Google Agent Development Kit (ADK) vs Microsoft Agent Framework
Detailed side-by-side comparison to help you choose the right tool
Google Agent Development Kit (ADK)
🔴DeveloperAI Agent Framework
Google's open-source, code-first framework for building, evaluating, and deploying AI agents. Optimized for Gemini but works with any LLM.
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ContactMicrosoft Agent Framework
AI Agent Framework
Microsoft's unified open-source framework for building AI agents and multi-agent systems, combining AutoGen's multi-agent patterns with Semantic Kernel's enterprise features into a single Python and .NET SDK.
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Google Agent Development Kit (ADK) - Pros & Cons
Pros
- ✓Completely free and open-source
- ✓Model-agnostic despite Google origins
- ✓Strong Gemini optimization
- ✓Built-in evaluation framework
- ✓Backed by Google's engineering team
- ✓Clean Python-first API
- ✓Integrates with Vertex AI for production
Cons
- ✗Requires Python programming knowledge
- ✗Newer framework with smaller community than LangChain
- ✗Documentation still maturing
- ✗Best features tied to Google ecosystem
- ✗Steeper learning curve than no-code alternatives
- ✗Limited third-party integrations compared to competitors
Microsoft Agent Framework - Pros & Cons
Pros
- ✓Only major agent framework with genuine first-class .NET support — if your team writes C#, this is essentially your only serious option
- ✓Combines AutoGen's proven multi-agent research patterns with Semantic Kernel's production-grade enterprise features
- ✓Free and open-source (MIT) with no licensing traps — only pay for the models and compute you use
- ✓Checkpointing and time-travel debugging are genuinely useful features that most competing frameworks lack
- ✓MCP and A2A protocol support future-proofs agent interoperability as these standards mature
- ✓Backed by Microsoft with dedicated teams, extensive documentation, and Azure integration for managed hosting
Cons
- ✗Still in public preview (GA targeted Q1 2026) — APIs may change, and production deployment carries preview-stage risk
- ✗Microsoft's framework churn track record creates trust issues: developers burned by AutoGen → Semantic Kernel → Agent Framework migrations are understandably skeptical
- ✗Documentation is improving but still reflects the merger — some pages reference AutoGen or Semantic Kernel concepts that have been reorganized
- ✗The learning curve is steep for teams new to multi-agent patterns: understanding when to use agent vs. workflow orchestration takes experimentation
- ✗Community ecosystem is smaller than LangChain's — fewer pre-built tools, integrations, and tutorials available
- ✗Python SDK may lag .NET in certain edge cases, given Microsoft's natural .NET-first development culture
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