AutoGen vs Google Agent Development Kit

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-08-15
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At a glance

DimensionAutoGenGoogle Agent Development Kit
PricingFree (open-source, MIT license)Free (open-source, Apache 2.0 license)
Primary FocusMulti-agent conversation orchestration with flexible rolesProduction-grade multi-agent systems with deterministic graph workflows
Language SupportPython (primary), community extensionsPython, TypeScript, Go, Java, Kotlin
Key IntegrationsOpenAI, Azure OpenAI, Hugging Face, LLaMA, Mistral, ClaudeGemini, Gemma, Claude, Ollama, vLLM, LiteLLM, Apigee, Google Cloud
Deployment & MonitoringSelf-hosted, basic Docker support (sandboxing)Cloud Run, GKE, Apigee AI Gateway, built-in observability (logs, metrics, traces)
Latest ReleaseNo recent newsADK 2.0 GA - graph workflows, collaborative agents, Kotlin support (Nov 2025)

If you need flexible multi-agent experimentation with any LLM, choose AutoGen. For production-grade enterprise deployments with deterministic logic, multi-language SDKs, and Google Cloud integration, Google ADK is the stronger choice, especially with ADK 2.0's graph workflows and Kotlin support.

AutoGen
AutoGen

Open-source framework for building multi-agent AI workflows.

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Google Agent Development Kit
Google Agent Development Kit

Google's open-source framework for building production-grade AI agents across Python, TypeScript, Go, Java, and Kotlin.

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Pricing
Free
Free
Plans
$0/mo
$0/mo
Popularity
5.3k views
4.9k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
APIDesktopWeb
WebCLIAPI
Categories
🕸️ Agent Frameworks & Orchestration
🕸️ Agent Frameworks & Orchestration
Features
Multi-agent conversation orchestration
Flexible agent role definition
Customizable conversation patterns (sequential, group chat, nested)
Integration with various LLMs via abstract interface
Extensible tool use
Human-in-the-loop support
Open-source (MIT license) with community contributions
AutoGen Studio visual prototyping UI
Code execution sandboxing (requires Docker)
Modular agent composition
Event-driven core for scalable systems
Distributed agents via gRPC
MCP server integration via McpWorkbench
OpenAI Assistant API integration
Multi-language SDKs: Python, TypeScript, Go, Java, Kotlin
Graph workflows for deterministic logic with AI reasoning
Collaborative agents for multi-agent coordination
Agent team configuration and orchestration
Support for Gemini, Gemma, Claude models
Model routing through Ollama, vLLM, LiteLLM, LiteRT-LM
Google Search grounding integration
Apigee AI Gateway deployment option
Built-in logging, metrics, and traces
Agents CLI for scaffolding, testing, evaluation, deployment
Web interface and visual builder
REST API for agent control
MCP and A2A protocol support
Session management and context caching
Deployment to Cloud Run and GKE
Integrations
OpenAI
Azure OpenAI
Hugging Face
LLaMA
Mistral
Claude
Gemini
Gemma
Ollama
vLLM
LiteLLM
LiteRT-LM
Apigee AI Gateway
Google Cloud Run
Google Kubernetes Engine (GKE)
Google Search
MCP
A2A Protocol

Who should pick which

  • Researcher exploring multi-agent dynamics
    Pick: AutoGen

    AutoGen's flexible role definitions and support for many LLMs allow rapid prototyping of multi-agent conversations for research.

  • Enterprise developer building a production agent system
    Pick: Google Agent Development Kit

    ADK's graph workflows, multi-language SDKs, enterprise observability, and Google Cloud integration are built for production scalability.

  • Hackathon participant needing fast multi-language support
    Pick: Google Agent Development Kit

    ADK's support for Python, TypeScript, Go, Java, and Kotlin, plus CLI tools, enable rapid prototyping across languages.

  • Developer integrating with non-Google LLMs (e.g., Mistral, Claude)
    Pick: AutoGen

    AutoGen directly supports many LLMs like Mistral and Claude out-of-the-box, while ADK has more limited model integrations.

  • Team using Google Cloud infrastructure
    Pick: Google Agent Development Kit

    ADK natively integrates with Cloud Run, GKE, and Apigee, making deployment and monitoring seamless for Google Cloud users.

Frequently Asked Questions

AutoGen vs Google Agent Development Kit: which should you choose?

If you need flexible multi-agent experimentation with any LLM, choose AutoGen. For production-grade enterprise deployments with deterministic logic, multi-language SDKs, and Google Cloud integration, Google ADK is the stronger choice, especially with ADK 2.0's graph workflows and Kotlin support.

Which framework is better for production deployments?

Google ADK is better for production due to built-in observability, deterministic graph workflows, and direct deployment to Cloud Run/GKE with Apigee integration.

Can I use models other than Gemini with Google ADK?

Yes, ADK supports Claude, Gemma, and model routing via Ollama, vLLM, and LiteLLM, but primary integration is with Gemini.

Does AutoGen support deterministic workflows?

AutoGen focuses on flexible conversation orchestration, not deterministic graph logic. ADK 2.0 introduces graph workflows for deterministic control.

Which framework supports more programming languages?

Google ADK supports Python, TypeScript, Go, Java, and Kotlin. AutoGen primarily supports Python.

Is there a visual UI for prototyping in AutoGen?

Yes, AutoGen includes AutoGen Studio for visual prototyping. Google ADK provides CLI tools but no dedicated UI.

How do the licenses compare?

AutoGen is MIT licensed; Google ADK is Apache 2.0. Both are permissive open-source licenses.

What is the latest version of Google ADK?

As of Nov 2025, ADK 2.0 GA is live, introducing graph workflows, collaborative agents, and Kotlin support.

Which framework is easier to learn for beginners?

AutoGen may be simpler for small multi-agent experiments. Google ADK has a steeper learning curve due to broader scope but provides comprehensive documentation.

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Last reviewed: May 12, 2026