Which is more popular, LangChain or LangChain.js-LLM-Template?

LangChain has more GitHub stars (147,399 vs 330).

Which is more actively developed, LangChain or LangChain.js-LLM-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.js-LLM-Template has had no commit in 43 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.js-LLM-Template.
Pick LangChain for: the agent engineering platform. Pick LangChain.js-LLM-Template for: this is a LangChain LLM template that allows you to train your own custom AI LLM.

From GitHub data refreshed daily.

LangChainopen-source

The agent engineering platform

This is a LangChain LLM template that allows you to train your own custom AI LLM.

+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

+Simple markdown-based training data format that's easy to organize and maintain
+Built on the robust LangChain.js framework with established patterns and community support
+Includes Replit integration for quick deployment and experimentation without local setup

-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

•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 internal company chatbots trained on documentation and knowledge bases
•Creating domain-specific AI assistants for specialized fields like legal, medical, or technical domains
•Rapid prototyping of custom AI applications that need to understand proprietary or niche content

Which is more popular, LangChain or LangChain.js-LLM-Template?: LangChain has more GitHub stars (147,399 vs 330).
Which is more actively developed, LangChain or LangChain.js-LLM-Template?: LangChain had more commits in the last 90 days (542 vs 0).
Should I use LangChain or LangChain.js-LLM-Template?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.