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.