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Its companion LlamaCloud service adds hosted document parsing, extraction, indexing, and agent deployment.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fllamaindex.svg",{"slug":79,"display_name":80,"description":81},{"category_id":232,"name":233,"slug":234},{"subcategory_id":236,"name":237,"slug":238},[357],{"category_id":232,"name":233,"slug":234,"is_primary":3,"display_order":7},[359],{"subcategory_id":236,"name":237,"slug":238,"category_id":232,"is_primary":3,"display_order":7},[361,363,366,369,373,377],{"tag_id":82,"name":189,"slug":190,"tag_type":362},"technology",{"tag_id":64,"name":364,"slug":365,"tag_type":362},"TypeScript","typescript",{"tag_id":172,"name":6,"slug":367,"tag_type":368},"open-source","feature",{"tag_id":370,"name":371,"slug":372,"tag_type":368},12,"Self-hostable","self-hostable",{"tag_id":374,"name":375,"slug":376,"tag_type":368},13,"Free Tier","free-tier",{"tag_id":378,"name":379,"slug":380,"tag_type":368},16,"AI-powered","ai-powered",{"learning_curve":382,"flexibility":383,"performance":384,"popularity":385,"portability":386},"A basic RAG pipeline takes only a few lines, but getting good retrieval means learning node parsers, index types, retrievers, and Workflows, and the documentation has shifted with several API reorganisations.","Every stage from loading and chunking to retrieval, reranking, and agent orchestration can be swapped or customised, with hundreds of integrations and support for any model provider.","Retrieval and parsing quality on complex documents is its strength, especially with LlamaParse, while agent execution speed is bounded by the chosen model rather than framework overhead.","Around 52K GitHub stars and years as the default name for Python RAG make it widely recognised among developers who build with LLMs, second only to LangChain in that space.","MIT-licensed, model-agnostic, and self-hostable, with agents that can be exported from LlamaCloud and run on any infrastructure.","2026-10-01",{"vendor_id":389,"name":349,"slug":350,"website_url":390,"logo_url":31,"logo_bg":77},222,"https:\u002F\u002Fwww.llamaindex.ai","https:\u002F\u002Fdevelopers.llamaindex.ai","https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index","LlamaIndex is an **open-source framework for data-aware LLM applications**, best known for retrieval-augmented generation (RAG) and, increasingly, for agents that work over documents. It started in 2022 as GPT Index and grew into one of the two most widely used Python AI application frameworks alongside LangChain, with a TypeScript version (LlamaIndex.TS) for JavaScript teams.\n\nThe framework covers the full path from raw data to an answer: hundreds of **data connectors** through LlamaHub, document loaders and node parsers, embedding and vector-store integrations, indexes, query engines, and chat engines. On top of that, **Workflows** is its event-driven orchestration layer, where typed steps emit and consume events with shared state, resource injection, and built-in observability. Agents using function calling or ReAct patterns are built on Workflows, and multi-agent setups hand work between them. It is model-agnostic, supporting OpenAI, Anthropic, Gemini, Mistral, local models through Ollama, and many others.\n\nThe company behind it now positions itself as a **document processing platform for AI**. Its commercial LlamaCloud service provides LlamaParse, an agentic OCR and parsing service for complex PDFs, tables, and scans, alongside structured extraction, managed indexes, and LlamaAgents, which packages document-agent templates such as invoice extraction that can be served locally with the `llamactl` CLI and deployed to LlamaCloud or exported for self-hosting. LlamaCloud is billed in credits with a free monthly allowance.\n\nThe framework itself is **MIT-licensed** and free to run anywhere. Teams usually choose LlamaIndex when retrieval quality over messy documents is the hard part of the problem, and LangChain or LangGraph when the work is mostly tool orchestration and control flow.","The document processing platform for AI agents.",[396,397,398,399,400,401,402],"Hundreds of data connectors through LlamaHub","Indexes, retrievers, query engines, and chat engines for RAG","Event-driven Workflows with typed state for agents and multi-agent systems","Model-agnostic support for hosted APIs and local models","LlamaParse agentic OCR for complex PDFs, tables, and scans","LlamaAgents document-agent templates deployable with the llamactl CLI","Python and TypeScript versions",[404,405,406,407],"Strongest out-of-the-box tooling for ingesting and retrieving from messy real-world documents","LlamaParse handles tables and scanned PDFs that basic loaders mangle","Workflows give agents explicit, typed control flow without a heavy graph DSL","Large integration catalogue for vector stores, embedding models, and data sources",[409,410,411,412],"Frequent API reshuffles have left many older tutorials and examples outdated","The best parsing quality sits in the paid, credit-metered LlamaCloud service","TypeScript version trails the Python package in features","Less suited than LangGraph to agents that are mostly tool orchestration rather than retrieval",{},[],[416,418,420,422,424],{"tier_name":6,"price":7,"billing_period":8,"features":417},[10,11],{"tier_name":13,"price":7,"billing_period":8,"features":419},[15,16],{"tier_name":18,"price":19,"billing_period":8,"features":421},[21,22],{"tier_name":24,"price":25,"billing_period":8,"features":423},[27,28],{"tier_name":30,"price":31,"billing_period":8,"features":425},[33,34],{"tool_id":348,"agent_type":427,"primary_language":189,"open_source":3,"hosting_model":428,"github_stars":429,"github_stars_checked_at":430,"created_at":431,"updated_at":431},"framework","both",52343,"2026-09-30","2026-10-01T11:47:17.615377",[],[],1790856099248]