spb/tools-spboucher-ai

1{2  "slug": "llamaindex",3  "name": "LlamaIndex",4  "aliases": ["llama_index", "GPT Index", "LlamaIndex Framework"],5  "vendor": { "name": "LlamaIndex, Inc.", "slug": "llamaindex-inc", "website": "https://www.llamaindex.ai" },6  "homepage_url": "https://www.llamaindex.ai/",7  "docs_url": "https://developers.llamaindex.ai/python/framework/",8  "repo_url": "https://github.com/run-llama/llama_index",9  "pricing_url": "https://www.llamaindex.ai/pricing",10  "license_url": "https://github.com/run-llama/llama_index/blob/main/LICENSE",11  "product_type": "framework",12  "product_status": "active",13  "summary": "Open-source Python framework for retrieval-augmented agents over your data, with connectors, indexes, query engines and event-driven workflows, alongside the commercial LlamaParse document platform",14  "description": "LlamaIndex is an MIT-licensed Python toolkit for building LLM agents and retrieval pipelines over private data. Its building blocks are data connectors (loaders for files, APIs and databases), indexes backed by many vector stores, query and chat engines, agents with tools, and Workflows, an event-driven system for multi-step processes with branching, retries and human review that can be deployed as services. A retrieval pipeline is treated as one tool among others that an agent may call. Documentation moved in 2026 from docs.llamaindex.ai to developers.llamaindex.ai, and a TypeScript edition exists alongside the Python core.\n\nThe company behind the framework sells LlamaParse, a hosted document-processing platform (formerly named LlamaCloud) that parses, extracts, classifies and splits documents and offers a managed index; its output feeds directly into framework indexes. Plans for the hosted platform range from a free tier through Starter, Pro and Enterprise, while the framework itself stays free. LlamaIndex is a good fit when documents are the center of the problem; teams needing generic agent orchestration may find LangGraph or Pydantic AI more direct, and the breadth of integrations means dependency management deserves attention.",15  "capabilities": ["Data connectors for files, APIs and databases", "Indexes over many vector stores with query and chat engines", "Agents with tools and event-driven Workflows", "Evaluation and observability hooks", "Integration with LlamaParse for document parsing and extraction", "Python core with a TypeScript edition"],16  "workflows": ["Index a folder of PDFs and answer questions with citations", "Build a workflow that parses invoices, extracts fields and routes exceptions to a human", "Combine a retrieval tool with web search in one agent"],17  "advantages": ["Document-centric design with mature retrieval abstractions", "MIT license and broad integration catalog", "Commercial parsing service available when needed"],18  "limitations": ["Many optional dependencies to manage", "Hosted LlamaParse features are proprietary and metered", "Product renames (LlamaCloud to LlamaParse) date older guides"],19  "prerequisites": ["Python 3.10+", "An LLM and embedding provider or local equivalents"],20  "audiences": ["Developers building RAG applications", "Teams processing document-heavy workflows", "Researchers indexing corpora for question answering"],21  "alternatives": ["langchain", "langgraph", "pydantic-ai", "ragas"],22  "categories": [{ "slug": "agent-frameworks", "primary": true }, { "slug": "embeddings-vector-search", "label": "Retrieval and indexing abstractions" }],23  "tags": ["rag", "agents", "llm", "open-source", "python", "typescript", "documents"],24  "use_cases": ["build-agents"],25  "operating_systems": ["macos", "linux", "windows"],26  "execution_modes": ["local", "self-hosted", "cloud"],27  "interfaces": ["library", "api"],28  "deployment_models": ["embedded", "saas"],29  "integrations": ["OpenAI", "Anthropic", "Ollama", "Chroma", "Qdrant", "Weaviate", "pgvector", "Langfuse", "LlamaParse"],30  "data_formats": ["PDF", "DOCX", "HTML", "Markdown", "JSON", "CSV"],31  "languages": ["Python", "TypeScript"],32  "source_model": "open-source",33  "license_spdx": "MIT",34  "pricing_model": ["open-source", "free", "freemium", "enterprise"],35  "pricing_checked_at": "2026-11-02",36  "self_hostable": true,37  "sources": [38    { "url": "https://www.llamaindex.ai/", "type": "official_site", "title": "LlamaIndex", "supports": ["name", "summary"], "checked_at": "2026-11-02" },39    { "url": "https://developers.llamaindex.ai/python/framework/", "type": "official_docs", "title": "LlamaIndex Framework documentation", "supports": ["capabilities", "license_spdx", "languages"], "checked_at": "2026-11-02", "note": "States MIT license and the LlamaCloud→LlamaParse rename" },40    { "url": "https://github.com/run-llama/llama_index/blob/main/LICENSE", "type": "license_page", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" },41    { "url": "https://www.llamaindex.ai/pricing", "type": "pricing_page", "supports": ["pricing_model"], "checked_at": "2026-11-02" }42  ],43  "confidence": "high",44  "discovery_method": "editorial-research",45  "depth": "standard",46  "editorial_status": "reviewed",47  "visibility": "public",48  "featured": false,49  "author": "batch-A"50}51