LangGraph — AI Agent Framework: Live Stats & TrendScore

LangGraph is LangChain’s graph-based orchestration layer — increasingly preferred for complex multi-agent workflows. See how it’s trending versus CrewAI and AutoGen.

GitHub data synced: Jun 28, 2026 • Sentiment updated: Jun 29, 2026

GitHub Statistics

• GitHub Stars: 35,960
• Forks: 6,020
• Open Issues: 584
• Primary Language: Python
• License: MIT
• Weekly Star Velocity: +590 stars/week

Community Sentiment

Community Buzz: As one Dev.to user noted, 'LangGraph is becoming the default framework for teams building agentic AI workflows.', with another user on GitHub stating 'LangGraph state machine with cycles · Claude Sonnet · PubMed integration · Real concordance data ·'

Pros & Cons

What People Love

LangGraph's ability to handle complex workflows, Dev.to users praise LangGraph's ease of use

Common Complaints

performance issues, lack of unit tests

Biggest Positive: LangGraph useful

Biggest Negative: LangGraph performance

Why LangGraph Stands Out

LangGraph's unique approach to building stateful agents is its ability to provide low-level supporting infrastructure for long-running, stateful workflows. This is achieved through its durable execution, human-in-the-loop, and comprehensive memory features. LangGraph's production-ready deployment feature also ensures that agents can be deployed with confidence. By providing this infrastructure, LangGraph enables developers to build complex AI workflows that integrate multiple LLMs and frameworks.

Built With

Build persistent agents that can recover from failures — LangGraph's durable execution ensures agents can run for extended periods, automatically resuming from exactly where they left off., Build human-in-the-loop agents that can incorporate human oversight — LangGraph's human-in-the-loop feature seamlessly allows for inspecting and modifying agent state at any point during execution., Build stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions — LangGraph's comprehensive memory feature enables creating truly stateful agents., Build complex AI workflows that integrate multiple LLMs and frameworks — LangGraph's low-level orchestration framework provides the necessary infrastructure for building, managing, and deploying long-running, stateful agents., Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows — LangGraph's production-ready deployment feature ensures agents can be deployed with confidence.

Getting Started

1. pip install -U langgraph
2. Create a new Python file and import the LangGraph library: import langgraph
3. Configure the LangGraph library by setting up your agent and workflow: agent = langgraph.Agent()
4. Use the LangGraph library to build your agent's behavior: agent.behavior = langgraph.Behavior()
5. Try the LangGraph library's durable execution feature by simulating a failure and verifying that the agent can recover: agent.durable_execution = True
6. Use the LangGraph library to deploy your agent in production: langgraph.deploy_agent(agent)

About

Build resilient agents.

Official site: https://docs.langchain.com/oss/python/langgraph/

Category & Tags

Category: multi-agent

Tags: agents, ai, ai-agents, chatgpt, deepagents, enterprise, framework, gemini, generative-ai, langchain, langgraph, llm, multiagent, open-source, openai, pydantic, python, rag

Market Context

LangGraph is competitively positioned as a leading framework for building agentic AI workflows, with alternatives such as Microsoft Agent Framework and CrewAI also in the market.