LangChain vs Onyx
Side-by-side comparison of two AI agent tools
Short answer
- LangChain is growing faster: +23,217 GitHub stars in the last 30 days vs +240 for Onyx.
- Pick LangChain for: the agent engineering platform. Pick Onyx for: open Source AI Platform - AI Chat with advanced features that works with every LLM.
From GitHub data refreshed daily.
LangChainopen-source
The agent engineering platform
O
Onyxopen-source
Open Source AI Platform - AI Chat with advanced features that works with every LLM
Metrics
| LangChain | Onyx | |
|---|---|---|
| Stars | 147.4k | 32.3k |
| Star velocity /mo | 23.2k | 240 |
| Commits (90d) | 546 | 1.6k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9025020701905048 | 0.8102610712797703 |
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
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
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
FAQ
- Which is more popular, LangChain or Onyx?
- LangChain has more GitHub stars (147,383 vs 32,316).
- Which is more actively developed, LangChain or Onyx?
- Onyx had more commits in the last 90 days (1,557 vs 546).
- Should I use LangChain or Onyx?
- 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.