Go Micro

Go Micro is an agent harness and service framework for Go.

Overview

A harness is the runtime around an agent: the tools it can call, the memory it keeps, the guardrails that bound it, the workflows that trigger it, the services it depends on, and the protocols other agents use to reach it.

Go Micro gives you the harness as Go code. Build an agent and it gets a model, memory, tools, planning, delegation, guardrails, and service discovery; it is reachable over MCP and A2A. Write services and every endpoint becomes an AI-callable tool. Orchestrate the deterministic parts with durable flows. Agents, services, and flows share one runtime because an agent is a distributed system, and building one is building a service.

Sponsors

<a href="https://go-micro.dev/blog/2026/03/04/building-the-ai-native-future-of-go-micro-with-claude.html"><img src="https://upload.wikimedia.org/wikipedia/commons/7/78/Anthropic_logo.svg" height="26" /></a>    <a href="https://go-micro.dev/blog/2026/06/23/go-micro-joins-openai-s-codex-for-open-source.html"><img src="https://upload.wikimedia.org/wikipedia/commons/4/4d/OpenAI_Logo.svg" height="26" /></a>    <a href="https://go-micro.dev/blog/2026/05/28/atlas-cloud-sponsors-go-micro-300-ai-models-one-integration.html"><img src="https://www.atlascloud.ai/logo.svg" height="26" /></a>

Want to support Go Micro and see your logo here? Become a sponsor — reach out on Discord.

Community

Questions, ideas, or just want to build alongside us? Join the Discord.

Commercial Support

Running Go Micro in production, or building on it and want help? Paid support, consulting, training, and retainers are available directly from the maintainer — and they're what keep the project maintained. See Support for the tiers, or open a request.

Contents

• Quick Start
  • First agent on-ramp
• Why an Agent Harness
• Writing Services
• Building Agents — Plan & Delegate, Pluggable, Paid tools (x402), A2A
• Features
• CLI
• Autonomous improvement loop
• Multi-Service Projects
• Data Model
• AI Providers
• Examples
• Commercial Support
• Docs

Quick Start

Install the CLI:

# Binary (no Go required)
curl -fsSL https://go-micro.dev/install.sh | sh

# Or with Go
go install go-micro.dev/v6/cmd/micro@latest

If install or PATH checks fail, use the install troubleshooting guide before scaffolding your first service.

Fastest start — no API key

Scaffold a service, run it, call it:

micro new helloworld
cd helloworld
micro run

Prefer Docker? The micro image (Docker Hub micro/micro or GitHub Container Registry ghcr.io/micro/go-micro) bundles the CLI and its runtime dependencies:

docker pull micro/micro:latest          # or ghcr.io/micro/go-micro:latest
docker run --rm -it micro/micro new helloworld
docker run --rm -it --network host -v "$(pwd)":/micro/helloworld micro/micro run

Then in another terminal:

curl -X POST http://localhost:8080/api/helloworld/Helloworld.Call \
  -H 'Content-Type: application/json' -d '{"name":"World"}'

This install → scaffold → run → call path is covered by no-secret CI harnesses. To verify just the local installer and first-run CLI boundaries without network access or provider keys, use:

make install-smoke

To verify the focused CLI inner-loop contract — scaffold → run/chat/inspect → deploy dry-run — use:

make inner-loop

To run only the ordered 0→hero services → agents → workflows transcript that CI guards, use:

make zero-to-hero-transcript

To run the broader local contract (including that transcript, chat/inspect CLI boundaries, and deploy dry-run), use:

make harness

First agent on-ramp

After install and the first micro new/micro run smoke check, take the walkable agent path in this order:

1. Install troubleshooting — verify the binary installer or go install, PATH, micro --version, and the no-secret smoke path before agent work.

Run make docs-wayfinding to verify the focused no-secret docs/CLI contract that keeps these README and website commands aligned with the installed CLI.

2. micro agent demo — print the provider-free first-agent demo command and next docs steps from the installed CLI.
3. micro agent quickcheck (or micro agent debug) — when scaffold → run → chat → inspect stalls, print the short recovery map before you dive into the full debugging guide.
4. micro examples — print the maintained provider-free runnable examples in copy/paste order.
5. micro zero-to-hero — print the maintained one-command no-secret lifecycle harness and runnable examples.
6. Examples wayfinding index — choose the smallest no-secret first-agent, maintained 0→hero support reference, and next interop examples from one map.
7. Smallest first-agent example — run one service-backed agent with a mock model and no provider key.
8. No-secret first-agent transcript — run the maintained support agent with a mock model and see services → agents → workflows succeed without a key.
9. Your First Agent — build a service-backed agent and talk to it with micro chat.
10. Debugging your agent — use micro agent preflight before micro run, micro agent doctor after micro run, then micro chat and micro inspect agent <name> to recover run history, memory, and provider checks when the first conversation does something unexpected.
11. 0→hero Reference — complete the services → agents → workflows loop with scaffold, run, chat, inspect, flow history, and deploy dry-run commands that match the maintained harness.

Autonomous improvement loop

Want the same services → agents → workflows lifecycle applied to your repository? micro loop scaffolds the autonomous improvement loop used by Go Micro itself: a North Star, ranked issue queue, role prompts, GitHub Actions workflows, and verification for CI-gated PRs.

micro loop init --roles all
micro loop verify

Before turning on the schedule, configure a dispatch token such as CODEX_TRIGGER_TOKEN, protect the default branch with required CI checks (go build ./..., go test ./..., and golangci-lint run ./... for this repository), and seed .github/loop/PRIORITIES.md with one scoped issue per increment. See the micro loop quickstart for the setup checklist and operating model.

Generate from a prompt — with an LLM key

Set a provider key, describe what you want, and the AI designs services, writes handlers, compiles, and starts them:

export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY, GEMINI_API_KEY, ...
micro run --prompt "a task management system with categories" --provider anthropic

The AI designs the architecture, you review it, then it generates handlers with real business logic, compiles them, and starts them:

Services:
  ● task — Task management with status tracking
  ● project — Project organization

Generate? [Y/n]

Micro
  Services:
    ● task
    ● project
  Agents:
    ◆ agent

Then talk to your services from the console:

> Create a project called Launch, then add three tasks to it

→ project_Project_Create({"name":"Launch"})
← {"record":{"id":"p1..."},"success":true}
→ task_Task_Create({"title":"Design specs","project_id":"p1..."})
→ task_Task_Create({"title":"Write code","project_id":"p1..."})
→ task_Task_Create({"title":"Ship it","project_id":"p1..."})

Created project Launch and added three tasks to it.

When you need a capability that doesn't exist, the agent generates a new service mid-conversation:

> I need to track shipping. Create a shipment for order 123 to London.

  ⚡ generating shipping service...
  ✓ shipping
  → shipping_Shipping_Create({"order_id":"123","destination":"London"})
  ← {"record":{"id":"xyz...","status":"pending"}}

  Created shipment for order 123 going to London.

Edit the generated code by hand at any time — re-running preserves your changes. Read more.

Why an Agent Harness

The first wave of agent frameworks helped developers put a model in a loop. The next problem is operating that loop: connecting it to real tools, scoping what it can touch, preserving state, routing work to specialists, recovering from failures, observing what happened, and letting other agents call it. That is harness work.

Go Micro's answer is to make the harness the same thing you already deploy:

• Tools are services — endpoint metadata becomes tool schema; RPC executes the call.
• Agents are services — they register, discover, load-balance, and expose Agent.Chat.
• Workflows are durable code paths — use flows when the path is known; dispatch to agents when it is not.
• Safety lives at execution — MaxSteps, LoopLimit, ApproveTool, and tool wrappers run where actions happen.
• Interop is built in — MCP for tools, A2A for agents, x402 for paid tools.

Use Go Micro when the agent has to operate a system, not just answer a prompt.

Writing Services

Under the hood, a service is a struct with methods. Doc comments and @example tags become tool descriptions for AI agents automatically.

package main

import (
    "context"

    "go-micro.dev/v6"
)

type Request struct {
    Name string `json:"name"`
}

type Response struct {
    Message string `json:"message"`
}

type Say struct{}

// Hello greets a person by name.
// @example {"name": "Alice"}
func (h *Say) Hello(ctx context.Context, req *Request, rsp *Response) error {
    rsp.Message = "Hello " + req.Name
    return nil
}

func main() {
    service := micro.NewService("greeter")
    service.Handle(new(Say))
    service.Run()
}

Run it and everything is accessible — REST, gRPC, MCP, agent playground:

micro run
# Dashboard:   http://localhost:8080
# API:         http://localhost:8080/api/{service}/{method}
# Agent:       http://localhost:8080/agent
# MCP Tools:   http://localhost:8080/mcp/tools

You can also scaffold a service from a template:

micro new helloworld
micro new contacts --template crud

Building Agents

An Agent is a service with an LLM inside it. It has a proto-defined Agent.Chat RPC endpoint, registers in the registry, and is callable like any service:

agent := micro.NewAgent("task-mgr",
    micro.AgentServices("task", "project"),
    micro.AgentPrompt("You manage tasks and projects. You understand deadlines and priorities."),
    micro.AgentProvider("anthropic"),
)
agent.Run()

The agent discovers its services from the registry, scopes its tools to their endpoints, and maintains c