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 (33,428 commits in the last 90 days).
- hermes-agent is growing faster: +5,710 GitHub stars in the last 30 days vs +0 for LangChain-Streamlit Template.
From GitHub data refreshed daily.
h
hermes-agentopen-source
The agent that grows with you
Metrics
| hermes-agent | LangChain-Streamlit Template | |
|---|---|---|
| Stars | 250.9k | 298 |
| Star velocity /mo | 5.7k | 0.3157894736842105 |
| Commits (90d) | 33.4k | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.946551175635728 | 0.139064714840521 |
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 (250,877 vs 298).
- Which is more actively developed, hermes-agent or LangChain-Streamlit Template?
- hermes-agent had more commits in the last 90 days (33,428 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.