LangGraph vs Multi-GPT

Side-by-side comparison of two AI agent tools

Short answer

  • Multi-GPT has had no commit in 40 months; LangGraph is actively maintained (132 commits in the last 90 days).
  • LangGraph is growing faster: +2,365 GitHub stars in the last 30 days vs +1 for Multi-GPT.
  • Pick LangGraph for: build resilient language agents as graphs. Pick Multi-GPT for: an experimental open-source attempt to make GPT-4 fully autonomous.

From GitHub data refreshed daily.

LangGraphopen-source

Build resilient language agents as graphs.

Multi-GPTopen-source

An experimental open-source attempt to make GPT-4 fully autonomous.

Metrics

LangGraphMulti-GPT
Stars42.7k565
Star velocity /mo2.4k0.631578947368421
Commits (90d)1320
Releases (6m)100
Downloads (30d, npm + PyPI)43.7M—
Overall score0.80913195306925360.1479328868844223

Pros

  • +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
  • +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
  • +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
  • +多代理协作机制：不同专家可以发挥各自优势，理论上比单一代理能处理更复杂的任务
  • +完整的记忆系统：支持长短期记忆管理，支持多种后端（Redis、Pinecone、Milvus、Weaviate）
  • +互联网访问能力：具备搜索和信息收集功能，可以访问流行网站和平台获取实时信息

Cons

  • -Low-level framework requires more technical expertise and setup compared to high-level agent builders
  • -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
  • -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
  • -实验性项目：稳定性和可靠性未经充分验证，可能存在未知风险
  • -配置复杂：需要多个 API 密钥和记忆后端设置，学习和部署门槛较高
  • -资源消耗大：运行多个 GPT-4 实例会显著增加 API 调用成本和计算资源需求

Use Cases

  • •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
  • •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
  • •Stateful agents that must maintain context and memory across multiple sessions and interactions
  • •复杂研究项目：需要整合多个学科知识和专业技能的研究任务
  • •长期项目管理：需要持续记忆和状态跟踪的项目，如产品开发或学术研究
  • •自动化信息工作流：大规模信息收集、分析和处理任务的自动化
View LangGraph DetailsView Multi-GPT Details

FAQ

Which is more popular, LangGraph or Multi-GPT?
LangGraph has more GitHub stars (42,656 vs 565).
Which is more actively developed, LangGraph or Multi-GPT?
LangGraph had more commits in the last 90 days (132 vs 0).
Should I use LangGraph or Multi-GPT?
Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.