LangChain vs LangChain
Side-by-side comparison of two AI agent tools
Short answer
- LangChain has had no commit in 32 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 +2 for LangChain.
- Pick LangChain for: the agent engineering platform. Pick LangChain for: reference implementations of several LangChain agents as Streamlit apps.
From GitHub data refreshed daily.
LangChainopen-source
The agent engineering platform
LangChainopen-source
Reference implementations of several LangChain agents as Streamlit apps
Metrics
| LangChain | LangChain | |
|---|---|---|
| Stars | 147.4k | 1.6k |
| Star velocity /mo | 23.1k | 2.2105263157894735 |
| Commits (90d) | 542 | 0 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 169.4M | — |
| Overall score | 0.8918400192125109 | 0.16744302204886327 |
Pros
- +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
- +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
Cons
- -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
- -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
Use Cases
- •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
- •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
FAQ
- Which is more popular, LangChain or LangChain?
- LangChain has more GitHub stars (147,399 vs 1,644).
- Which is more actively developed, LangChain or LangChain?
- LangChain had more commits in the last 90 days (542 vs 0).
- Should I use LangChain or LangChain?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.