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Asim Aslam 888dbbca4a
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Refactor AI tool handling and enhance CLI command documentation (#2920)
* refactor(ai): rename ToolSet to Tools, simplify wiring with WithTools

Move tool discovery/execution fully into the ai package as ai.Tools
(formerly ai.ToolSet), and simplify the usage model:

- NewTools(reg, ai.ToolClient(c)) takes the execution client as an
  option instead of threading it through Handler(c) per call
- New ai.WithTools(tools) option wires the tool handler into a model
  in one call, replacing ai.WithToolHandler(set.Handler(c))
- ai.DiscoverTools(reg) for one-shot discovery

Before:
  set := ai.NewToolSet(reg)
  list, _ := set.Discover()
  m := ai.New(p, ai.WithToolHandler(set.Handler(client)))

After:
  tools := ai.NewTools(reg, ai.ToolClient(client))
  list, _ := tools.Discover()
  m := ai.New(p, ai.WithTools(tools))

Update ai/flow, micro chat, README, ai integration doc, Atlas Cloud
guide, and blog posts 3/8/9/10.

* feat(cli): add per-interface commands (registry, broker, store, config)

Map go-micro's core interfaces onto the CLI so the framework's
building blocks are inspectable and manipulable from the terminal:

  micro registry list/get/watch       service discovery
  micro broker publish/subscribe      pub/sub messaging
  micro store read/write/delete/list  persistence
  micro config get/dump               dynamic config (from env)

Structured pluggably in cmd/micro/resource: each interface is one
file exposing a Command() func, all wired through a commandFuncs
slice in resource.go. Adding a new resource command is a single
file plus one slice entry. Shared printJSON/fail helpers keep
output and errors consistent across commands.

Each command's verbs mirror the interface methods. Output is JSON
for structured data, raw for single values. Update README and
getting-started with an "inspecting the framework" section.

* docs: update CLI README with all new commands

Add documentation for commands that were missing from the CLI README:
- micro new --template (crud, pubsub, api)
- micro api (standalone HTTP gateway)
- micro registry list/get/watch
- micro broker publish/subscribe
- micro store read/write/delete/list
- micro config get/dump
- micro chat (interactive LLM agent)
- micro flow run/exec (event-driven orchestration)
- micro mcp serve/list/test

Organized into sections: API Gateway, Inspecting the Framework
(registry, broker, store, config), and AI & Agents (chat, flow, mcp).

* refactor(ai): move History from caller to Request field

History is now pure state (no Generate method). Instead, pass it
via Request.History and call ai.Generate(ctx, model, req):

Before:
  hist := ai.NewHistory("system prompt", 50)
  resp, _ := hist.Generate(ctx, model, prompt, tools)

After:
  hist := ai.NewHistory(50)
  resp, _ := ai.Generate(ctx, model, &ai.Request{
      Prompt:       prompt,
      SystemPrompt: "system prompt",
      Tools:        tools,
      History:      hist,
  })

The model is always the thing you call. History is context you
pass in. ai.Generate() handles the bookkeeping: prepends
accumulated messages before the call, records the exchange after.

NewHistory no longer takes a system prompt (it belongs on the
Request, where it always did).

Update micro chat, ai/flow, and all blog posts/docs.

* refactor(ai): make History a plain message accumulator

History no longer has Generate or touches the model. It's just
Add/Messages/Reset/Len with truncation — a helper for building
Request.Messages across turns.

Before:
  hist := ai.NewHistory(50)
  resp, _ := ai.Generate(ctx, m, &ai.Request{History: hist, ...})

After:
  hist := ai.NewHistory(50)
  hist.Add("user", prompt)
  resp, _ := m.Generate(ctx, &ai.Request{Messages: hist.Messages(), ...})
  hist.Add("assistant", resp.Reply)

Remove History field from Request. Remove package-level
ai.Generate(ctx, model, req) wrapper — users call m.Generate()
directly, which is the interface method. History is a convenience
for accumulating messages, not a participant in generation.

Update micro chat, ai/flow, blog posts 9 and 10.

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-05-30 16:20:38 +01:00
..

AI Package

The ai package provides simple, high-level interfaces for AI model providers. It supports text generation (Model), image generation (ImageModel), and video generation (VideoModel).

Interfaces

Text Generation (Model)

The Model interface follows the same patterns as other go-micro packages (Registry, Client, Broker):

type Model interface {
    Init(...Option) error
    Options() Options
    Generate(ctx context.Context, req *Request, opts ...GenerateOption) (*Response, error)
    Stream(ctx context.Context, req *Request, opts ...GenerateOption) (Stream, error)
    String() string
}

Quick Start

import (
    "context"
    "go-micro.dev/v5/ai"
    _ "go-micro.dev/v5/ai/anthropic"
    _ "go-micro.dev/v5/ai/openai"
)

// Create a model
m := ai.New("openai",
    ai.WithAPIKey("your-api-key"),
    ai.WithModel("gpt-4o"),
)

// Generate a response
req := &ai.Request{
    Prompt:       "What is Go?",
    SystemPrompt: "You are a helpful programming assistant",
}

resp, err := m.Generate(context.Background(), req)
if err != nil {
    log.Fatal(err)
}

fmt.Println(resp.Reply)

Image Generation (ImageModel)

type ImageModel interface {
    GenerateImage(ctx context.Context, req *ImageRequest, opts ...GenerateOption) (*ImageResponse, error)
    String() string
}
import (
    "go-micro.dev/v5/ai"
    _ "go-micro.dev/v5/ai/atlascloud"
)

ig := ai.NewImage("atlascloud",
    ai.WithAPIKey("your-api-key"),
)

resp, err := ig.GenerateImage(context.Background(), &ai.ImageRequest{
    Prompt: "A Go gopher in space",
    Size:   "1024x1024",
})

fmt.Println(resp.Images[0].URL)

Providers that support image generation: Atlas Cloud, OpenAI.

Video Generation (VideoModel)

type VideoModel interface {
    GenerateVideo(ctx context.Context, req *VideoRequest, opts ...GenerateOption) (*VideoResponse, error)
    String() string
}
import (
    "go-micro.dev/v5/ai"
    _ "go-micro.dev/v5/ai/atlascloud"
)

vg := ai.NewVideo("atlascloud",
    ai.WithAPIKey("your-api-key"),
)

resp, err := vg.GenerateVideo(context.Background(), &ai.VideoRequest{
    Prompt:   "Microservices nodes animating with data flowing between them",
    Images:   []string{"https://example.com/diagram.png"}, // optional: image-to-video
    Duration: 6,
})

fmt.Println(resp.URL)

Providers that support video generation: Atlas Cloud.

Options

Configure the model using functional options:

m := ai.New("anthropic",
    ai.WithAPIKey("your-key"),              // Required
    ai.WithModel("claude-sonnet-4-20250514"), // Optional, uses provider default
    ai.WithBaseURL("https://api.anthropic.com"), // Optional, uses provider default
)

You can also update options after creation:

m.Init(
    ai.WithModel("gpt-4o-mini"),
    ai.WithAPIKey("new-key"),
)

Using Tools

The model can automatically execute tool calls when provided with a tool handler:

// Define a tool handler
toolHandler := func(name string, input map[string]any) (result any, content string) {
    // Execute the tool and return results
    switch name {
    case "get_weather":
        return map[string]string{"temp": "72F"}, `{"temp": "72F"}`
    default:
        return nil, `{"error": "unknown tool"}`
    }
}

// Create model with tool handler
m := ai.New("openai",
    ai.WithAPIKey("your-key"),
    ai.WithToolHandler(toolHandler),
)

// Provide tools in the request
req := &ai.Request{
    Prompt: "What's the weather?",
    SystemPrompt: "You are a helpful assistant",
    Tools: []ai.Tool{
        {
            Name:        "get_weather",
            Description: "Get current weather",
            Properties: map[string]any{
                "location": map[string]any{
                    "type": "string",
                    "description": "City name",
                },
            },
        },
    },
}

// Generate will automatically call tools and return final answer
resp, err := m.Generate(context.Background(), req)
fmt.Println(resp.Answer) // Final answer after tool execution

Response Structure

type Response struct {
    Reply     string      // Initial reply from model
    ToolCalls []ToolCall  // Tools the model wants to call
    Answer    string      // Final answer (after tool execution if handler provided)
}
  • Reply: The model's first response
  • ToolCalls: List of tools the model requested (if any)
  • Answer: The final answer after tools are executed (only set if ToolHandler is provided)

Supported Providers

Anthropic Claude

m := ai.New("anthropic",
    ai.WithAPIKey("sk-ant-..."),
    ai.WithModel("claude-sonnet-4-20250514"), // default
)

Default model: claude-sonnet-4-20250514 Default base URL: https://api.anthropic.com

OpenAI GPT

m := ai.New("openai",
    ai.WithAPIKey("sk-..."),
    ai.WithModel("gpt-4o"), // default
)

Default model: gpt-4o Default base URL: https://api.openai.com

Google Gemini

m := ai.New("gemini",
    ai.WithAPIKey("your-key"),
    ai.WithModel("gemini-2.5-flash"), // default
)

Default model: gemini-2.5-flash Default base URL: https://generativelanguage.googleapis.com

Google Gemini uses its own API format with system_instruction, contents (not messages), and functionDeclarations for tool calling. The provider handles the translation automatically.

Groq

m := ai.New("groq",
    ai.WithAPIKey("your-key"),
    ai.WithModel("llama-3.3-70b-versatile"), // default
)

Default model: llama-3.3-70b-versatile Default base URL: https://api.groq.com/openai

Groq provides ultra-fast inference for open-weight models via an OpenAI-compatible endpoint.

Mistral

m := ai.New("mistral",
    ai.WithAPIKey("your-key"),
    ai.WithModel("mistral-large-latest"), // default
)

Default model: mistral-large-latest Default base URL: https://api.mistral.ai

Mistral AI is a European AI company offering high-performance models via an OpenAI-compatible endpoint.

Together AI

m := ai.New("together",
    ai.WithAPIKey("your-key"),
    ai.WithModel("meta-llama/Llama-3.3-70B-Instruct-Turbo"), // default
)

Default model: meta-llama/Llama-3.3-70B-Instruct-Turbo Default base URL: https://api.together.xyz

Together AI provides fast inference for open-weight models via an OpenAI-compatible endpoint.

Atlas Cloud

m := ai.New("atlascloud",
    ai.WithAPIKey("your-key"),
    ai.WithModel("llama-3.3-70b"), // default
)

Default model: llama-3.3-70b Default base URL: https://api.atlascloud.ai

Atlas Cloud is an enterprise AI infrastructure platform offering high-performance LLM APIs. It exposes an OpenAI-compatible chat completions endpoint with tool calling support.

Auto-Detection

Use AutoDetectProvider() to detect the provider from a base URL:

provider := ai.AutoDetectProvider("https://api.anthropic.com")
// Returns "anthropic"

m := ai.New(provider, ai.WithAPIKey("..."))

Adding a New Provider

See the full AI Provider Integration Guide for a step-by-step walkthrough, checklist, and design notes.

Quick summary:

  1. Create ai/yourprovider/yourprovider.go implementing ai.Model.
  2. Call ai.Register("yourprovider", ...) in init().
  3. Add tests in ai/yourprovider/yourprovider_test.go.
  4. Users enable the provider with a blank import:
import _ "go-micro.dev/v5/ai/yourprovider"

We welcome contributions and sponsorships from AI infrastructure companies — see the guide for details.

Comparison with Other Packages

The ai package follows the same patterns as other go-micro packages:

Registry:

r := registry.NewRegistry(registry.Addrs("..."))
r.Register(service)

Client:

c := client.NewClient(client.Retries(3))
c.Call(ctx, req, rsp)

AI:

m := ai.New("openai", ai.WithAPIKey("..."))
m.Generate(ctx, req)

All use:

  • Init() to update options
  • Options() to get current options
  • String() to get the implementation name
  • Functional options pattern

Testing

go test ./ai/...

Examples

See the server implementation for a complete example of using the ai package with tool execution.