AI Frameworks · microsoft

semantic-kernel

Semantic Kernel is a Microsoft-maintained, open-source SDK for building AI agents and multi-agent systems that work with any LLM. It provides tools for orchestration, plugins, memory, and vector database integration across Python, .NET, and Java.

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Source: GitHub — github.com/microsoft/semantic-kernel

28.3k

GitHub stars

4.7k

Forks

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	microsoft/semantic-kernel
Owner	microsoft
Primary language	C#
License	MIT — OSI-approved
Stars	28.3k
Forks	4.7k
Open issues	260
Latest release	dotnet-1.78.0 (2026-07-07)
Last updated	2026-07-07
Source	https://github.com/microsoft/semantic-kernel

What semantic-kernel is

Model-agnostic agent framework supporting OpenAI, Azure OpenAI, Hugging Face, and local LLMs. Offers plugin architecture, structured output, multi-agent orchestration, vector DB connectors (Azure AI Search, Elasticsearch, Chroma), and process workflow modeling. Licensed under MIT.

Quickstart

Get the semantic-kernel source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/microsoft/semantic-kernel.git2cd semantic-kernel3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-agent AI orchestration systems

Build systems of specialized agents that collaborate on complex workflows. Well-suited for enterprise automation requiring agent coordination, memory management, and plugin chaining.

LLM-powered business applications

Quickly integrate cutting-edge LLM capabilities into existing enterprise applications. Supports multiple LLM providers and abstracts provider-specific APIs for easier switching.

Agent frameworks with tool/plugin extensibility

Create agents that access custom native code functions, prompt templates, OpenAPI specs, and Model Context Protocol (MCP) plugins. Ideal for RAG systems and domain-specific agent tooling.

Implementation considerations

Async-first architecture requires proficiency with async/await patterns across Python, C#, or Java. Team skill match essential before adoption.
LLM service integration requires API keys (OpenAI, Azure OpenAI). Plan for cost management, rate limiting, and fallback provider strategies.
Multi-agent systems add operational complexity; logging, observability, and error handling across distributed agent calls must be architected upfront.
Plugin ecosystem supports native code, OpenAPI, MCP, and prompt templates. Choose plugin model early; mixing paradigms may increase cognitive load.
Vector DB integration works with multiple providers but requires selecting and managing a backend (Azure AI Search, Elasticsearch, Chroma, etc.).

When to avoid it — and what to weigh

Lightweight, single-agent chatbot — If you need only a simple conversational interface without multi-agent orchestration or advanced plugin architecture, the overhead may exceed requirements. Consider lighter SDKs.
Project requires guaranteed long-term stability signal — Semantic Kernel is being superseded by Microsoft Agent Framework 1.0 (noted as enterprise successor). While actively maintained, new development should evaluate MAF first.
Strict vendor lock-in constraints — Strong Microsoft ecosystem integration (Azure, OpenAI plugins). If avoiding any Microsoft strategic dependencies is required, evaluate alternatives.
Real-time, ultra-low-latency requirements — Framework targets enterprise orchestration, not real-time inference. Async/await patterns add latency; benchmark against hard latency SLAs.

License & commercial use

MIT License—permissive open-source license allowing commercial use, modification, and distribution with minimal restrictions. Original copyright and license text must be included.

MIT is a standard, OSI-approved permissive license explicitly allowing commercial use. No licensing restrictions on building proprietary applications with Semantic Kernel. No warranty or liability indemnity provided; review liability and support requirements independently.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

LLM API keys and credentials must be managed securely (environment variables shown in examples). No explicit mention of encryption at rest/transit for vector stores. Input sanitization and prompt injection risks common to LLM applications; framework does not abstract these. Agent-to-Agent (A2A) communication security model not detailed in README. Review authentication and authorization for multi-agent systems in production. Evaluate whether local ONNX deployment meets data residency/compliance requirements vs. cloud LLM APIs.

Alternatives to consider

LangChain / LangGraph

Mature multi-agent orchestration with extensive integration ecosystem. Python-first; less native .NET support. Larger community but steeper learning curve for some workflows.

Microsoft Agent Framework (MAF) 1.0

Official successor to Semantic Kernel, targeting enterprise use. Production-ready with long-term support commitment. Better choice if forward compatibility and unified Microsoft strategy are priorities.

Anthropic Claude SDK / OpenAI Assistants API

Lightweight, vendor-native solutions for single-agent chatbots. Lower operational overhead but less multi-agent orchestration and plugin flexibility. Simpler for constrained use cases.

Software development agency

Build on semantic-kernel with DEV.co software developers

Semantic Kernel provides the framework to orchestrate multi-agent systems and integrate LLMs into your enterprise applications. Start with the Python or .NET quickstart.

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semantic-kernel FAQ

Is Semantic Kernel being deprecated?

No; it remains active and maintained. However, Microsoft Agent Framework (MAF) 1.0 is positioned as the enterprise-ready successor with long-term support. Migration guide available. New enterprise projects should evaluate MAF first.

Does Semantic Kernel lock me into a specific LLM provider?

No. It is model-agnostic and supports OpenAI, Azure OpenAI, Hugging Face, NVIDIA NIM, and local LLMs (Ollama, LMStudio, ONNX). You can switch providers without rewriting core agent logic.

Can I use Semantic Kernel in production?

Yes. It is stable (v1.78.0) and actively maintained by Microsoft. Production deployments require careful observability, error handling, cost management for LLM calls, and testing of multi-agent failure scenarios.

What languages does Semantic Kernel support?

Python (3.10+), .NET (.NET 10.0+), and Java (JDK 17+). Feature parity is planned across languages; Python and .NET are most mature.

Software development & web development with DEV.co

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Ready to build AI agents?

Semantic Kernel provides the framework to orchestrate multi-agent systems and integrate LLMs into your enterprise applications. Start with the Python or .NET quickstart.

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