Google ADK-Go Tutorial: Build AI Agents in Go (2025)

Google launched ADK-Go on November 7, 2025 – an open-source toolkit that brings enterprise-grade AI agent development to Go developers. For the first time, backend teams can build sophisticated multi-agent systems without switching to Python. With version 0.2.0 already live and 5,570+ GitHub stars, ADK-Go proves Google is serious about meeting developers “where they are” as AI agents move from experiments to production.

Go developers have been locked out of the AI agent revolution. LangChain, CrewAI, and AutoGen are all Python-only, forcing backend teams into tech stack fragmentation. ADK-Go changes this by delivering official Google backing, 30+ database integrations out of the box, and multi-agent orchestration in under 100 lines of code.

What Google ADK-Go Is and Why It Exists

ADK-Go extends Google’s Agent Development Kit to the Go ecosystem. The framework is code-first – you define agents directly in Go code, not YAML or UI configurations. This enables testability, version control, and CI/CD integration that production teams demand.

The toolkit is model-agnostic despite being optimized for Gemini. It supports Anthropic Claude and OpenAI GPT, preventing vendor lock-in. Deployment flexibility spans local development, Docker containers, Google Cloud Run, and Kubernetes. Google built ADK-Go to solve three pain points: debugging challenges, version control difficulties, and deployment inflexibility.

Repository structure reveals the architecture: core agent implementation, tool management, model integration layer, execution orchestration, HTTP server for deployment, state management, and session handling. The framework supports four agent types – LLM agents powered by language models, Sequential agents for workflow orchestration, Parallel agents leveraging Go’s goroutines for concurrent execution, and Loop agents for iterative tasks.

Key Features That Set ADK-Go Apart

Multi-agent orchestration in under 100 lines of code is the headline feature. Sequential agents execute sub-agents in order for dependent workflows. Parallel agents execute concurrently – a travel planner finding flights and hotels simultaneously before synthesizing results. Loop agents iterate repeatedly, perfect for refinement tasks. Go’s native concurrency gives ADK-Go a performance edge over Python alternatives.

MCP Toolbox integration delivers 30+ database connectors with zero configuration. PostgreSQL, MySQL, MongoDB, Redis – provide a connection string and get natural language to SQL translation automatically. This eliminates weeks of integration work. Custom tools are dead simple: write a Go function, and ADK auto-converts it.

The Development UI is where ADK-Go shows maturity. Run `adk run` and access localhost:8080 for visual debugging. Inspect exact LLM prompts and responses in real-time. Test agents, visualize execution flow, and demo to stakeholders without writing UI code. Google learned from LangChain’s debugging pain points and shipped a solution.

Code-first development means agents are just Go code. Unit tests for custom tools. Integration tests for workflows. Git for version control. Code review for agent definitions. This is software engineering applied to AI, not YAML wrangling or drag-and-drop UI hell.

Getting Started: Installation and First Agent

Installation is one command:

go get google.golang.org/adk

Creating your first agent requires minimal boilerplate. Import packages, create a model with your API key, and configure an LLM agent:

import (
    "google.golang.org/adk/agent"
    "google.golang.org/adk/model"
    "google.golang.org/adk/tool"
)

// Create Gemini model
geminiModel := model.NewGemini(apiKey)

// Create agent with tools
timeAgent := agent.NewLLM(
    "TimeAgent",
    geminiModel,
    agent.WithDescription("Provides time information"),
    agent.WithTools(tool.GoogleSearch),
)

That’s a working agent in 15 lines. Google claims less than 20 minutes to first agent. For once, the marketing isn’t overselling.

Launch the Development UI to see your agent in action:

adk run  # Access at localhost:8080

Multi-agent workflows are equally straightforward. Create specialized agents and compose them:

codeReviewAgent := agent.NewLLM("CodeReviewer", model, ...)
docAgent := agent.NewLLM("Documenter", model, ...)

workflow := workflowagents.NewSequential(
    "CodeReviewPipeline",
    codeReviewAgent,
    docAgent,
)

result := workflow.Execute(ctx, "Review PR #123")

Sequential workflows in 10 lines. Parallel workflows leverage Go’s goroutines automatically. This is the composability Python frameworks promise but deliver through complex abstractions.

When to Use Google ADK-Go and When to Skip It

ADK-Go is ideal if you’re running a Go backend stack with microservices, APIs, or data pipelines. Performance-critical agent workflows benefit from compiled Go’s speed over interpreted Python. Google Cloud and Gemini users get tight integration advantages. Multi-agent systems needing orchestration fit ADK-Go’s strengths perfectly.

Skip ADK-Go if your team is Python-only – learning Go adds friction that outweighs benefits. LangChain’s 1000+ integrations matter if you need massive ecosystem breadth. Frontend-focused teams should use Genkit, Google’s TypeScript framework. API stability concerns are valid – v0.2.0 is still evolving, and the release notes mention “significant code refactoring.”

Compared to LangChain, ADK-Go offers lower learning curve, faster execution (compiled versus interpreted), and simpler multi-agent setup. LangChain wins on community size and ecosystem breadth. Choose based on your stack, not framework popularity.

Limitations and Gotchas to Know

Folder structure requirements are strict. Exact naming conventions matter, and error messages won’t help when you get it wrong. Follow official examples precisely until you internalize the patterns.

Built-in tools only work with Gemini models. You can attach only one built-in tool per agent. You cannot mix built-in tools with custom tools in the same agent. This isn’t a dealbreaker – custom tools are easy – but know the constraints going in.

Rapid API evolution is expected for v0.2.0. The release notes cite “significant code refactoring.” Pin your version in `go.mod` for production deployments. Test upgrades carefully. Monitor the GitHub repository for breaking changes.

Community size is small compared to LangChain’s massive ecosystem. Fewer tutorials exist. Stack Overflow answers are limited. This improves as adoption grows, but early adopters accept this trade-off for Go-native AI capabilities.

Resources and Next Steps

Bookmark these essential resources. Official documentation provides getting started guides and API reference. GitHub repository hosts source code, issues, and releases. Sample code repository contains working examples like travel planners and code reviewers.

The Google Developers Blog announcement explains the “why” behind ADK-Go’s creation. Reddit’s r/agentdevelopmentkit offers community support and discussions.

Next steps: Install ADK-Go with `go get google.golang.org/adk`. Clone the samples repository and run examples to see agents in action. Launch the Development UI with `adk run` to experience visual debugging. Start simple with a single LLM agent, then compose multi-agent workflows as you build confidence.

Go developers finally have a native AI agent toolkit. No Python context switching. No language barrier. Just idiomatic Go code that builds production-grade agent systems. Google’s investment in ADK-Go signals long-term commitment to multi-language support. The 5,570+ GitHub stars and active development prove developers are responding.

Production-ready doesn’t mean perfect. Version 0.2.0 signals ongoing evolution. Early adopters get competitive advantages at the cost of API changes and smaller communities. That’s the trade-off. For Go backend teams building AI agent systems, ADK-Go isn’t just an option – it’s the right tool for the job.