FastAgency vs Agno
Side-by-side comparison of two AI agent tools
Short answer
- FastAgency has had no commit in 9 months; Agno is actively maintained (351 commits in the last 90 days).
- Agno is growing faster: +560 GitHub stars in the last 30 days vs +3 for FastAgency.
- Pick FastAgency for: the fastest way to bring multi-agent workflows to production. Pick Agno for: build, run, manage agentic software at scale.
From GitHub data refreshed daily.
FastAgencyopen-source
The fastest way to bring multi-agent workflows to production.
Agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| FastAgency | Agno | |
|---|---|---|
| Stars | 548 | 42.5k |
| Star velocity /mo | 2.526315789473684 | 560.0526315789474 |
| Commits (90d) | 0 | 351 |
| Releases (6m) | 0 | 10 |
| Overall score | 0.16848510206411044 | 0.7960554542558297 |
Pros
- +Unified interface for deploying AG2 workflows to production with minimal code changes
- +Supports both web chat applications and REST API services from the same codebase
- +Built-in scaling capabilities with distributed architecture and message broker coordination
- +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
- -Dependent on AG2 framework, limiting flexibility to other agent frameworks
- -Relatively small community with 532 GitHub stars compared to major frameworks
- -Limited documentation available in the provided materials for advanced features
- -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
- •Deploying AG2 multi-agent chatbots as web applications for customer service or support
- •Creating REST API services that expose agent workflows for integration with existing systems
- •Building scalable distributed agent systems that coordinate across multiple servers or datacenters
- •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, FastAgency or Agno?
- Agno has more GitHub stars (42,524 vs 548).
- Which is more actively developed, FastAgency or Agno?
- Agno had more commits in the last 90 days (351 vs 0).
- Should I use FastAgency 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.