LangGraph vs Agno
Side-by-side comparison of two AI agent tools
Short answer
- LangGraph is growing faster: +2,370 GitHub stars in the last 30 days vs +558 for Agno.
- Pick LangGraph for: build resilient language agents as graphs. Pick Agno for: build, run, manage agentic software at scale.
From GitHub data refreshed daily.
LangGraphopen-source
Build resilient language agents as graphs.
Agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| LangGraph | Agno | |
|---|---|---|
| Stars | 42.7k | 42.5k |
| Star velocity /mo | 2.4k | 558.2539682539682 |
| Commits (90d) | 132 | 351 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8220244037908294 | 0.814447863455959 |
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
- +Production-ready runtime with built-in scalability, session isolation, and native tracing capabilities
- +Comprehensive monitoring and management through AgentOS UI for testing, debugging, and production oversight
- +Simple development experience - build sophisticated agents with memory and tools in approximately 20 lines of Python code
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
- -Python-focused platform with limited examples for other programming languages
- -Requires multiple dependencies and proper configuration of API keys and database connections
- -May have a learning curve for implementing complex multi-agent workflows and team coordination
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
- •Building production AI agents with persistent state, memory, and custom tool integrations for customer service or automation
- •Creating multi-agent teams and workflows for complex business processes that require coordination between specialized agents
- •Enterprise deployment of AI agents with comprehensive monitoring, user session management, and production-grade reliability requirements
FAQ
- Which is more popular, LangGraph or Agno?
- LangGraph has more GitHub stars (42,656 vs 42,524).
- Which is more actively developed, LangGraph or Agno?
- Agno had more commits in the last 90 days (351 vs 132).
- Should I use LangGraph or Agno?
- 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.