Comparisons / Anthropic Agent SDK vs AutoGen
Anthropic Agent SDK vs AutoGen: Which Agent Framework to Use?
Anthropic Agent SDK the anthropic agent sdk packages claude code's agent loop as a library. AutoGen autogen by microsoft models agents as conversableagents that chat with each other. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
Anthropic Agent SDK
3.1k
582
Python
MIT
2023-01-17
Anthropic
Google, Spark Capital
Yes
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Anthropic Agent SDK | AutoGen | Plain Python |
|---|---|---|---|
| Agent | Claude agent with built-in tools, MCP servers, and system prompt | ConversableAgent with system_message, llm_config | A function that POSTs to /messages and returns the response |
| Tools | Built-in tools (bash, file read/write, web) + MCP server connections | register_for_llm() and register_for_execution() | A dict of callables: tools = {"bash": run_command, "read": read_file} |
| Agent Loop | SDK's internal agentic loop with automatic tool dispatch | — | A while loop: call LLM, check for tool_use blocks, execute, repeat |
| Sub-Agents | Agents invoke other agents as tools via the SDK | — | A function that calls another function: result = research_agent(query) |
| Lifecycle Hooks | 18 hook events: pre/post tool call, message, error, etc. | — | if/else checks inside your loop: if should_log: log(event) |
| MCP Integration | One-line MCP server config for Playwright, Slack, GitHub, etc. | — | HTTP client calls to each service: requests.post(slack_url, payload) |
| Conversation | — | Two-agent chat with initiate_chat(), message history | A messages array that grows with each turn |
| Multi-Agent | — | GroupChat with GroupChatManager, speaker selection | Multiple agent functions called in sequence on shared messages |
| Nested Chats | — | register_nested_chats() for sub-task handling | A task queue (BFS) — agent schedules follow-ups via a tool |
| Termination | — | is_termination_msg callback, max_consecutive_auto_reply | The while loop exits when no tool_calls or max_turns reached |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both Anthropic Agent SDK and AutoGen wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use Anthropic Agent SDK
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.
What the Anthropic Agent SDK does
The Anthropic Agent SDK takes Claude Code — the coding agent used by hundreds of thousands of developers — and ships it as a Python and TypeScript library. You get the same agent loop, built-in tools (bash execution, file read/write, web search), and context management that Claude Code uses internally. The standout feature is MCP (Model Context Protocol) integration: connect Playwright, Slack, GitHub, databases, and hundreds of other servers with a single config line. The SDK also provides 18 lifecycle hooks that let you intercept tool calls, messages, errors, and other events. This gives you fine-grained control over agent behavior without modifying the core loop. It's less a framework and more a productized agent runtime.
The plain Python equivalent
The agent loop is a while loop that POSTs to the /messages API, checks for tool_use blocks in the response, executes the matching function from a tools dict, appends the result to messages, and repeats. Built-in tools are just functions: bash is subprocess.run(), file reading is open().read(), web search is an HTTP call to a search API. MCP integration is HTTP client calls to each service — there's nothing magical about connecting to Slack or GitHub beyond knowing their API endpoints. Lifecycle hooks are if/else checks at specific points in your loop. The entire agent — tool dispatch, sub-agent delegation, logging — fits in about 60 lines. The SDK's value isn't in the pattern (which is simple) but in the pre-built tool implementations and MCP plumbing.
When to use AutoGen
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.
What AutoGen does
AutoGen's core abstraction is the ConversableAgent — an agent that can send and receive messages. Two agents chat by alternating turns on a shared message history. GroupChat extends this to N agents, with a GroupChatManager that selects the next speaker (round-robin, random, or LLM-based selection). Nested chats allow an agent to spin up a sub-conversation to handle a complex subtask before returning to the main thread. AutoGen also provides code execution sandboxes, letting agents write and run code as part of their conversation. The framework thinks in terms of conversations, not chains or graphs. This makes it natural for workflows where agents need to debate, critique, or iteratively refine outputs together.
The plain Python equivalent
A ConversableAgent is a function that takes a messages array, calls the LLM with a system prompt, and returns the assistant message. Two-agent chat is a while loop where you alternate between calling agent_a(messages) and agent_b(messages), appending each response. GroupChat is the same loop but with a speaker selection step — either rotate through a list or ask the LLM "who should speak next?" and call that agent function. Nested chats are a function call within the loop: pause the main conversation, run a sub-loop with different agents, and inject the result back. Tool registration is adding functions to a tools dict with their JSON schemas. The conversation-as-primitive model is just messages arrays passed between functions.
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.
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