LangChain vs LlamaIndex vs AutoGen
LangChain is a general-purpose framework for chaining LLM calls, tools, and — via LangGraph — building stateful agents. LlamaIndex specializes in data ingestion, indexing, and retrieval (RAG). AutoGen (from Microsoft) focuses on orchestrating conversations among multiple agents. They overlap, but each has a center of gravity, and the right choice depends on whether your problem is orchestration, retrieval, or multi-agent coordination.
At a glance
| Framework | Center of gravity | Best for | Watch-outs |
|---|---|---|---|
| LangChain / LangGraph | Orchestration + agents | Chaining tools/LLMs; stateful agent graphs | Broad surface; pin versions, keep it lean |
| LlamaIndex | Data & retrieval (RAG) | Ingesting, indexing, and querying your corpora | Less of an agent framework on its own |
| AutoGen | Multi-agent conversation | Agents that collaborate/critique each other | Governance/observability you must add yourself |
LangChain (and LangGraph)
LangChain is the most general option: connectors to models and tools, chains, and — through LangGraph — stateful, controllable agent workflows (supervisors, multi-step graphs). Choose it when you need flexible orchestration across tools and models, or explicit agent control flow. Its breadth is also its risk: keep dependencies pinned and the graph minimal.
LlamaIndex
LlamaIndex is purpose-built for the data side of LLM apps: loading documents, chunking, indexing, and retrieval. If your core problem is high-quality RAG over your own corpora, LlamaIndex gives you the richest retrieval primitives. Many teams use LlamaIndex for retrieval inside a LangChain/LangGraph orchestration.
AutoGen
AutoGen structures problems as conversations between multiple agents that plan, critique, and hand off to one another. It shines for research-style, multi-agent workflows. What it does not give you out of the box is enterprise governance — per-agent identity, scoped credentials, audit — which you must layer on.
The framework isn't the hard part
Choosing a framework is a day-one decision. The parts that determine whether an agent system survives production are the ones no framework hands you: identity per agent, scoped and ephemeral credentials, tiered autonomy, audit trails, evaluation, and tenancy isolation — the definition of governed AI agents. Pick the framework that fits your problem, then engineer governance around it.
How Citadel helps
Citadel builds multi-agent systems and RAG platforms on whichever framework fits — and wraps them in the governance and security that gets them approved for production.