herdr vs Agno

Side-by-side comparison of two AI agent tools

Short answer

  • herdr is growing faster: +3,900 GitHub stars in the last 30 days vs +560 for Agno.
  • Pick herdr for: the runtime your coding agents live on. Pick Agno for: build, run, manage agentic software at scale.

From GitHub data refreshed daily.

h
herdropen-source

the runtime your coding agents live on

Agnoopen-source

Build, run, manage agentic software at scale.

Metrics

herdrAgno
Stars42.0k42.5k
Star velocity /mo3.9k560.0526315789474
Commits (90d)783351
Releases (6m)1010
Overall score0.88597798416234160.7960554542558297

Pros

    • +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

      • -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 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
        View herdr DetailsView Agno Details

        FAQ

        Which is more popular, herdr or Agno?
        Agno has more GitHub stars (42,524 vs 42,035).
        Which is more actively developed, herdr or Agno?
        herdr had more commits in the last 90 days (783 vs 351).
        Should I use herdr 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.