A side-by-side of two agent frameworks for building AI agents — live GitHub data, languages, and what each is best at.
Short answer: AutoGen leads AutoGen vs LlamaIndex by community traction (★ 60k vs ★ 52k). Pick AutoGen for conversational multi-agent; pick LlamaIndex for RAG.
✓ Live data verified
| AutoGen | LlamaIndex | |
|---|---|---|
| GitHub stars | ★ 60k | ★ 52k |
| Language | Python | Python |
| Category | Agent frameworks | Agent frameworks |
| Best for | conversational multi-agent | RAG |
| Repository | microsoft/autogen | run-llama/llama_index |
AutoGen and LlamaIndex are both credible choices. By community traction, AutoGen leads (★ 60k). Pick AutoGen for conversational multi-agent; pick LlamaIndex for RAG.
Both are credible agent frameworks. By community traction AutoGen leads (★ 60k). Pick AutoGen for conversational multi-agent; pick LlamaIndex for RAG.
AutoGen is Microsoft's framework for multi-agent conversation, with a programming model for agents that talk to each other and tools.. LlamaIndex is Data framework for connecting LLMs to private data — indexing, retrieval, and agentic RAG over your documents..
AutoGen has more — ★ 60k vs ★ 52k (live counts).
Often yes — many teams combine agent frameworks. Check each tool's docs for interop; they solve overlapping but not identical problems.
AutoGen is primarily Python; LlamaIndex is primarily Python.
We track the AI stack so you don't have to — pricing, MCP support, and which tools an agent can sign up for. Free.