LlamaIndex
LlamaIndex · Data Framework for LLM Apps and RAG Pipelines
A Python framework for connecting LLM applications to documents, databases, and APIs. Handles data ingestion, indexing, retrieval, and agent orchestration for RAG and document-grounded AI systems.
Best for
Developers building RAG systems and document-grounded agents who need intelligent data parsing, indexing, and retrieval — especially teams with large or complex document sets
Not ideal for
General-purpose agent orchestration without a document/data focus — LangGraph or CrewAI are more orchestration-focused; non-Python environments
Who it's for
Python developers and data teams building AI applications grounded in documents, databases, or proprietary data — especially those working on RAG, knowledge bases, or document-intensive workflows
Capabilities
- Document parsing and data ingestion: PDFs, markdown, HTML, databases, APIs, and 300+ integration packages
- Intelligent chunking and embedding: automatically split documents and create vector embeddings
- Retrieval pipelines: flexible retrieval strategies including BM25, vector search, hybrid, and fusion methods
- Workflows and AgentWorkflow for agent and multi-agent apps, with human-in-the-loop events, branching, and loops
- LlamaParse platform: agentic OCR, parsing (130+ formats), extraction, indexing, and LlamaAgents
- Multi-modal support: process text, images, and structured data within RAG pipelines
- Free tier: 10,000 LlamaParse credits/month without paid commitment
Limitations
- Strongest as a data/retrieval layer — it provides Workflows and AgentWorkflow, but orchestration is less of a focus than in LangGraph or CrewAI
- Python only — LlamaIndex.TS has been deprecated
- LlamaParse is a separate paid, credit-based service; the company's strategic focus is now LlamaParse rather than the OSS framework
- RAG pipeline complexity grows quickly with large document sets — retrieval quality and cost scale with data size
Use cases
- Building document Q&A and knowledge-base chatbots grounded in proprietary documents
- Retrieval-augmented generation (RAG) pipelines over private or internal data
- Structured data extraction from PDFs, scanned documents, and unstructured text
- Research tools that synthesize answers from specific document collections
- Enterprise knowledge management systems where accuracy and source attribution matter
- Adding data grounding to agent workflows — LlamaIndex as the data layer inside LangGraph or CrewAI
Our take
LlamaIndex is strongest as a data grounding framework, even though it also ships Workflows and AgentWorkflow for agent apps. It excels at one thing: taking documents, PDFs, databases, or APIs and making them queryable by LLMs. The distinction matters: CrewAI, LangGraph, and AutoGen orchestrate agent behavior and task execution; LlamaIndex makes data accessible to those agents. The practical comparison is clear. If your problem is 'I need agents to work together,' choose CrewAI or LangGraph. If your problem is 'I need to answer questions about my documents' or 'I need RAG over private data,' choose LlamaIndex. Most teams use both: LangGraph or CrewAI for orchestration, LlamaIndex for data retrieval. The framework is still maintained with regular 0.14.x releases, but the company's strategic focus is now LlamaParse, its document-processing platform. For teams with large document sets or complex parsing requirements, LlamaParse is worth evaluating to offload document handling.
Who should use it
Python developers and data teams building RAG systems, chatbots, or document-grounded AI apps — especially those with large document collections, complex parsing needs, or strict accuracy requirements. Also teams building multi-agent systems who need a robust data retrieval layer.
Who should skip it
Teams building general-purpose agent orchestration without a document focus — use LangGraph or CrewAI instead. Non-Python environments or those needing visual workflow builders. Teams with simple Q&A requirements who don't need advanced retrieval strategies.
Strengths
- Excellent data ingestion and parsing — handles PDFs, images, databases, APIs, and 300+ integration packages
- Flexible retrieval strategies — vector search, BM25, hybrid, reranking, and custom pipelines for optimal results
- Production-grade document processing — LlamaParse offers managed agentic OCR, parsing, extraction, and indexing
- Composable with agent frameworks — use LlamaIndex as the data layer inside LangGraph, CrewAI, or AutoGen
- Comprehensive documentation and active community — many tutorials and real-world examples available
- Free tier with meaningful limits — 10,000 LlamaParse credits/month is sufficient for prototyping
Weaknesses
- Orchestration is a secondary focus — Workflows support human-in-the-loop and branching, but LangGraph and CrewAI are more orchestration-centric
- Python only — LlamaIndex.TS has been deprecated
- Advanced document parsing via LlamaParse requires paid credits beyond the free tier
- RAG pipeline quality and cost scale with data volume — large document sets require optimization
- Requires some domain knowledge about retrieval strategies (vector search vs BM25, chunk size, embedding models) to get optimal results
Where LlamaIndex excels
Enterprise knowledge base chatbot
A company's support team uses LlamaIndex to index their help docs, FAQs, and product manuals. The LLM retrieves relevant sections and synthesizes answers. With LlamaParse's managed parsing, they handle new documents automatically.
Legal document analysis
A legal AI app uses LlamaIndex to index contracts, regulations, and case law. Lawyers query the system for clause analysis or risk assessment. LlamaIndex's multi-modal support handles scanned PDFs and the retrieval quality is critical (wrong sources are costly).
RAG layer inside a LangGraph agent system
A research team builds a LangGraph workflow where one agent node queries indexed research papers via LlamaIndex. LangGraph controls the workflow logic and branching; LlamaIndex handles the data retrieval. Neither framework alone would be sufficient.
Customer onboarding with personalized guidance
An onboarding AI indexes product guides, feature docs, and company-specific policies. LlamaIndex retrieves the right guidance for each customer context. Streaming answers back means users see relevant help in real time.
LlamaIndex vs. competitors
LlamaIndex vs. LangGraph
LangGraph is an agent orchestration framework — you define graphs of agent steps, branching, and state. LlamaIndex is a data framework — you index documents and retrieve answers from them. LangGraph solves 'how do I coordinate agent execution?' LlamaIndex solves 'how do I ground LLM answers in my data?' Most teams use both: LangGraph for orchestration, LlamaIndex for retrieval.
LlamaIndex vs. CrewAI
CrewAI orchestrates teams of agents with defined roles and tasks. LlamaIndex indexes and retrieves from documents. CrewAI answers 'how do agents collaborate on tasks?' LlamaIndex answers 'how do I make my data queryable?' Use CrewAI when you need multi-agent task execution; use LlamaIndex as a tool inside that execution (e.g., one agent queries a knowledge base via LlamaIndex).
LlamaIndex vs. LangChain
LangChain is a general LLM integration library with chains, memory, and retrieval (built on top of libraries like vector databases). LlamaIndex is specialized in data ingestion, chunking, and retrieval for RAG. Both can be used together: LangChain for composing chains, LlamaIndex for robust document handling. LlamaIndex is stronger for heavy document workloads; LangChain offers more flexibility for custom integrations.
LlamaIndex vs. Semantic Kernel
Semantic Kernel is Microsoft's agent framework across .NET, Python, and Java with enterprise features. LlamaIndex is Python-only and focused on data retrieval. Semantic Kernel answers 'how do I build and run agents?' LlamaIndex answers 'how do I make documents retrievable?' Semantic Kernel can integrate LlamaIndex for data grounding, but the scopes are different.
Frequently asked questions
Is LlamaIndex an agent framework?
Partly. LlamaIndex describes its open-source framework as a way to build agentic applications, and it includes Workflows and AgentWorkflow for agent and multi-agent apps. Its core strength, though, is data: parsing documents, creating indexes, and retrieving relevant information for LLM queries. Frameworks like LangGraph and CrewAI are more focused on orchestrating agent behavior and task execution. You can use LlamaIndex as a data layer inside those frameworks — e.g., one agent node in a LangGraph workflow queries a LlamaIndex-indexed knowledge base.
What is LlamaIndex used for?
LlamaIndex is used for building retrieval-augmented generation (RAG) systems and document-grounded AI apps. It handles document ingestion (PDFs, databases, APIs), intelligent chunking, embedding, and retrieval. Typical use cases: knowledge-base chatbots, document Q&A systems, legal or medical document analysis, and data retrieval layers inside agent workflows.
When should I use LlamaIndex instead of LangGraph or CrewAI?
Use LlamaIndex when your core problem is 'I need to make my documents queryable and ground LLM answers in data.' Use LangGraph or CrewAI when your core problem is 'I need agents to orchestrate tasks and collaborate.' Most teams use both: LangGraph/CrewAI for orchestration, LlamaIndex for data retrieval. If you are choosing just one, answer this question: Am I building an agent workflow or a retrieval system?
How is LlamaIndex different from LangChain?
LangChain is a broader library for LLM integrations, memory, chains, and retrieval. LlamaIndex is specialized and lightweight, focused on document parsing, indexing, and retrieval for RAG. Both can coexist in the same project — LangChain for general composition, LlamaIndex for sophisticated document handling. LlamaIndex is often the better choice if your bottleneck is data retrieval quality.
Do I need LlamaParse, or can I use LlamaIndex open-source for free?
LlamaIndex open-source is free and handles document parsing, indexing, and retrieval locally, and LiteParse is a free local parser. LlamaParse is the company's managed document-processing platform, adding agentic OCR for scanned PDFs, extraction, indexing, and deployed document agents. It has a free tier of 10,000 credits/month, then Starter ($50/month) and Pro ($500/month) plans. For prototyping or small document sets, open-source is sufficient; for production workloads with large or complex documents, LlamaParse can be worth the cost.
Integrations & fit
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FreeCrewAI
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FreemiumAbout LlamaIndex
LlamaIndex is a data framework for building AI applications grounded in your own data — documents, PDFs, databases, and APIs. Unlike orchestration-first agent frameworks like LangGraph or CrewAI, LlamaIndex specializes in data grounding: parsing documents, chunking them intelligently, creating embeddings, and retrieval. It enables developers to build retrieval-augmented generation (RAG) pipelines and document-aware agents that synthesize answers from private data. The open-source Python library handles ingestion, indexing, and retrieval. It also provides event-driven Workflows and AgentWorkflow for agent and multi-agent apps, with built-in human-in-the-loop events, branching, and loops. Important context: the company's primary focus has shifted to LlamaParse, its enterprise document-processing platform (agentic OCR, parsing, extraction, indexing, and deployed document agents via LlamaAgents), plus LiteParse, a free local open-source parser; the open-source framework remains maintained with regular 0.14.x releases. LlamaParse has a free tier of 10,000 credits/month, with paid plans at Starter ($50/month, 40,000 credits) and Pro ($500/month, 400,000 credits) and custom Enterprise pricing (1,000 credits = $1.25). LlamaIndex is often used inside multi-agent systems alongside LangGraph or CrewAI rather than as the primary orchestration framework.
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