Compare AgentFlow against other Python AI agent frameworks

If you are evaluating Python frameworks for production AI agents, this section compares AgentFlow to the most popular alternatives. Each comparison shows the same use case implemented in both frameworks, a TL;DR table of architectural differences, and a short migration guide.

Pick a comparison

• AgentFlow vs LangGraph. Graph-based runtimes head-to-head
• AgentFlow vs CrewAI. Role-based crews vs typed graphs
• AgentFlow vs AutoGen. Microsoft AutoGen vs AgentFlow
• AgentFlow vs LlamaIndex Agents. RAG-first agents vs runtime-first agents
• AgentFlow vs Google ADK. Google Agent Development Kit alternative
• Best Python agent framework in 2026. A roundup with our recommendations

What AgentFlow brings to the comparison

AgentFlow is an open-source Python framework for building production-grade multi-agent systems. The runtime ships with:

• Graph-based orchestration. Typed StateGraph with conditional edges, sub-graphs, and recursion limits
• Persistence built in.InMemoryCheckpointer for dev, PgCheckpointer (Postgres + Redis) for production
• REST API and CLI.agentflow api serves any compiled graph at /v1/graph/invoke, /v1/graph/stream
• Typed TypeScript client.@10xscale/agentflow-client for invoking and streaming from any frontend
• Hosted playground. Test a deployed graph in the browser without writing client code

That stack means you do not glue together langchain + fastapi + a custom React fetcher to ship an agent. The runtime, API, and client come from one project.

If you are migrating, start with Get started. The API matches the patterns you already know from graph-based frameworks, and most LangGraph or CrewAI agents port over in a single sitting.