LangGraph
Framework for building stateful, multi-actor AI applications as graphs. LangGraph enables complex agent workflows with cycles, conditional branching, and human-in-the-loop checkpoints.
MIT
TypeScript / Python
Why LangGraph?
Building complex multi-step agent workflows
You need stateful agents with memory across steps
Human-in-the-loop approval flows
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitSimple single-turn AI responses
You don't need agent orchestration complexity
Your team is new to graph-based programming patterns
Pricing
Free tier & paid plans
100% free, open-source (MIT)
Free & open-source
LangSmith tracing is paid; the framework itself is free
Alternative Tools
Other options worth considering
The original LLM orchestration framework with a huge pre-built ecosystem of chains, agents, memory, and tool integrations. Very high adoption but community sentiment has shifted — frequent breaking changes are a known pain point.
Framework for orchestrating role-based AI agents that collaborate to solve complex tasks. Each agent has specialized roles, goals, and tools, working together like a software development team.
Often Used Together
Complementary tools that pair well with LangGraph
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/langchain-ai/langgraph
npm install @langchain/langgraphpip install langgraphQuick Start
Copy and adapt to get going fast
import { StateGraph, END } from '@langchain/langgraph';
const workflow = new StateGraph({ channels: { messages: { value: (x, y) => x.concat(y) } } });
workflow.addNode('agent', async (state) => {
const response = await llm.invoke(state.messages);
return { messages: [response] };
});
workflow.setEntryPoint('agent');
workflow.addEdge('agent', END);
const app = workflow.compile();
const result = await app.invoke({ messages: [{ role: 'user', content: 'Hello' }] });Code Examples
Common usage patterns
ReAct agent
Build a Reason+Act agent with tools
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(model="claude-sonnet-4-6")
tools = [search_tool, calculator_tool]
agent = create_react_agent(model, tools)
result = agent.invoke({"messages": [("user", "What's 15% of the GDP of France?")]})Human approval checkpoint
Pause workflow for human review
from langgraph.checkpoint.memory import MemorySaver
checkpointer = MemorySaver()
app = workflow.compile(checkpointer=checkpointer, interrupt_before=["tools"])
config = {"configurable": {"thread_id": "1"}}
result = app.invoke(inputs, config=config)
# Human reviews, then resume:
app.invoke(None, config=config)Community Notes
Real experiences from developers who've used this tool