Comparisons / Rasa vs Semantic Kernel
Rasa vs Semantic Kernel: Which Agent Framework to Use?
Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants. 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
Rasa
21.1k
4.9k
Python
Apache-2.0
2016-10-14
Rasa Technologies
Rasa Pro / Rasa Cloud
Yes
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 | Rasa | Semantic Kernel |
|---|---|---|
| Agent | Rasa agent with NLU pipeline, dialogue policies, and action server | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| NLU | NLU pipeline: tokenizer, featurizer, intent classifier, entity extractor | — |
| Dialogue | Stories/Rules YAML + dialogue policies for conversation flow | — |
| Tools | Custom actions running on a separate action server via HTTP | — |
| Slots | Typed slots for tracking entities and state across turns | — |
| CALM | LLM for understanding + deterministic `Flows` for business logic | — |
| 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 |
Rasa vs Semantic Kernel, head to head
Rasa Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants.
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 Rasa if
Pick Rasa if rasa is purpose-built for production conversational AI with enterprise requirements — on-premise deployment, regulatory compliance, deterministic business logic. For general-purpose agents or simple chatbots, an LLM with a system prompt and a few tools is faster to build and more flexible. Rasa 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; Rasa would force you to translate.
What both add
Both Rasa 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 Rasa 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 →