Comparisons / AutoGen vs AWS Strands Agents
AutoGen vs AWS Strands Agents: Which Agent Framework to Use?
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. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
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 |
AutoGen vs AWS Strands Agents, head to head
AutoGen AutoGen by Microsoft models agents as ConversableAgents that chat with each other.
AWS Strands Agents AWS Strands Agents is a lightweight, model-driven Python SDK for building agents released by AWS in May 2025.
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 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. AutoGen is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; AWS Strands Agents would force you to translate.
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. AWS Strands Agents is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; AutoGen would force you to translate.
What both add
Both AutoGen and AWS Strands Agents 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 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 →