Semantic Kernel is live · free · last verified 13 Nov 2026 · viability healthy
Semantic Kernel
Microsoft's open-source SDK for building AI agents and copilots with plugins, memory, and multi-agent orchestration.
Pick Semantic Kernel if you're a .NET or Azure team shipping a copilot, agent, or stateful approval workflow and you want Microsoft-supported orchestration with built-in security filters and observability. Documented connectors to Microsoft Graph, Entra ID, and Azure Cognitive Search mean your agent can reach protected internal data without custom glue. If your stack isn't Microsoft — or you want a broader set of model providers and a larger third-party ecosystem — LangChain and LlamaIndex are the usual alternatives. For quick prototypes, a lighter framework will get you to a demo faster than setting up a kernel, plugins, and middleware first.
Verdict generatedVerified 10h ago · liveness 73/100 · cite: rightaichoice.com/tools/semantic-kernel
- .NET developers building copilots and AI agents
- Enterprise teams standardized on Microsoft Azure
- Multi-step AI workflows needing durable, stateful execution
- LLM applications that require a memory and context layer
- Cloud-agnostic deployments (the SDK leans toward Azure)
- Non-Microsoft ecosystems such as AWS or Google Cloud
- Teams wanting a large third-party prompt-template library
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Skip Semantic Kernel if your team runs primarily on AWS or Google Cloud and needs cloud-agnostic deployment, since the SDK's documented connectors and enterprise components assume Azure, Microsoft Graph, and Entra ID.
Semantic Kernel is an open-source SDK released under the MIT license, so the software itself carries no license fee at any team size. Your real spend sits underneath it: Azure OpenAI or OpenAI inference, Azure Cognitive Search for retrieval, and the engineering time to build plugins, processes, and observability, which is where a small team's budget actually goes.
In short
Semantic Kernel — Microsoft's open-source SDK for building AI agents and copilots with plugins, memory, and multi-agent orchestration. Best for .NET developers building copilots and AI agents, Enterprise teams standardized on Microsoft Azure, Multi-step AI workflows needing durable, stateful execution. Free to use.
What is Semantic Kernel?
Semantic Kernel — Microsoft's open-source SDK for building AI agents and copilots with plugins, memory, and multi-agent orchestration. Best for .NET developers building copilots and AI agents, Enterprise teams standardized on Microsoft Azure, Multi-step AI workflows needing durable, stateful execution. Free to use.
Semantic Kernel is an open-source SDK from Microsoft for building AI agents and copilots inside your own applications. You compose capabilities as plugins — reusable functions that wrap APIs, databases, or Microsoft Graph — then let the kernel plan and orchestrate calls, with a memory layer supplying context to the model. The Process Framework supports stateful, long-running workflows, and the Agent Framework coordinates multiple agents (including group-chat patterns) for tasks like internal IT support. Enterprise components cover observability and security filters for responsible-AI policy enforcement. Documented language support is C#, Python, and Java; the SDK connects to Azure OpenAI, OpenAI, Microsoft 365 Copilot, Microsoft Graph, Azure Cognitive Search, Entity Framework, ASP.NET Core, Blazor, Power Platform, and Microsoft Entra ID. It differs from LangChain mainly in its Microsoft-stack orientation and its emphasis on durable processes rather than prompt-chain experimentation, which makes it a fit for .NET teams already on Azure.
Key features
- Plugin-based composition of reusable functions
- Memory layer for supplying context to models
- Process Framework for stateful, durable workflows
- Agent Framework for multi-agent coordination
- Group chat pattern for multi-agent systems
- Security filters for AI policy enforcement
- Observability and telemetry hooks
- Multi-language SDK support (C#, Python, Java)
- Azure OpenAI connectivity
- OpenAI connectivity
- Microsoft 365 Copilot extension
- Microsoft Graph data access
- Kernel extensibility through middleware
- Semantic functions and plans
- Connectors for external data sources
Our long-form take written Jul 24, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-11-08
Limitations
- Semantic Kernel is oriented toward Microsoft's stack; you get the most out of it when Azure services, Microsoft Graph, and Entra ID are already in your environment.
- C# and Python are the primary SDK targets, and Java support is described as newer — some capabilities, including the Process Framework, are documented as .NET-only, so polyglot teams may not have feature parity across languages.
- The concepts take time to absorb: kernels, plugins, semantic functions, plans, and middleware all have to be understood before you can build something useful, which makes the SDK a poor fit for quick prototypes.
- Its community is smaller than LangChain's, so fewer third-party examples and integrations exist outside what Microsoft documents.
as of 2026-10-01
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Is Semantic Kernel still active?
Semantic Kernel is live · free · last verified 13 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is Semantic Kernel? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about Semantic Kernel?
What people actually say about Semantic Kernel — is it worth it?
Our community sentiment research written Aug 15, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Semantic Kernel — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 13 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Semantic Kernel 23 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed SSL certificate
- — changed SSL certificate, domain expiry
- — changed pricing fit, hidden costs, switching out, description, our verdict, switching in, who should skip it, setup time
- — changed SSL certificate
- — changed pricing page
Showing the 6 most recent of 23 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Semantic Kernel cost in 2026?
Semantic Kernel: Free to use. Pricing last verified 13 Nov 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Semantic Kernel's pricing actually pencils out — and where peers do it cheaper.
Semantic Kernel is an open-source SDK released under the MIT license, so the software itself carries no license fee at any team size. Your real spend sits underneath it: Azure OpenAI or OpenAI inference, Azure Cognitive Search for retrieval, and the engineering time to build plugins, processes, and observability, which is where a small team's budget actually goes.
Has Semantic Kernel's pricing changed?
No plan price for Semantic Kernel has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Semantic Kernel?
Pick Semantic Kernel if you are .NET developers building copilots and AI agents, Enterprise teams standardized on Microsoft Azure, Multi-step AI workflows needing durable, stateful execution, LLM applications that require a memory and context layer.
Look elsewhere if you are Cloud-agnostic deployments (the SDK leans toward Azure), Non-Microsoft ecosystems such as AWS or Google Cloud, Teams wanting a large third-party prompt-template library, No-code teams looking for minimal setup before first value.
Skip it if Skip Semantic Kernel if your team runs primarily on AWS or Google Cloud and needs cloud-agnostic deployment, since the SDK's documented connectors and enterprise components assume Azure, Microsoft Graph, and Entra ID.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Semantic Kernel actually fits — and what changes day-one when you adopt it.
You start from the Semantic Kernel quick start and samples, define a plugin that wraps an Entra ID-protected internal API, and register it on the kernel alongside your Azure OpenAI service.
Outcome: A working agent that answers internal questions by calling your own systems rather than only the model.
You model each approval step as a process step in the Process Framework so state survives restarts, then add security filters over the model calls and wire up telemetry.
Outcome: A durable approval workflow you can observe and audit, instead of a script that loses state on failure.
You build a custom agent with the Agent Framework, connect it to Microsoft Graph for organizational data, and run a group-chat pattern across specialist agents.
Outcome: Copilot users get answers routed through internal data and coordinated specialist agents.
Use cases
- Build a customer-service agent in C# that calls Entra ID-protected internal APIs.
- Ship a Java-based RAG agent over an enterprise Azure AI Search index.
- Orchestrate a long-running approval workflow using the Process Framework.
- Extend Microsoft 365 Copilot with a custom agent connected to internal data.
- Deploy a multi-agent group-chat system for internal IT support.
Setup time & first value
How long it actually takes to get something useful out of Semantic Kernel — broken out by persona, not the marketing-page minute.
Expect an afternoon for a developer already comfortable in C# or Python to run the quick start and get a plugin responding through Azure OpenAI. Getting to a first useful agent with real internal data is typically days, not hours, because you'll be defining plugins, wiring authentication, and learning kernel and plugin concepts. A durable Process Framework workflow or multi-agent system is a
Switching to or from Semantic Kernel
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangChain: re-implement chains as plugins and semantic functions, and move multi-step logic into the Process Framework for durable state.
- →From hand-rolled OpenAI SDK calls: wrap existing API calls as plugins so the kernel can plan and invoke them.
- →From custom RAG scripts: keep your vector store and register retrieval as a plugin backed by Azure Cognitive Search.
- ↗To LangChain: port plugins back to tools and chains, expecting to lose the Process Framework's durable state handling.
- ↗To LlamaIndex: move retrieval-heavy logic into its index abstractions if your agent is mostly RAG.
- ↗To a direct model SDK: extract plugin functions into plain functions once orchestration is no longer needed.
Integrations
Resources & guides
- Resourcelearn.microsoft.com
Semantic Kernel documentation
Semantic Kernel documentation.
- Resourcelearn.microsoft.com
How to quickly start with Semantic Kernel
Follow along with Semantic Kernel's guides to quickly learn how to use the SDK
- Resourcelearn.microsoft.com
In-depth Semantic Kernel Demos
Go deeper with additional Demos to learn how to use Semantic Kernel.
- Conceptslearn.microsoft.com
Understanding the kernel in Semantic Kernel
Learn about the central component of Semantic Kernel and how it works
- Conceptslearn.microsoft.com
Plugins in Semantic Kernel
Learn how to use AI plugins in Semantic Kernel
Tutorials & Learning
Official links
What are the best Semantic Kernel alternatives?
The closest alternatives to Semantic Kernel we list are AutoGen, Microsoft Agent Framework and TaskWeaver. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| AutoGen | Free | Free | — |
| Microsoft Agent Framework | Paid | Pricing not published | — |
| TaskWeaver | Free | Free | — |
Alternatives to Semantic Kernel
View all
AutoGen
AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.
Microsoft Agent Framework
Microsoft Agent Framework is an open-source SDK for building agentic AI agents and workflows in Python or C#, now with the GA Agent Harness
TaskWeaver
Microsoft's open-source, code-first Python framework for building stateful, data-intensive conversational AI agents.
Tools that pair well with Semantic Kernel
Common stack mates teams adopt alongside Semantic Kernel, with the specific reason each pairing earns its keep.
AutoGen
AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.
Microsoft Agent Framework
Microsoft Agent Framework is an open-source SDK for building agentic AI agents and workflows in Python or C#, now with the GA Agent Harness
TaskWeaver
Microsoft's open-source, code-first Python framework for building stateful, data-intensive conversational AI agents.
Semantic Kernel vs its closest rival
Semantic Kernel's closest head-to-head is with Autogen.
Our Semantic Kernel vs Autogen verdict written May 12, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Head-to-head comparisons
Autogen vs Semantic Kernel
Verdict being re-reviewed against current pricing and data.
Langgraph vs Semantic Kernel
Verdict being re-reviewed against current pricing and data.
Langchain vs Semantic Kernel
If you're a .NET shop on Azure building production copilots, Semantic Kernel is the no-brainer——it's free, deeply integrated with Microsoft's stack, and the process framework handles durable workflows. But if you need multi-step agent orchestration with serious observability, evaluation, and deployment tooling, LangChain wins—especially with LangSmith's recent AI-driven issue detection and tuned evaluators. For non-Microsoft stacks, skip Semantic Kernel's Azure lock-in and go LangChain.
GeneratedFrequently asked questions
For makers of Semantic Kernel
- Get a Verified Listing for Semantic Kernel ($39): a written decision within 1 working day.
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