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 LangGraph vs LlamaIndex by community traction (★ 51k vs ★ 39k). Pick LangGraph for stateful multi-agent workflows; pick LlamaIndex for RAG.
✓ Live data verified
| LangGraph | LlamaIndex | |
|---|---|---|
| GitHub stars | ★ 39k | ★ 51k |
| Language | Python | Python |
| Category | Agent frameworks | Agent frameworks |
| Best for | stateful multi-agent workflows | RAG |
| Repository | langchain-ai/langgraph | run-llama/llama_index |
LangGraph and LlamaIndex are both credible choices. By community traction, LlamaIndex leads (★ 51k). Pick LangGraph for stateful multi-agent workflows; pick LlamaIndex for RAG.
Both are credible agent frameworks. By community traction LlamaIndex leads (★ 51k). Pick LangGraph for stateful multi-agent workflows; pick LlamaIndex for RAG.
LangGraph is Graph-based orchestration for stateful, multi-actor agent workflows with explicit control flow and checkpointing.. LlamaIndex is Data framework for connecting LLMs to private data — indexing, retrieval, and agentic RAG over your documents..
LlamaIndex has more — ★ 51k vs ★ 39k (live counts).
Often yes — many teams combine agent frameworks. Check each tool's docs for interop; they solve overlapping but not identical problems.
LangGraph is primarily Python; LlamaIndex is primarily Python.
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