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 DSPy by community traction (★ 52k vs ★ 37k). Pick LlamaIndex for RAG; pick DSPy for prompt optimization.
✓ Live data verified
| LlamaIndex | DSPy | |
|---|---|---|
| GitHub stars | ★ 52k | ★ 37k |
| Language | Python | Python |
| Category | Agent frameworks | Agent frameworks |
| Best for | RAG | prompt optimization |
| Repository | run-llama/llama_index | stanfordnlp/dspy |
LlamaIndex and DSPy are both credible choices. By community traction, LlamaIndex leads (★ 52k). Pick LlamaIndex for RAG; pick DSPy for prompt optimization.
Both are credible agent frameworks. By community traction LlamaIndex leads (★ 52k). Pick LlamaIndex for RAG; pick DSPy for prompt optimization.
LlamaIndex is Data framework for connecting LLMs to private data — indexing, retrieval, and agentic RAG over your documents.. DSPy is Programming — not prompting — language models: compile declarative pipelines into optimized prompts/weights..
LlamaIndex has more — ★ 52k vs ★ 37k (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; DSPy 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.