Comparisons / AutoGen vs AWS Strands Agents
AutoGen vs AWS Strands Agents: Which Agent Framework to Use?
AutoGen vs AWS Strands Agents, head to head
AutoGen and AWS Strands Agents 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.
AutoGen by Microsoft models agents as ConversableAgents that chat with each other.
AWS Strands Agents is a lightweight, model-driven Python SDK for building agents released by AWS in May 2025.
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 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; AWS Strands Agents will feel like translation if they don't.
Pick AWS Strands Agents if
Pick AWS Strands Agents if aWS Strands fits AWS-heavy teams that want a thin SDK, native MCP, and a hosted runtime via Bedrock AgentCore. The model-driven design is genuinely lighter than LangChain — but for teams not on AWS, plain Python is closer to what Strands is doing than any other framework on this list. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.
By the numbers
By the numbers
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
AWS Strands Agents
4.2k
380
Python
Apache-2.0
2025-05-01
AWS
Amazon Web Services
Designed to run on Bedrock AgentCore for hosted deploy + observability
Yes
Used by: Amazon Q Developer, AWS Glue, AWS internal teams
github.com/strands-agents/sdk-python→GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AutoGen | AWS Strands Agents |
|---|---|---|
| Agent | `ConversableAgent` with `system_message`, `llm_config` | `Agent(model, tools, system_prompt)` with the model running its own tool-call loop |
| Tools | `register_for_llm()` and `register_for_execution()` | `@tool` decorator on Python functions; type hints become the schema |
| Conversation | Two-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` | — |
| Loop | — | Implicit — the model decides when to call tools and when to stop |
| Multi-agent | — | `Graph`, `Swarm`, agents-as-tools, and a workflow primitive |
| MCP | — | First-class MCP server + client support out of the box |
| Deploy | — | Bedrock AgentCore for hosted runtime, observability, identity |
Or build your own in 60 lines
Both AutoGen and AWS Strands Agents 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 →