LlamaIndex is a Python framework for agentic applications that need to work over your own data. It is known for its ingestion and retrieval layer, but it also offers first-class agents and workflows.
What it is for
- Building agents that query company documents, databases and APIs
- Creating event-driven workflows with deterministic steps and agent steps
- Indexing large content sets and retrieving relevant chunks for the model
- Combining several data sources behind a single query interface
How it works
- Documents go through ingestion, chunking, indexing and vector storage
- Agents use those retrieval tools to answer from the content
- Workflows describe steps and events, enabling branching and parallelism
- It integrates with dozens of vector stores, models and external systems
- The same core serves both Python and TypeScript
Availability
- Open-source core under the MIT licence, published on PyPI and npm
- The company maintains commercial parsing and cloud products; the open framework remains active
Points to note
- Answer quality depends on your chunking and indexing strategy
- The breadth of integrations demands deliberate choices to avoid dependency bloat
- If your case has no private data, lighter frameworks may be enough