Go Micro vs Google Agent Development Kit (ADK)

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

	Go Micro Agents	Google Agent Development Kit (ADK) Agents
Tagline	Agent harness and microservice framework, in one Go runtime.	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Free· Open source (Apache 2.0). Commercial support available separately; hosted model access via Atlas Cloud is billed by that provider.	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	Model-agnostic: Claude (Anthropic), OpenAI, and 300+ models via Atlas Cloud	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	—	—
Use cases	Production AI agents that call internal microservicesAgent-to-agent systems with service discoveryMCP tool servers backed by existing gRPC/HTTP APIsDurable long-running agent workflowsSelf-hosted agent products in GoMulti-model routing across Claude, OpenAI, and open modelsChat-frontends over existing service estatesHuman-in-the-loop workflows with checkpointed resume	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	Unifies agent runtime and microservice framework — one binary handles both the LLM loop and the surrounding services. Automatically exposes service endpoints as MCP tools, so agents can call your existing services without hand-written adapters. Durable, checkpointed workflows survive restarts and long tool calls without re-running side effects. Every abstraction (registry, broker, transport, store, model) is a Go interface — genuinely pluggable. Apache 2.0 with a large existing Go Micro community (23k+ GitHub stars) and no vendor lock-in on models. Local dev loop is tight: `micro new`, `micro run` with hot reload, and `micro chat` to talk to the agent from the terminal.	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	Go-only — if your team's stack is Python or TypeScript, most of the surrounding ecosystem (LangChain, LlamaIndex, DSPy) doesn't apply. The framework is opinionated about services; teams that just want a thin agent library will find it heavier than they need. Documentation and examples for the newer AI-agent surface are thinner than the mature microservice docs. Model routing through Atlas Cloud is convenient but adds a hosted dependency and its own billing if you use it. Durable workflows require you to think about idempotency and checkpoint boundaries — not a drop-in for casual scripts.	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	go-micro.dev	google.github.io

Pick Go Micro if

✅ Unifies agent runtime and microservice framework — one binary handles both the LLM loop and the surrounding services.
✅ Automatically exposes service endpoints as MCP tools, so agents can call your existing services without hand-written adapters.
✅ Durable, checkpointed workflows survive restarts and long tool calls without re-running side effects.
✅ Every abstraction (registry, broker, transport, store, model) is a Go interface — genuinely pluggable.

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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