Comparisons / Anthropic Agent SDK vs AutoGen

Anthropic Agent SDK vs AutoGen: Which Agent Framework to Use?

Anthropic Agent SDK vs AutoGen, head to head

Anthropic Agent SDK and AutoGen both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

The Anthropic Agent SDK packages Claude Code's agent loop as a library.

AutoGen by Microsoft models agents as ConversableAgents that chat with each other.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

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. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.

Full Anthropic Agent SDKcomparison →

Pick AutoGen if

Pick AutoGen if autoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler. The tradeoffs in its intro should match how your team already thinks about agents; Anthropic Agent SDK will feel like translation if they don't.

Full AutoGencomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. Anthropic Agent SDK has its own class hierarchy and tool registration conventions; AutoGen has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

Anthropic Agent SDK

GitHub Stars

3.1k

Forks

582

Language

Python

License

MIT

Created

2023-01-17

Created by

Anthropic

Backed by

Google, Spark Capital

Production ready

Yes

github.com/anthropics/anthropic-sdk-python

AutoGen

GitHub Stars

56.7k

Forks

8.5k

Language

Python

License

CC-BY-4.0

Created

2023-08-18

Created by

Microsoft Research

github.com/microsoft/autogen

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptAnthropic Agent SDKAutoGen
AgentClaude agent with built-in tools, MCP servers, and system prompt`ConversableAgent` with `system_message`, `llm_config`
ToolsBuilt-in tools (`bash`, file read/write, web) + MCP server connections`register_for_llm()` and `register_for_execution()`
Agent LoopSDK's internal agentic loop with automatic tool dispatch
Sub-AgentsAgents invoke other agents as tools via the SDK
Lifecycle Hooks18 hook events: pre/post tool call, message, error, etc.
MCP IntegrationOne-line MCP server config for Playwright, Slack, GitHub, etc.
ConversationTwo-agent chat with `initiate_chat()`, message history
Multi-Agent`GroupChat` with `GroupChatManager`, speaker selection
Nested Chats`register_nested_chats()` for sub-task handling
Termination`is_termination_msg` callback, `max_consecutive_auto_reply`

Or build your own in 60 lines

Both Anthropic Agent SDK and AutoGen 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 →