FastAgency vs Ruflo

Side-by-side comparison of two AI agent tools

Short answer

  • FastAgency has had no commit in 9 months; Ruflo is actively maintained (900 commits in the last 90 days).
  • Ruflo is growing faster: +1,940 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 Ruflo for: agent framework for multi-agent swarms, autonomous workflows, adaptive memory, and vector RAG.

From GitHub data refreshed daily.

FastAgencyopen-source

The fastest way to bring multi-agent workflows to production.

R
Rufloopen-source

Agent framework for multi-agent swarms, autonomous workflows, adaptive memory, and vector RAG

Metrics

FastAgencyRuflo
Stars54873.8k
Star velocity /mo2.5263157894736841.9k
Commits (90d)0900
Releases (6m)010
Overall score0.168485102064110440.8795362848711274

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

    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

      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

        FAQ

        Which is more popular, FastAgency or Ruflo?
        Ruflo has more GitHub stars (73,768 vs 548).
        Which is more actively developed, FastAgency or Ruflo?
        Ruflo had more commits in the last 90 days (900 vs 0).
        Should I use FastAgency or Ruflo?
        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.