Comparisons / AWS Strands Agents vs Semantic Kernel
AWS Strands Agents vs Semantic Kernel: Which Agent Framework to Use?
AWS Strands Agents is a lightweight, model-driven Python SDK for building agents released by AWS in May 2025. Semantic Kernel is Microsoft's enterprise SDK for building AI agents. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
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→Semantic Kernel
27.6k
4.5k
C#
MIT
2023-02-27
Microsoft
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AWS Strands Agents | Semantic Kernel |
|---|---|---|
| Agent | `Agent(model, tools, system_prompt)` with the model running its own tool-call loop | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| Tools | `@tool` decorator on Python functions; type hints become the schema | — |
| 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 | — |
| Tools / Plugins | — | `KernelPlugin` with `@kernel_function` decorators, typed parameters |
| Planning | — | `StepwisePlanner`, `HandlebarsPlanner` for multi-step decomposition |
| Memory | — | `SemanticTextMemory` with embeddings and vector stores |
| Orchestration | — | `Kernel.invoke()` with plugin resolution and filter pipeline |
| Multi-Language | — | C#, Python, Java SDKs with shared abstractions |
AWS Strands Agents vs Semantic Kernel, head to head
AWS Strands Agents AWS Strands Agents is a lightweight, model-driven Python SDK for building agents released by AWS in May 2025.
Semantic Kernel Semantic Kernel is Microsoft's enterprise SDK for building AI agents.
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 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; Semantic Kernel would force you to translate.
Pick Semantic Kernel if
Pick Semantic Kernel if semantic Kernel earns its complexity in enterprise environments with Azure OpenAI, .NET backends, and existing Microsoft infrastructure. But the core agent pattern — LLM call, tool dispatch, loop — is identical to what you can build in 60 lines of Python. Semantic Kernel 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.
What both add
Both AWS Strands Agents and Semantic Kernel 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 AWS Strands Agents and Semantic Kernel 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 →