Langfuse vs Agno
Side-by-side comparison of two AI agent tools
Short answer
- Langfuse is growing faster: +1,807 GitHub stars in the last 30 days vs +560 for Agno.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick Agno for: build, run, manage agentic software at scale.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
Agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| Langfuse | Agno | |
|---|---|---|
| Stars | 35.3k | 42.5k |
| Star velocity /mo | 1.8k | 560.0526315789474 |
| Commits (90d) | 2.0k | 351 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8971312686464765 | 0.7960554542558297 |
Pros
- +Open source with MIT license allowing full customization and transparency, plus active community support
- +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
- +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK
- +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
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
- -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
- •Production LLM application monitoring to track performance, costs, and identify issues in real-time
- •Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- •LLM evaluation and testing to measure model performance across different datasets and use cases
- •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, Langfuse or Agno?
- Agno has more GitHub stars (42,524 vs 35,329).
- Which is more actively developed, Langfuse or Agno?
- Langfuse had more commits in the last 90 days (2,013 vs 351).
- Should I use Langfuse 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.