LlamaIndex

LlamaIndex is the framework most teams reach for when retrieval quality matters. Maintained by LlamaIndex Inc., it started as a RAG-first toolkit and has since expanded into full agent support built on the same data foundation. With 49,467 GitHub stars, 1,934 contributors, and over 11.6 million PyPI monthly downloads, it is one of the most widely adopted open source AI agent frameworks in production today.

Where LangChain focuses on broad orchestration across model providers and CrewAI specializes in role-based multi-agent teams, LlamaIndex occupies the retrieval-heavy end of the spectrum. Its design philosophy is straightforward: before an agent can act, it needs the right context. The framework gives practitioners the deepest set of indexing primitives available, then layers agent workflows on top. If your application depends on finding the right chunk of a PDF, the right row in a table, or the right image in a corpus before an LLM processes it, LlamaIndex is the natural starting point.

Licensed under MIT and written as a mixed polyglot framework with both Python and TypeScript SDKs, LlamaIndex is built by the same team that maintains LlamaCloud, a managed platform for parsing, indexing, and retrieval at scale.

LlamaIndex is primarily code-first and imperative. You build pipelines by composing objects in Python or TypeScript, not by writing configuration files or visual graphs.

The core abstractions are:

• Indices: Data structures that organize your documents for retrieval. LlamaIndex supports vector indices, summary indices, knowledge graph indices, and structured indices, and tree-based indices. You can combine them into a single query engine.
• Query Engines: The interface that selects the right index and retrieval strategy for a given query. You ask a question; the query engine determines whether to use vector search, keyword search, structured querying, or a hybrid.
• Retriever Tools: Wrapper objects that expose a query engine as a callable tool for an agent. This is how agents interact with data without manual routing.
• Agent Workflows: Event-driven orchestration that chains retrieval steps with tool calls, branching, parallelism, and human-in-the-loop checkpoints.

Control flow is handled through two layers. For simple RAG, you build an index, attach it to a retriever, and query directly. For agentic tasks, you define a workflow as a sequence of steps connected by events. The agent receives a user request, selects tools from its available toolset, calls them in order, and reflects on results before deciding whether to stop or continue.

The framework supports statefulness through memory components and persistence through its storage layer. You can serialize indices to disk, load them across sessions, and share them between agents.

The single strongest feature in LlamaIndex is its index architecture. You are not limited to a single vector store. You can build a vector index for semantic search, a keyword index for exact matches, a summary index for document-level questions, and a knowledge graph index for relationship queries, then compose them into a single retriever that routes to the appropriate index per query. This matters in production because real data is heterogeneous. A single embedding model cannot capture every retrieval scenario.

Document parsing is the bottleneck for most enterprise RAG deployments. LlamaParse is a production-grade parser that handles PDFs, slides, scanned documents, handwritten text, tables, charts, and complex layouts. It uses vision-language models for layout-aware extraction and runs auto-correction loops that detect and fix errors automatically. Over 1 billion documents have been processed through the platform, and it supports 50-plus unstructured file types. You can use it standalone or integrate it directly into a LlamaIndex pipeline.

Agentic behavior in LlamaIndex is event-driven. A workflow consists of steps that fire on events: a retrieval step emits a "retrieved" event, a tool call step emits a "tool result" event, a reflection step evaluates the result and emits either a "continue" or "stop" event. This model supports branching (route different documents to different tools), parallelism (run multiple retrievals simultaneously), and human-in-the-loop pauses. Workflows are durable: on failure, you can replay from the last checkpoint.

Streaming is supported at both the retrieval and generation layers. Memory can be short-term (conversation history) or long-term (summarized across sessions). The framework has first-class support for tracing and evaluation: you can log every retrieval, tool call, and LLM request, then evaluate response quality against ground-truth datasets. Both self-hostable (open source) and cloud-hosted (LlamaCloud) deployment options are available.

LlamaIndex supports multi-agent setups, but its coordination model is more structured than CrewAI or AutoGen. You typically define one orchestrator agent that routes tasks to specialized sub-agents, each with its own tools. Sub-agents do not negotiate among themselves; the orchestrator manages all delegation. This design works well for document-grounded workflows where the task structure is known ahead of time but is less flexible for emergent multi-agent collaboration.

The most common deployment is retrieval pipelines over heterogeneous enterprise documents. A legal team might have PDFs, scanned contracts, spreadsheets, and email threads covering the same subject. LlamaIndex handles the ingestion and indexing of each format through its connector library (LlamaHub) and LlamaParse. The evaluation tools let teams measure retrieval precision before going to production.

Teams building internal copilots or customer support bots need every answer to cite its source. LlamaIndex agents produce grounded responses by default: the retrieval step returns document chunks, the LLM generates answers from those chunks, and the agent attaches source metadata to each piece of the response. This is critical for regulated industries where ungrounded answers are unacceptable.

Some documents contain text, tables, images, and audio. LlamaIndex supports separate retrievers for each modality: text chunks go to a text retriever, images go to an image retriever, tables go to a structured data retriever. A single query can merge results across modalities. A question like "what did the chart on page 14 show about Q3 revenue" retrieves both the table row and the surrounding text explanation.

LlamaIndex is a poor fit for applications that require complex multi-agent negotiation, emergent agent roles, or decentralized coordination. If your use case involves agents that discover each other and negotiate task assignment at runtime, CrewAI or AutoGen serve better. It also has a steeper learning curve for simple use cases. If you only need a basic chatbot over a single PDF, a raw API call to a model with system prompts may be faster to prototype.

Installation is straightforward for both SDKs:

Python:

1pip install llama-index

TypeScript:

1npm install llamaindex

The first 20 lines of a working RAG pipeline in Python look like this:

1. Load documents using a reader (PDF reader, CSV reader, directory reader).
2. Parse them with LlamaParse (optional but recommended for complex layouts).
3. Build an index (VectorStoreIndex from the parsed documents).
4. Create a query engine from the index.
5. Ask questions.

Beyond the framework itself, you need an LLM provider key (OpenAI, Anthropic, or any model accessible through LlamaIndex's LLM integrations) and a vector store for production-scale deployments (Pinecone, Weaviate, Qdrant, or Chroma for local testing). For observability, you can use the built-in tracing or plug in tools like Arize or Weights and Biases.

The official documentation lives at docs.llamaindex.ai. Community support is active on Discord (20,000-plus members) and Reddit at r/LlamaIndex. For managed infrastructure, LlamaCloud handles parsing, indexing, retrieval, and deployment with a free tier that includes 10,000 credits per month.

LlamaIndex vs LangChain. LangChain offers broader ecosystem support for LLM providers, vector stores, and model chaining patterns. LlamaIndex offers deeper retrieval primitives and better document parsing. Choose LangChain when your application needs to switch between multiple LLM providers or chain together diverse tools from different ecosystems. Choose LlamaIndex when retrieval accuracy is the primary performance constraint and your documents are messy or multi-modal.

LlamaIndex vs CrewAI. CrewAI excels at role-based multi-agent teams where agents have defined responsibilities and interact through task delegation. LlamaIndex handles multi-agent coordination but within a stricter orchestrator pattern. Choose CrewAI when you need agents with distinct personas that negotiate task execution. Choose LlamaIndex when every agent decision must be grounded in a data source and citations are non-negotiable.

LlamaIndex vs Pydantic AI. Pydantic AI focuses on type-safe agent definitions using Python's type system. LlamaIndex focuses on retrieval infrastructure. They complement each other. Some teams use Pydantic AI to define agent schemas and LlamaIndex to manage the data layer beneath them.