LangChain vs LlamaHub
Side-by-side comparison of two AI agent tools
Short answer
- LlamaHub has had no commit in 31 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 LlamaHub.
- Pick LangChain for: the agent engineering platform. Pick LlamaHub for: a library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain.
From GitHub data refreshed daily.
LangChainopen-source
The agent engineering platform
LlamaHubopen-source
A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain
Metrics
| LangChain | LlamaHub | |
|---|---|---|
| Stars | 147.4k | 3.5k |
| Star velocity /mo | 23.1k | -2.3684210526315788 |
| Commits (90d) | 542 | 0 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 169.4M | — |
| Overall score | 0.8918400192125109 | 0.1127555220681511 |
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
- +Extensive community-contributed collection of data loaders and integrations for popular LLM frameworks
- +Simplified data ingestion with ready-to-use connectors for major platforms like Google Workspace, Notion, and Slack
- +Well-documented examples and Jupyter notebooks demonstrating real-world data agent implementations
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
- -Repository is archived and read-only, with no new development or maintenance
- -All functionality has been migrated to the main llama-index repository, making this version obsolete
- -Installation may be deprecated as the PyPI package redirects users to the updated implementation
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
- •Legacy projects that need to maintain compatibility with older LlamaIndex versions
- •Learning from historical examples of data loader implementations and patterns
- •Understanding the evolution of LlamaIndex's integration ecosystem before consulting current documentation
FAQ
- Which is more popular, LangChain or LlamaHub?
- LangChain has more GitHub stars (147,399 vs 3,460).
- Which is more actively developed, LangChain or LlamaHub?
- LangChain had more commits in the last 90 days (542 vs 0).
- Should I use LangChain or LlamaHub?
- 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.