# microsoft/semantic-kernel

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27,262 stars · 4,464 forks · C# · mit

## Links

- GitHub: https://github.com/microsoft/semantic-kernel
- Homepage: https://aka.ms/semantic-kernel
- awesome-repositories: https://awesome-repositories.com/repository/microsoft-semantic-kernel.md

## Topics

`ai` `artificial-intelligence` `llm` `openai` `sdk`

## Description

Semantic Kernel is an artificial intelligence orchestration framework designed to integrate large language models with existing codebases. It functions as an agentic workflow engine, providing a standardized interface that connects generative models to traditional application logic, data sources, and external tools to automate complex, multi-step business tasks.

The platform distinguishes itself through a modular plugin architecture and a planner-based reasoning engine that decomposes high-level goals into executable sequences of functions. By utilizing a connector-based abstraction layer, it decouples core orchestration logic from specific model providers and vector databases, allowing for consistent retrieval and execution across diverse infrastructure.

The framework includes a middleware-based request pipeline for managing cross-cutting concerns such as telemetry and safety filtering, alongside a prompt template engine for dynamic context injection. These components support the development of scalable, enterprise-ready systems that maintain security and compliance while coordinating multiple language models and specialized tools.

## Tags

### Artificial Intelligence & ML

- [Agent Orchestration Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-orchestration-frameworks.md) — Building intelligent systems that coordinate multiple language models and external tools to complete complex, multi-step business tasks.
- [AI Orchestration Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-orchestration-frameworks.md) — A development platform that integrates large language models with existing code to automate complex tasks and business workflows.
- [Model Abstraction Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-abstraction-layers.md) — A standardized interface layer decouples core orchestration logic from specific large language model providers and their proprietary API protocols.
- [Prompt Engineering Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-engineering-tools.md) — A text-processing system that dynamically injects runtime variables and context into structured prompts before dispatching them to an external model.
- [Agentic Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-runtimes.md) — A runtime environment for building autonomous agents that execute multi-step processes by chaining together specialized functions and tools.
- [Function Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/function-orchestrators.md) — A modular system where discrete functional units are dynamically discovered and executed by a central planner to satisfy complex user requests.
- [Task Planners](https://awesome-repositories.com/f/artificial-intelligence-ml/task-planners.md) — An automated reasoning engine that breaks high-level goals into sequences of executable steps by analyzing available function signatures and their metadata.
- [Vector Retrieval Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-retrieval-abstractions.md) — A unified interface layer that maps diverse vector databases and storage providers into a consistent retrieval mechanism for context-aware AI operations.
- [Enterprise AI Integration Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/enterprise-ai-integration-tools.md) — Connecting generative artificial intelligence models to proprietary corporate data and existing software infrastructure while maintaining strict security and compliance standards.
- [Language-Agnostic Connectors](https://awesome-repositories.com/f/artificial-intelligence-ml/language-agnostic-connectors.md) — A standardized interface layer that decouples the core orchestration logic from specific large language model providers and their proprietary API protocols.
- [LLM Integration Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-integration-layers.md) — Providing a standardized software interface that connects generative artificial intelligence models to traditional application logic and data sources.
- [Modular AI Components](https://awesome-repositories.com/f/artificial-intelligence-ml/modular-ai-components.md) — Creating flexible and reusable AI components that can be easily swapped, tested, and scaled within larger software applications.

### Business & Productivity Software

- [Workflow Automation Engines](https://awesome-repositories.com/f/business-productivity-software/workflow-automation-engines.md) — Streamlining repetitive business processes by chaining together automated reasoning steps and external service calls into a single cohesive pipeline.

### Software Engineering & Architecture

- [Middleware Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/middleware-pipelines.md) — A chain of interceptors that process prompts and responses to inject cross-cutting concerns like logging, telemetry, and safety filtering during execution.
- [Plugin Architectures](https://awesome-repositories.com/f/software-engineering-architecture/plugin-architectures.md) — Add custom functionality by plugging in external components that integrate seamlessly with existing logic to meet unique project requirements without modifying the core structure of your application. ([source](https://learn.microsoft.com/en-us/semantic-kernel/overview/))