LangChain-Streamlit Template vs OpenHuman

Side-by-side comparison of two AI agent tools

Short answer

  • LangChain-Streamlit Template has had no commit in 21 months; OpenHuman is actively maintained (22,774 commits in the last 90 days).
  • OpenHuman is growing faster: +2,510 GitHub stars in the last 30 days vs +0 for LangChain-Streamlit Template.

From GitHub data refreshed daily.

O
OpenHumanopen-source

OpenHuman is the fastest, cheapest, most efficient open-source agent harness. Written in Rust

Metrics

LangChain-Streamlit TemplateOpenHuman
Stars29840.5k
Star velocity /mo0.31578947368421052.5k
Commits (90d)022.8k
Releases (6m)010
Overall score0.1390647148405210.9308227395695856

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, LangChain-Streamlit Template or OpenHuman?
        OpenHuman has more GitHub stars (40,486 vs 298).
        Which is more actively developed, LangChain-Streamlit Template or OpenHuman?
        OpenHuman had more commits in the last 90 days (22,774 vs 0).
        Should I use LangChain-Streamlit Template or OpenHuman?
        Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.