Chassis vs Google Agent Development Kit (ADK)

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Chassis Agents	Google Agent Development Kit (ADK) Agents
Tagline	Open-source tool that auto-packages ML models into production-ready Docker containers with a prediction API.	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Free· Free, open source (Apache-style community project)	Free· Framework itself is free and open-source (Apache 2.0). Costs come from the underlying model provider (e.g. Gemini API / Vertex AI usage) and any hosting infrastructure (Cloud Run, GKE, Agent Engine).
Model	—	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	6.9 / 10	—
Use cases	model-packagingedge-deploymentml-containerizationmlopskubernetes-serving	Multi-agent research assistantCustomer support triage agentRAG chatbot backed by Vertex AI SearchCode review and refactoring agentBigQuery natural-language analytics agentDocument processing pipelineVoice/streaming conversational agentInternal tool-use agent orchestrating APIsEvaluation and regression testing of LLM workflowsEnterprise workflow automation on Agent Engine
Pros	One Python call turns a trained model into a Docker prediction container Cross-compiles for x86 and ARM, including Jetson and Raspberry Pi Framework-agnostic across Scikit-learn, PyTorch, TensorFlow Fully open source with no vendor lock-in to Modzy	Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly Rich multi-agent primitives out of the box: SequentialAgent, ParallelAgent, LoopAgent, and hierarchical sub-agents Tight Google Cloud integration for deployment (Cloud Run, GKE, Agent Engine) plus native BigQuery/Vertex Search tools Evaluation harness with trajectory-level scoring is included, not a separate paid add-on First-class MCP (Model Context Protocol) client and server support
Cons	Not an AI model itself, just deployment glue Release activity has slowed; community support is the main channel Requires Docker installed locally and some wrapper code	Documentation and examples lean heavily on Gemini + Google Cloud; non-Google paths work but feel like second-class citizens API surface is still evolving — breaking changes between minor versions have been common through 2025-2026 Multi-agent orchestration primitives are powerful but the graph/callback model has a real learning curve compared to a plain prompt loop Agent Engine deployment is convenient but locks you into GCP billing and quotas TypeScript/Kotlin support lags the Python SDK in features and community examples
Website	chassisml.io	google.github.io

Pick Chassis if

✅ One Python call turns a trained model into a Docker prediction container
✅ Cross-compiles for x86 and ARM, including Jetson and Raspberry Pi
✅ Framework-agnostic across Scikit-learn, PyTorch, TensorFlow
✅ Fully open source with no vendor lock-in to Modzy

Pick Google Agent Development Kit (ADK) if

✅ Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product
✅ Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only
✅ Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously
✅ Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly

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