Microsoft Agent Framework vs Semantic Kernel

Detailed side-by-side comparison to help you choose the right tool

Microsoft Agent Framework

🟡Low Code

Agent Platforms

Enterprise multi-agent platform integrated with Azure AI and Microsoft 365.

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Starting Price

Contact

Semantic Kernel

🔴Developer

AI Agent Builders

SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.

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Starting Price

Free

Feature Comparison

Scroll horizontally to compare details.

FeatureMicrosoft Agent FrameworkSemantic Kernel
CategoryAgent PlatformsAI Agent Builders
Pricing Plans10 tiers11 tiers
Starting PriceContactFree
Key Features
    • Workflow Runtime
    • Tool and API Connectivity
    • State and Context Handling

    Microsoft Agent Framework - Pros & Cons

    Pros

    • Deep Microsoft ecosystem integration
    • Enterprise-grade security and compliance
    • Visual development tools
    • Strong Azure AI integration
    • Excellent for Microsoft shops

    Cons

    • Locked into Microsoft ecosystem
    • Can be expensive for small teams
    • Limited flexibility outside Microsoft tools

    Semantic Kernel - Pros & Cons

    Pros

    • Plugin system lets you expose existing C#/Python methods to LLMs with a single decorator — minimal refactoring needed
    • First-class .NET support with dependency injection, middleware patterns, and enterprise conventions C# teams already know
    • Azure OpenAI deep integration with managed identity, content safety filters, and enterprise deployment patterns baked in
    • OpenTelemetry-based observability provides production-grade tracing and metrics without third-party tooling
    • Multi-language SDK (C#, Python, Java) with feature parity goals, enabling polyglot enterprise teams

    Cons

    • C# SDK is significantly ahead of Python and Java in features — cross-language parity lags by months
    • Planner reliability varies — automatic function chaining can produce unexpected plans requiring careful prompt engineering
    • Heavier abstraction layer than direct API calls — adds complexity for simple use cases that don't need plugins
    • Community and ecosystem are smaller than LangChain's, resulting in fewer third-party integrations and examples

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    🔒 Security & Compliance Comparison

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    Security FeatureMicrosoft Agent FrameworkSemantic Kernel
    SOC2
    GDPR
    HIPAA
    SSO
    Self-Hosted✅ Yes
    On-Prem✅ Yes
    RBAC
    Audit Log
    Open Source✅ Yes
    API Key Auth
    Encryption at Rest
    Encryption in Transit
    Data Residency
    Data Retentionconfigurable
    🦞

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