Semantic Kernel

Semantic Kernel is Microsoft's open-source framework for building AI agents and integrating LLMs into C#, Python, or Java applications. It's the enterprise-grade choice when your stack is already in the Microsoft ecosystem, your compliance team wants audit hooks, and you need the same abstraction layer to work reliably across three programming languages at once.

The AI framework space has a Python bias. Most tutorials, most blog posts, most open-source examples assume you're writing Python, and the frameworks themselves started there. If your stack is .NET, you've been watching this play out from the outside, picking up Python wrappers around libraries that were never designed for your runtime, or building proprietary glue code that nobody else maintains.

Semantic Kernel is Microsoft's answer to that problem. It launched as a C# framework and grew from there, and in 2026 it's genuinely multi-language in a way most "we support Python and JavaScript" frameworks are not. The version numbering, the core abstractions, the plugin model: all of it is kept consistent across C#, Python, and Java. A team that publishes an internal plugin in C# can describe it with OpenAPI and share it with a Python team's agents. The architecture assumes that kind of cross-language collaboration is real, not hypothetical.

Quick verdict

Semantic Kernel is the right call for .NET teams, Azure-heavy organizations, and any company where compliance and audit trails are non-negotiable. It's not the fastest path to a working prototype if you're starting in Python, and its community footprint is smaller than LangChain. But if your infrastructure is already in Azure and you need an agent framework with genuine enterprise controls, Semantic Kernel is the most complete option in that specific lane.

What is Semantic Kernel, exactly?

Semantic Kernel is an open-source middleware framework from Microsoft that connects your existing code to large language models. It started as a research project inside Microsoft, became the backbone of Microsoft 365 Copilot, and shipped as a stable open-source SDK in 2023. As of May 2026, the .NET release is at version 1.75 and the repository has nearly 28,000 GitHub stars.

The core idea is straightforward: you have functions and services that already exist in your codebase, and you want an LLM to be able to call them. Semantic Kernel wraps those functions as plugins, exposes them to the model through a standardized tool-calling interface, and handles the round-trips between model requests and actual function execution. The kernel is the middleware layer that sits between your code and the model, translating in both directions.

What distinguishes it from simpler tool-calling wrappers is the depth of the surrounding infrastructure. There are planners that let models decompose multi-step goals into function call sequences. There is an Agent Framework for multi-agent orchestration with handoff patterns. There is a Process Framework for long-running stateful workflows. And there are hooks and filters at every layer that let you intercept, log, modify, or block what the model does, which is where the enterprise compliance story lives.

Microsoft uses Semantic Kernel internally across multiple products. The Fortune 500 companies that are on Azure OpenAI and building copilot-style applications are largely using it or building on top of abstractions it introduced. That production exposure shows in the design. This is not a framework built to demo well in a notebook; it's built to survive a year of production traffic, a compliance audit, and a team turnover.

The features that matter

Multi-language parity (C#, Python, Java)

The multi-language support in Semantic Kernel is more genuine than the label suggests. The core concepts, Kernel, Plugin, KernelFunction, ChatCompletionAgent, Process, are kept in sync across all three SDKs. When Microsoft ships a new capability, it targets all three languages. The .NET SDK tends to lead on timing, but Python and Java are not afterthoughts.

For teams that mix languages, this matters in a specific practical way. Your infrastructure team might write plugin implementations in C# against internal APIs. Your data science team might build agent workflows in Python. With Semantic Kernel, those plugins can be described with OpenAPI specs that both environments consume, using the same semantic model for how tools are defined and called. That interoperability is built into the framework's design rather than bolted on.

Java support is the youngest of the three and carries a smaller community ecosystem, but the SDK itself is complete. Teams building on Spring Boot or in environments where Java is the enterprise standard have a first-class path here that doesn't exist in most other frameworks in this space.

Plugins and the function-calling abstraction

Plugins are the central unit of Semantic Kernel. A plugin is a collection of functions that the kernel can expose to an LLM as tools. Writing a plugin in C# means annotating a class and its methods with descriptions that the model can read. The framework handles serializing those descriptions into the tool definition format the model expects and deserializing the model's function call requests back into actual method invocations.

The description quality matters. The kernel passes your function descriptions directly to the model, so a well-described plugin produces dramatically better function selection than a vague one. Semantic Kernel's documentation leans into this and provides patterns for writing descriptions that actually guide model behavior. It's a simple concept with a high skill ceiling.

Beyond native functions, Semantic Kernel supports importing OpenAPI specs directly as plugins. If you have a REST API documented with OpenAPI, you can load its spec into the kernel and the model can call any of its endpoints as tools without you writing wrapper code. For teams with large existing API surfaces, this is a substantial time-saver. It also means Semantic Kernel plugins are compatible with the format used by Microsoft 365 Copilot extensions, which matters if that's part of your roadmap.

Planners for multi-step orchestration

Planners let the model take a high-level goal and break it down into a sequence of plugin calls that achieve it. The default approach in current Semantic Kernel leans on the model's native function-calling to handle this sequencing. The model sees the available plugins, receives the goal, and decides which functions to call and in what order, with the kernel executing each call and feeding results back.

This is simpler than Semantic Kernel's earlier stepwise and sequential planners, which generated explicit execution plans in structured formats. Those older planners still exist for specific use cases, but the current best practice is to use the model's built-in reasoning to drive the sequence. The practical effect is that planning quality is tied to your model's function-calling ability, which means it benefits directly from every generation of model improvement without requiring framework changes.

For workflows where you need more determinism than a model-driven loop provides, the Process Framework handles that case separately.

Azure and Microsoft 365 integration

The Azure integrations in Semantic Kernel are first-class in a way that reflects Microsoft building this for its own products. Azure OpenAI, Azure AI Search, Azure Cosmos DB, and Azure AI Foundry all have native connectors. You configure them with the same credential patterns you use everywhere else in Azure, and they participate in the same observability pipelines. If your organization is already using Azure Monitor and Application Insights, the kernel's telemetry hooks feed into that infrastructure without extra work.

The Microsoft 365 angle is more specific. Semantic Kernel's plugin format is the same format used by Microsoft 365 Copilot extensions. If you build an internal tool as a Semantic Kernel plugin, it can also be published as a Copilot extension. That compatibility is not incidental; it's architectural, and it means agent work done in Semantic Kernel has a direct path to the M365 surface if your organization is on that platform.

For teams entirely outside the Azure ecosystem, these integrations are irrelevant rather than harmful. Semantic Kernel works fine with OpenAI directly, with Hugging Face, with NVIDIA-hosted models, and with local models through Ollama or LM Studio. The Azure path is the one Microsoft optimizes for; the others work.

Process Framework for workflow orchestration

The Process Framework is the part of Semantic Kernel that handles what happens when an agent loop is not the right model. Agent loops are good at exploratory, goal-directed tasks where the model decides what to do next. They're less good at workflows that need to run for hours, survive infrastructure failures, involve human approval steps, and produce consistent audit trails.

The Process Framework lets you model a business workflow as a set of explicit steps and transitions. Each step can involve an LLM call, a plugin invocation, a human-in-the-loop check, or a wait for an external event. The framework manages state across these steps and handles the persistence needed for long-running workflows.

This is Semantic Kernel's clearest answer to use cases like document approval pipelines, multi-stage data processing with human review, or customer service escalation flows. These are workflows that exist in enterprises and don't fit neatly into an agent loop or a simple chain. The Process Framework is purpose-built for them and is more opinionated than a general orchestration layer.

Pricing

Semantic Kernel is MIT-licensed and free to use in any context, commercial or otherwise. Microsoft provides no paid tier; there is nothing to buy. Your only costs are the API calls you make to whatever model provider you choose.

If you're using Azure OpenAI, you're billed by Azure at standard rates for the model and token volume. If you're using OpenAI directly, same story. Local models through Ollama cost nothing beyond compute. The framework itself adds no overhead charge and collects no usage data for billing.

The total cost of a Semantic Kernel project depends almost entirely on your model usage patterns. A light internal copilot with modest traffic is cheap. An enterprise application processing thousands of requests per day with GPT-4o or Claude Sonnet is a real line item. Because you control the model routing and can implement caching and token management through the kernel's hooks, you have more cost-control flexibility than in managed agent platforms where the model routing is opaque.

Where Semantic Kernel wins

The strongest case for Semantic Kernel is a .NET team at a company already running on Azure. In that environment, you're not fighting the framework. Your Azure credentials work. Your existing APIs become plugins with minimal effort. Your telemetry flows into the monitoring you already have. The Process Framework covers your stateful workflow needs. And you're writing C#, not learning Python to access AI tooling built for a different runtime.

The compliance angle is real and underappreciated in framework comparisons. The hooks and filters that let you intercept every model request and response, log it, modify it, block it if it violates a policy, are enterprise security infrastructure. For financial services, healthcare, and government customers who need to demonstrate that they know what the model is doing at every step, this is not a nice-to-have. Most lighter frameworks don't have it, or have it as an afterthought.

Multi-agent scenarios with the handoff orchestration pattern are another area where Semantic Kernel holds up well. The May 2026 blog coverage of the handoff pattern, where agents make contextual decisions about whether to handle a request themselves or pass it to a specialist agent with carry-forward context, reflects active development in this area. It's not a static framework coasting on past releases.

Where Semantic Kernel loses

The Python experience is solid but not exceptional. Python developers evaluating agent frameworks will find that LangChain has more tutorials, more community examples, and faster prototyping for pure Python use cases. LangGraph has a more expressive model for stateful workflows. AutoGen has a stronger community around research-adjacent multi-agent patterns. If your team is Python-native and has no Azure dependency, Semantic Kernel is a reasonable choice but not the obvious one.

The documentation is thorough but dense. Microsoft's Learn platform is thorough, and the volume of content is not the problem. The problem is that the concepts are interconnected in ways that require reading through several layers before the overall architecture becomes clear. Getting to a working mental model takes longer than in frameworks with a flatter learning curve.

The community footprint is also smaller than the star count suggests. The 28,000 stars reflect Microsoft's brand and the Azure audience, but the volume of community blog posts, third-party tutorials, and Stack Overflow answers is thinner than for LangChain. When you hit an unusual problem, you're more likely to be reading source code than finding a thread where someone solved it last month.

Who Semantic Kernel is built for

The primary audience is enterprise development teams at organizations that are invested in the Microsoft stack. .NET shops building internal copilots, customer-facing AI features, or process automation that touches Azure services are the core case. If that describes your team, Semantic Kernel fits more naturally than anything Python-first.

Java teams in enterprise environments are a second genuine use case. If your organization standardizes on Java and you need an agent framework that isn't Python, Semantic Kernel is one of the few serious options with full Java support and an active maintenance commitment.

Python teams with Azure dependencies are a valid third group. The Python SDK is capable, and if your model infrastructure is on Azure OpenAI and your team is comfortable with slightly more verbosity in exchange for a stable, Microsoft-backed abstraction layer, Semantic Kernel is defensible.

What Semantic Kernel is not built for: fast prototyping in a research context, small Python-first teams without enterprise compliance needs, or anyone who wants a framework that "just works" in five lines of code. The framework's strength is also its weight: it's built for environments where that weight is justified.

Semantic Kernel vs the alternatives

Semantic Kernel vs LangChain

LangChain is the most widely used AI framework in the Python ecosystem, with a larger community, more third-party integrations, and more tutorials. For Python teams, LangChain is faster to get started with and easier to find help for. Semantic Kernel beats it on multi-language support, Azure integration depth, and enterprise compliance features. If you're in .NET, the comparison is simple: LangChain has no real C# story. If you're in Python, you should evaluate both before committing.

Semantic Kernel vs AutoGen

AutoGen is Microsoft's other multi-agent framework, also open-source, focused on conversational multi-agent patterns where agents interact in structured dialogues. The two frameworks have distinct design philosophies. AutoGen centers on agent-to-agent conversation as the coordination mechanism. Semantic Kernel centers on plugins, planning, and the kernel as middleware. They've been kept separate rather than merged, which means you're choosing a design philosophy as much as a framework. For production enterprise workflows with defined steps and audit needs, Semantic Kernel is the stronger pick. For research-oriented multi-agent experiments and conversational agent patterns, AutoGen has more community traction.

Semantic Kernel vs LangGraph

LangGraph is the LangChain team's graph-based state machine framework, designed for complex workflows with explicit control flow. It's Python and TypeScript only. If you're building a workflow where you want to explicitly draw the states and transitions and have full control over the execution graph, LangGraph is more expressive than Semantic Kernel's Process Framework. If you need C# or Java support, or you want Azure integration and compliance controls alongside the workflow model, Semantic Kernel covers ground that LangGraph can't.

Semantic Kernel vs Microsoft Copilot Studio

Microsoft Copilot Studio is the low-code and managed counterpart to Semantic Kernel. Copilot Studio lets you build agents through a configured UI without writing SDK code. Semantic Kernel is the code-first framework underneath. They're complementary rather than competitive. Teams that need custom code, complex plugin logic, or tight programmatic control reach for Semantic Kernel. Teams that want to assemble agent behaviors from preconfigured components and have governance handled at the platform level use Copilot Studio. Enterprise organizations often use both: Copilot Studio for broadly deployed copilots, Semantic Kernel for the custom backend plugins those copilots call.

Getting started

The fastest path to a working Semantic Kernel project depends on your language.

For C#, add the NuGet package: dotnet add package Microsoft.SemanticKernel. Create a kernel with your model configuration, write a class with annotated methods as your plugin, add it to the kernel, and invoke the kernel with a prompt. The full working example is about thirty lines of code. From there, add additional plugins for each capability you want the model to access.

For Python, pip install semantic-kernel and the setup mirrors the C# approach. Configure your AI service, define a plugin, register it, invoke. The Python documentation has interactive notebooks that let you run examples without any project setup.

For Java, add the Maven or Gradle dependency, follow the same conceptual pattern. The Java examples on GitHub are the best starting point since the Java community examples are fewer in the broader web.

In all three languages, the recommended progression is: start with a single plugin and a single model configuration, get the function-calling loop working with something simple, then add complexity. The Process Framework, multi-agent handoff, and Azure integrations are features you add as your requirements grow. Starting with all of them simultaneously makes the learning curve steeper than it needs to be.

The best AI agent for coding comparison covers how Semantic Kernel-backed tools stack up against specialized coding agents if that's a specific use case you're evaluating.

The bottom line

Semantic Kernel has a specific thesis: enterprise teams deserve an AI framework that speaks their language, fits their compliance requirements, and integrates with the infrastructure they already run. That thesis is most convincing for .NET teams on Azure, and it's genuinely convincing there. The framework has production pedigree, a strong version stability commitment, and active development in areas like multi-agent orchestration and process workflows that reflect real enterprise needs in 2026.

Outside that core case, the calculus gets more nuanced. Python teams without Azure dependencies will find faster paths with other frameworks. Research-oriented teams will find AutoGen or LangGraph more experimental-friendly. Small teams that just want something that works will find Semantic Kernel more framework than they need.

But for the audience it's designed for, Semantic Kernel earns its place. Microsoft built this to solve its own problem of integrating AI into enterprise software at scale. Solving that same problem for other enterprises is what the framework does best.

Key features

• Genuine multi-language parity across C#, Python, and Java
• Plugin system maps native functions and OpenAPI specs to LLM tool calls
• Process Framework for long-running, stateful workflow orchestration
• Native integration with Azure OpenAI, Azure AI Search, and Microsoft 365
• Telemetry, filters, and hooks for responsible AI at enterprise scale
• Supports OpenAI, Hugging Face, NVIDIA, Ollama, and other model providers
• Agent Framework with multi-agent handoff orchestration