A data framework for connecting LLMs to private and enterprise data — ingestion, indexing, retrieval, and agent workflows over your own content.
Agent Development comparison
Pricing, pros, cons, and ideal use cases — side by side.
A data framework for connecting LLMs to private and enterprise data — ingestion, indexing, retrieval, and agent workflows over your own content.
An open-source framework from Microsoft Research for building multi-agent applications, where agents converse to solve tasks together.
| LlamaIndex | Microsoft AutoGen | |
|---|---|---|
| Pricing | FreemiumOpen-source framework is free. LlamaCloud (managed parsing and indexing) is sold on usage-based paid plans. | FreeOpen-source (MIT), free to use. You pay only for the underlying model API calls. |
| Category | Agent Development | Agent Development |
| Ideal for | Teams building RAG over internal dataEngineering orgs standardizing ingestion and retrievalEnterprises with large, messy document sets | Engineering teams prototyping multi-agent systemsResearch and innovation groupsTeams already in the Microsoft ecosystem |
Microsoft AutoGen is the lighter-weight option (Free), while LlamaIndex sits higher on the pricing ladder (Freemium). LlamaIndex is built around teams building rag over internal data; Microsoft AutoGen leans more toward engineering teams prototyping multi-agent systems. Shortlist the one whose strengths line up with your biggest constraint.
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