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Semantic Kernel

Semantic Kernel is a Microsoft-developed framework for building Artificial Intelligence (AI) applications that orchestrate large language models, traditional code, and external data and services through a unified programming model (AI orchestration framework).

  • Orchestrates large language models, conventional functions, and external services as composable “skills” (AI orchestration / application framework).
  • Provides planners and stepwise execution to break down user goals into sequences of AI and non-AI operations (workflow automation / task planning).
  • Offers connectors and plugins for integrating with data sources and Software-as-a-Service (SaaS) systems (systems integration / data access).
  • Supports multiple host languages and runtimes, such as .NET, Python, and Java, for embedding AI into existing applications (developer framework).
  • Implements prompt templating, memory, and context management for grounding model interactions on enterprise data and logic (prompt engineering / context management).

More About Semantic Kernel

Semantic Kernel is an AI application framework from Microsoft that targets scenarios where enterprises need to coordinate large language models (LLMs), conventional code, and external systems within one programmable environment (AI orchestration framework). It focuses on enabling developers to embed Large Language Model (LLM) capabilities such as Natural Language Understanding (NLU), text generation, and reasoning into existing applications, while still relying on established application logic, security, and data access patterns.

At the core of Semantic Kernel is a skill-based abstraction (application framework) that treats both AI prompts and conventional functions as reusable building blocks. A skill can wrap an LLM prompt, a native method in a host language like C#, Python, or Java, or a call to an external Application Programming Interface (API). These skills are then composed into pipelines that can fulfill user requests or application workflows. This approach allows teams to mix deterministic code with probabilistic model behavior under a single programming model.

Semantic Kernel includes planning capabilities (workflow automation) that assist with decomposing high-level goals into ordered steps. Planners can generate a plan that chains multiple skills, determine inputs and outputs between them, and execute the plan step by step. This supports scenarios such as multi-step task execution, agent-like behaviors, and contextual decision flows where LLMs select or parameterize which skills to invoke.

The framework provides prompt templating, memories, and context management (prompt engineering / context management). Prompt templates allow developers to define parameterized prompts and bind runtime data into them. Memory components support storing and retrieving information that can be surfaced back to the model as context, including semantic or vector-based retrieval from enterprise data sources. This helps ground LLM responses on business data and application state.

Semantic Kernel integrates with external systems and data sources through connectors and plugins (systems integration). Official materials describe integration patterns for services such as Microsoft 365, Azure OpenAI Service, and other REST-based endpoints, as well as patterns for accessing enterprise data stores. These integrations allow LLM operations to read and act on documents, emails, records, and line-of-business systems while retaining the surrounding application’s access control and compliance posture.

The project supports multiple languages and runtimes (developer tools), including SDKs for .NET, Python, and Java, so teams can embed AI capabilities in existing applications, microservices, and back-end services. It is designed to align with Azure AI and Microsoft cloud offerings but can also work with compatible LLM endpoints where supported by the SDKs. Within an enterprise directory or catalog, Semantic Kernel fits into categories such as AI orchestration frameworks, LLM application frameworks, and developer tools for integrating large language models with operational systems and data.