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Vadym Barda
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Welcome to the LangGraph how-to guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph.
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## Core
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## Basics
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The core guides show how to address common needs when building out AI workflows, with special focus placed on [ReAct](https://arxiv.org/abs/2210.03629)-style agents with [tool calling](https://python.langchain.com/docs/modules/model_io/chat/function_calling/) (agents that <strong>Re</strong>ason and **Act** to accomplish tasks).
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---
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hide_comments: true
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hide:
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- toc
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- navigation
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---
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# 🦜🕸️LangGraph
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[](https://pepy.tech/project/langgraph)
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[](https://github.com/langchain-ai/langgraph/issues)
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[](https://discord.com/channels/1038097195422978059/1170024642245832774)
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⚡ Build language agents as graphs ⚡
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!!! note "Python version :material-language-python:"
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Looking for the JS version? Click [:fontawesome-brands-square-js: here](https://github.com/langchain-ai/langgraphjs) ([:simple-readme: JS docs](https://langchain-ai.github.io/langgraphjs/)).
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## Overview
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Suppose you're building a customer support assistant. You want your assistant to be able to:
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1. Use tools to respond to questions
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2. Connect with a human if needed
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3. Be able to pause the process indefinitely and resume whenever the human responds
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LangGraph makes this all easy. First install:
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```bash
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pip install -U langgraph
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```
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Then define your assistant:
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```python
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import json
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from langchain_anthropic import ChatAnthropic
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langgraph.checkpoint.sqlite import SqliteSaver
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from langgraph.graph import END, MessageGraph
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from langgraph.prebuilt.tool_node import ToolNode
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# Define the function that determines whether to continue or not
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def should_continue(messages):
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last_message = messages[-1]
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# If there is no function call, then we finish
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if not last_message.tool_calls:
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return END
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else:
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return "action"
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# Define a new graph
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workflow = MessageGraph()
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tools = [TavilySearchResults(max_results=1)]
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model = ChatAnthropic(model="claude-3-haiku-20240307").bind_tools(tools)
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workflow.add_node("agent", model)
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workflow.add_node("action", ToolNode(tools))
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workflow.set_entry_point("agent")
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# Conditional agent -> action OR agent -> END
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workflow.add_conditional_edges(
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"agent",
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should_continue,
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)
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# Always transition `action` -> `agent`
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workflow.add_edge("action", "agent")
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memory = SqliteSaver.from_conn_string(":memory:") # Here we only save in-memory
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# Setting the interrupt means that any time an action is called, the machine will stop
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app = workflow.compile(checkpointer=memory, interrupt_before=["action"])
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```
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Now, run the graph:
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```python
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# Run the graph
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thread = {"configurable": {"thread_id": "4"}}
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for event in app.stream("what is the weather in sf currently", thread, stream_mode="values"):
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event[-1].pretty_print()
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```
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We configured the graph to **wait** before executing the `action`. The `SqliteSaver` persists the state. Resume at any time.
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```python
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for event in app.stream(None, thread, stream_mode="values"):
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event[-1].pretty_print()
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```
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The graph orchestrates everything:
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- The `MessageGraph` contains the agent's "Memory"
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- Conditional edges enable dynamic routing between the chatbot, tools, and the user
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- Persistence makes it easy to stop, resume, and even rewind for full control over your application
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With LangGraph, you can build complex, stateful agents without getting bogged down in manual state and interrupt management. Just define your nodes, edges, and state schema - and let the graph take care of the rest.
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## Tutorials
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Consult the [Tutorials](tutorials/index.md) to learn more about building with LangGraph, including advanced use cases.
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## How-To Guides
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Check out the [How-To Guides](how-tos/index.md) for instructions on handling common tasks with LangGraph
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## Reference
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For documentation on the core APIs, check out the [Reference](reference/graphs.md) docs.
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## Conceptual Guides
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Once you've learned the basics, if you want to further understand LangGraph's core abstractions, check out the [Conceptual Guides](./concepts/index.md).
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## Why LangGraph?
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LangGraph is framework agnostic (each node is a regular python function). It extends the core Runnable API (shared interface for streaming, async, and batch calls) to make it easy to:
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- Seamless state management across multiple turns of conversation or tool usage
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- The ability to flexibly route between nodes based on dynamic criteria
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- Smooth switching between LLMs and human intervention
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- Persistence for long-running, multi-session applications
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If you're building a straightforward DAG, Runnables are a great fit. But for more complex, stateful applications with nonlinear flows, LangGraph is the perfect tool for the job.
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{!README.md!}
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