Best Python AI agent frameworks in 2026
The Python agent ecosystem has consolidated around a handful of credible frameworks. Each has a clear identity and a set of teams it serves well. This roundup is opinionated. We maintain AgentFlow, so we will tell you when we think it is the right pick and when it is not.
How we score
For each framework, we score five dimensions that matter for production teams:
- Orchestration model. How you express multi-step / multi-agent flows
- Persistence. Checkpointing, threads, resumability
- Production server. What it takes to expose the agent over HTTP
- Frontend story. First-party clients for TypeScript / JavaScript
- Provider neutrality. Locking you in vs leaving the door open
The frameworks at a glance
Snapshot of the major Python agent frameworks. Categories are deliberately broad. See each comparison for nuance.
| Dimension | AgentFlow | The field |
|---|---|---|
| AgentFlow | Typed graph runtime with built-in API + TS client | Open source, MIT, multi-provider |
| LangGraph | — | Graph runtime (open source) + LangGraph Platform (paid) |
| CrewAI | — | Role-based crews; OSS + CrewAI Enterprise |
| AutoGen (Microsoft) | — | Conversational multi-agent + AutoGen Studio |
| LlamaIndex Agents | — | RAG-first agents on top of LlamaIndex indexes |
| Google ADK | — | Gemini- and Vertex AI-optimized agent kit |
Our picks
Best for production multi-agent products: AgentFlow
If you are building a real product. A chat surface, a co-pilot, an internal automation. AgentFlow gives you the most "deployable out of the box" of the major frameworks: typed graphs, persistent threads with PgCheckpointer, a REST + SSE server (agentflow api), and a typed TypeScript client (@10xscale/agentflow-client). MIT-licensed, no required SaaS account.
Choose AgentFlow if: you want one Python project that handles orchestration, state, the API, and the frontend SDK without gluing five libraries together. → Get started
Best for the LangChain ecosystem: LangGraph
If your codebase is already deep into LangChain. Runnables, retrievers, LangSmith. LangGraph keeps that ecosystem cohesive. The graph mental model is similar to AgentFlow's, so most of the patterns transfer either way. You will assemble FastAPI / SSE yourself unless you adopt LangGraph Platform.
Choose LangGraph if: the LangChain dependency tree is already a load-bearing part of your stack. → AgentFlow vs LangGraph
Best for prototype-friendly role-based crews: CrewAI
CrewAI's "Researcher → Writer → Editor" DSL is genuinely the fastest way to write a multi-agent script. For prototypes, internal tools, and one-off automations, it is hard to beat. Production characteristics (debuggability, persistence, API serving) require more glue.
Choose CrewAI if: you want roles + tasks + sequential or hierarchical processes, and your deployment story is "run this Python script on a worker." → AgentFlow vs CrewAI
Best for research and conversational experiments: AutoGen
AutoGen 0.4's actor model and group-chat primitives are powerful for academic experiments and emergent multi-agent conversations. AutoGen Studio is a great tool for designing flows visually. The production server tier is BYO.
Choose AutoGen if: you are exploring novel multi-agent dynamics, working in a Microsoft / Azure ecosystem, or want to see what emergent agent conversations look like. → AgentFlow vs AutoGen
Best for document-heavy RAG: LlamaIndex Agents
If your product is "chat with my PDFs," "query a corpus," or "search and summarise documents," LlamaIndex's retrieval, indexing, and parsing stack is best in class. The agent layer is a thin wrapper on top. Pleasant for single-agent RAG, lighter on multi-agent orchestration.
Choose LlamaIndex Agents if: retrieval is the product. (And consider pairing it with AgentFlow when you outgrow the agent layer.) → AgentFlow vs LlamaIndex Agents
Best for committed Vertex AI users: Google ADK
If you are all-in on Gemini and Vertex AI, ADK is the official Google path with first-party support and Vertex AI Agent Engine for hosted execution. Provider neutrality and MIT licensing are not strengths.
Choose Google ADK if: Vertex AI is the answer for your team across data, models, and ops. → AgentFlow vs Google ADK
A quick decision tree
| Your situation | Start with |
|---|---|
| Building a stateful multi-agent product with a frontend | AgentFlow |
| Already heavy in LangChain | LangGraph |
| Spinning up a 3-role crew in 30 minutes | CrewAI |
| Research / experiments with multi-agent conversations | AutoGen |
| RAG over documents is the core feature | LlamaIndex Agents (often + AgentFlow for the runtime) |
| All-in on Vertex AI | Google ADK |
Why "best" depends on what you measure
Two teams can pick different frameworks for the same use case and both be right. The critical questions:
- Where does your code live in 12 months? If you are migrating to a new vendor, single-provider frameworks are riskier.
- What is your deployment story? A built-in server saves weeks; a paid hosting tier might save more or might lock you in.
- What language is your product surface? Python-only stacks feel different from full-stack apps with TypeScript frontends.
- How much glue can your team maintain? Every "easy hello world" hides a different production budget.
When you have answers, the choice usually narrows to two frameworks. The compare pages above run head-to-head between AgentFlow and each of the others. Read the one that maps to your second-place option.