Comparisons / Mastra vs Semantic Kernel
Mastra vs Semantic Kernel: Which Agent Framework to Use?
Mastra vs Semantic Kernel, head to head
Mastra and Semantic Kernel 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.
Mastra is a TypeScript-first framework for building AI agents, from the team behind Gatsby.
Semantic Kernel is Microsoft's enterprise SDK for building AI agents.
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 Mastra if
Pick Mastra if mastra is the best option for TypeScript teams that want a batteries-included agent framework without leaving the Node.js ecosystem. The workflow engine and Studio are genuinely productive. For simple agents or Python teams, the plain approach avoids an unnecessary dependency. The tradeoffs in its intro should match how your team already thinks about agents; Semantic Kernel will feel like translation if they don't.
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. The tradeoffs in its intro should match how your team already thinks about agents; Mastra will feel like translation if they don't.
By the numbers
By the numbers
Mastra
22.7k
1.8k
TypeScript
Apache-2.0
2024-08-06
Mastra AI
Spark Capital, Y Combinator
Series A ($22M, Apr 2026 — $35M total)
244.0k
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 | Mastra | Semantic Kernel |
|---|---|---|
| Agent | `new Agent({ model, instructions, tools })` with automatic tool dispatch | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| Tools | `createTool({ name, schema, execute })` with Zod validation | — |
| Workflows | `Workflow` class with `.step()`, `.then()`, `.branch()` for orchestration | — |
| RAG | Built-in document syncing, chunking, embedding, and vector search | — |
| Memory | Short-term thread memory + long-term vector memory across sessions | `SemanticTextMemory` with embeddings and vector stores |
| Studio | Mastra Studio: local GUI for testing agents, viewing traces, debugging | — |
| Tools / Plugins | — | `KernelPlugin` with `@kernel_function` decorators, typed parameters |
| Planning | — | `StepwisePlanner`, `HandlebarsPlanner` for multi-step decomposition |
| Orchestration | — | `Kernel.invoke()` with plugin resolution and filter pipeline |
| Multi-Language | — | C#, Python, Java SDKs with shared abstractions |
Or build your own in 60 lines
Both Mastra 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 →