Comparisons / Anthropic Agent SDK vs ControlFlow
Anthropic Agent SDK vs ControlFlow: Which Agent Framework to Use?
The Anthropic Agent SDK packages Claude Code's agent loop as a library. ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Anthropic Agent SDK
3.1k
582
Python
MIT
2023-01-17
Anthropic
Google, Spark Capital
Yes
ControlFlow
1.5k
120
Python
Apache-2.0
2024-05-01
Prefect
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Anthropic Agent SDK | ControlFlow |
|---|---|---|
| Agent | Claude agent with built-in tools, MCP servers, and system prompt | `cf.Agent()` with name, model, instructions, and tool access |
| Tools | Built-in tools (`bash`, file read/write, web) + MCP server connections | Python functions passed to `Task()` or `Agent()` as tool lists |
| Agent Loop | SDK's internal agentic loop with automatic tool dispatch | — |
| Sub-Agents | Agents invoke other agents as tools via the SDK | — |
| Lifecycle Hooks | 18 hook events: pre/post tool call, message, error, etc. | — |
| MCP Integration | One-line MCP server config for Playwright, Slack, GitHub, etc. | — |
| Task | — | `cf.Task()` with `result_type`, `instructions`, `agents`, and `dependencies` |
| Flow | — | `@cf.flow` decorator composing tasks with dependency resolution |
| Multi-Agent | — | Multiple `cf.Agent()` instances assigned to different tasks in one flow |
| Observability | — | Built-in Prefect integration for logging, retries, and monitoring |
Anthropic Agent SDK vs ControlFlow, head to head
Anthropic Agent SDK The Anthropic Agent SDK packages Claude Code's agent loop as a library.
ControlFlow ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick Anthropic Agent SDK if
Pick Anthropic Agent SDK if the Anthropic Agent SDK's real value is packaging Claude Code's battle-tested agent loop with built-in tools and MCP integration. If you want a production agent that reads files, runs commands, and connects to services, it saves significant plumbing. For understanding how agents work, the plain version is more instructive. Anthropic Agent SDK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; ControlFlow would force you to translate.
Pick ControlFlow if
Pick ControlFlow if controlFlow's task-centric model is a genuinely different way to think about agent orchestration — define what you want, not how to get it. The Prefect integration adds real production value. But if your workflow is linear and your tasks are simple, plain function composition does the same job with less ceremony. ControlFlow is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Anthropic Agent SDK would force you to translate.
What both add
Both Anthropic Agent SDK and ControlFlow pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both Anthropic Agent SDK and ControlFlow implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →