AutoGen vs Google Agent Development Kit

AutoGen excels at multi-agent conversation orchestration with flexible agent role definition, customizable conversation patterns, and human-in-the-loop support. Its open architecture allows integration with many LLMs (OpenAI, Azure, Hugging Face, etc.) and includes AutoGen Studio for visual prototyping. However, it lacks built-in production monitoring and relies on Docker for code sandboxing. Google ADK, especially with ADK 2.0 GA, introduces graph-based workflows with deterministic logic, multi-agent orchestration, and streaming support. It supports multiple SDKs (Python, TypeScript, Go, Java, Kotlin), making it accessible to broader teams. ADK integrates deeply with Google Cloud (Apigee, Cloud Run, GKE) and offers enterprise observability (logging, metrics, traces). It also supports model routing via Ollama, vLLM, and LiteLLM, plus protocols like MCP and A2A. AutoGen is better for research and flexible agent collaboration, while ADK is optimized for production scalability and deterministic workflows. The news confirms ADK 2.0's graph workflows and Kotlin support, further strengthening its enterprise appeal.

Both tools are free and open-source under permissive licenses (AutoGen: MIT, Google ADK: Apache 2.0). There are no direct usage costs, but infrastructure costs differ. AutoGen requires self-hosted compute for LLM calls and Docker for sandboxing, so costs vary based on cloud provider and models used. Google ADK offers free tiers for Google Cloud services (e.g., Cloud Run free tier, Gemini API free tier up to 60 requests/min). For production, ADK leverages Google Cloud's pay-as-you-go model but also supports self-hosting. Considering pricing, ADK may have lower initial infrastructure costs due to free tiers, while AutoGen's Docker requirement might add overhead. Both are cost-effective for developers, but ADK provides more built-in enterprise-grade tooling (observability, deployment) that could reduce operational costs at scale.