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Asim Aslam 1bc886fa82 Introduce Agent abstraction and integrate with chat router (#2939)
* 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>
2026-06-05 10:25:14 +01:00

275 行
5.8 KiB
Go

// Package agent provides the Agent abstraction for Go Micro.
//
// An Agent is a service with an LLM inside it. It registers a Chat
// RPC endpoint, discovers its assigned services' tools, and
// orchestrates them intelligently.
//
// agent := micro.NewAgent("task-mgr",
// micro.AgentServices("task"),
// micro.AgentPrompt("You manage tasks."),
// micro.AgentProvider("anthropic"),
// )
// agent.Run()
package agent
import (
"context"
"encoding/json"
"fmt"
"strings"
"sync"
"go-micro.dev/v5/ai"
pb "go-micro.dev/v5/agent/proto"
"go-micro.dev/v5/server"
"go-micro.dev/v5/store"
_ "go-micro.dev/v5/ai/anthropic"
_ "go-micro.dev/v5/ai/atlascloud"
_ "go-micro.dev/v5/ai/gemini"
_ "go-micro.dev/v5/ai/groq"
_ "go-micro.dev/v5/ai/mistral"
_ "go-micro.dev/v5/ai/openai"
_ "go-micro.dev/v5/ai/together"
)
// Agent is the interface for an AI agent that manages services.
type Agent interface {
Name() string
Init(...Option)
Options() Options
Ask(ctx context.Context, message string) (*Response, error)
Run() error
Stop() error
String() string
}
// Response is what an agent returns from Chat.
type Response struct {
Reply string
ToolCalls []ai.ToolCall
Agent string
}
type agentImpl struct {
opts Options
model ai.Model
tools *ai.Tools
hist *ai.History
server server.Server
mu sync.Mutex
}
// New creates a new Agent.
func New(opts ...Option) Agent {
return &agentImpl{
opts: newOptions(opts...),
}
}
func (a *agentImpl) Name() string {
return a.opts.Name
}
func (a *agentImpl) Init(opts ...Option) {
for _, o := range opts {
o(&a.opts)
}
a.setup()
}
func (a *agentImpl) Options() Options {
return a.opts
}
func (a *agentImpl) String() string {
return "agent"
}
func (a *agentImpl) setup() {
var modelOpts []ai.Option
modelOpts = append(modelOpts, ai.WithAPIKey(a.opts.APIKey))
if a.opts.Model != "" {
modelOpts = append(modelOpts, ai.WithModel(a.opts.Model))
}
a.tools = ai.NewTools(a.opts.Registry, ai.ToolClient(a.opts.Client))
modelOpts = append(modelOpts, ai.WithToolHandler(a.tools.Handler()))
a.model = ai.New(a.opts.Provider, modelOpts...)
a.hist = ai.NewHistory(a.opts.HistoryLimit)
a.loadHistory()
}
// Ask sends a message and returns the agent's response.
// This is the programmatic API for direct use.
func (a *agentImpl) Ask(ctx context.Context, message string) (*Response, error) {
a.mu.Lock()
defer a.mu.Unlock()
if a.model == nil {
a.setup()
}
toolList, err := a.discoverTools()
if err != nil {
return nil, fmt.Errorf("discover tools: %w", err)
}
a.hist.Add("user", message)
resp, err := a.model.Generate(ctx, &ai.Request{
Prompt: message,
SystemPrompt: a.buildPrompt(),
Tools: toolList,
Messages: a.hist.Messages(),
})
if err != nil {
return nil, err
}
if resp.Reply != "" {
a.hist.Add("assistant", resp.Reply)
}
if resp.Answer != "" {
a.hist.Add("assistant", resp.Answer)
}
a.saveHistory()
reply := resp.Reply
if resp.Answer != "" {
if reply != "" {
reply += "\n\n"
}
reply += resp.Answer
}
return &Response{
Reply: reply,
ToolCalls: resp.ToolCalls,
Agent: a.opts.Name,
}, nil
}
// Chat implements the proto AgentHandler interface for RPC.
// @example {"message": "What tasks are overdue?"}
func (a *agentImpl) Chat(ctx context.Context, req *pb.ChatRequest, rsp *pb.ChatResponse) error {
resp, err := a.Ask(ctx, req.Message)
if err != nil {
return err
}
rsp.Reply = resp.Reply
rsp.Agent = resp.Agent
for _, tc := range resp.ToolCalls {
input, _ := json.Marshal(tc.Input)
rsp.ToolCalls = append(rsp.ToolCalls, &pb.ToolCall{
Id: tc.ID,
Name: tc.Name,
Input: string(input),
Result: tc.Result,
})
}
return nil
}
// Run starts the agent as a service with a Chat RPC endpoint.
func (a *agentImpl) Run() error {
if a.model == nil {
a.setup()
}
a.server = server.NewServer(
server.Name(a.opts.Name),
server.Registry(a.opts.Registry),
server.Metadata(map[string]string{
"type": "agent",
"services": strings.Join(a.opts.Services, ","),
}),
)
pb.RegisterAgentHandler(a.server, a)
if err := a.server.Start(); err != nil {
return fmt.Errorf("failed to start agent: %w", err)
}
fmt.Printf("Agent %s registered (manages: %s)\n", a.opts.Name, strings.Join(a.opts.Services, ", "))
ch := make(chan struct{})
<-ch
return nil
}
func (a *agentImpl) Stop() error {
if a.server != nil {
return a.server.Stop()
}
return nil
}
func (a *agentImpl) discoverTools() ([]ai.Tool, error) {
all, err := a.tools.Discover()
if err != nil {
return nil, err
}
var scoped []ai.Tool
for _, t := range all {
if strings.HasPrefix(t.OriginalName, a.opts.Name+".") {
continue
}
if len(a.opts.Services) == 0 {
scoped = append(scoped, t)
continue
}
for _, svc := range a.opts.Services {
if strings.HasPrefix(t.OriginalName, svc+".") {
scoped = append(scoped, t)
break
}
}
}
return scoped, nil
}
func (a *agentImpl) buildPrompt() string {
if a.opts.Prompt != "" {
return a.opts.Prompt
}
if len(a.opts.Services) > 0 {
return fmt.Sprintf("You are the %s agent. You manage these services: %s. Use the available tools to fulfill requests.",
a.opts.Name, strings.Join(a.opts.Services, ", "))
}
return fmt.Sprintf("You are the %s agent. Use the available tools to fulfill requests.", a.opts.Name)
}
func (a *agentImpl) historyKey() string {
return "agent/" + a.opts.Name + "/history"
}
func (a *agentImpl) loadHistory() {
recs, err := a.opts.Store.Read(a.historyKey())
if err != nil || len(recs) == 0 {
return
}
var messages []ai.Message
if err := json.Unmarshal(recs[0].Value, &messages); err != nil {
return
}
for _, m := range messages {
a.hist.Add(m.Role, m.Content)
}
}
func (a *agentImpl) saveHistory() {
data, err := json.Marshal(a.hist.Messages())
if err != nil {
return
}
a.opts.Store.Write(&store.Record{
Key: a.historyKey(),
Value: data,
})
}