LangChain vs Relevance AI
Side-by-side comparison with live GitHub signals. Last updated April 1, 2026.
Relevance AI
No-code platform for building and deploying multi-agent AI workflows
| Metric | LangChain | Relevance AI |
|---|---|---|
| GitHub Stars | 131.9K | — |
| Contributors | 469 | — |
| Last Commit | Mar 31, 2026 | — |
| Open Issues | 509 | — |
| License | open-source | commercial |
| Pricing | open-source | freemium |
| Free Tier | Yes | Yes |
| Category | frameworks | frameworks |
| Trending | No | No |
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About LangChain
LangChain is the most widely adopted framework for building LLM-powered applications. It provides modular abstractions for chains, agents, memory, retrievers, and tools, enabling developers to compose complex pipelines from reusable components. Available in both Python and JavaScript/TypeScript, LangChain integrates with every major LLM provider, vector store, and data source. Its ecosystem includes LangSmith for observability and LangGraph for stateful, graph-based agent workflows.
View full listingAbout Relevance AI
Relevance AI is a no-code platform for building multi-agent AI workflows. It provides a visual builder for creating agent teams with custom tools, knowledge bases, and integrations. Agents can be deployed as APIs, embedded in applications, or shared as standalone tools. Focuses on business teams building AI-powered internal tools.
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