Getting Started
June 22, 2026 · View on GitHub
Go Micro has three core abstractions:
| Abstraction | What | Constructor |
|---|---|---|
| Service | Capability — endpoints, data, business logic | micro.NewService("task") |
| Agent | Intelligence — manages services with an LLM | micro.NewAgent("task-mgr") |
| Flow | Orchestration — event-driven LLM triggers | micro.NewFlow("onboard") |
Prerequisites
- Go 1.21+ for development. The
curlinstall below gives you themicrobinary without Go, butmicro runcompiles your services, so you'll want Go installed to build them. - An LLM provider key (Anthropic, OpenAI, Gemini, …) only for the AI features —
micro run --prompt,micro chat, and agents. Plain services need no key. Set it before running, e.g.export ANTHROPIC_API_KEY=sk-ant-....
Install
# 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
@latestreports a version constraint conflict, pin the current major version explicitly, e.g.go install go-micro.dev/v6/cmd/micro@v6.1.0(see releases).
Quick Start: Generate from a Prompt
Describe what you need. The AI designs services, writes handlers, compiles, and starts them:
micro run --prompt "task management system"
You'll see the design, confirm, and services + agent start:
Services:
● task — Core task management
● project — Project organization
Generate? [Y/n]
Micro
Services:
● task
● project
Agents:
◆ agent
The interactive console lets you talk to your services immediately:
> Create a project called Launch, then add a task called 'Write docs'
→ project_Project_Create({"name":"Launch"})
← {"record":{"id":"p1..."},"success":true}
→ task_Task_Create({"title":"Write docs","project_id":"p1..."})
Created project Launch and added task 'Write docs' to it.
The console discovers services from the registry and orchestrates across them via the agent. Use micro run -d for detached mode without the console, or micro chat as a standalone command.
Quick Start: Write a Service
Create and run a service manually:
micro new helloworld
cd helloworld
micro run
Open http://localhost:8080 to see the dashboard, call endpoints, and chat with your service.
A service is a Go struct with methods. Doc comments and @example tags become tool descriptions for AI agents:
package main
import "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()
}
micro run gives you:
- Dashboard at
http://localhost:8080 - API Gateway at
http://localhost:8080/api/{service}/{method} - Agent Playground at
http://localhost:8080/agent - MCP Tools at
http://localhost:8080/mcp/tools - Hot Reload — auto-rebuild on file changes
micro new scaffolds a reflection-based service by default — plain Go types, no code generation, so go run . works with nothing else installed. If you prefer Protocol Buffers, add --proto (this requires the protoc toolchain; the command tells you what to install).
Templates are available for common patterns. These use Protocol Buffers, so they need the protoc toolchain (protoc, protoc-gen-go, protoc-gen-micro — micro new prints the install commands if they're missing):
micro new contacts --template crud
micro new events --template pubsub
micro new gateway --template api
Building Agents
An Agent is an intelligent layer that manages one or more services:
package main
import "go-micro.dev/v6"
func main() {
agent := micro.NewAgent("task-mgr",
micro.AgentServices("task", "project"),
micro.AgentPrompt("You manage tasks and projects. You understand deadlines, priorities, and assignments."),
micro.AgentProvider("anthropic"),
micro.AgentAPIKey("sk-ant-..."),
)
agent.Run()
}
An agent is a service — it has a proto-defined Agent.Chat RPC endpoint and registers in the registry like everything else. It:
- Discovers its services from the registry
- Only sees endpoints from its assigned services (scoped tools)
- Maintains conversation memory in the store (persists across restarts)
- Is callable via
micro call, the interactive console, or any go-micro client
Use it programmatically:
resp, _ := agent.Ask(ctx, "What tasks are overdue for Alice?")
fmt.Println(resp.Reply)
Or via the CLI:
micro agent list # list registered agents
micro call task-mgr Agent.Chat '{"message": "What tasks are overdue?"}'
When multiple agents are registered, the console routes to the right agent automatically.
Event-Driven Flows
A Flow subscribes to a broker topic and triggers an LLM when events arrive. You can define flows in code or run them from the CLI.
In code:
f := micro.NewFlow("onboard-user",
micro.FlowTrigger("events.user.created"),
micro.FlowPrompt("New user created: {{.Data}}. Send welcome email and create workspace."),
micro.FlowProvider("anthropic"),
micro.FlowAPIKey(os.Getenv("MICRO_AI_API_KEY")),
)
f.Register(service.Options().Registry, service.Options().Broker, service.Client())
From the CLI:
micro flow run --trigger events.user.created --prompt "New user: {{.Data}}. Send welcome email."
micro flow exec --prompt "Summarize all open tickets and email the report."
The flow discovers all services as tools and lets the LLM decide which RPCs to call in response to the event.
CLI Workflow
| Command | Purpose |
|---|---|
micro run --prompt "..." | Generate services + agent, start with interactive console |
micro run | Dev mode: hot reload, gateway, interactive console |
micro run -d | Detached mode (no console) |
micro chat | Standalone chat (when not using micro run) |
micro agent list | List registered agents |
micro flow run --trigger <topic> | Run an event-driven flow |
micro flow exec --prompt "..." | Execute a one-shot flow |
micro new myservice | Scaffold a service |
micro call service endpoint '{}' | Call a service or agent |
micro build | Compile production binaries |
micro deploy user@server | Deploy via SSH + systemd |
Next Steps
- AI Integration — how services, agents, MCP, and LLMs fit together
- Agent Design — the full agent interface specification
- MCP & AI Agents — MCP gateway, tool discovery, and auth
- Data Model — typed persistence with CRUD and queries
- Deployment — deploy via SSH + systemd