LlamaIndex is an open-source data framework designed to help developers build production-ready applications powered by large language models (LLMs). It provides tools to ingest data from a wide variety of sources, structure that data into indexes, and query it efficiently using LLMs. The platform supports retrieval-augmented generation (RAG) pipelines, agentic workflows, and multi-step reasoning over complex data. LlamaIndex offers both a Python and TypeScript library, along with LlamaCloud, a managed cloud service that provides hosted parsing, indexing, and retrieval infrastructure. LlamaCloud is aimed at teams that want to move beyond prototype-stage RAG systems into scalable, enterprise-grade deployments. The framework integrates with numerous LLM providers, vector databases, and data connectors, making it adaptable to diverse technology stacks. It is used by individual developers, startups, and large enterprises building AI assistants, document Q&A systems, knowledge management tools, and autonomous agents.
Target audience and deployment
- Solo / Freelancer
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- Self-hosted
- API
Key features
Use cases
- Build retrieval-augmented generation (RAG) pipelines
- Parse and index complex documents
- Develop autonomous AI agents
- Deploy production-grade knowledge assistants
- Query structured and unstructured enterprise data
Best for
- AI engineers who need to build and deploy production RAG pipelines over custom data
- Developers who need to integrate LLMs with diverse enterprise data sources
- Data teams who need to parse and structure complex documents for LLM consumption
- Startups who need a scalable framework for building LLM-powered products quickly
Integrations
Developer
LangChain, FastAPI
AI models included
OpenAI, Anthropic, Gemini, Mistral, Cohere, Hugging Face
Databases
Pinecone, Weaviate, Chroma, MongoDB, PostgreSQL
Other
Slack, Notion, Google Drive