DSPy vs LlamaIndex

Pick two tools from the same category to compare side-by-side.

	DSPy Open-source Python framework for programming, rather than prompting, language models.	LlamaIndex Data framework for connecting LLMs to private data, with RAG, agents, and event-driven workflows.
Category	framework	framework
Vendor type	open source	hybrid
Pricing	free	freemium
Strengths	+ Declarative, modular programs replace brittle prompt strings + Automatic prompt optimization against a defined metric + Portable across models and providers (uses LiteLLM under the hood) + Research-backed, with an active academic and OSS community	+ Extensive data connectors and RAG tooling + Strong document parsing and ingestion (LlamaParse/LlamaCloud) + Agents and event-driven workflows built in + Large ecosystem and integrations
Tradeoffs	− Optimizer-driven workflow has a distinct learning curve − Compilation and optimization can add cost and iteration time − Less of a turnkey agent runtime than orchestration-focused frameworks	− Broad API surface can be complex to navigate − Heritage is RAG-centric rather than agent-first − Frequent API changes across versions
Used in stacks	0	0