Which is more popular, LangChain or LangChain-Streamlit Template?

LangChain has more GitHub stars (147,399 vs 298).

Which is more actively developed, LangChain or LangChain-Streamlit Template?

LangChain had more commits in the last 90 days (542 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

LangChain-Streamlit Template has had no commit in 21 months; LangChain is actively maintained (542 commits in the last 90 days).
LangChain is growing faster: +23,097 GitHub stars in the last 30 days vs +0 for LangChain-Streamlit Template.

From GitHub data refreshed daily.

LangChainopen-source

The agent engineering platform

	LangChain	LangChain-Streamlit Template
Stars	147.4k	298
Star velocity /mo	23.1k	0.3157894736842105
Commits (90d)	542	0
Releases (6m)	10	0
Downloads (30d, npm + PyPI)	169.4M	—
Overall score	0.8918400192125109	0.139064714840521

+Extensive ecosystem with seamless integration between LangGraph, LangSmith, and hundreds of third-party components
+Future-proof architecture that adapts to evolving LLM technologies without requiring application rewrites
+Strong community support with 131k+ GitHub stars and comprehensive documentation for both Python and JavaScript

+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

-Significant learning curve due to the framework's extensive feature set and multiple abstraction layers
-Potential over-engineering for simple use cases that might be better served by direct API calls
-Heavy dependency on the LangChain ecosystem which can create vendor lock-in concerns

-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

•Building complex multi-agent systems that require planning, tool use, and coordination between different AI components
•Creating production LLM applications with observability, debugging, and deployment infrastructure via LangSmith
•Developing chatbots and conversational AI with memory, context management, and integration with external data sources

•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

Which is more popular, LangChain or LangChain-Streamlit Template?: LangChain has more GitHub stars (147,399 vs 298).
Which is more actively developed, LangChain or LangChain-Streamlit Template?: LangChain had more commits in the last 90 days (542 vs 0).
Should I use LangChain 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.