LangChain vs Agno
Side-by-side comparison of two AI agent tools
Short answer
- LangChain is growing faster: +23,097 GitHub stars in the last 30 days vs +560 for Agno.
- Pick LangChain for: the agent engineering platform. Pick Agno for: build, run, manage agentic software at scale.
From GitHub data refreshed daily.
LangChainopen-source
The agent engineering platform
Agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| LangChain | Agno | |
|---|---|---|
| Stars | 147.4k | 42.5k |
| Star velocity /mo | 23.1k | 560.0526315789474 |
| Commits (90d) | 542 | 351 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | 169.4M | 1.7M |
| Overall score | 0.8918400192125109 | 0.7960554542558297 |
Pros
- +Extensive ecosystem with seamless integration between LangGraph, LangSmith, and hundreds of third-party components
- +Future-proof architecture that adapts to evolving LLM technologies without requiring application rewrites
- +Strong community support with 131k+ GitHub stars and comprehensive documentation for both Python and JavaScript
- +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
- -Significant learning curve due to the framework's extensive feature set and multiple abstraction layers
- -Potential over-engineering for simple use cases that might be better served by direct API calls
- -Heavy dependency on the LangChain ecosystem which can create vendor lock-in concerns
- -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
- •Building complex multi-agent systems that require planning, tool use, and coordination between different AI components
- •Creating production LLM applications with observability, debugging, and deployment infrastructure via LangSmith
- •Developing chatbots and conversational AI with memory, context management, and integration with external data sources
- •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, LangChain or Agno?
- LangChain has more GitHub stars (147,399 vs 42,524).
- Which is more actively developed, LangChain or Agno?
- LangChain had more commits in the last 90 days (542 vs 351).
- Should I use LangChain 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.