A side-by-side of two agent frameworks for building AI agents — live GitHub data, languages, and what each is best at.
Short answer: LlamaIndex leads LlamaIndex vs Agno by community traction (★ 52k vs ★ 42k). Pick LlamaIndex for RAG; pick Agno for full-stack agents.
✓ Live data verified
| LlamaIndex | Agno | |
|---|---|---|
| GitHub stars | ★ 52k | ★ 42k |
| Language | Python | Python |
| Category | Agent frameworks | Agent frameworks |
| Best for | RAG | full-stack agents |
| Repository | run-llama/llama_index | agno-agi/agno |
LlamaIndex and Agno are both credible choices. By community traction, LlamaIndex leads (★ 52k). Pick LlamaIndex for RAG; pick Agno for full-stack agents.
Both are credible agent frameworks. By community traction LlamaIndex leads (★ 52k). Pick LlamaIndex for RAG; pick Agno for full-stack agents.
LlamaIndex is Data framework for connecting LLMs to private data — indexing, retrieval, and agentic RAG over your documents.. Agno is Batteries-included agent runtime — built-in memory, knowledge/RAG, and AgentOS, a control plane you run in your own cloud. Formerly Phidata..
LlamaIndex has more — ★ 52k vs ★ 42k (live counts).
Often yes — many teams combine agent frameworks. Check each tool's docs for interop; they solve overlapping but not identical problems.
LlamaIndex is primarily Python; Agno is primarily Python.
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