]
},
{
"cell_type": "markdown",
"id": "04d18c63-a0eb-45ac-86dc-0cc5bd683973",
"metadata": {},
"source": [
"## Prebuilt implementations"
]
},
{
"cell_type": "markdown",
"id": "e0e4ce57-f8de-4f37-836e-c1e1a02dd7b7",
"metadata": {},
"source": [
"LangGraph comes with prebuilt implementations of two of the most popular multi-agent architectures:\n",
"\n",
"- [supervisor](../../agents/multi-agent#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. You can use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent systems.\n",
"- [swarm](../../agents/multi-agent#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent systems."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/docs/how-tos/multi_agent.md
+1 -1
View File
@@ -130,7 +130,7 @@ def create_task_description_handoff_tool(
return handoff_tool
```
See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.ipynb#4-create-delegation-tasks) example for a full example of using [`Send()`][langgraph.types.Send] in handoffs.
See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.md#4-create-delegation-tasks) example for a full example of using [`Send()`][langgraph.types.Send] in handoffs.
## Build a multi-agent system
docs/docs/how-tos/run-id-langsmith.ipynb
-274
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docs/docs/how-tos/run-id-langsmith.md
+155
View File
@@ -0,0 +1,155 @@
# How to pass custom run ID or set tags and metadata for graph runs in LangSmith
!!! tip "Prerequisites"
This guide assumes familiarity with the following:
- [LangSmith Documentation](https://docs.smith.langchain.com)
- [LangSmith Platform](https://smith.langchain.com)
- [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig)
- [Add metadata and tags to traces](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain#add-metadata-and-tags-to-traces)
- [Customize run name](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain#customize-run-name)
Debugging graph runs can sometimes be difficult to do in an IDE or terminal. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read the [LangSmith documentation](https://docs.smith.langchain.com) for more information on how to get started.
To make it easier to identify and analyzed traces generated during graph invocation, you can set additional configuration at run time (see [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig)):
| **Field** | **Type** | **Description** |
|-------------|---------------------|--------------------------------------------------------------------------------------------------------------------|
| run_name | `str` | Name for the tracer run for this call. Defaults to the name of the class. |
| run_id | `UUID` | Unique identifier for the tracer run for this call. If not provided, a new UUID will be generated. |
| tags | `List[str]` | Tags for this call and any sub-calls (e.g., a Chain calling an LLM). You can use these to filter calls. |
| metadata | `Dict[str, Any]` | Metadata for this call and any sub-calls (e.g., a Chain calling an LLM). Keys should be strings, values should be JSON-serializable. |
LangGraph graphs implement the [LangChain Runnable Interface](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) and accept a second argument (`RunnableConfig`) in methods like `invoke`, `ainvoke`, `stream` etc.
The LangSmith platform will allow you to search and filter traces based on `run_name`, `run_id`, `tags` and `metadata`.
## TLDR
```python
import uuid
# Generate a random UUID -- it must be a UUID
config = {"run_id": uuid.uuid4()}, "tags": ["my_tag1"], "metadata": {"a": 5}}
# Works with all standard Runnable methods
# like invoke, batch, ainvoke, astream_events etc
graph.stream(inputs, config, stream_mode="values")
```
The rest of the how to guide will show a full agent.
## Setup
First, let's install the required packages and set our API keys
```python
%%capture --no-stderr
%pip install --quiet -U langgraph langchain_openai
```
```python
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
_set_env("LANGSMITH_API_KEY")
```
!!! tip
Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started [here](https://docs.smith.langchain.com).
## Define the graph
For this example we will use the [prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/).
```python
from langchain_openai import ChatOpenAI
from typing import Literal
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
# First we initialize the model we want to use.
model = ChatOpenAI(model="gpt-4o", temperature=0)
# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
# Define the graph
graph = create_react_agent(model, tools=tools)
```
## Run your graph
Now that we've defined our graph let's run it once and view the trace in LangSmith. In order for our trace to be easily accessible in LangSmith, we will pass in a custom `run_id` in the config.
This assumes that you have set your `LANGSMITH_API_KEY` environment variable.
Note that you can also configure what project to trace to by setting the `LANGCHAIN_PROJECT` environment variable, by default runs will be traced to the `default` project.
```python
import uuid
def print_stream(stream):
for s in stream:
message = s["messages"][-1]
if isinstance(message, tuple):
print(message)
else:
message.pretty_print()
inputs = {"messages": [("user", "what is the weather in sf")]}
config = {"run_name": "agent_007", "tags": ["cats are awesome"]}
print_stream(graph.stream(inputs, config, stream_mode="values"))
```
**Output:**
```
================================ Human Message ==================================
what is the weather in sf
================================== Ai Message ===================================
Tool Calls:
get_weather (call_9ZudXyMAdlUjptq9oMGtQo8o)
Call ID: call_9ZudXyMAdlUjptq9oMGtQo8o
Args:
city: sf
================================= Tool Message ==================================
Name: get_weather
It's always sunny in sf
================================== Ai Message ===================================
The weather in San Francisco is currently sunny.
```
## View the trace in LangSmith
Now that we've ran our graph, let's head over to LangSmith and view our trace. First click into the project that you traced to (in our case the default project). You should see a run with the custom run name "agent_007".
![LangSmith Trace View](assets/d38d1f2b-0f4c-4707-b531-a3c749de987f.png)
In addition, you will be able to filter traces after the fact using the tags or metadata provided. For example,
![LangSmith Filter View](assets/410e0089-2ab8-46bb-a61a-827187fd46b3.png)
docs/docs/how-tos/tool-calling.md
+2
View File
@@ -344,6 +344,8 @@ tool_node.invoke({"messages": [...]})
## Tool customization
For more control over tool behavior, use the `@tool` decorator.
### Parameter descriptions
Auto-generate descriptions from docstrings:
docs/docs/troubleshooting/studio.md
+2 -2
View File
@@ -17,7 +17,7 @@ Safari blocks plain-HTTP traffic on localhost. When running Studio with `langgra
```shell
# Requires @langchain/langgraph-cli>=0.0.26
npx @langchain/langgraph-cli dev
npx @langchain/langgraph-cli dev --tunnel
```
The command outputs a URL in this format:
@@ -55,7 +55,7 @@ Disable Brave Shields for LangSmith using the Brave icon in the URL bar.
```shell
# Requires @langchain/langgraph-cli>=0.0.26
npx @langchain/langgraph-cli dev
npx @langchain/langgraph-cli dev --tunnel
```
The command outputs a URL in this format:
docs/docs/tutorials/get-started/1-build-basic-chatbot.md
+1 -1
View File
@@ -112,7 +112,7 @@ This tells the graph to terminate after running the chatbot node.
## 6. Compile the graph
Before running the graph, we'll need to compile it. We can do so by calling `compile()`
on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
on the graph builder. This creates a `CompiledStateGraph` we can invoke on our state.
```python
graph = graph_builder.compile()
docs/docs/tutorials/get-started/2-add-tools.md
+6 -2
View File
@@ -23,12 +23,16 @@ pip install -U langchain-tavily
Configure your environment with your search engine API key:
```bash
```python
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("TAVILY_API_KEY")
```
```
TAVILY_API_KEY: ········
os.environ["TAVILY_API_KEY"]: "········"
```
## 3. Define the tool
docs/docs/tutorials/multi_agent/agent_supervisor.ipynb
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docs/docs/tutorials/multi_agent/agent_supervisor.md
+812
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@@ -0,0 +1,812 @@
# Multi-agent supervisor
[**Supervisor**](../../concepts/multi_agent.md#supervisor) is a multi-agent architecture where **specialized** agents are coordinated by a central **supervisor agent**. The supervisor agent controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements.
In this tutorial, you will build a supervisor system with two agents — a research and a math expert. By the end of the tutorial you will:
1. Build specialized research and math agents
2. Build a supervisor for orchestrating them with the prebuilt [`langgraph-supervisor`](https://langchain-ai.github.io/langgraph/agents/multi-agent/#supervisor)
3. Build a supervisor from scratch
4. Implement advanced task delegation
![diagram](assets/diagram.png)
## Setup
First, let's install required packages and set our API keys
```python
%%capture --no-stderr
%pip install -U langgraph langgraph-supervisor langchain-tavily "langchain[openai]"
```
```python
import getpass
import os
def _set_if_undefined(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"Please provide your {var}")
_set_if_undefined("OPENAI_API_KEY")
_set_if_undefined("TAVILY_API_KEY")
```
!!! tip
Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph.
## 1. Create worker agents
First, let's create our specialized worker agents — research agent and math agent:
* Research agent will have access to a web search tool using [Tavily API](https://tavily.com/)
* Math agent will have access to simple math tools (`add`, `multiply`, `divide`)
### Research agent
For web search, we will use `TavilySearch` tool from `langchain-tavily`:
```python
from langchain_tavily import TavilySearch
web_search = TavilySearch(max_results=3)
web_search_results = web_search.invoke("who is the mayor of NYC?")
print(web_search_results["results"][0]["content"])
```
**Output:**
```
Find events, attractions, deals, and more at nyctourism.com Skip Main Navigation Menu The Official Website of the City of New York Text Size Powered by Translate SearchSearch Primary Navigation The official website of NYC Home NYC Resources NYC311 Office of the Mayor Events Connect Jobs Search Office of the Mayor | Mayor's Bio | City of New York Secondary Navigation MayorBiographyNewsOfficials Eric L. Adams 110th Mayor of New York City Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve public safety, and build a stronger, healthier city that delivers for all New Yorkers. As the representative of one of the nation's largest counties, Eric fought tirelessly to grow the local economy, invest in schools, reduce inequality, improve public safety, and advocate for smart policies and better government that delivers for all New Yorkers.
```
To create individual worker agents, we will use LangGraph's prebuilt [agent](../../agents/agents.md).
```python
from langgraph.prebuilt import create_react_agent
research_agent = create_react_agent(
model="openai:gpt-4.1",
tools=[web_search],
prompt=(
"You are a research agent.\n\n"
"INSTRUCTIONS:\n"
"- Assist ONLY with research-related tasks, DO NOT do any math\n"
"- After you're done with your tasks, respond to the supervisor directly\n"
"- Respond ONLY with the results of your work, do NOT include ANY other text."
),
name="research_agent",
)
```
Let's [run the agent](../../agents/run_agents.md) to verify that it behaves as expected.
!!! note "We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely"
```python
from langchain_core.messages import convert_to_messages
def pretty_print_message(message, indent=False):
pretty_message = message.pretty_repr(html=True)
if not indent:
print(pretty_message)
return
indented = "\n".join("\t" + c for c in pretty_message.split("\n"))
print(indented)
def pretty_print_messages(update, last_message=False):
is_subgraph = False
if isinstance(update, tuple):
ns, update = update
# skip parent graph updates in the printouts
if len(ns) == 0:
return
graph_id = ns[-1].split(":")[0]
print(f"Update from subgraph {graph_id}:")
print("\n")
is_subgraph = True
for node_name, node_update in update.items():
update_label = f"Update from node {node_name}:"
if is_subgraph:
update_label = "\t" + update_label
print(update_label)
print("\n")
messages = convert_to_messages(node_update["messages"])
if last_message:
messages = messages[-1:]
for m in messages:
pretty_print_message(m, indent=is_subgraph)
print("\n")
```
```python
from langchain_core.messages import convert_to_messages
def pretty_print_message(message, indent=False):
pretty_message = message.pretty_repr(html=True)
if not indent:
print(pretty_message)
return
indented = "\n".join("\t" + c for c in pretty_message.split("\n"))
print(indented)
def pretty_print_messages(update, last_message=False):
is_subgraph = False
if isinstance(update, tuple):
ns, update = update
# skip parent graph updates in the printouts
if len(ns) == 0:
return
graph_id = ns[-1].split(":")[0]
print(f"Update from subgraph {graph_id}:")
print("\n")
is_subgraph = True
for node_name, node_update in update.items():
update_label = f"Update from node {node_name}:"
if is_subgraph:
update_label = "\t" + update_label
print(update_label)
print("\n")
messages = convert_to_messages(node_update["messages"])
if last_message:
messages = messages[-1:]
for m in messages:
pretty_print_message(m, indent=is_subgraph)
print("\n")
```
```python
for chunk in research_agent.stream(
{"messages": [{"role": "user", "content": "who is the mayor of NYC?"}]}
):
pretty_print_messages(chunk)
```
**Output:**
```
Update from node agent:
================================== Ai Message ==================================
Name: research_agent
Tool Calls:
tavily_search (call_U748rQhQXT36sjhbkYLSXQtJ)
Call ID: call_U748rQhQXT36sjhbkYLSXQtJ
Args:
query: current mayor of New York City
search_depth: basic
Update from node tools:
================================= Tool Message ==================================
Name: tavily_search
{"query": "current mayor of New York City", "follow_up_questions": null, "answer": null, "images": [], "results": [{"title": "List of mayors of New York City - Wikipedia", "url": "https://en.wikipedia.org/wiki/List_of_mayors_of_New_York_City", "content": "The mayor of New York City is the chief executive of the Government of New York City, as stipulated by New York City's charter.The current officeholder, the 110th in the sequence of regular mayors, is Eric Adams, a member of the Democratic Party.. During the Dutch colonial period from 1624 to 1664, New Amsterdam was governed by the Director of Netherland.", "score": 0.9039154, "raw_content": null}, {"title": "Office of the Mayor | Mayor's Bio | City of New York - NYC.gov", "url": "https://www.nyc.gov/office-of-the-mayor/bio.page", "content": "Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve", "score": 0.8405867, "raw_content": null}, {"title": "Eric Adams - Wikipedia", "url": "https://en.wikipedia.org/wiki/Eric_Adams", "content": "Eric Leroy Adams (born September 1, 1960) is an American politician and former police officer who has served as the 110th mayor of New York City since 2022. Adams was an officer in the New York City Transit Police and then the New York City Police Department (```
```
### Math agent
For math agent tools we will use [vanilla Python functions](../../how-tos/tool-calling.md#define-a-tool):
```python
def add(a: float, b: float):
"""Add two numbers."""
return a + b
def multiply(a: float, b: float):
"""Multiply two numbers."""
return a * b
def divide(a: float, b: float):
"""Divide two numbers."""
return a / b
math_agent = create_react_agent(
model="openai:gpt-4.1",
tools=[add, multiply, divide],
prompt=(
"You are a math agent.\n\n"
"INSTRUCTIONS:\n"
"- Assist ONLY with math-related tasks\n"
"- After you're done with your tasks, respond to the supervisor directly\n"
"- Respond ONLY with the results of your work, do NOT include ANY other text."
),
name="math_agent",
)
```
Let's run the math agent:
```python
for chunk in math_agent.stream(
{"messages": [{"role": "user", "content": "what's (3 + 5) x 7"}]}
):
pretty_print_messages(chunk)
```
**Output:**
```
Update from node agent:
================================== Ai Message ==================================
Name: math_agent
Tool Calls:
add (call_p6OVLDHB4LyCNCxPOZzWR15v)
Call ID: call_p6OVLDHB4LyCNCxPOZzWR15v
Args:
a: 3
b: 5
Update from node tools:
================================= Tool Message ==================================
Name: add
8.0
Update from node agent:
================================== Ai Message ==================================
Name: math_agent
Tool Calls:
multiply (call_EoaWHMLFZAX4AkajQCtZvbli)
Call ID: call_EoaWHMLFZAX4AkajQCtZvbli
Args:
a: 8
b: 7
Update from node tools:
================================= Tool Message ==================================
Name: multiply
56.0
Update from node agent:
================================== Ai Message ==================================
Name: math_agent
56
```
## 2. Create supervisor with `langgraph-supervisor`
To implement out multi-agent system, we will use [`create_supervisor`][langgraph_supervisor.supervisor.create_supervisor] from the prebuilt `langgraph-supervisor` library:
```python
from langgraph_supervisor import create_supervisor
from langchain.chat_models import init_chat_model
supervisor = create_supervisor(
model=init_chat_model("openai:gpt-4.1"),
agents=[research_agent, math_agent],
prompt=(
"You are a supervisor managing two agents:\n"
"- a research agent. Assign research-related tasks to this agent\n"
"- a math agent. Assign math-related tasks to this agent\n"
"Assign work to one agent at a time, do not call agents in parallel.\n"
"Do not do any work yourself."
),
add_handoff_back_messages=True,
output_mode="full_history",
).compile()
```
```python
from IPython.display import display, Image
display(Image(supervisor.get_graph().draw_mermaid_png()))
```
![Graph](assets/output.png)
**Note:** When you run this code, it will generate and display a visual representation of the supervisor graph showing the flow between the supervisor and worker agents.
Let's now run it with a query that requires both agents:
* research agent will look up the necessary GDP information
* math agent will perform division to find the percentage of NY state GDP, as requested
```python
for chunk in supervisor.stream(
{
"messages": [
{
"role": "user",
"content": "find US and New York state GDP in 2024. what % of US GDP was New York state?",
}
]
},
):
pretty_print_messages(chunk, last_message=True)
final_message_history = chunk["supervisor"]["messages"]
```
**Output:**
```
Update from node supervisor:
================================= Tool Message ==================================
Name: transfer_to_research_agent
Successfully transferred to research_agent
Update from node research_agent:
================================= Tool Message ==================================
Name: transfer_back_to_supervisor
Successfully transferred back to supervisor
Update from node supervisor:
================================= Tool Message ==================================
Name: transfer_to_math_agent
Successfully transferred to math_agent
Update from node math_agent:
================================= Tool Message ==================================
Name: transfer_back_to_supervisor
Successfully transferred back to supervisor
Update from node supervisor:
================================== Ai Message ==================================
Name: supervisor
In 2024, the US GDP was $29.18 trillion and New York State's GDP was $2.297 trillion. New York State accounted for approximately 7.87% of the total US GDP in 2024.
```
## 3. Create supervisor from scratch
Let's now implement this same multi-agent system from scratch. We will need to:
1. [Set up how the supervisor communicates](#set-up-agent-communication) with individual agents
2. [Create the supervisor agent](#create-supervisor-agent)
3. Combine supervisor and worker agents into a [single multi-agent graph](#create-multi-agent-graph).
### Set up agent communication
We will need to define a way for the supervisor agent to communicate with the worker agents. A common way to implement this in multi-agent architectures is using **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify:
- **destination**: target agent to transfer to
- **payload**: information to pass to that agent
We will implement handoffs via **handoff tools** and give these tools to the supervisor agent: when the supervisor calls these tools, it will hand off control to a worker agent, passing the full message history to that agent.
```python
from typing import Annotated
from langchain_core.tools import tool, InjectedToolCallId
from langgraph.prebuilt import InjectedState
from langgraph.graph import StateGraph, START, MessagesState
from langgraph.types import Command
def create_handoff_tool(*, agent_name: str, description: str | None = None):
name = f"transfer_to_{agent_name}"
description = description or f"Ask {agent_name} for help."
@tool(name, description=description)
def handoff_tool(
state: Annotated[MessagesState, InjectedState],
tool_call_id: Annotated[str, InjectedToolCallId],
) -> Command:
tool_message = {
"role": "tool",
"content": f"Successfully transferred to {agent_name}",
"name": name,
"tool_call_id": tool_call_id,
}
# highlight-next-line
return Command(
# highlight-next-line
goto=agent_name, # (1)!
# highlight-next-line
update={**state, "messages": state["messages"] + [tool_message]}, # (2)!
# highlight-next-line
graph=Command.PARENT, # (3)!
)
return handoff_tool
# Handoffs
assign_to_research_agent = create_handoff_tool(
agent_name="research_agent",
description="Assign task to a researcher agent.",
)
assign_to_math_agent = create_handoff_tool(
agent_name="math_agent",
description="Assign task to a math agent.",
)
```
1. Name of the agent or node to hand off to.
2. Take the agent's messages and add them to the parent's state as part of the handoff. The next agent will see the parent state.
3. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
### Create supervisor agent
Then, let's create the supervisor agent with the handoff tools we just defined. We will use the prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
```python
supervisor_agent = create_react_agent(
model="openai:gpt-4.1",
tools=[assign_to_research_agent, assign_to_math_agent],
prompt=(
"You are a supervisor managing two agents:\n"
"- a research agent. Assign research-related tasks to this agent\n"
"- a math agent. Assign math-related tasks to this agent\n"
"Assign work to one agent at a time, do not call agents in parallel.\n"
"Do not do any work yourself."
),
name="supervisor",
)
```
### Create multi-agent graph
Putting this all together, let's create a graph for our overall multi-agent system. We will add the supervisor and the individual agents as subgraph [nodes](../../concepts/low_level.md#nodes).
```python
from langgraph.graph import END
# Define the multi-agent supervisor graph
supervisor = (
StateGraph(MessagesState)
# NOTE: `destinations` is only needed for visualization and doesn't affect runtime behavior
.add_node(supervisor_agent, destinations=("research_agent", "math_agent", END))
.add_node(research_agent)
.add_node(math_agent)
.add_edge(START, "supervisor")
# always return back to the supervisor
.add_edge("research_agent", "supervisor")
.add_edge("math_agent", "supervisor")
.compile()
)
```
Notice that we've added explicit [edges](../../concepts/low_level.md#edges) from worker agents back to the supervisor — this means that they are guaranteed to return control back to the supervisor. If you want the agents to respond directly to the user (i.e., turn the system into a router, you can remove these edges).
```python
from IPython.display import display, Image
display(Image(supervisor.get_graph().draw_mermaid_png()))
```
![Graph](assets/multi-output.png)
**Note:** When you run this code, it will generate and display a visual representation of the multi-agent supervisor graph showing the flow between the supervisor and worker agents.
With the multi-agent graph created, let's now run it!
```python
for chunk in supervisor.stream(
{
"messages": [
{
"role": "user",
"content": "find US and New York state GDP in 2024. what % of US GDP was New York state?",
}
]
},
):
pretty_print_messages(chunk, last_message=True)
final_message_history = chunk["supervisor"]["messages"]
```
**Output:**
```
Update from node supervisor:
================================= Tool Message ==================================
Name: transfer_to_research_agent
Successfully transferred to research_agent
Update from node research_agent:
================================== Ai Message ==================================
Name: research_agent
- US GDP in 2024 is projected to be about $28.18 trillion USD (Statista; CBO projection).
- New York State's nominal GDP for 2024 is estimated at approximately $2.16 trillion USD (various economic reports).
- New York State's share of US GDP in 2024 is roughly 7.7%.
Sources:
- https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/
- https://nyassembly.gov/Reports/WAM/2025economic_revenue/2025_report.pdf?v=1740533306
Update from node supervisor:
================================= Tool Message ==================================
Name: transfer_to_math_agent
Successfully transferred to math_agent
Update from node math_agent:
================================== Ai Message ==================================
Name: math_agent
US GDP in 2024: $28.18 trillion
New York State GDP in 2024: $2.16 trillion
Percentage of US GDP from New York State: 7.67%
Update from node supervisor:
================================== Ai Message ==================================
Name: supervisor
Here are your results:
- 2024 US GDP (projected): $28.18 trillion USD
- 2024 New York State GDP (estimated): $2.16 trillion USD
- New York State's share of US GDP: approximately 7.7%
If you need the calculation steps or sources, let me know!
```
Let's examine the full resulting message history:
```python
for message in final_message_history:
message.pretty_print()
```
**Output:**
```
================================ Human Message ==================================
find US and New York state GDP in 2024. what % of US GDP was New York state?
================================== Ai Message ===================================
Name: supervisor
Tool Calls:
transfer_to_research_agent (call_KlGgvF5ahlAbjX8d2kHFjsC3)
Call ID: call_KlGgvF5ahlAbjX8d2kHFjsC3
Args:
================================= Tool Message ==================================
Name: transfer_to_research_agent
Successfully transferred to research_agent
================================== Ai Message ===================================
Name: research_agent
Tool Calls:
tavily_search (call_ZOaTVUA6DKrOjWQldLhtrsO2)
Call ID: call_ZOaTVUA6DKrOjWQldLhtrsO2
Args:
query: US GDP 2024 estimate or actual
search_depth: advanced
tavily_search (call_QsRAasxW9K03lTlqjuhNLFbZ)
Call ID: call_QsRAasxW9K03lTlqjuhNLFbZ
Args:
query: New York state GDP 2024 estimate or actual
search_depth: advanced
================================= Tool Message ==================================
Name: tavily_search
{"query": "US GDP 2024 estimate or actual", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://www.advisorperspectives.com/dshort/updates/2025/05/29/gdp-gross-domestic-product-q1-2025-second-estimate", "title": "Q1 GDP Second Estimate: Real GDP at -0.2%, Higher Than Expected", "content": "> Real gross domestic product (GDP) decreased at an annual rate of 0.2 percent in the first quarter of 2025 (January, February, and March), according to the second estimate released by the U.S. Bureau of Economic Analysis. In the fourth quarter of 2024, real GDP increased 2.4 percent. The decrease in real GDP in the first quarter primarily reflected an increase in imports, which are a subtraction in the calculation of GDP, and a decrease in government spending. These movements were partly [...] by [Harry Mamaysky](https://www.advisor```
```
!!! important
You can see that the supervisor system appends **all** of the individual agent messages (i.e., their internal tool-calling loop) to the full message history. This means that on every supervisor turn, supervisor agent sees this full history. If you want more control over:
* **how inputs are passed to agents**: you can use LangGraph [`Send()`][langgraph.types.Send] primitive to directly send data to the worker agents during the handoff. See the [task delegation](#4-create-delegation-tasks) example below
* **how agent outputs are added**: you can control how much of the agent's internal message history is added to the overall supervisor message history by wrapping the agent in a separate node function:
```python
def call_research_agent(state):
# return agent's final response,
# excluding inner monologue
response = research_agent.invoke(state)
# highlight-next-line
return {"messages": response["messages"][-1]}
```
## 4. Create delegation tasks
So far the individual agents relied on **interpreting full message history** to determine their tasks. An alternative approach is to ask the supervisor to **formulate a task explicitly**. We can do so by adding a `task_description` parameter to the `handoff_tool` function.
```python
from langgraph.types import Send
def create_task_description_handoff_tool(
*, agent_name: str, description: str | None = None
):
name = f"transfer_to_{agent_name}"
description = description or f"Ask {agent_name} for help."
@tool(name, description=description)
def handoff_tool(
# this is populated by the supervisor LLM
task_description: Annotated[
str,
"Description of what the next agent should do, including all of the relevant context.",
],
# these parameters are ignored by the LLM
state: Annotated[MessagesState, InjectedState],
) -> Command:
task_description_message = {"role": "user", "content": task_description}
agent_input = {**state, "messages": [task_description_message]}
return Command(
# highlight-next-line
goto=[Send(agent_name, agent_input)],
graph=Command.PARENT,
)
return handoff_tool
assign_to_research_agent_with_description = create_task_description_handoff_tool(
agent_name="research_agent",
description="Assign task to a researcher agent.",
)
assign_to_math_agent_with_description = create_task_description_handoff_tool(
agent_name="math_agent",
description="Assign task to a math agent.",
)
supervisor_agent_with_description = create_react_agent(
model="openai:gpt-4.1",
tools=[
assign_to_research_agent_with_description,
assign_to_math_agent_with_description,
],
prompt=(
"You are a supervisor managing two agents:\n"
"- a research agent. Assign research-related tasks to this assistant\n"
"- a math agent. Assign math-related tasks to this assistant\n"
"Assign work to one agent at a time, do not call agents in parallel.\n"
"Do not do any work yourself."
),
name="supervisor",
)
supervisor_with_description = (
StateGraph(MessagesState)
.add_node(
supervisor_agent_with_description, destinations=("research_agent", "math_agent")
)
.add_node(research_agent)
.add_node(math_agent)
.add_edge(START, "supervisor")
.add_edge("research_agent", "supervisor")
.add_edge("math_agent", "supervisor")
.compile()
)
```
!!! note
We're using [`Send()`][langgraph.types.Send] primitive in the `handoff_tool`. This means that instead of receiving the full `supervisor` graph state as input, each worker agent only sees the contents of the `Send` payload. In this example, we're sending the task description as a single "human" message.
Let's now running it with the same input query:
```python
for chunk in supervisor_with_description.stream(
{
"messages": [
{
"role": "user",
"content": "find US and New York state GDP in 2024. what % of US GDP was New York state?",
}
]
},
subgraphs=True,
):
pretty_print_messages(chunk, last_message=True)
```
**Output:**
```
Update from subgraph supervisor:
Update from node agent:
================================== Ai Message ==================================
Name: supervisor
Tool Calls:
transfer_to_research_agent (call_tk8q8py8qK6MQz6Kj6mijKua)
Call ID: call_tk8q8py8qK6MQz6Kj6mijKua
Args:
task_description: Find the 2024 GDP (Gross Domestic Product) for both the United States and New York state, using the most up-to-date and reputable sources available. Provide both GDP values and cite the data sources.
Update from subgraph research_agent:
Update from node agent:
================================== Ai Message ==================================
Name: research_agent
Tool Calls:
tavily_search (call_KqvhSvOIhAvXNsT6BOwbPlRB)
Call ID: call_KqvhSvOIhAvXNsT6BOwbPlRB
Args:
query: 2024 United States GDP value from a reputable source
search_depth: advanced
tavily_search (call_kbbAWBc9KwCWKHmM5v04H88t)
Call ID: call_kbbAWBc9KwCWKHmM5v04H88t
Args:
query: 2024 New York state GDP value from a reputable source
search_depth: advanced
Update from subgraph research_agent:
Update from node tools:
================================= Tool Message ==================================
Name: tavily_search
{"query": "2024 United States GDP value from a reputable source", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://www.focus-economics.com/countries/united-states/", "title": "United States Economy Overview - Focus Economics", "content": "The United States' Macroeconomic Analysis:\n------------------------------------------\n\n**Nominal GDP of USD 29,185 billion in 2024.**\n\n**Nominal GDP of USD 29,179 billion in 2024.**\n\n**GDP per capita of USD 86,635 compared to the global average of USD 10,589.**\n\n**GDP per capita of USD 86,652 compared to the global average of USD 10,589.**\n\n**Average real GDP growth of 2.5% over the last decade.**\n\n**Average real GDP growth of ```
```
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docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb
+3 -3
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@@ -49,7 +49,7 @@
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental"
"%pip install -U langgraph langchain_community langchain_anthropic langchain-tavily langchain_experimental"
]
},
{
@@ -121,10 +121,10 @@
"from typing import Annotated, List\n",
"\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langchain_tavily import TavilySearch\n",
"from langchain_core.tools import tool\n",
"\n",
"tavily_tool = TavilySearchResults(max_results=5)\n",
"tavily_tool = TavilySearch(max_results=5)\n",
"\n",
"\n",
"@tool\n",
docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb
+3 -3
View File
@@ -37,7 +37,7 @@
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U langchain_community langchain_anthropic langchain_experimental matplotlib langgraph"
"%pip install -U langchain_community langchain_anthropic langchain-tavily langchain_experimental matplotlib langgraph"
]
},
{
@@ -92,11 +92,11 @@
"source": [
"from typing import Annotated\n",
"\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langchain_tavily import TavilySearch\n",
"from langchain_core.tools import tool\n",
"from langchain_experimental.utilities import PythonREPL\n",
"\n",
"tavily_tool = TavilySearchResults(max_results=5)\n",
"tavily_tool = TavilySearch(max_results=5)\n",
"\n",
"# Warning: This executes code locally, which can be unsafe when not sandboxed\n",
"\n",
docs/docs/tutorials/overview.md
-21
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@@ -1,21 +0,0 @@
# Examples
The pages in this section provide end-to-end examples for the following topics:
## General
- [Agentic RAG](./rag/langgraph_adaptive_rag.ipynb)
- [Agent Supervisor](./multi_agent/agent_supervisor.ipynb)
- [SQL agent](./sql-agent.ipynb)
- [Graph runs in LangSmith](../how-tos/run-id-langsmith.ipynb)
## LangGraph Platform
- [Set up custom authentication](./auth/getting_started.md)
- [Make conversations private](./auth/resource_auth.md)
- [Connect an authentication provider](./auth/add_auth_server.md)
- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
- [Use RemoteGraph](../how-tos/use-remote-graph.md)
- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-langgraph-platform.ipynb)
- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md)
- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)
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# Agentic RAG
In this tutorial we will build a [retrieval agent](https://python.langchain.com/docs/tutorials/qa_chat_history). Retrieval agents are useful when you want an LLM to make a decision about whether to retrieve context from a vectorstore or respond to the user directly.
By the end of the tutorial we will have done the following:
1. Fetch and preprocess documents that will be used for retrieval.
2. Index those documents for semantic search and create a retriever tool for the agent.
3. Build an agentic RAG system that can decide when to use the retriever tool.
![Screenshot 2024-02-14 at 3.43.58 PM.png](assets/screenshot_2024_02_14_3_43_58_pm.png)
## Setup
Let's download the required packages and set our API keys:
```python
%%capture --no-stderr
%pip install -U --quiet langgraph "langchain[openai]" langchain-community langchain-text-splitters
```
```python
import getpass
import os
def _set_env(key: str):
if key not in os.environ:
os.environ[key] = getpass.getpass(f"{key}:")
_set_env("OPENAI_API_KEY")
```
!!! tip
Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph.
## 1. Preprocess documents
1. Fetch documents to use in our RAG system. We will use three of the most recent pages from [Lilian Weng's excellent blog](https://lilianweng.github.io/). We'll start by fetching the content of the pages using `WebBaseLoader` utility:
```python
from langchain_community.document_loaders import WebBaseLoader
urls = [
"https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
"https://lilianweng.github.io/posts/2024-07-07-hallucination/",
"https://lilianweng.github.io/posts/2024-04-12-diffusion-video/",
]
docs = [WebBaseLoader(url).load() for url in urls]
```
```python
docs[0][0].page_content.strip()[:1000]
```
2. Split the fetched documents into smaller chunks for indexing into our vectorstore:
```python
from langchain_text_splitters import RecursiveCharacterTextSplitter
docs_list = [item for sublist in docs for item in sublist]
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=100, chunk_overlap=50
)
doc_splits = text_splitter.split_documents(docs_list)
```
```python
doc_splits[0].page_content.strip()
```
## 2. Create a retriever tool
Now that we have our split documents, we can index them into a vector store that we'll use for semantic search.
1. Use an in-memory vector store and OpenAI embeddings:
```python
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
vectorstore = InMemoryVectorStore.from_documents(
documents=doc_splits, embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()
```
2. Create a retriever tool using LangChain's prebuilt `create_retriever_tool`:
```python
from langchain.tools.retriever import create_retriever_tool
retriever_tool = create_retriever_tool(
retriever,
"retrieve_blog_posts",
"Search and return information about Lilian Weng blog posts.",
)
```
3. Test the tool:
```python
retriever_tool.invoke({"query": "types of reward hacking"})
```
## 3. Generate query
Now we will start building components ([nodes](../../concepts/low_level.md#nodes) and [edges](../../concepts/low_level.md#edges)) for our agentic RAG graph. Note that the components will operate on the [`MessagesState`](../../concepts/low_level.md#messagesstate) — graph state that contains a `messages` key with a list of [chat messages](https://python.langchain.com/docs/concepts/messages/).
1. Build a `generate_query_or_respond` node. It will call an LLM to generate a response based on the current graph state (list of messages). Given the input messages, it will decide to retrieve using the retriever tool, or respond directly to the user. Note that we're giving the chat model access to the `retriever_tool` we created earlier via `.bind_tools`:
```python
from langgraph.graph import MessagesState
from langchain.chat_models import init_chat_model
response_model = init_chat_model("openai:gpt-4.1", temperature=0)
def generate_query_or_respond(state: MessagesState):
"""Call the model to generate a response based on the current state. Given
the question, it will decide to retrieve using the retriever tool, or simply respond to the user.
"""
response = (
response_model
# highlight-next-line
.bind_tools([retriever_tool]).invoke(state["messages"])
)
return {"messages": [response]}
```
2. Try it on a random input:
```python
input = {"messages": [{"role": "user", "content": "hello!"}]}
generate_query_or_respond(input)["messages"][-1].pretty_print()
```
**Output:**
```
================================== Ai Message ==================================
Hello! How can I help you today?
```
3. Ask a question that requires semantic search:
```python
input = {
"messages": [
{
"role": "user",
"content": "What does Lilian Weng say about types of reward hacking?",
}
]
}
generate_query_or_respond(input)["messages"][-1].pretty_print()
```
**Output:**
```
================================== Ai Message ==================================
Tool Calls:
retrieve_blog_posts (call_tYQxgfIlnQUDMdtAhdbXNwIM)
Call ID: call_tYQxgfIlnQUDMdtAhdbXNwIM
Args:
query: types of reward hacking
```
## 4. Grade documents
1. Add a [conditional edge](../../concepts/low_level.md#conditional-edges) — `grade_documents` — to determine whether the retrieved documents are relevant to the question. We will use a model with a structured output schema `GradeDocuments` for document grading. The `grade_documents` function will return the name of the node to go to based on the grading decision (`generate_answer` or `rewrite_question`):
```python
from pydantic import BaseModel, Field
from typing import Literal
GRADE_PROMPT = (
"You are a grader assessing relevance of a retrieved document to a user question. \n "
"Here is the retrieved document: \n\n {context} \n\n"
"Here is the user question: {question} \n"
"If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n"
"Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."
)
# highlight-next-line
class GradeDocuments(BaseModel):
"""Grade documents using a binary score for relevance check."""
binary_score: str = Field(
description="Relevance score: 'yes' if relevant, or 'no' if not relevant"
)
grader_model = init_chat_model("openai:gpt-4.1", temperature=0)
def grade_documents(
state: MessagesState,
) -> Literal["generate_answer", "rewrite_question"]:
"""Determine whether the retrieved documents are relevant to the question."""
question = state["messages"][0].content
context = state["messages"][-1].content
prompt = GRADE_PROMPT.format(question=question, context=context)
response = (
grader_model
# highlight-next-line
.with_structured_output(GradeDocuments).invoke(
[{"role": "user", "content": prompt}]
)
)
score = response.binary_score
if score == "yes":
return "generate_answer"
else:
return "rewrite_question"
```
2. Run this with irrelevant documents in the tool response:
```python
from langchain_core.messages import convert_to_messages
input = {
"messages": convert_to_messages(
[
{
"role": "user",
"content": "What does Lilian Weng say about types of reward hacking?",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "1",
"name": "retrieve_blog_posts",
"args": {"query": "types of reward hacking"},
}
],
},
{"role": "tool", "content": "meow", "tool_call_id": "1"},
]
)
}
grade_documents(input)
```
3. Confirm that the relevant documents are classified as such:
```python
input = {
"messages": convert_to_messages(
[
{
"role": "user",
"content": "What does Lilian Weng say about types of reward hacking?",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "1",
"name": "retrieve_blog_posts",
"args": {"query": "types of reward hacking"},
}
],
},
{
"role": "tool",
"content": "reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering",
"tool_call_id": "1",
},
]
)
}
grade_documents(input)
```
## 5. Rewrite question
1. Build the `rewrite_question` node. The retriever tool can return potentially irrelevant documents, which indicates a need to improve the original user question. To do so, we will call the `rewrite_question` node:
```python
REWRITE_PROMPT = (
"Look at the input and try to reason about the underlying semantic intent / meaning.\n"
"Here is the initial question:"
"\n ------- \n"
"{question}"
"\n ------- \n"
"Formulate an improved question:"
)
def rewrite_question(state: MessagesState):
"""Rewrite the original user question."""
messages = state["messages"]
question = messages[0].content
prompt = REWRITE_PROMPT.format(question=question)
response = response_model.invoke([{"role": "user", "content": prompt}])
return {"messages": [{"role": "user", "content": response.content}]}
```
2. Try it out:
```python
input = {
"messages": convert_to_messages(
[
{
"role": "user",
"content": "What does Lilian Weng say about types of reward hacking?",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "1",
"name": "retrieve_blog_posts",
"args": {"query": "types of reward hacking"},
}
],
},
{"role": "tool", "content": "meow", "tool_call_id": "1"},
]
)
}
response = rewrite_question(input)
print(response["messages"][-1]["content"])
```
**Output:**
```
What are the different types of reward hacking described by Lilian Weng, and how does she explain them?
```
## 6. Generate an answer
1. Build `generate_answer` node: if we pass the grader checks, we can generate the final answer based on the original question and the retrieved context:
```python
GENERATE_PROMPT = (
"You are an assistant for question-answering tasks. "
"Use the following pieces of retrieved context to answer the question. "
"If you don't know the answer, just say that you don't know. "
"Use three sentences maximum and keep the answer concise.\n"
"Question: {question} \n"
"Context: {context}"
)
def generate_answer(state: MessagesState):
"""Generate an answer."""
question = state["messages"][0].content
context = state["messages"][-1].content
prompt = GENERATE_PROMPT.format(question=question, context=context)
response = response_model.invoke([{"role": "user", "content": prompt}])
return {"messages": [response]}
```
2. Try it:
```python
input = {
"messages": convert_to_messages(
[
{
"role": "user",
"content": "What does Lilian Weng say about types of reward hacking?",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "1",
"name": "retrieve_blog_posts",
"args": {"query": "types of reward hacking"},
}
],
},
{
"role": "tool",
"content": "reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering",
"tool_call_id": "1",
},
]
)
}
response = generate_answer(input)
response["messages"][-1].pretty_print()
```
**Output:**
```
================================== Ai Message ==================================
Lilian Weng categorizes reward hacking into two types: environment or goal misspecification, and reward tampering. She considers reward hacking as a broad concept that includes both of these categories. Reward hacking occurs when an agent exploits flaws or ambiguities in the reward function to achieve high rewards without performing the intended behaviors.
```
## 7. Assemble the graph
* Start with a `generate_query_or_respond` and determine if we need to call `retriever_tool`
* Route to next step using `tools_condition`:
* If `generate_query_or_respond` returned `tool_calls`, call `retriever_tool` to retrieve context
* Otherwise, respond directly to the user
* Grade retrieved document content for relevance to the question (`grade_documents`) and route to next step:
* If not relevant, rewrite the question using `rewrite_question` and then call `generate_query_or_respond` again
* If relevant, proceed to `generate_answer` and generate final response using the `ToolMessage` with the retrieved document context
```python
from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import ToolNode
from langgraph.prebuilt import tools_condition
workflow = StateGraph(MessagesState)
# Define the nodes we will cycle between
workflow.add_node(generate_query_or_respond)
workflow.add_node("retrieve", ToolNode([retriever_tool]))
workflow.add_node(rewrite_question)
workflow.add_node(generate_answer)
workflow.add_edge(START, "generate_query_or_respond")
# Decide whether to retrieve
workflow.add_conditional_edges(
"generate_query_or_respond",
# Assess LLM decision (call `retriever_tool` tool or respond to the user)
tools_condition,
{
# Translate the condition outputs to nodes in our graph
"tools": "retrieve",
END: END,
},
)
# Edges taken after the `action` node is called.
workflow.add_conditional_edges(
"retrieve",
# Assess agent decision
grade_documents,
)
workflow.add_edge("generate_answer", END)
workflow.add_edge("rewrite_question", "generate_query_or_respond")
# Compile
graph = workflow.compile()
```
Visualize the graph:
```python
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
```
![Graph](assets/agentic-rag-output.png)
## 8. Run the agentic RAG
```python
for chunk in graph.stream(
{
"messages": [
{
"role": "user",
"content": "What does Lilian Weng say about types of reward hacking?",
}
]
}
):
for node, update in chunk.items():
print("Update from node", node)
update["messages"][-1].pretty_print()
print("\n\n")
```
**Output:**
```
Update from node generate_query_or_respond
================================== Ai Message ==================================
Tool Calls:
retrieve_blog_posts (call_NYu2vq4km9nNNEFqJwefWKu1)
Call ID: call_NYu2vq4km9nNNEFqJwefWKu1
Args:
query: types of reward hacking
Update from node retrieve
================================= Tool Message ==================================
Name: retrieve_blog_posts
(Note: Some work defines reward tampering as a distinct category of misalignment behavior from reward hacking. But I consider reward hacking as a broader concept here.)
At a high level, reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering.
Why does Reward Hacking Exist?#
Pan et al. (2022) investigated reward hacking as a function of agent capabilities, including (1) model size, (2) action space resolution, (3) observation space noise, and (4) training time. They also proposed a taxonomy of three types of misspecified proxy rewards:
Let's Define Reward Hacking#
Reward shaping in RL is challenging. Reward hacking occurs when an RL agent exploits flaws or ambiguities in the reward function to obtain high rewards without genuinely learning the intended behaviors or completing the task as designed. In recent years, several related concepts have been proposed, all referring to some form of reward hacking:
Update from node generate_answer
================================== Ai Message ==================================
Lilian Weng categorizes reward hacking into two types: environment or goal misspecification, and reward tampering. She considers reward hacking as a broad concept that includes both of these categories. Reward hacking occurs when an agent exploits flaws or ambiguities in the reward function to achieve high rewards without performing the intended behaviors.
```
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# Build a SQL agent
In this tutorial, we will walk through how to build an agent that can answer questions about a SQL database.
At a high level, the agent will:
1. Fetch the available tables from the database
2. Decide which tables are relevant to the question
3. Fetch the schemas for the relevant tables
4. Generate a query based on the question and information from the schemas
5. Double-check the query for common mistakes using an LLM
6. Execute the query and return the results
7. Correct mistakes surfaced by the database engine until the query is successful
8. Formulate a response based on the results
!!! warning "Security note"
Building Q&A systems of SQL databases requires executing model-generated SQL queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your agent's needs. This will mitigate though not eliminate the risks of building a model-driven system.
## 1. Setup
Let's first install some dependencies. This tutorial uses SQL database and tool abstractions from [langchain-community](https://python.langchain.com/docs/concepts/architecture/#langchain-community). We will also require a LangChain [chat model](https://python.langchain.com/docs/concepts/chat_models/).
```python
%%capture --no-stderr
%pip install -U langgraph langchain_community "langchain[openai]"
```
!!! tip
Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph.
### Select a LLM
First we [initialize our LLM](https://python.langchain.com/docs/how_to/chat_models_universal_init/). Any model supporting [tool-calling](https://python.langchain.com/docs/integrations/chat/#featured-providers) should work. We use OpenAI below.
```python
from langchain.chat_models import init_chat_model
llm = init_chat_model("openai:gpt-4.1")
```
### Configure the database
We will be creating a SQLite database for this tutorial. SQLite is a lightweight database that is easy to set up and use. We will be loading the `chinook` database, which is a sample database that represents a digital media store.
Find more information about the database [here](https://www.sqlitetutorial.net/sqlite-sample-database/).
For convenience, we have hosted the database (`Chinook.db`) on a public GCS bucket.
```python
import requests
url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db"
response = requests.get(url)
if response.status_code == 200:
# Open a local file in binary write mode
with open("Chinook.db", "wb") as file:
# Write the content of the response (the file) to the local file
file.write(response.content)
print("File downloaded and saved as Chinook.db")
else:
print(f"Failed to download the file. Status code: {response.status_code}")
```
We will use a handy SQL database wrapper available in the `langchain_community` package to interact with the database. The wrapper provides a simple interface to execute SQL queries and fetch results:
```python
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
print(f"Dialect: {db.dialect}")
print(f"Available tables: {db.get_usable_table_names()}")
print(f'Sample output: {db.run("SELECT * FROM Artist LIMIT 5;")}')
```
**Output:**
```
Dialect: sqlite
Available tables: ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']
Sample output: [(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains')]
```
### Tools for database interactions
`langchain-community` implements some built-in tools for interacting with our `SQLDatabase`, including tools for listing tables, reading table schemas, and checking and running queries:
```python
from langchain_community.agent_toolkits import SQLDatabaseToolkit
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
tools = toolkit.get_tools()
for tool in tools:
print(f"{tool.name}: {tool.description}\n")
```
**Output:**
```
sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields.
sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3
sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database.
sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!
```
## 2. Using a prebuilt agent
Given these tools, we can initialize a pre-built agent in a single line. To customize our agents behavior, we write a descriptive system prompt.
```python
from langgraph.prebuilt import create_react_agent
system_prompt = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run,
then look at the results of the query and return the answer. Unless the user
specifies a specific number of examples they wish to obtain, always limit your
query to at most {top_k} results.
You can order the results by a relevant column to return the most interesting
examples in the database. Never query for all the columns from a specific table,
only ask for the relevant columns given the question.
You MUST double check your query before executing it. If you get an error while
executing a query, rewrite the query and try again.
DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the
database.
To start you should ALWAYS look at the tables in the database to see what you
can query. Do NOT skip this step.
Then you should query the schema of the most relevant tables.
""".format(
dialect=db.dialect,
top_k=5,
)
agent = create_react_agent(
llm,
tools,
prompt=system_prompt,
)
```
!!! note
This system prompt includes a number of instructions, such as always running specific tools before or after others. In the [next section](#3-customizing-the-agent), we will enforce these behaviors through the graph's structure, providing us a greater degree of control and allowing us to simplify the prompt.
Let's run this agent on a sample query and observe its behavior:
```python
question = "Which genre on average has the longest tracks?"
for step in agent.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
```
**Output:**
```
================================ Human Message =================================
Which genre on average has the longest tracks?
================================== Ai Message ==================================
Tool Calls:
sql_db_list_tables (call_d8lCgywSroCgpVl558nmXKwA)
Call ID: call_d8lCgywSroCgpVl558nmXKwA
Args:
================================= Tool Message =================================
Name: sql_db_list_tables
Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:
sql_db_schema (call_nNf6IIUcwMYLIkE0l6uWkZHe)
Call ID: call_nNf6IIUcwMYLIkE0l6uWkZHe
Args:
table_names: Genre, Track
================================= Tool Message =================================
Name: sql_db_schema
CREATE TABLE "Genre" (
"GenreId" INTEGER NOT NULL,
"Name" NVARCHAR(120),
PRIMARY KEY ("GenreId")
)
/*
3 rows from Genre table:
GenreId Name
1 Rock
2 Jazz
3 Metal
*/
CREATE TABLE "Track" (
"TrackId" INTEGER NOT NULL,
"Name" NVARCHAR(200) NOT NULL,
"AlbumId" INTEGER,
"MediaTypeId" INTEGER NOT NULL,
"GenreId" INTEGER,
"Composer" NVARCHAR(220),
"Milliseconds" INTEGER NOT NULL,
"Bytes" INTEGER,
"UnitPrice" NUMERIC(10, 2) NOT NULL,
PRIMARY KEY ("TrackId"),
FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"),
FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),
FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")
)
/*
3 rows from Track table:
TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice
1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99
2 Balls to the Wall 2 2 1 None 342562 5510424 0.99
3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99
*/
================================== Ai Message ==================================
Tool Calls:
sql_db_query_checker (call_urTRmtiGtTxkwHtscec7Fd2K)
Call ID: call_urTRmtiGtTxkwHtscec7Fd2K
Args:
query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds
FROM Track
JOIN Genre ON Track.GenreId = Genre.GenreId
GROUP BY Genre.Name
ORDER BY AvgMilliseconds DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query_checker
\`\`\`sql
SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds
FROM Track
JOIN Genre ON Track.GenreId = Genre.GenreId
GROUP BY Genre.Name
ORDER BY AvgMilliseconds DESC
LIMIT 1;
\`\`\`
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_RNMqyUEMv0rvy0UxSwrXY2AV)
Call ID: call_RNMqyUEMv0rvy0UxSwrXY2AV
Args:
query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds
FROM Track
JOIN Genre ON Track.GenreId = Genre.GenreId
GROUP BY Genre.Name
ORDER BY AvgMilliseconds DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query
[('Sci Fi & Fantasy', 2911783.0384615385)]
================================== Ai Message ==================================
The genre with the longest average track length is "Sci Fi & Fantasy," with an average duration of about 2,911,783 milliseconds (approximately 48.5 minutes) per track.
```
This worked well enough: the agent correctly listed the tables, obtained the schemas, wrote a query, checked the query, and ran it to inform its final response.
!!! tip
You can inspect all aspects of the above run, including steps taken, tools invoked, what prompts were seen by the LLM, and more in the [LangSmith trace](https://smith.langchain.com/public/bd594960-73e3-474b-b6f2-db039d7c713a/r).
## 3. Customizing the agent
The prebuilt agent lets us get started quickly, but at each step the agent has access to the full set of tools. Above, we relied on the system prompt to constrain its behavior— for example, we instructed the agent to always start with the "list tables" tool, and to always run a query-checker tool before executing the query.
We can enforce a higher degree of control in LangGraph by customizing the agent. Below, we implement a simple ReAct-agent setup, with dedicated nodes for specific tool-calls. We will use the same [state](../../concepts/low_level.md#state) as the pre-built agent.
We construct dedicated nodes for the following steps:
- Listing DB tables
- Calling the "get schema" tool
- Generating a query
- Checking the query
Putting these steps in dedicated nodes lets us (1) force tool-calls when needed, and (2) customize the prompts associated with each step.
```python
from typing import Literal
from langchain_core.messages import AIMessage
from langchain_core.runnables import RunnableConfig
from langgraph.graph import END, START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode
get_schema_tool = next(tool for tool in tools if tool.name == "sql_db_schema")
get_schema_node = ToolNode([get_schema_tool], name="get_schema")
run_query_tool = next(tool for tool in tools if tool.name == "sql_db_query")
run_query_node = ToolNode([run_query_tool], name="run_query")
# Example: create a predetermined tool call
def list_tables(state: MessagesState):
tool_call = {
"name": "sql_db_list_tables",
"args": {},
"id": "abc123",
"type": "tool_call",
}
tool_call_message = AIMessage(content="", tool_calls=[tool_call])
list_tables_tool = next(tool for tool in tools if tool.name == "sql_db_list_tables")
tool_message = list_tables_tool.invoke(tool_call)
response = AIMessage(f"Available tables: {tool_message.content}")
return {"messages": [tool_call_message, tool_message, response]}
# Example: force a model to create a tool call
def call_get_schema(state: MessagesState):
# Note that LangChain enforces that all models accept `tool_choice="any"`
# as well as `tool_choice=<string name of tool>`.
llm_with_tools = llm.bind_tools([get_schema_tool], tool_choice="any")
response = llm_with_tools.invoke(state["messages"])
return {"messages": [response]}
generate_query_system_prompt = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run,
then look at the results of the query and return the answer. Unless the user
specifies a specific number of examples they wish to obtain, always limit your
query to at most {top_k} results.
You can order the results by a relevant column to return the most interesting
examples in the database. Never query for all the columns from a specific table,
only ask for the relevant columns given the question.
DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
""".format(
dialect=db.dialect,
top_k=5,
)
def generate_query(state: MessagesState):
system_message = {
"role": "system",
"content": generate_query_system_prompt,
}
# We do not force a tool call here, to allow the model to
# respond naturally when it obtains the solution.
llm_with_tools = llm.bind_tools([run_query_tool])
response = llm_with_tools.invoke([system_message] + state["messages"])
return {"messages": [response]}
check_query_system_prompt = """
You are a SQL expert with a strong attention to detail.
Double check the {dialect} query for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins
If there are any of the above mistakes, rewrite the query. If there are no mistakes,
just reproduce the original query.
You will call the appropriate tool to execute the query after running this check.
""".format(dialect=db.dialect)
def check_query(state: MessagesState):
system_message = {
"role": "system",
"content": check_query_system_prompt,
}
# Generate an artificial user message to check
tool_call = state["messages"][-1].tool_calls[0]
user_message = {"role": "user", "content": tool_call["args"]["query"]}
llm_with_tools = llm.bind_tools([run_query_tool], tool_choice="any")
response = llm_with_tools.invoke([system_message, user_message])
response.id = state["messages"][-1].id
return {"messages": [response]}
```
Finally, we assemble these steps into a workflow using the Graph API. We define a [conditional edge](../../concepts/low_level.md#conditional-edges) at the query generation step that will route to the query checker if a query is generated, or end if there are no tool calls present, such that the LLM has delivered a response to the query.
```python
def should_continue(state: MessagesState) -> Literal[END, "check_query"]:
messages = state["messages"]
last_message = messages[-1]
if not last_message.tool_calls:
return END
else:
return "check_query"
builder = StateGraph(MessagesState)
builder.add_node(list_tables)
builder.add_node(call_get_schema)
builder.add_node(get_schema_node, "get_schema")
builder.add_node(generate_query)
builder.add_node(check_query)
builder.add_node(run_query_node, "run_query")
builder.add_edge(START, "list_tables")
builder.add_edge("list_tables", "call_get_schema")
builder.add_edge("call_get_schema", "get_schema")
builder.add_edge("get_schema", "generate_query")
builder.add_conditional_edges(
"generate_query",
should_continue,
)
builder.add_edge("check_query", "run_query")
builder.add_edge("run_query", "generate_query")
agent = builder.compile()
```
We visualize the application below:
```python
from IPython.display import Image, display
from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles
display(Image(agent.get_graph().draw_mermaid_png()))
```
![Graph](./output.png)
**Note:** When you run this code, it will generate and display a visual representation of the SQL agent graph showing the flow between the different nodes (list_tables → call_get_schema → get_schema → generate_query → check_query → run_query).
We can now invoke the graph exactly as before:
```python
question = "Which genre on average has the longest tracks?"
for step in agent.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
```
**Output:**
```
================================ Human Message =================================
Which genre on average has the longest tracks?
================================== Ai Message ==================================
Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:
sql_db_schema (call_qxKtYiHgf93AiTDin9ez5wFp)
Call ID: call_qxKtYiHgf93AiTDin9ez5wFp
Args:
table_names: Genre,Track
================================= Tool Message =================================
Name: sql_db_schema
CREATE TABLE "Genre" (
"GenreId" INTEGER NOT NULL,
"Name" NVARCHAR(120),
PRIMARY KEY ("GenreId")
)
/*
3 rows from Genre table:
GenreId Name
1 Rock
2 Jazz
3 Metal
*/
CREATE TABLE "Track" (
"TrackId" INTEGER NOT NULL,
"Name" NVARCHAR(200) NOT NULL,
"AlbumId" INTEGER,
"MediaTypeId" INTEGER NOT NULL,
"GenreId" INTEGER,
"Composer" NVARCHAR(220),
"Milliseconds" INTEGER NOT NULL,
"Bytes" INTEGER,
"UnitPrice" NUMERIC(10, 2) NOT NULL,
PRIMARY KEY ("TrackId"),
FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"),
FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),
FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")
)
/*
3 rows from Track table:
TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice
1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99
2 Balls to the Wall 2 2 1 None 342562 5510424 0.99
3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99
*/
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_RPN3GABMfb6DTaFTLlwnZxVN)
Call ID: call_RPN3GABMfb6DTaFTLlwnZxVN
Args:
query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength
FROM Track
JOIN Genre ON Track.GenreId = Genre.GenreId
GROUP BY Genre.GenreId
ORDER BY AvgTrackLength DESC
LIMIT 1;
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_PR4s8ymiF3ZQLaoZADXtdqcl)
Call ID: call_PR4s8ymiF3ZQLaoZADXtdqcl
Args:
query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength
FROM Track
JOIN Genre ON Track.GenreId = Genre.GenreId
GROUP BY Genre.GenreId
ORDER BY AvgTrackLength DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query
[('Sci Fi & Fantasy', 2911783.0384615385)]
================================== Ai Message ==================================
The genre with the longest tracks on average is "Sci Fi & Fantasy," with an average track length of approximately 2,911,783 milliseconds.
```
!!! tip
See [LangSmith trace](https://smith.langchain.com/public/94b8c9ac-12f7-4692-8706-836a1f30f1ea/r) for the above run.
## Next steps
Check out [this guide](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph) for evaluating LangGraph applications, including SQL agents like this one, using LangSmith.
docs/mkdocs.yml
+8 -6
View File
@@ -109,6 +109,7 @@ nav:
- Agent architectures: concepts/agentic_concepts.md
- Guides:
- guides/index.md
- LangGraph APIs:
- Graph API:
- Overview: concepts/low_level.md
@@ -268,25 +269,26 @@ nav:
- Environment variables: cloud/reference/env_var.md
- Examples:
- examples/index.md
- Template applications: concepts/template_applications.md # TODO: make tutorial
- Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb
- Agent Supervisor: tutorials/multi_agent/agent_supervisor.ipynb
- SQL agent: tutorials/sql-agent.ipynb
- Agentic RAG: tutorials/rag/langgraph_agentic_rag.md
- Agent Supervisor: tutorials/multi_agent/agent_supervisor.md
- SQL agent: tutorials/sql/sql-agent.md
- Prebuilt chat UI: agents/ui.md
- Graph runs in LangSmith: how-tos/run-id-langsmith.ipynb
- Graph runs in LangSmith: how-tos/run-id-langsmith.md
- LangGraph Platform:
- Authentication:
- tutorials/auth/getting_started.md
- tutorials/auth/resource_auth.md
- tutorials/auth/add_auth_server.md
- Use RemoteGraph: how-tos/use-remote-graph.md
- Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-langgraph-platform.ipynb
# combine with how-tos/autogen-integration.ipynb
- Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-integration.md
- Front-end and generative UI:
- Integrate LangGraph into a React app: cloud/how-tos/use_stream_react.md
- Implement generative UI with LangGraph: cloud/how-tos/generative_ui_react.md
- Additional resources:
- additional-resources/index.md
- agents/prebuilt.md # NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
- LangGraph Academy course: https://academy.langchain.com/courses/intro-to-langgraph
- Case studies: adopters.md
docs/overrides/main.html
+7 -2
View File
@@ -184,7 +184,12 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
visibility: hidden;
}
/* Dark mode banner links */
/* Dark mode banner styling */
[data-md-color-scheme="slate"] .md-banner {
background-color: #CFC9FA; /* Same light purple for both modes */
color: #000000;
}
[data-md-color-scheme="slate"] .md-banner a {
color: #000000;
}
@@ -220,5 +225,5 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
{% endblock %}
{% block announce %}
<strong>We are growing and hiring for multiple roles for LangChain, LangGraph and LangSmith. <a href="https://www.langchain.com/careers" target="_blank" rel="noopener noreferrer"> Join our team!</a></strong>
Our <a href="https://academy.langchain.com/courses/ambient-agents/?utm_medium=internal&utm_source=docs&utm_campaign=q2-2025_ambient-agents_co" target="_blank">Building Ambient Agents with LangGraph</a> course is now available on LangChain Academy!
{% endblock %}
examples/human_in_the_loop/breakpoints.ipynb
-1
View File
@@ -1 +0,0 @@
extract_images.py
-70
View File
@@ -1,70 +0,0 @@
#!/usr/bin/env python3
"""
Script to extract images from the graph-api.ipynb notebook and save them to assets folder.
"""
import json
import base64
import os
from pathlib import Path
def extract_images_from_notebook(notebook_path, assets_dir):
"""Extract images from notebook and save them to assets directory."""
# Read the notebook
with open(notebook_path, 'r') as f:
notebook = json.load(f)
# Create assets directory if it doesn't exist
os.makedirs(assets_dir, exist_ok=True)
image_count = 0
# Process each cell
for cell_idx, cell in enumerate(notebook['cells']):
if cell['cell_type'] == 'code':
# Check if this cell contains draw_mermaid_png
source = ''.join(cell.get('source', []))
if 'draw_mermaid_png' in source:
print(f"Found draw_mermaid_png in cell {cell_idx}")
# Check for outputs with images
if 'outputs' in cell:
for output_idx, output in enumerate(cell['outputs']):
if output.get('output_type') == 'display_data':
data = output.get('data', {})
# Check for PNG data
if 'image/png' in data:
png_data = data['image/png']
# Decode base64 data
try:
image_bytes = base64.b64decode(png_data)
# Generate filename
image_count += 1
filename = f"graph_api_image_{image_count}.png"
filepath = os.path.join(assets_dir, filename)
# Save the image
with open(filepath, 'wb') as img_file:
img_file.write(image_bytes)
print(f"Saved image: {filepath}")
except Exception as e:
print(f"Error decoding image {image_count}: {e}")
print(f"Extracted {image_count} images to {assets_dir}")
return image_count
if __name__ == "__main__":
notebook_path = "docs/docs/how-tos/graph-api.ipynb"
assets_dir = "docs/docs/how-tos/assets"
if os.path.exists(notebook_path):
count = extract_images_from_notebook(notebook_path, assets_dir)
print(f"Successfully extracted {count} images")
else:
print(f"Notebook not found: {notebook_path}")
libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py
+1 -1
View File
@@ -452,7 +452,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
while True:
try:
yield asyncio.run_coroutine_threadsafe(
anext(aiter_), # noqa: F821
anext(aiter_), # type: ignore[arg-type] # noqa: F821
self.loop,
).result()
except StopAsyncIteration:
libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py
+1 -1
View File
@@ -877,7 +877,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
while True:
try:
yield asyncio.run_coroutine_threadsafe(
anext(aiter_), # noqa: F821
anext(aiter_), # type: ignore[arg-type] # noqa: F821
self.loop,
).result()
except StopAsyncIteration:
libs/checkpoint-postgres/langgraph/store/postgres/base.py
+3 -5
View File
@@ -448,10 +448,8 @@ class BasePostgresStore(Generic[C]):
score_operator, post_operator = get_distance_operator(self)
post_operator = post_operator.replace("scored", "uniq")
vector_type = (
cast(PostgresIndexConfig, self.index_config)
.get("ann_index_config", {})
.get("vector_type", "vector")
vector_type = self.index_config.get("ann_index_config", {}).get(
"vector_type", "vector"
)
# For hamming bit vectors, or “regular” vectors
@@ -1141,7 +1139,7 @@ def _get_vector_type_ops(store: BasePostgresStore) -> str:
if not store.index_config:
return "vector_cosine_ops"
config = cast(PostgresIndexConfig, store.index_config)
config = store.index_config
index_config = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
vector_type = cast(str, index_config.get("vector_type", "vector"))
if vector_type not in ("vector", "halfvec"):
libs/checkpoint-postgres/uv.lock
Generated
+106 -90
View File
@@ -1,10 +1,6 @@
version = 1
revision = 2
requires-python = ">=3.9"
resolution-markers = [
"python_full_version >= '3.12.4'",
"python_full_version < '3.12.4'",
]
[[package]]
name = "annotated-types"
@@ -32,11 +28,11 @@ wheels = [
[[package]]
name = "certifi"
version = "2025.4.26"
version = "2025.6.15"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" }
sdist = { url = "https://files.pythonhosted.org/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b", size = 158753, upload-time = "2025-06-15T02:45:51.329Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3", size = 159618, upload-time = "2025-04-26T02:12:27.662Z" },
{ url = "https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057", size = 157650, upload-time = "2025-06-15T02:45:49.977Z" },
]
[[package]]
@@ -205,7 +201,7 @@ name = "exceptiongroup"
version = "1.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "typing-extensions", marker = "python_full_version < '3.12.4'" },
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
wheels = [
@@ -290,7 +286,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "0.3.60"
version = "0.3.68"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -301,9 +297,9 @@ dependencies = [
{ name = "tenacity" },
{ name = "typing-extensions" },
]
sdist = { url = "https://files.pythonhosted.org/packages/5b/75/95129aaada92980a002a31e002610a80af3c8967ae7884710372e89cdde0/langchain_core-0.3.60.tar.gz", hash = "sha256:63dd1bdf7939816115399522661ca85a2f3686a61440f2f46ebd86d1b028595b", size = 557456, upload-time = "2025-05-15T15:23:23.642Z" }
sdist = { url = "https://files.pythonhosted.org/packages/23/20/f5b18a17bfbe3416177e702ab2fd230b7d168abb17be31fb48f43f0bb772/langchain_core-0.3.68.tar.gz", hash = "sha256:312e1932ac9aa2eaf111b70fdc171776fa571d1a86c1f873dcac88a094b19c6f", size = 563041, upload-time = "2025-07-03T17:02:28.704Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/2d/bc/344f5b11fdfe0e27f7064d2e829921a791461dc32e5ed285fe6325518c26/langchain_core-0.3.60-py3-none-any.whl", hash = "sha256:2ccdf06b12e699b1b0962bc02837056c075b4981c3d13f82a4d4c30bb22ea3dc", size = 437890, upload-time = "2025-05-15T15:23:22.278Z" },
{ url = "https://files.pythonhosted.org/packages/f9/da/c89be0a272993bfcb762b2a356b9f55de507784c2755ad63caec25d183bf/langchain_core-0.3.68-py3-none-any.whl", hash = "sha256:5e5c1fbef419590537c91b8c2d86af896fbcbaf0d5ed7fdcdd77f7d8f3467ba0", size = 441405, upload-time = "2025-07-03T17:02:27.115Z" },
]
[[package]]
@@ -385,7 +381,7 @@ dev = [
[[package]]
name = "langsmith"
version = "0.3.42"
version = "0.4.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -396,53 +392,54 @@ dependencies = [
{ name = "requests-toolbelt" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/3a/44/fe171c0b0fb0377b191aebf0b7779e0c7b2a53693c6a01ddad737212495d/langsmith-0.3.42.tar.gz", hash = "sha256:2b5cbc450ab808b992362aac6943bb1d285579aa68a3a8be901d30a393458f25", size = 345619, upload-time = "2025-05-03T03:07:17.873Z" }
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libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py
+1 -1
View File
@@ -203,7 +203,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
while True:
try:
yield asyncio.run_coroutine_threadsafe(
anext(aiter_), # noqa: F821
anext(aiter_), # type: ignore[arg-type] # noqa: F821
self.loop,
).result()
except StopAsyncIteration:
libs/checkpoint-sqlite/uv.lock
Generated
+106 -90
View File
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libs/cli/examples/graphs/storm.py
+2 -2
View File
@@ -285,7 +285,7 @@ class AnswerWithCitations(BaseModel):
@property
def as_str(self) -> str:
return f"{self.answer}\n\nCitations:\n\n" + "\n".join(
f"[{i+1}]: {url}" for i, url in enumerate(self.cited_urls)
f"[{i + 1}]: {url}" for i, url in enumerate(self.cited_urls)
)
@@ -553,7 +553,7 @@ async def conduct_interviews(state: ResearchState):
def format_conversation(interview_state):
messages = interview_state["messages"]
convo = "\n".join(f"{m.name}: {m.content}" for m in messages)
return f'Conversation with {interview_state["editor"].name}\n\n' + convo
return f"Conversation with {interview_state['editor'].name}\n\n" + convo
async def refine_outline(state: ResearchState):
libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py
+1 -1
View File
@@ -17,7 +17,7 @@ model_oai = ChatOpenAI(temperature=0)
model_anth = model_anth.bind_tools(tools)
model_oai = model_oai.bind_tools(tools)
prompt = open("prompt.txt").read()
prompt = open(Path(__file__).parent.parent / "prompt.txt").read()
subprompt = open(Path(__file__).parent / "subprompt.txt").read()
libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py
+1 -1
View File
@@ -17,7 +17,7 @@ model_oai = ChatOpenAI(temperature=0)
model_anth = model_anth.bind_tools(tools)
model_oai = model_oai.bind_tools(tools)
prompt = open("prompt.txt").read()
prompt = open(Path(__file__).parent.parent / "prompt.txt").read()
subprompt = open(Path(__file__).parent / "subprompt.txt").read()
libs/cli/examples/langgraph.json
+1
View File
@@ -8,6 +8,7 @@
"scikit-learn",
"./graphs"
],
"keep_pkg_tools": false,
"graphs": {
"agent": "./graphs/agent.py:graph",
"storm": "./graphs/storm.py:graph"
libs/cli/langgraph_cli/config.py
+103 -24
View File
@@ -471,21 +471,61 @@ class Config(TypedDict, total=False):
"""Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.
"""
keep_pkg_tools: Optional[Union[bool, list[str]]]
"""Optional. Control whether to retain Python packaging tools in the final image.
Allowed tools are: "pip", "setuptools", "wheel".
You can also set to true to include all packaging tools.
"""
PIP_CLEANUP_LINES = """# -- Ensure user deps didn't inadvertently overwrite langgraph-api
_BUILD_TOOLS = ("pip", "setuptools", "wheel")
def _get_pip_cleanup_lines(
install_cmd: str,
to_uninstall: Optional[tuple[str]],
pip_installer: Literal["uv", "pip"],
) -> str:
commands = [
f"""# -- Ensure user deps didn't inadvertently overwrite langgraph-api
RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && \
touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py
touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py
RUN PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir --no-deps -e /api
# -- End of ensuring user deps didn't inadvertently overwrite langgraph-api --
# -- Removing pip from the final image ~<:===~~~ --
RUN pip uninstall -y pip setuptools wheel && \
rm -rf /usr/local/lib/python*/site-packages/pip* /usr/local/lib/python*/site-packages/setuptools* /usr/local/lib/python*/site-packages/wheel* && \
find /usr/local/bin -name "pip*" -delete || true
# pip removal for wolfi
RUN rm -rf /usr/lib/python*/site-packages/pip* /usr/lib/python*/site-packages/setuptools* /usr/lib/python*/site-packages/wheel* && \
find /usr/bin -name "pip*" -delete || true
{uv_removal}
# -- End of pip removal --"""
# -- Removing build deps from the final image ~<:===~~~ --"""
]
if to_uninstall:
for pack in to_uninstall:
if pack not in _BUILD_TOOLS:
raise ValueError(
f"Invalid build tool: {pack}; must be one of {', '.join(_BUILD_TOOLS)}"
)
packs_str = " ".join(sorted(to_uninstall))
commands.append(f"RUN pip uninstall -y {packs_str}")
# Ensure the directories are removed entirely
packages_rm = " ".join(
f"/usr/local/lib/python*/site-packages/{pack}*" for pack in to_uninstall
)
if "pip" in to_uninstall:
packages_rm += ' && find /usr/local/bin -name "pip*" -delete || true'
commands.append(f"RUN rm -rf {packages_rm}")
wolfi_packages_rm = " ".join(
f"/usr/lib/python*/site-packages/{pack}*" for pack in to_uninstall
)
if "pip" in to_uninstall:
wolfi_packages_rm += ' && find /usr/bin -name "pip*" -delete || true'
commands.append(f"RUN rm -rf {wolfi_packages_rm}")
if pip_installer == "uv":
commands.append(
f"RUN uv pip uninstall --system {packs_str} && rm /usr/bin/uv /usr/bin/uvx"
)
else:
if pip_installer == "uv":
commands.append(
"RUN rm /usr/bin/uv /usr/bin/uvx\n# -- End of build deps removal --"
)
return "\n".join(commands)
def _parse_version(version_str: str) -> tuple[int, int]:
@@ -563,6 +603,7 @@ def validate_config(config: Config) -> Config:
"checkpointer": config.get("checkpointer"),
"ui": config.get("ui"),
"ui_config": config.get("ui_config"),
"keep_pkg_tools": config.get("keep_pkg_tools"),
}
if config.get("node_version"):
@@ -602,8 +643,7 @@ def validate_config(config: Config) -> Config:
if not config["graphs"]:
raise click.UsageError(
"No graphs found in config. "
"Add at least one graph to 'graphs' dictionary."
"No graphs found in config. Add at least one graph to 'graphs' dictionary."
)
# Validate image_distro config
@@ -636,6 +676,22 @@ def validate_config(config: Config) -> Config:
f"Invalid http.app format: '{http_conf['app']}'. "
"Must be in format './path/to/file.py:attribute_name'"
)
if keep_pkg_tools := config.get("keep_pkg_tools"):
if isinstance(keep_pkg_tools, list):
for tool in keep_pkg_tools:
if tool not in _BUILD_TOOLS:
raise ValueError(
f"Invalid keep_pkg_tools: '{tool}'. "
"Must be one of 'pip', 'setuptools', 'wheel'."
)
elif keep_pkg_tools is True:
pass
else:
raise ValueError(
f"Invalid keep_pkg_tools: '{keep_pkg_tools}'. "
"Must be bool or list[str] (with values"
" 'pip', 'setuptools', and/or 'wheel')."
)
return config
@@ -1129,6 +1185,27 @@ def _image_supports_uv(base_image: str) -> bool:
return version >= min_uv
def get_build_tools_to_uninstall(config: Config) -> tuple[str]:
keep_pkg_tools = config.get("keep_pkg_tools")
if not keep_pkg_tools:
return _BUILD_TOOLS
if keep_pkg_tools is True:
return ()
expected = _BUILD_TOOLS
if isinstance(keep_pkg_tools, list):
for tool in keep_pkg_tools:
if tool not in expected:
raise ValueError(
f"Invalid build tool to uninstall: {tool}. Expected one of {expected}"
)
return tuple(sorted(set(_BUILD_TOOLS) - set(keep_pkg_tools)))
else:
raise ValueError(
f"Invalid value for keep_pkg_tools: {keep_pkg_tools}."
" Expected True or a list containing any of {expected}."
)
def python_config_to_docker(
config_path: pathlib.Path,
config: Config,
@@ -1136,20 +1213,18 @@ def python_config_to_docker(
) -> tuple[str, dict[str, str]]:
"""Generate a Dockerfile from the configuration."""
pip_installer = config.get("pip_installer", "auto")
build_tools_to_uninstall = get_build_tools_to_uninstall(config)
if pip_installer == "auto":
if _image_supports_uv(base_image):
pip_installer = "uv"
else:
pip_installer = "pip"
if pip_installer == "uv":
install_cmd = "uv pip install --system"
uv_removal = "RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx"
elif pip_installer == "pip":
install_cmd = "pip install"
uv_removal = ""
else:
if _image_supports_uv(base_image):
install_cmd = "uv pip install --system"
uv_removal = "RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx"
else:
install_cmd = "pip install"
uv_removal = ""
raise ValueError(f"Invalid pip_installer: {pip_installer}")
# configure pip
pip_install = f"PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir -c /api/constraints.txt"
@@ -1181,7 +1256,7 @@ def python_config_to_docker(
)
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)}'
pip_reqs_str += f"{os.linesep}RUN {pip_install} {' '.join('-r ' + r for _, r in local_deps.pip_reqs)}"
pip_reqs_str = f"""# -- Installing local requirements --
{pip_reqs_str}
# -- End of local requirements install --"""
@@ -1298,7 +1373,11 @@ ADD {relpath} /deps/{name}
js_inst_str,
"",
# Add pip cleanup after all installations are complete
PIP_CLEANUP_LINES.format(install_cmd=install_cmd, uv_removal=uv_removal),
_get_pip_cleanup_lines(
install_cmd=install_cmd,
to_uninstall=build_tools_to_uninstall,
pip_installer=pip_installer,
),
"",
f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
]
libs/cli/langgraph_cli/templates.py
+1 -1
View File
@@ -123,7 +123,7 @@ def _download_repo_with_requests(repo_url: str, path: str) -> None:
)
except error.HTTPError as e:
click.secho(
f"❌ Error: Failed to download repository.\n" f"Details: {e}\n",
f"❌ Error: Failed to download repository.\nDetails: {e}\n",
fg="red",
bold=True,
err=True,
libs/cli/pyproject.toml
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-cli"
version = "0.3.3"
version = "0.3.4"
description = "CLI for interacting with LangGraph API"
authors = []
requires-python = ">=3.9"
libs/cli/schemas/schema.json
+35 -1
View File
@@ -134,6 +134,23 @@
],
"description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
},
"keep_pkg_tools": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n"
},
"pip_installer": {
"anyOf": [
{
@@ -298,6 +315,23 @@
],
"description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
},
"keep_pkg_tools": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n"
},
"pip_installer": {
"anyOf": [
{
@@ -505,7 +539,7 @@
},
"disable_meta": {
"type": "boolean",
"description": "Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n"
"description": "Optional. If True, all meta endpoints (/openapi.json, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n"
},
"disable_runs": {
"type": "boolean",
libs/cli/schemas/schema.v0.json
+35 -1
View File
@@ -134,6 +134,23 @@
],
"description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
},
"keep_pkg_tools": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n"
},
"pip_installer": {
"anyOf": [
{
@@ -298,6 +315,23 @@
],
"description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
},
"keep_pkg_tools": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n"
},
"pip_installer": {
"anyOf": [
{
@@ -505,7 +539,7 @@
},
"disable_meta": {
"type": "boolean",
"description": "Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n"
"description": "Optional. If True, all meta endpoints (/openapi.json, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n"
},
"disable_runs": {
"type": "boolean",
libs/cli/schemas/version.schema.json
+46
View File
@@ -0,0 +1,46 @@
{
"$id": "https://github.com/langchain-ai/langgraph/libs/cli/schemas/version.schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "LangGraph Platform configuration (when building via the langgraph-cli).",
"type": "object",
"oneOf": [
{
"allOf": [
{
"oneOf": [
{
"properties": {
"version": {
"type": "string",
"maxLength": 0
}
},
"required": ["version"]
},
{
"not": {
"required": ["version"]
}
}
]
},
{
"$ref": "https://raw.githubusercontent.com/langchain-ai/langgraph/main/libs/cli/schemas/schema.json"
}
]
},
{
"allOf": [
{
"properties": {
"version": { "const": "v0" }
},
"required": ["version"]
},
{
"$ref": "https://raw.githubusercontent.com/langchain-ai/langgraph/main/libs/cli/schemas/schema.v0.json"
}
]
}
]
}
libs/cli/tests/unit_tests/cli/test_cli.py
+10 -9
View File
@@ -10,13 +10,14 @@ from pathlib import Path
from click.testing import CliRunner
from langgraph_cli.cli import cli, prepare_args_and_stdin
from langgraph_cli.config import PIP_CLEANUP_LINES, Config, validate_config
from langgraph_cli.config import Config, _get_pip_cleanup_lines, validate_config
from langgraph_cli.docker import DEFAULT_POSTGRES_URI, DockerCapabilities, Version
from langgraph_cli.util import clean_empty_lines
FORMATTED_CLEANUP_LINES = PIP_CLEANUP_LINES.format(
FORMATTED_CLEANUP_LINES = _get_pip_cleanup_lines(
install_cmd="uv pip install --system",
uv_removal="RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx",
to_uninstall=("pip", "setuptools", "wheel"),
pip_installer="uv",
)
DEFAULT_DOCKER_CAPABILITIES = DockerCapabilities(
version_docker=Version(26, 1, 1),
@@ -280,9 +281,9 @@ def test_version_option() -> None:
assert result.exit_code == 0, "Expected exit code 0 for --version option"
# Check that the output contains the correct version information
assert (
"LangGraph CLI, version" in result.output
), "Expected version information in output"
assert "LangGraph CLI, version" in result.output, (
"Expected version information in output"
)
def test_dockerfile_command_basic() -> None:
@@ -570,6 +571,6 @@ def test_build_generate_proper_build_context():
build_context_pattern = re.compile(r"--build-context\s+(\w+)=([^\s]+)")
build_contexts = re.findall(build_context_pattern, result.output)
assert (
len(build_contexts) == 2
), f"Expected 2 build contexts, but found {len(build_contexts)}"
assert len(build_contexts) == 2, (
f"Expected 2 build contexts, but found {len(build_contexts)}"
)
libs/cli/tests/unit_tests/cli/test_templates.py
+9 -9
View File
@@ -42,18 +42,18 @@ def test_create_new_with_mocked_download(mock_urlopen: MagicMock) -> None:
# Verify CLI command execution and success
assert result.exit_code == 0, result.output
assert (
"New project created" in result.output
), "Expected success message in output."
assert "New project created" in result.output, (
"Expected success message in output."
)
# Verify that the directory is not empty
assert os.listdir(temp_dir), "Expected files to be created in temp directory."
# Check for a known file in the extracted content
extracted_files = [f.name for f in Path(temp_dir).glob("*")]
assert (
"test-file.txt" in extracted_files
), "Expected 'test-file.txt' in the extracted content."
assert "test-file.txt" in extracted_files, (
"Expected 'test-file.txt' in the extracted content."
)
def test_invalid_template_id() -> None:
@@ -65,6 +65,6 @@ def test_invalid_template_id() -> None:
# Verify the command failed and proper message is displayed
assert result.exit_code != 0, "Expected non-zero exit code for invalid template."
assert (
"Template 'invalid-template-id' not found" in result.output
), "Expected error message in output."
assert "Template 'invalid-template-id' not found" in result.output, (
"Expected error message in output."
)
libs/cli/tests/unit_tests/test_config.py
+54 -3
View File
@@ -9,7 +9,8 @@ import click
import pytest
from langgraph_cli.config import (
PIP_CLEANUP_LINES,
_BUILD_TOOLS,
_get_pip_cleanup_lines,
config_to_compose,
config_to_docker,
docker_tag,
@@ -18,9 +19,10 @@ from langgraph_cli.config import (
)
from langgraph_cli.util import clean_empty_lines
FORMATTED_CLEANUP_LINES = PIP_CLEANUP_LINES.format(
FORMATTED_CLEANUP_LINES = _get_pip_cleanup_lines(
install_cmd="uv pip install --system",
uv_removal="RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx",
to_uninstall=("pip", "setuptools", "wheel"),
pip_installer="uv",
)
PATH_TO_CONFIG = pathlib.Path(__file__).parent / "test_config.json"
@@ -51,6 +53,7 @@ def test_validate_config():
"http": None,
"ui": None,
"ui_config": None,
"keep_pkg_tools": None,
**expected_config,
}
assert actual_config == expected_config
@@ -77,6 +80,7 @@ def test_validate_config():
"http": None,
"ui": None,
"ui_config": None,
"keep_pkg_tools": None,
}
actual_config = validate_config(expected_config)
assert actual_config == expected_config
@@ -925,6 +929,53 @@ def test_config_to_docker_pip_installer():
assert "uv pip install --system" in docker_default
def test_config_retain_build_tools():
graphs = {"agent": "./graphs/agent.py:graph"}
base_config = {
"python_version": "3.11",
"dependencies": ["."],
"graphs": graphs,
}
config_true = validate_config(
{**copy.deepcopy(base_config), "keep_pkg_tools": True}
)
docker_true, _ = config_to_docker(
PATH_TO_CONFIG, config_true, "langchain/langgraph-api:0.2.47"
)
assert not any(
"/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_true
for pckg in _BUILD_TOOLS
)
assert "RUN pip uninstall -y pip setuptools wheel" not in docker_true
config_false = validate_config(
{**copy.deepcopy(base_config), "keep_pkg_tools": False}
)
docker_false, _ = config_to_docker(
PATH_TO_CONFIG, config_false, "langchain/langgraph-api:0.2.47"
)
assert all(
"/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_false
for pckg in _BUILD_TOOLS
)
assert "RUN pip uninstall -y pip setuptools wheel" in docker_false
config_list = validate_config(
{**copy.deepcopy(base_config), "keep_pkg_tools": ["pip", "setuptools"]}
)
docker_list, _ = config_to_docker(
PATH_TO_CONFIG, config_list, "langchain/langgraph-api:0.2.47"
)
assert all(
"/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_list
for pckg in ("wheel",)
)
assert not any(
"/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_list
for pckg in ("pip", "setuptools")
)
assert "RUN pip uninstall -y wheel" in docker_list
assert "RUN pip uninstall -y pip setuptools" not in docker_list
# config_to_compose
def test_config_to_compose_simple_config():
graphs = {"agent": "./agent.py:graph"}
libs/cli/uv.lock
Generated
+328 -249
View File
@@ -2,10 +2,9 @@ version = 1
revision = 2
requires-python = ">=3.9"
resolution-markers = [
"python_full_version >= '3.12.4' and python_full_version < '4.0'",
"python_full_version >= '3.11' and python_full_version < '3.12.4'",
"python_full_version >= '4.0'",
"python_full_version < '3.11'",
"python_full_version >= '3.11'",
"python_full_version == '3.10.*'",
"python_full_version < '3.10'",
]
[[package]]
@@ -45,11 +44,11 @@ wheels = [
[[package]]
name = "certifi"
version = "2025.4.26"
version = "2025.6.15"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" }
sdist = { url = "https://files.pythonhosted.org/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b", size = 158753, upload-time = "2025-06-15T02:45:51.329Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3", size = 159618, upload-time = "2025-04-26T02:12:27.662Z" },
{ url = "https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057", size = 157650, upload-time = "2025-06-15T02:45:49.977Z" },
]
[[package]]
@@ -199,14 +198,33 @@ wheels = [
name = "click"
version = "8.1.8"
source = { registry = "https://pypi.org/simple" }
resolution-markers = [
"python_full_version < '3.10'",
]
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
{ name = "colorama", marker = "python_full_version < '3.10' and sys_platform == 'win32'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593, upload-time = "2024-12-21T18:38:44.339Z" }
wheels = [
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libs/langgraph/bench/react_agent.py
+2 -2
View File
@@ -15,7 +15,7 @@ from langgraph.pregel import Pregel
def react_agent(n_tools: int, checkpointer: Optional[BaseCheckpointSaver]) -> Pregel:
class FakeFuntionChatModel(FakeMessagesListChatModel):
class FakeFunctionChatModel(FakeMessagesListChatModel):
def bind_tools(self, functions: list):
return self
@@ -40,7 +40,7 @@ def react_agent(n_tools: int, checkpointer: Optional[BaseCheckpointSaver]) -> Pr
description="",
)
model = FakeFuntionChatModel(
model = FakeFunctionChatModel(
responses=[
AIMessage(
content="",
libs/langgraph/langgraph/constants.py
-1
View File
@@ -119,7 +119,6 @@ RESERVED = {
CONFIG_KEY_STREAM,
CONFIG_KEY_STREAM_WRITER,
CONFIG_KEY_STORE,
CONFIG_KEY_CHECKPOINT_MAP,
CONFIG_KEY_RESUMING,
CONFIG_KEY_TASK_ID,
CONFIG_KEY_CHECKPOINT_MAP,
libs/langgraph/langgraph/types.py
+1 -1
View File
@@ -59,7 +59,7 @@ StreamMode = Literal[
- `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
- `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by get_state().
- `"tasks"`: Emit events when tasks start and finish, including their results and errors.
- `"debug"`: Emit "checlkpoints" and "tasks" events, for debugging purposes.
- `"debug"`: Emit "checkpoints" and "tasks" events, for debugging purposes.
"""
StreamWriter = Callable[[Any], None]
libs/langgraph/langgraph/utils/runnable.py
+1 -1
View File
@@ -83,7 +83,7 @@ def _unset_config_context(token: Token[RunnableConfig | None], run: Any = None)
"""Set the child Runnable config + tracing context.
Args:
config: The config to set.
token: The config token to reset.
"""
var_child_runnable_config.reset(token)
if run is not None:
libs/langgraph/tests/__snapshots__/test_large_cases.ambr
+1 -1
View File
@@ -218,7 +218,7 @@
"id": [
"tests",
"test_large_cases",
"FakeFuntionChatModel"
"FakeFunctionChatModel"
],
"name": "agent"
}
libs/langgraph/tests/test_large_cases.py
+4 -4
View File
@@ -2377,7 +2377,7 @@ def test_message_graph(
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.tools import tool
class FakeFuntionChatModel(FakeMessagesListChatModel):
class FakeFunctionChatModel(FakeMessagesListChatModel):
def bind_functions(self, functions: list):
return self
@@ -2403,7 +2403,7 @@ def test_message_graph(
tools = [search_api]
model = FakeFuntionChatModel(
model = FakeFunctionChatModel(
responses=[
AIMessage(
content="",
@@ -3100,7 +3100,7 @@ def test_root_graph(
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.tools import tool
class FakeFuntionChatModel(FakeMessagesListChatModel):
class FakeFunctionChatModel(FakeMessagesListChatModel):
def bind_functions(self, functions: list):
return self
@@ -3126,7 +3126,7 @@ def test_root_graph(
tools = [search_api]
model = FakeFuntionChatModel(
model = FakeFunctionChatModel(
responses=[
AIMessage(
content="",
libs/langgraph/tests/test_large_cases_async.py
+2 -2
View File
@@ -2066,7 +2066,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.tools import tool
class FakeFuntionChatModel(FakeMessagesListChatModel):
class FakeFunctionChatModel(FakeMessagesListChatModel):
def bind_functions(self, functions: list):
return self
@@ -2077,7 +2077,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
tools = [search_api]
model = FakeFuntionChatModel(
model = FakeFunctionChatModel(
responses=[
AIMessage(
content="",
libs/langgraph/tests/test_runnable.py
+2 -2
View File
@@ -85,7 +85,7 @@ def test_runnable_callable_injectable_arguments() -> None:
"""
# Test Optional[BaseStore] annotation.
def func_optional_store(inputs: Any, store: Optional[BaseStore]) -> str: # noqa: UP007
def func_optional_store(inputs: Any, store: Optional[BaseStore]) -> str: # noqa: UP045
"""Test function that accepts an optional store parameter."""
assert store is None
return "success"
@@ -159,7 +159,7 @@ async def test_runnable_callable_injectable_arguments_async() -> None:
"""
# Test Optional[BaseStore] annotation.
def func_optional_store(inputs: Any, store: Optional[BaseStore]) -> str: # noqa: UP007
def func_optional_store(inputs: Any, store: Optional[BaseStore]) -> str: # noqa: UP045
"""Test function that accepts an optional store parameter."""
assert store is None
return "success"
libs/langgraph/uv.lock
Generated
+560 -517
View File
File diff suppressed because it is too large Load Diff
libs/prebuilt/uv.lock
Generated
+103 -87
View File
@@ -1,10 +1,6 @@
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revision = 2
requires-python = ">=3.9"
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