Lagent vs Agno
Side-by-side comparison of two AI agent tools
Short answer
- Agno is growing faster: +560 GitHub stars in the last 30 days vs +7 for Lagent.
- Pick Lagent for: a lightweight framework for building LLM-based agents. Pick Agno for: build, run, manage agentic software at scale.
From GitHub data refreshed daily.
Lagentopen-source
A lightweight framework for building LLM-based agents
Agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| Lagent | Agno | |
|---|---|---|
| Stars | 2.3k | 42.5k |
| Star velocity /mo | 7.421052631578947 | 560.0526315789474 |
| Commits (90d) | 0 | 351 |
| Releases (6m) | 1 | 10 |
| Downloads (30d, npm + PyPI) | 1.3K | — |
| Overall score | 0.23866145350294984 | 0.7960554542558297 |
Pros
- +PyTorch-inspired design makes agent workflows intuitive for ML practitioners familiar with neural network concepts
- +Built-in memory management automatically handles message storage and state persistence across agent interactions
- +Lightweight architecture with clean abstractions that simplify multi-agent system development and reduce boilerplate code
- +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
- -Limited to source installation only, which may complicate deployment in production environments
- -Documentation appears minimal based on available information, potentially creating barriers for new users
- -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 conversational AI systems that require multiple specialized agents working together on complex tasks
- •Research prototyping for multi-agent reinforcement learning and collaborative AI experiments
- •Creating intelligent automation workflows where different LLM agents handle specific aspects of a larger process
- •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, Lagent or Agno?
- Agno has more GitHub stars (42,524 vs 2,281).
- Which is more actively developed, Lagent or Agno?
- Agno had more commits in the last 90 days (351 vs 0).
- Should I use Lagent 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.