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

LangChain has more GitHub stars (1,644 vs 298).

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

LangChain-Streamlit Template had more commits in the last 90 days (0 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 is growing faster: +2 GitHub stars in the last 30 days vs +0 for LangChain-Streamlit Template.

From GitHub data refreshed daily.

LangChainopen-source

Reference implementations of several LangChain agents as Streamlit apps

	LangChain-Streamlit Template	LangChain
Stars	298	1.6k
Star velocity /mo	0.3157894736842105	2.2105263157894735
Commits (90d)	0	0
Releases (6m)	0	0
Overall score	0.139064714840521	0.16744302204886327

+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

+Multiple complete, working examples covering diverse agent patterns from basic chat to complex document Q&A systems
+Ready-to-deploy Streamlit applications with live demos available for immediate testing and exploration
+Demonstrates best practices for LangChain-Streamlit integration including callback handling, memory management, and user feedback collection

-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

-Some examples use potentially unsafe tools like PythonAstREPLTool that are vulnerable to arbitrary code execution
-Limited to the LangChain ecosystem and may not showcase integration with other agent frameworks or libraries
-Most examples require external API keys and services to run fully, creating setup barriers for immediate testing

•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

•Rapid prototyping of conversational AI agents with interactive web interfaces for testing and demonstration
•Building document Q&A systems that can chat about custom content and provide contextual answers from uploaded files
•Creating natural language interfaces for database queries and data analysis tools

Which is more popular, LangChain-Streamlit Template or LangChain?: LangChain has more GitHub stars (1,644 vs 298).
Which is more actively developed, LangChain-Streamlit Template or LangChain?: LangChain-Streamlit Template had more commits in the last 90 days (0 vs 0).
Should I use LangChain-Streamlit Template or LangChain?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.