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RAG & agent frameworks compared

The leading open-source frameworks for building RAG pipelines and multi-agent systems, compared on focus, source, and setup.

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FieldLlamaIndex

Open-source framework for building agentic LLM applications over private data with ingestion, indexes, retrieval, RAG, tools, workflows, and evaluation.

Open dossier 		LangGraph

Agent orchestration framework for building stateful, controllable, multi-step LLM and agent workflows.

Open dossier 		CrewAI

Framework and platform for building multi-agent workflows, role-based agents, process automation, and AI crews.

Open dossier 		Microsoft AutoGen

Open-source framework for building multi-agent AI applications, conversations, workflows, and autonomous systems.

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Notes Safety ✓ Privacy ✓ Safety · Privacy ✓ Safety · Privacy · Safety ✓ Privacy ✓
Categorytoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorLlamaIndexLangChainCrewAIMicrosoft
Added2026-06-032026-04-272026-04-272026-04-27
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes✓LlamaIndex retrieval, RAG, structured extraction, and agent workflows improve access to private data, but they do not prove that generated answers, retrieved context, or tool calls are correct or safe. Data connectors, readers, parsers, indexes, tools, query engines, workflows, and MCP integrations can access private files, SaaS systems, databases, APIs, and vector stores; review permissions before connecting them. Retrieved documents, metadata, parsed tables, user uploads, tool descriptions, and external connector results become model-facing context and can contain stale, malicious, or prompt-injection-like instructions. Persistent indexes, vector stores, document stores, and local storage directories can outlive the original experiment; define cleanup, retention, migration, and access-control rules before indexing sensitive data. Optional LlamaParse, LlamaCloud, or hosted document-agent workflows can upload documents or extracted content to hosted services and should be reviewed separately from local open-source framework use. Evaluation and observability results are quality signals, not proof that a RAG pipeline, agent, extraction workflow, or document workflow is production-ready.— missing— missing✓AutoGen runs multi-agent workflows that can execute code and call external tools autonomously; sandbox execution and review agent actions before granting tool or system access.
Privacy notes✓LlamaIndex workflows can process source documents, chunks, metadata, embeddings, prompts, retrieved context, generated answers, tool arguments, tool outputs, traces, evaluation datasets, and callback data. Model and embedding providers may receive document snippets, user questions, generated summaries, extracted fields, or metadata unless a local or approved private provider path is used. Connectors can ingest private repositories, tickets, PDFs, spreadsheets, databases, chats, notes, emails, or cloud files; verify that ingestion scope matches the user's authorization. Vector stores, persisted indexes, chat stores, document stores, and exported eval reports may retain data outside the source system's native permissions, deletion policy, and audit controls. Optional hosted parsing, OCR, extraction, indexing, or agent services should be assessed for upload scope, retention, residency, access controls, and incident response before processing confidential documents.✓LangGraph sends prompts and graph state to your configured model provider (including Claude); persisted state and checkpoints can contain message and tool-call data.— missing✓AutoGen agents send prompts, code, and tool outputs to the configured LLM provider(s); review what data your agents transmit and each provider's data-handling and retention terms.
Prerequisites
  • Python project and dependency manager for installing `llama-index`, `llama-index-core`, and the model, embedding, vector store, reader, or integration packages needed by the application.
  • Approved data sources, file paths, SaaS connectors, databases, or document repositories to ingest, parse, index, and query.
  • Model provider credentials, embedding provider credentials, local model configuration, or gateway configuration for generation, embeddings, reranking, and structured extraction.
  • Reviewed storage backend for indexes, vector stores, document stores, chat stores, cache data, traces, and persisted retrieval artifacts.
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