* docs: compare Go Micro with Google ADK in the comparison guide Adds a 'vs Agent Frameworks (Google ADK)' section: ADK builds an agent, Go Micro builds the distributed system the agent lives in (agents are services in the mesh). Covers the category difference, a feature table, when to choose each, and MCP/A2A interoperability. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * docs: replace ADK comparison slogan with concrete explanation State plainly what each tool provides (ADK builds an agent process; Go Micro builds the surrounding service mesh) instead of marketing phrasing. * lint: apply golangci-lint autofixes; exclude ST1003 and demo errcheck Mechanical, behaviour-preserving fixes applied by 'golangci-lint run --fix': gofmt, misspell (US spelling), usestdlibvars (http.Method*/Status*), unconvert, and the auto-fixable staticcheck simplifications (QF*, S1017/S1019/S1023/S1039). Config: exclude ST1003 (remaining offenders are exported API renames, e.g. web.Id, which would break compatibility) and skip errcheck for examples/ and internal/harness/ (demo code where fire-and-forget is intentional). Build and test compilation verified. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL * lint: WIP cleanup checkpoint (errcheck config + partial fixes) Checkpoint of an in-progress golangci-lint cleanup (background pass). Builds cleanly; lint is not yet zero. Follow-up commit will complete the cleanup and switch CI to a blocking full-tree lint. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CmdEY7pYmV5zzwCjNJ4ykL --------- Co-authored-by: Claude <noreply@anthropic.com>
Go Micro Examples
This directory contains runnable examples demonstrating various go-micro features and patterns.
Quick Start
Each example can be run with go run . from its directory.
Examples
hello-world
Basic RPC service demonstrating core concepts:
- Service creation and registration
- Handler implementation
- Client calls
- Health checks
Run it:
cd hello-world
go run .
web-service
HTTP web service with service discovery:
- HTTP handlers
- Service registration
- Health checks
- JSON REST API
Run it:
cd web-service
go run .
multi-service
Multiple services in a single binary — the modular monolith pattern:
- Isolated server, client, store, and cache per service
- Shared registry and broker for inter-service communication
- Coordinated lifecycle with
service.Group - Start monolith, split later when you need to scale independently
Run it:
cd multi-service
go run .
deployment
Docker Compose deployment with MCP gateway, Consul registry, and Jaeger tracing:
- Production-like architecture in one
docker-compose up - Standalone MCP gateway connected to service registry
- Distributed tracing with OpenTelemetry + Jaeger
MCP Examples
See the mcp/ directory for AI agent integration examples:
- hello - Minimal MCP service (start here)
- crud - CRUD contact book with full agent documentation
- workflow - Cross-service orchestration via AI agents
- documented - All MCP features with auth scopes
agent-demo
Multi-service project management app (Projects, Tasks, Team) with seed data and agent playground integration.
agent-plan-delegate
The two built-in agent capabilities in a small multi-agent system:
- plan — an agent records an ordered plan in its store-backed memory before doing multi-step work
- delegate — an agent hands a subtask to another agent (over RPC if it's registered, else to an ephemeral sub-agent)
agent-wrap-tool
Middleware around an agent's tool execution with AgentWrapTool, the tool-side analogue of client/server wrappers:
- observe — time every tool call and record per-tool metrics, correlated by call ID
- retry — re-run a call whose result is an error, recovering from a transient failure before the model sees it
flow-durable
A workflow as ordered, checkpointed steps that survives a crash and resumes where it stopped:
- steps — a flow is a task with stages (
reserve → charge → confirm), not just one LLM turn - Checkpoint — each step is persisted; on
Resume, completed steps are not re-run (no duplicate side effects)
support
A real-world support desk — the "zero to hero" shape in one runnable file:
- services (
customers,tickets,notify) become the agent's tools automatically - flow turns a
ticket.createdevent into work for the agent (the event is the prompt) - guardrail — the agent triages freely but can't email a customer without passing the approval gate
Coming Soon
- pubsub-events - Event-driven architecture with NATS
- grpc-integration - Using go-micro with gRPC
Prerequisites
Some examples require external dependencies:
- NATS:
docker run -p 4222:4222 nats:latest - Consul:
docker run -p 8500:8500 consul:latest agent -dev -ui -client=0.0.0.0 - Redis:
docker run -p 6379:6379 redis:latest
Contributing
To add a new example:
- Create a new directory
- Add a descriptive README.md
- Include working code with comments
- Add to this index
- Ensure it runs with
go run .