hermes-agent vs LangChain-Streamlit Template

Side-by-side comparison of two AI agent tools

Short answer

  • LangChain-Streamlit Template has had no commit in 21 months; hermes-agent is actively maintained (32,889 commits in the last 90 days).
  • hermes-agent is growing faster: +5,760 GitHub stars in the last 30 days vs +0 for LangChain-Streamlit Template.

From GitHub data refreshed daily.

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hermes-agentopen-source

The agent that grows with you

Metrics

hermes-agentLangChain-Streamlit Template
Stars250.7k298
Star velocity /mo5.8k0.31746031746031744
Commits (90d)32.9k0
Releases (6m)100
Overall score0.95017616633487660.14836664799885807

Pros

    • +Provides a complete template structure for rapid LangGraph agent deployment with minimal setup required
    • +Seamlessly integrates Streamlit's interactive UI capabilities with LangChain's powerful agent framework
    • +Includes built-in LangSmith support for comprehensive monitoring, debugging, and performance optimization of deployed agents

    Cons

      • -Requires manual customization of the load_chain function, which may be challenging for beginners
      • -Template is specifically designed for chatbot interfaces, limiting flexibility for other types of AI applications
      • -Depends on external API keys (OpenAI) and cloud services for full functionality

      Use Cases

        • •Building and deploying conversational AI prototypes for testing LangGraph agent workflows
        • •Creating interactive demos to showcase LangGraph capabilities to stakeholders or clients
        • •Developing production-ready chatbot applications with monitoring and debugging capabilities

        FAQ

        Which is more popular, hermes-agent or LangChain-Streamlit Template?
        hermes-agent has more GitHub stars (250,690 vs 298).
        Which is more actively developed, hermes-agent or LangChain-Streamlit Template?
        hermes-agent had more commits in the last 90 days (32,889 vs 0).
        Should I use hermes-agent or LangChain-Streamlit Template?
        Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.