LlamaIndex
The data framework for building LLM applications over custom knowledge sources
LlamaIndex is an orchestration framework purpose-built for retrieval-augmented generation and knowledge-intensive agents. It provides structured data connectors, index types (vector, summary, keyword), and query engines that let agents reason accurately over your proprietary data rather than relying purely on model weights.
Agents that need to answer questions over large corpora — docs, PDFs, databases, APIs — with high accuracy and citation support.
Engineers building knowledge-retrieval products. Accessible for solo builders; production RAG pipelines often require tuning. Open source core; cloud managed service available.
Agent Architecture Fit
LlamaIndex occupies the retrieval layer of an agent blueprint. When your agent needs to answer a question, LlamaIndex handles the fetch: it queries your indexed data, ranks results, and formats them into context the LLM can reason over. Pair it with an orchestration layer (LangChain, LangGraph) for full agent logic, or use LlamaIndex's own agent abstractions for simpler retrieval-first workflows.
Next step
Your agent starts with a blueprint.
A blueprint tells you which tools to use, where they fit, and how they connect — before you write a line of code.
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