micro--go-micro
1bc886fa82
* docs: Agent interface design sketch Proposes Agent as a top-level abstraction alongside Service in the micro package. Agent manages services — scoped tools, system prompt, conversation memory, registry-discoverable. Design only, no implementation. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: Agent as a first-class abstraction Introduce micro.NewAgent() alongside micro.New() — Agent is to intelligence what Service is to capability. Agent interface: - Chat(ctx, message) (*Response, error) — core interaction method - Run() — registers in registry, subscribes to broker, blocks - Stop() — graceful shutdown - Scoped tools — only sees endpoints of its assigned services - Persistent memory — conversation history stored in store - Agent-to-agent — communication via broker topics Top-level API: agent := micro.NewAgent("task-mgr", micro.AgentServices("task"), micro.AgentPrompt("You manage tasks."), micro.AgentProvider("anthropic"), ) agent.Run() https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: wire agents into chat router, add micro agent CLI, expose Flow Three top-level abstractions: micro.New("task") — Service (capability) micro.NewAgent("task-mgr") — Agent (intelligence) micro.NewFlow("onboard-user") — Flow (event-driven orchestration) micro chat as router: - Discovers agents from registry on startup - Single agent: routes directly - Multiple agents: LLM classifies intent, dispatches to right agent via route_to_agent tool - No agents: falls back to current direct-service behaviour - Banner shows discovered agents micro agent CLI: - micro agent list — shows registered agents and their services - micro agent describe <name> — shows agent details from registry https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: move flow to top level, update docs for three abstractions Package structure now consistent: service/ — Service (capability) agent/ — Agent (intelligence) flow/ — Flow (event-driven orchestration) ai/flow/ kept as backward-compatible re-export. Updated across all surfaces: - CLAUDE.md: added agent/ and flow/ to project structure - README: added "Building Agents" section with NewAgent() examples, updated features table (Agents, Flows, Chat router), CLI table (agent list, agent describe), docs links - Website: features grid shows Services, Agents, Flows as the three pillars alongside generation, MCP, and pluggable architecture - micro.go: Flow imported from top-level flow/ package https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * docs: rewrite getting-started, fix ai-integration import paths Getting started now covers all three abstractions: - Service (write handlers, micro run, templates) - Agent (micro.NewAgent, scoped tools, memory, CLI) - Flow (event-driven LLM orchestration) Leads with prompt-based generation, then manual service creation. ai-integration.md: fixed flow import path from go-micro.dev/v5/ai/flow to go-micro.dev/v5/flow, updated stack diagram to show agent/flow/chat. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * blog: Introducing micro.NewAgent() Post 16 — announces Agent as a first-class abstraction. Shows the API (NewAgent, AgentServices, AgentPrompt, AgentProvider), scoped tools, persistent memory, multi-service agents, multi-agent systems, and the three-abstraction comparison table (Service/Agent/Flow). https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: fix agent registration, blog post 16 Agent registration: - Add node with address so mDNS can discover agents - Store type and services in node metadata (mDNS requirement) - Connect broker before subscribing, non-fatal if broker unavailable - Print registration confirmation on Run() Agent/chat discovery: - Check both service-level and node-level metadata for type=agent (mDNS stores metadata on nodes, not services) Blog post 16: "Introducing micro.NewAgent()" — announces the Agent abstraction with code examples, comparison table, multi-agent patterns. Tested end-to-end: micro run → micro agent list discovers the agent → micro chat routes to it → agent calls service endpoints. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: agents are proper services with RPC Chat endpoint Refactored agent to use server.Server instead of fake registry entries. An agent now: - Creates a real RPC server with server.Name(agentName) - Registers an Agent.Chat handler callable via standard RPC - Sets server metadata type=agent, services=x,y for discovery - No more fake addresses or broker hacks micro chat calls agents via RPC (client.Call) instead of creating local agent instances. The registry stays clean — agents are real services with real endpoints. Removed broker dependency from agent options. Agent-to-agent communication is just RPC like everything else. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: agent uses proto-defined RPC interface Added agent/proto/agent.proto with Agent service definition: rpc Chat(ChatRequest) returns (ChatResponse) Agent now implements the generated AgentHandler interface and registers via pb.RegisterAgentHandler. The Chat endpoint is a standard proto-based RPC callable by any go-micro client. Renamed the programmatic API from Chat() to Ask() to avoid collision with the proto handler method name. micro chat calls agents via standard RPC with JSON-encoded request/response — no special types needed on the caller side. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd * feat: generate agent alongside services, update all docs micro run --prompt now generates an agent binary that manages all the generated services. The agent reads MICRO_AI_PROVIDER and MICRO_AI_API_KEY from the environment. micro run propagates these when started with --prompt. Run banner shows services and agents separately. Updated README, getting-started guide, and landing page to show the complete flow: generate → services + agent start → micro chat routes to agent → agent orchestrates services. https://claude.ai/code/session_01QTp4SshuVmLAvvEGJe4TJd --------- Co-authored-by: Claude <noreply@anthropic.com>
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CLAUDE.md - Go Micro Project Guide
Project Overview
Go Micro is a framework for distributed systems development in Go. It provides pluggable abstractions for service discovery, RPC, pub/sub, config, auth, storage, and more.
The framework is evolving into an AI-native platform where every microservice is automatically accessible to AI agents via the Model Context Protocol (MCP).
Build & Test
# Run all tests
make test
# Run tests for a specific package
go test ./gateway/mcp/...
go test ./ai/...
go test ./model/...
# Lint
make lint
# Format
make fmt
# Build CLI
go build -o micro ./cmd/micro
# Run locally with hot reload
micro run
Project Structure
go-micro/
├── agent/ # Agent abstraction (intelligent service management)
├── ai/ # AI model providers (Anthropic, OpenAI, Gemini, etc.)
├── auth/ # Authentication (JWT, no-op)
├── broker/ # Message broker (NATS, RabbitMQ)
├── cache/ # Caching (Redis)
├── client/ # RPC client (gRPC)
├── cmd/micro/ # CLI tool (run, deploy, mcp, build, server)
├── codec/ # Message codecs (JSON, Proto)
├── config/ # Dynamic config (env, file, etcd, NATS)
├── errors/ # Error handling
├── events/ # Event system (NATS JetStream)
├── flow/ # Event-driven LLM orchestration
├── gateway/
│ ├── api/ # REST API gateway
│ └── mcp/ # MCP gateway (core AI integration)
│ └── deploy/ # Helm charts for MCP gateway
├── health/ # Health checking
├── logger/ # Logging
├── metadata/ # Context metadata
├── model/ # Typed data models (CRUD, queries, schemas)
├── registry/ # Service discovery (mDNS, Consul, etcd)
├── selector/ # Client-side load balancing
├── server/ # RPC server
├── service/ # Service interface + profiles
├── store/ # Data persistence (Postgres, NATS KV)
├── transport/ # Network transport
├── wrapper/ # Middleware (auth, trace, metrics)
├── examples/ # Working examples
└── internal/ # Non-public: docs, utils, test harness
Key Architectural Decisions
- Plugin architecture: All abstractions use Go interfaces. Defaults work out of the box, everything is swappable.
- Progressive complexity: Zero-config for development, full control for production.
- AI-native by default: Every service is automatically an MCP tool. No extra code needed.
- In-repo plugins: Plugins live in the main repo to avoid version compatibility issues.
- Reflection-based registration: Handlers are registered via reflection for minimal boilerplate.
Code Conventions
- Standard Go conventions (gofmt, golint)
- Functional options pattern for configuration (
WithX()functions) - Interface-first design: define the interface, then implement
- Tests alongside code (not in separate test directories)
- Commit messages: imperative mood, concise summary line
Current Focus & Priorities (March 2026)
Status
- Q1 2026 (MCP Foundation): COMPLETE
- Q2 2026 (Agent DX): COMPLETE (100%)
- Q3 2026 (Production): 50% complete (ahead of schedule)
Priority 1: Agent Showcase & Examples
Build compelling demos showing agents interacting with go-micro services in realistic scenarios.
Priority 2: Additional Protocol Support
- gRPC reflection-based MCP
- HTTP/3 support
Priority 3: Kubernetes & Deployment
- Helm Charts for MCP gateway
- Kubernetes Operator with CRDs
Recently Completed
micro newMCP Templates - Scaffolds MCP-enabled services with doc comments,@exampletags,WithMCP().--no-mcpto opt out.- CRUD Example - Contact book service with 6 operations, rich agent docs (
examples/mcp/crud/) - Migration Guide - "Add MCP to Existing Services" guide with 3 approaches
- Troubleshooting Guide - Common MCP issues and solutions
- Error Handling Guide - Patterns for agent-friendly error responses
- Documentation Guides - Six guides: AI-native services, MCP security, tool descriptions, agent patterns, error handling, troubleshooting
- WithMCP Option - One-line MCP setup (
gateway/mcp/option.go) - Agent Playground Redesign - Chat-focused UI with collapsible tool calls
- Standalone Gateway Binary -
micro-mcp-gatewaywith Docker support - WebSocket Transport - Bidirectional JSON-RPC 2.0 streaming (
gateway/mcp/websocket.go) - OpenTelemetry Integration - Full span instrumentation with W3C trace context (
gateway/mcp/otel.go) - LlamaIndex SDK - Python package with RAG examples (
contrib/go-micro-llamaindex/)
Key Files
| Purpose | File |
|---|---|
| MCP Gateway | gateway/mcp/mcp.go |
| MCP Docs | gateway/mcp/DOCUMENTATION.md |
| AI Interface | ai/model.go |
| Model Layer | model/model.go |
| CLI Entry | cmd/micro/main.go |
| MCP CLI | cmd/micro/mcp/ |
| Server (run/server) | cmd/micro/server/server.go |
| Roadmap | internal/docs/ROADMAP_2026.md |
| Status | internal/docs/CURRENT_STATUS_SUMMARY.md |
| Changelog | CHANGELOG.md |
| Docs Site | internal/website/docs/ |
Roadmap & Status Documents
- ROADMAP.md - General framework roadmap
- internal/docs/ROADMAP_2026.md - AI-native era roadmap with business model
- internal/docs/CURRENT_STATUS_SUMMARY.md - Quick status overview
- internal/docs/PROJECT_STATUS_2026.md - Detailed technical status
- internal/docs/IMPLEMENTATION_SUMMARY.md - Implementation notes
- CHANGELOG.md - What changed and when
Contributing
See CONTRIBUTING.md for full guidelines. Key points:
- Open an issue before large changes
- Include tests for new features
- Run
make testandmake lintbefore submitting - Follow commit message format:
type: description(e.g.,feat: add WebSocket transport)