Semantic Kernel

This Semantic Kernel review evaluates Microsoft's open-source SDK designed to integrate large language models into enterprise applications. Semantic Kernel provides a structured approach to building AI agents, orchestrating multi-step plans, and connecting LLMs with existing code through a plugin architecture. In this review, we examine its core capabilities, integration ecosystem, pricing model, and how it compares to competing frameworks like LangChain and AutoGen for teams building production AI systems.

Overview

Semantic Kernel is an open-source SDK developed by Microsoft, first released in 2023 and actively maintained under the Microsoft umbrella alongside Azure AI services. It supports C#, Python, and Java, making it one of the few LLM orchestration frameworks with first-class support for statically typed languages beyond Python. The project has accumulated over 22,000 GitHub stars and sees regular contributions from both Microsoft engineers and the open-source community.

The framework targets enterprise development teams already invested in the Microsoft ecosystem — those building on Azure OpenAI Service, using .

NET for backend services, or deploying through Azure DevOps pipelines. Semantic Kernel positions itself as the orchestration layer between your application logic and LLM providers, handling prompt management, function calling, and multi-step planning without requiring a full rewrite of existing codebases. Microsoft uses Semantic Kernel internally in products like Microsoft 365 Copilot and Bing Chat, which serves as a credibility signal for production readiness.

Key Features and Architecture

Semantic Kernel's architecture centers on three core abstractions: the Kernel, Plugins, and Planners. The Kernel acts as the central dependency injection container, managing AI services, plugins, and memory. Plugins encapsulate reusable functions — both native code functions (C#, Python, or Java methods) and semantic functions (prompt templates with variable substitution). Planners use an LLM to decompose complex user goals into a sequence of plugin calls, then execute them step by step.

AI Connectors and Model Support. Semantic Kernel integrates with Azure OpenAI Service, OpenAI API, Hugging Face models, and Google AI (Gemini) through a unified connector interface. Teams can swap between providers without rewriting orchestration logic. The SDK supports chat completion, text generation, embeddings, and image generation through these connectors.

Function Calling and Tool Use. The framework implements OpenAI-compatible function calling natively. Developers annotate C# or Python methods with metadata, and the SDK automatically generates the JSON schema that LLMs use to invoke those functions. This eliminates manual prompt engineering for tool use scenarios and supports nested function calls across multiple plugins.

Memory and Vector Store Integration. Semantic Kernel provides built-in abstractions for vector databases including Azure AI Search, Pinecone, Qdrant, Chroma, and PostgreSQL with pgvector. The memory layer handles text chunking, embedding generation, and similarity search, enabling retrieval-augmented generation (RAG) patterns without external libraries.

Agent Framework. The newer Agents module supports multi-agent orchestration where specialized agents collaborate on tasks. Agents can be backed by OpenAI Assistants API or custom implementations, with support for handoffs between agents, shared conversation history, and parallel execution. This positions Semantic Kernel as a direct competitor to frameworks like AutoGen for multi-agent workflows.

Process Framework. Semantic Kernel includes a process orchestration layer for building stateful, long-running AI workflows. Processes define steps as a directed graph, with support for branching, looping, and human-in-the-loop checkpoints — addressing a gap that many competing frameworks leave to external workflow engines like Airflow or Temporal.

Ideal Use Cases

Enterprise .

NET teams adding AI capabilities. Semantic Kernel is the strongest choice for organizations with existing C# codebases on Azure. The native .

NET SDK, Azure OpenAI integration, and dependency injection patterns align with enterprise .

NET conventions. Teams can expose existing business logic as plugins without rewriting services.

Multi-model orchestration. Teams that need to route different tasks to different LLM providers — for example, using GPT-4o for complex reasoning and a smaller model for classification — benefit from the unified connector abstraction. Switching models requires a configuration change, not a code change.

RAG applications on Azure. The built-in memory connectors for Azure AI Search and the embedding pipeline make Semantic Kernel a natural fit for document Q&A and knowledge retrieval systems deployed on Azure infrastructure.

Don't use Semantic Kernel if your team works exclusively in Python and wants maximum community ecosystem breadth. LangChain's Python ecosystem has roughly 10x the third-party integrations. Similarly, if you need a simple LLM wrapper without planning or plugin overhead, Semantic Kernel's abstraction layers add unnecessary complexity.

Pricing and Licensing

Semantic Kernel is released under the MIT License at $0 cost for the SDK itself. There are no paid tiers, no usage limits on the framework, and no commercial license requirements. The entire codebase — including the C#, Python, and Java SDKs — is available on GitHub.

However, the total cost of running Semantic Kernel depends on the AI services you connect:

Microsoft does not charge for Semantic Kernel usage, telemetry, or support through the open-source channel. Enterprise teams seeking dedicated support can access it through their existing Microsoft Enterprise Agreement or Azure support plans, which start at $100/month for the Developer tier.

Pros and Cons

Pros:

• First-class C# and Java support sets it apart from Python-only frameworks like LangChain and CrewAI

• Native Azure OpenAI integration with managed identity and enterprise security features built in

• Plugin architecture enables incremental AI adoption — wrap existing business logic without rewrites

• Multi-agent and process frameworks handle complex workflows that competitors delegate to external tools

• Active Microsoft backing with internal usage in Copilot products validates production readiness

• Comprehensive vector store abstractions for RAG across 8 supported backends including Qdrant and Chroma

Cons:

• Python SDK lags behind C# in feature parity — new capabilities often ship to .

NET first, with Python following weeks later

• Steeper learning curve than LangChain due to enterprise patterns like dependency injection and kernel configuration
• Community ecosystem is smaller: fewer third-party plugins, tutorials, and Stack Overflow answers compared to LangChain
• Documentation quality varies between languages — Java SDK documentation is notably sparse compared to C# resources

Alternatives and How It Compares

LangChain is the better choice when your team works primarily in Python and needs the broadest integration ecosystem. LangChain supports over 700 third-party integrations versus Semantic Kernel's more curated set. Choose LangChain for rapid prototyping or when you need connectors for niche data sources. Choose Semantic Kernel when you need C# support or tighter Azure integration.

AutoGen, also from Microsoft, focuses specifically on multi-agent conversation patterns. If your primary use case is multi-agent collaboration with complex turn-taking and human-in-the-loop workflows, AutoGen provides a more opinionated and streamlined API for that specific pattern. Semantic Kernel is the better fit when you need a general-purpose SDK that covers agents, RAG, and function calling in a single framework.

CrewAI excels at role-based agent orchestration where agents have defined personas and collaborate on tasks. It offers a simpler mental model for multi-agent systems but lacks Semantic Kernel's breadth in areas like vector store integration, process orchestration, and multi-language support.

Haystack by deepset is the strongest alternative for teams focused primarily on RAG and document processing pipelines. Haystack's pipeline abstraction is more mature for search-centric use cases, while Semantic Kernel offers broader coverage for agent-based workflows beyond retrieval.

We evaluated these frameworks based on documentation quality, integration breadth, language support, and production deployment patterns observed across enterprise AI projects.