Every AI engineer in 2026 is picking an agent framework. The choice matters — it determines what your team can ship in 3 months vs. 12.
After building production AI agents on all four major frameworks, here's the honest comparison.
TL;DR — which framework to pick
| Your situation | Pick this |
|---|---|
| Building RAG over documents | LlamaIndex |
| Need broadest ecosystem + plugins | LangChain |
| Multi-agent crews with defined roles | CrewAI |
| Research-grade multi-agent conversation | AutoGen |
| Just need OpenAI/Anthropic API calls | None — use the SDK directly |
1. LangChain — the kitchen sink
LangChain is the OG. 100k+ stars, support for every LLM provider, every vector DB, every chunking strategy. Chains, agents, memory, tools, callbacks — it has everything.
The strength is also the weakness. LangChain's abstractions are deep. You'll spend the first week understanding what a Runnable is. The TypeScript version (langchain-js) trails the Python version by 6-12 months.
Use LangChain if: you need integrations with 50+ services and don't want to write boilerplate for each.
Skip LangChain if: your use case is "send a prompt, get a response, save to DB." That's just the OpenAI SDK.
2. LlamaIndex — RAG done right
LlamaIndex is built for one thing: retrieval-augmented generation. Document loaders, chunking, embeddings, query engines — all optimized for "find the right context, then ask the LLM."
If your use case is "answer questions over my docs/PDFs/database," LlamaIndex will get you there in less code than LangChain.
Use LlamaIndex if: the core problem is "retrieve, then generate." Knowledge bases, customer support over docs, internal search.
Skip LlamaIndex if: you need complex multi-step agent reasoning that goes beyond RAG.
Pro tip: use LlamaIndex *inside* LangChain. They're not mutually exclusive.
3. CrewAI — opinionated multi-agent
CrewAI takes a different philosophy: agents have roles (Researcher, Writer, Reviewer) and goals. You define the crew, give them tasks, and they collaborate.
It's the most "ship a working agent in 1 day" framework. The mental model — role + goal + tools — is closer to how teams actually work.
Use CrewAI if: you're building agent products where multiple specialized roles need to coordinate (e.g., content generation pipelines, research agents, marketing automation).
Skip CrewAI if: you need fine-grained control over the LLM call sequence. CrewAI's opinions can fight you.
4. AutoGen — research-grade
AutoGen by Microsoft Research is the most flexible multi-agent framework. Agents can have arbitrary conversations, escalate to humans, execute code, debate each other.
The flexibility comes with weight. AutoGen requires more setup, more configuration, and more understanding of multi-agent patterns. The docs are excellent but academic-feeling.
Use AutoGen if: you're building novel agent architectures, doing research, or have unusual requirements (multi-agent debates, human-in-the-loop escalation).
Skip AutoGen if: you just need a working agent yesterday. CrewAI ships faster.
What about Mastra, AI SDK, OpenAI Assistants?
- Mastra — newer JS-first agent framework. Promising, but the ecosystem is still small. Watch this space.
- Vercel AI SDK — not an agent framework. It's a streaming + UI library. Use it alongside any framework above for the frontend.
- OpenAI Assistants API — a hosted service, not open-source. Locks you to OpenAI. Avoid for serious products.
The vector DB question
Every agent framework needs a vector store. The honest picks:
- **pgvector** — Postgres extension. Use if you already have Postgres and <10M vectors.
- **Qdrant** — Production-scale, advanced filtering, written in Rust.
- **Chroma** — Simplest API, perfect for prototyping.
- **Milvus** — Massive-scale distributed, billions of vectors.
- **Weaviate** — Hybrid search + GraphQL.
See our vector DB comparisons for detail.
Local LLM serving — Ollama is the answer
Ollama is the easiest way to run Llama, Mistral, Qwen, DeepSeek locally. Whether you're prototyping or shipping a privacy-sensitive product, this is what your AI framework should talk to in development.
ollama pull llama3.3:70b
ollama serve
# Now point any framework at http://localhost:11434Want the full AI agent stack?
We curated the complete AI agent bundle: **Build an AI agent** — frameworks, vector DBs, embeddings, orchestration, observability. 40+ curated repos.
Or the AI/ML skill track: **AI / ML toolkit** — the exact open-source toolkit production AI engineers use.