Go SDK

Use docker-agent as a Go library to embed AI agents in your applications.

Overview

docker-agent can be used as a Go library, allowing you to build AI agents directly into your Go applications. This gives you full programmatic control over agent creation, tool integration, and execution.

Import Path

import "github.com/docker/docker-agent/pkg/..."

Core Packages

Package	Purpose
`pkg/agent`	Agent creation and configuration
`pkg/runtime`	Agent execution and event streaming
`pkg/session`	Conversation state management
`pkg/team`	Multi-agent team composition
`pkg/tools`	Tool interface and utilities
`pkg/tools/builtin`	Built-in tools (shell, filesystem, etc.)
`pkg/model/provider/*`	Model provider clients
`pkg/config/latest`	Configuration types
`pkg/environment`	Environment and secrets
`pkg/embeddedchat`	Headless chat session for embedding the agent runtime in a custom UI
`pkg/tui/components/toolconfirm`	Tool-confirmation policy: `Decision` enum, `BuildPermissionPattern`, key bindings, and rejection-reason presets. Share this instead of copying the permission-pattern logic.
`pkg/tui/service`	`StaticSessionState` — a `SessionStateReader` with conservative fixed values, for rendering message/tool views outside the full TUI app. Replaces hand-rolled nine-method stubs.
`pkg/tui/animation`	`Stopper` / `StopView` — animation lifecycle contract. Call `StopAnimation` on views removed from the UI to prevent leaked tick subscriptions.
`pkg/tui/components/transcript`	Embedded transcript view with read-only `Messages()` accessor for observing conversation structure in host tests and persistence layers.

Embedding TUI Components

When building custom UIs on top of docker-agent’s TUI primitives, four packages define the contracts that keep the runtime and the UI in sync:

pkg/tui/components/toolconfirm — import this package for the permission-decision policy rather than copying the pattern-building logic. The Decision enum, BuildPermissionPattern helper, and rejection-reason presets are the canonical source of truth: whatever pattern is shown to the user in the confirmation dialog is exactly the pattern granted to the runtime.
pkg/tui/service — use StaticSessionState as a stub SessionStateReader when rendering individual message or tool views outside the full TUI app. It returns conservative fixed values for all nine interface methods, eliminating the need for hand-rolled stubs.
pkg/tui/animation — implement animation.Stopper on any view that owns a tick-based animation. Call StopAnimation whenever a view is removed from the UI hierarchy to prevent leaked time.Tick subscriptions from firing against a dead view.
pkg/tui/components/transcript — embed the transcript view for displaying conversation history. Use the Messages() method to read the current slice of transcript messages (treat as read-only — mutations desync renders). This is useful for host-side tests asserting on chat history, and for persistence layers that need to snapshot conversation state.

Headless Embedded Chat (`pkg/embeddedchat`)

pkg/embeddedchat is a thin wrapper around the docker-agent runtime that lets you drive an agent from your own UI instead of running docker-agent’s Bubble Tea application. It handles runtime construction, event projection, and conversation state, exposing a simple Send / Confirm / Restart / Close API.

Creating a session

import (
    "context"
    "fmt"
    "strings"

    dagentcfg "github.com/docker/docker-agent/pkg/config"
    dagentruntime "github.com/docker/docker-agent/pkg/runtime"
    "github.com/docker/docker-agent/pkg/embeddedchat"
)

chat, err := embeddedchat.New(ctx, embeddedchat.Config{
    // AgentSource can be a file path, raw YAML bytes, or an OCI reference.
    AgentSource: dagentcfg.NewBytesSource("agent", []byte(agentYAML)),
})
if err != nil {
    return err
}
defer chat.Close()

Sending a message and reading events

Send appends the user message to the conversation and returns a channel of Event values. Drain the channel until it closes.

events, err := chat.Send(ctx, "Hello! What can you do?")
if err != nil {
    return err
}

var response strings.Builder
for ev := range events {
    switch {
    case ev.Text != "":
        response.WriteString(ev.Text)
    case ev.Tool != nil && ev.Tool.NeedsConfirmation:
        // Approve the pending tool call (use ResumeApproveSession to allow all).
        if err := chat.Confirm(ctx, dagentruntime.ResumeApprove()); err != nil {
            return err
        }
    case ev.Tool != nil && ev.Tool.Finished:
        fmt.Printf("[tool %s finished]\n", ev.Tool.Def.Name)
    case ev.Err != nil:
        fmt.Printf("error: %v\n", ev.Err)
    case ev.Done:
        fmt.Println("\n[turn complete]")
    }
}
fmt.Print(response.String())

Restarting the conversation

To start a fresh conversation without recreating the runtime:

if err := chat.Restart(); err != nil {
    return err
}

Event types

Field	When set
`Text`	Assistant text delta; accumulate into a string for the full reply.
`Tool`	A tool call started, needs confirmation, or finished.
`Tool.NeedsConfirmation`	Runtime is blocked until `Confirm` is called.
`Tool.Finished`	Tool call completed; `Tool.IsError` is true if it errored.
`Err`	A user-facing runtime error; no further content events follow.
`Done`	Clean end of turn; no more events.
`RuntimeEvent`	The original `runtime.Event` for callers that need the full stream.

For advanced use (custom elicitation, raw event inspection), call chat.Runtime() to access the underlying runtime.Runtime directly.

Optional Provider Build Tags

By default docker-agent includes all four cloud providers (OpenAI, Anthropic, Google, Amazon Bedrock). When embedding docker-agent in your own binary you can compile out unneeded providers — together with their transitive SDK dependencies — to reduce binary size.

Each provider is gated by a negative build tag prefixed docker_agent_ to avoid collisions with your own project’s tags:

Build tag	Provider dropped	Major dependency removed
`docker_agent_no_openai`	OpenAI	`github.com/openai/openai-go`
`docker_agent_no_anthropic`	Anthropic	`github.com/anthropics/anthropic-sdk-go` (partial — see note)
`docker_agent_no_google`	Google / Vertex AI	`google.golang.org/genai`, Vertex auth stack, and indirectly the Anthropic and OpenAI SDKs via Vertex Model Garden
`docker_agent_no_bedrock`	Amazon Bedrock	`github.com/aws/aws-sdk-go-v2` stack (the largest provider dependency tree)

To build without Bedrock and OpenAI:

go build -tags 'docker_agent_no_bedrock docker_agent_no_openai' ./...

Requesting a model whose provider was compiled out fails at construction time with a clear "not compiled into this build" error. The dmr (Docker Model Runner) provider and the rule-based router are always compiled in.

Anthropic + Google dependency

The Google provider's Vertex Model Garden support also imports the Anthropic SDK, so the Anthropic dependency is only fully removed when both docker_agent_no_anthropic and docker_agent_no_google are set.

RAG Toolset (cgo-free builds)

The RAG toolset (type: rag) uses a tree-sitter code parser that requires cgo. When building without cgo — or when you want to drop the cgo dependency entirely — do not import the pkg/rag package in your binary.

By default the RAG toolset is opt-in: it is only linked when you blank-import its package:

import (
    _ "github.com/docker/docker-agent/pkg/tools/builtin/rag" // register RAG toolset
)

Without this import, a config that declares type: rag fails with a “toolset type not registered” error at startup. If your application does not use RAG, simply omit the blank import; the rest of docker-agent works without cgo.

Basic Example

Create a simple agent and run it:

package main

import (
    "context"
    "fmt"
    "log"
    "os/signal"
    "syscall"

    "github.com/docker/docker-agent/pkg/agent"
    "github.com/docker/docker-agent/pkg/config/latest"
    "github.com/docker/docker-agent/pkg/environment"
    "github.com/docker/docker-agent/pkg/model/provider/openai"
    "github.com/docker/docker-agent/pkg/runtime"
    "github.com/docker/docker-agent/pkg/session"
    "github.com/docker/docker-agent/pkg/team"
)

func main() {
    ctx, cancel := signal.NotifyContext(context.Background(),
        syscall.SIGINT, syscall.SIGTERM)
    defer cancel()

    if err := run(ctx); err != nil {
        log.Fatal(err)
    }
}

func run(ctx context.Context) error {
    // Create model provider
    llm, err := openai.NewClient(
        ctx,
        &latest.ModelConfig{
            Provider: "openai",
            Model:    "gpt-4o",
        },
        environment.NewDefaultProvider(),
    )
    if err != nil {
        return err
    }

    // Create agent
    assistant := agent.New(
        "root",
        "You are a helpful assistant.",
        agent.WithModel(llm),
        agent.WithDescription("A helpful assistant"),
    )

    // Create team and runtime
    t := team.New(team.WithAgents(assistant))
    rt, err := runtime.New(t)
    if err != nil {
        return err
    }

    // Run with a user message
    sess := session.New(
        session.WithUserMessage("What is 2 + 2?"),
    )

    messages, err := rt.Run(ctx, sess)
    if err != nil {
        return err
    }

    // Print the response
    fmt.Println(messages[len(messages)-1].Message.Content)
    return nil
}

Custom Tools

Define custom tools for your agent:

package main

import (
    "context"
    "encoding/json"
    "fmt"

    "github.com/docker/docker-agent/pkg/tools"
)

// Define the tool's input schema
type AddNumbersArgs struct {
    A int `json:"a"`
    B int `json:"b"`
}

// Implement the tool handler
func addNumbers(_ context.Context, toolCall tools.ToolCall) (*tools.ToolCallResult, error) {
    var args AddNumbersArgs
    if err := json.Unmarshal([]byte(toolCall.Function.Arguments), &args); err != nil {
        return nil, err
    }

    result := args.A + args.B
    return tools.ResultSuccess(fmt.Sprintf("%d", result)), nil
}

func main() {
    // Create the tool definition
    addTool := tools.Tool{
        Name:        "add",
        Category:    "math",
        Description: "Add two numbers together",
        Parameters:  tools.MustSchemaFor[AddNumbersArgs](),
        Handler:     addNumbers,
    }

    // Use with an agent
    calculator := agent.New(
        "root",
        "You are a calculator. Use the add tool for arithmetic.",
        agent.WithModel(llm),
        agent.WithTools(addTool),
    )
    // ...
}

Streaming Responses

Process events as they happen:

func runStreaming(ctx context.Context, rt runtime.Runtime, sess *session.Session) error {
    events := rt.RunStream(ctx, sess)

    for event := range events {
        switch e := event.(type) {
        case *runtime.StreamStartedEvent:
            fmt.Println("Stream started")

        case *runtime.AgentChoiceEvent:
            // Print response chunks as they arrive
            fmt.Print(e.Content)

        case *runtime.ToolCallEvent:
            fmt.Printf("\n[Tool call: %s]\n", e.ToolCall.Function.Name)

        case *runtime.ToolCallConfirmationEvent:
            // Auto-approve tool calls
            rt.Resume(ctx, runtime.ResumeRequest{
                Type: runtime.ResumeTypeApproveSession,
            })

        case *runtime.ToolCallResponseEvent:
            fmt.Printf("[Tool response: %s]\n", e.Response)

        case *runtime.StreamStoppedEvent:
            fmt.Println("\nStream stopped")

        case *runtime.ErrorEvent:
            return fmt.Errorf("error: %s", e.Error)
        }
    }

    return nil
}

Multi-Agent Teams

Create agents that delegate to sub-agents:

package main

import (
    "github.com/docker/docker-agent/pkg/agent"
    "github.com/docker/docker-agent/pkg/team"
    "github.com/docker/docker-agent/pkg/tools/builtin"
)

func createTeam(llm provider.Provider) *team.Team {
    // Create a child agent
    researcher := agent.New(
        "researcher",
        "You research topics thoroughly.",
        agent.WithModel(llm),
        agent.WithDescription("Research specialist"),
    )

    // Create root agent with sub-agents
    coordinator := agent.New(
        "root",
        "You coordinate research tasks.",
        agent.WithModel(llm),
        agent.WithDescription("Team coordinator"),
        agent.WithSubAgents(researcher),
        agent.WithToolSets(builtin.NewTransferTaskTool()),
    )

    return team.New(team.WithAgents(coordinator, researcher))
}

Built-in Tools

Use docker-agent’s built-in tools:

import (
    "github.com/docker/docker-agent/pkg/config"
    "github.com/docker/docker-agent/pkg/tools/builtin"
)

func createAgentWithBuiltinTools(llm provider.Provider) *agent.Agent {
    // Runtime config for tools that need it
    rtConfig := &config.RuntimeConfig{
        Config: config.Config{
            WorkingDir: "/path/to/workdir",
        },
    }

    return agent.New(
        "root",
        "You are a developer assistant.",
        agent.WithModel(llm),
        agent.WithToolSets(
            // Shell tool for running commands
            builtin.NewShellTool(os.Environ(), rtConfig),
            // Filesystem tools
            builtin.NewFilesystemTool(rtConfig.Config.WorkingDir),
            // Think tool for reasoning
            builtin.NewThinkTool(),
            // Todo tool for task tracking
            builtin.NewTodoTool(),
        ),
    )
}

HTTP Middleware / Transport Wrappers

Use options.WithHTTPTransportWrapper to inject HTTP middleware into the transport chain of all provider clients built by docker-agent. This is useful for request tracing, injecting custom headers, collecting metrics, or any other cross-cutting concern at the HTTP layer.

import (
    "net/http"

    "github.com/docker/docker-agent/pkg/model/provider/options"
)

type headerTransport struct {
    base http.RoundTripper
}

func (t *headerTransport) RoundTrip(req *http.Request) (*http.Response, error) {
    req = req.Clone(req.Context())
    req.Header.Set("X-Request-Source", "my-app")
    return t.base.RoundTrip(req)
}

// Example: add a custom header to every outbound LLM request
wrapper := options.WithHTTPTransportWrapper(
    func(base http.RoundTripper) http.RoundTripper {
        return &headerTransport{base: base}
    },
)

client, err := openai.NewClient(ctx, &latest.ModelConfig{
    Provider: "openai",
    Model:    "gpt-4o",
}, env, wrapper)

The wrapper receives the already-instrumented transport (OpenTelemetry, SSE decompression, Desktop proxy support) as its base argument, so wrapping it preserves all built-in behaviour.

Supported providers: Anthropic, OpenAI, Gemini (GeminiAPI backend), Bedrock. Works in both direct and gateway/proxy mode.

Vertex AI not supported

Vertex AI uses an ADC-managed HTTP client that docker-agent cannot intercept. When a transport wrapper is set, docker-agent falls back to the GeminiAPI backend instead of Vertex AI — a debug message is logged.

In gateway mode the wrapper is called on every LLM request because gateway clients are rebuilt each call for short-lived auth tokens. In direct mode it is called once at client construction. Rate-limit responses (HTTP 429) are classified as non-retryable by the runtime and cause the model chain to skip to the next fallback, so wrappers that track per-request outcomes will observe these as failures rather than retried calls.

Returning nil from your wrapper function is not allowed; docker-agent logs a warning and keeps the original transport instead.

Using Different Providers

import (
    "github.com/docker/docker-agent/pkg/model/provider/anthropic"
    "github.com/docker/docker-agent/pkg/model/provider/gemini"
    "github.com/docker/docker-agent/pkg/model/provider/openai"
)

// OpenAI
openaiClient, _ := openai.NewClient(ctx, &latest.ModelConfig{
    Provider: "openai",
    Model:    "gpt-4o",
}, env)

// Anthropic
anthropicClient, _ := anthropic.NewClient(ctx, &latest.ModelConfig{
    Provider: "anthropic",
    Model:    "claude-sonnet-4-5",
}, env)

// Google Gemini
geminiClient, _ := gemini.NewClient(ctx, &latest.ModelConfig{
    Provider: "google",
    Model:    "gemini-3.5-flash",
}, env)

Session Options

import "github.com/docker/docker-agent/pkg/session"

sess := session.New(
    // Set a title for the session
    session.WithTitle("Code Review Task"),

    // Add user message
    session.WithUserMessage("Review this code for bugs"),

    // Limit iterations
    session.WithMaxIterations(20),
)

Error Handling

messages, err := rt.Run(ctx, sess)
if err != nil {
    if errors.Is(err, context.Canceled) {
        // User cancelled
        log.Println("Operation cancelled")
        return nil
    }
    if errors.Is(err, context.DeadlineExceeded) {
        // Timeout
        log.Println("Operation timed out")
        return nil
    }
    // Other error
    return fmt.Errorf("runtime error: %w", err)
}

// Check for errors in the event stream
for event := range rt.RunStream(ctx, sess) {
    if errEvent, ok := event.(*runtime.ErrorEvent); ok {
        return fmt.Errorf("stream error: %s", errEvent.Error)
    }
}

Complete Example

See the examples/golibrary directory for complete working examples:

simple/ — Basic agent with no tools
tool/ — Custom tool implementation
stream/ — Streaming event handling
multi/ — Multi-agent with sub-agents
builtintool/ — Using built-in tools

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Go SDK

Overview

Core Packages

Embedding TUI Components

Headless Embedded Chat (pkg/embeddedchat)

Creating a session

Sending a message and reading events

Restarting the conversation

Event types

Optional Provider Build Tags

RAG Toolset (cgo-free builds)

Basic Example

Custom Tools

Streaming Responses

Multi-Agent Teams

Built-in Tools

HTTP Middleware / Transport Wrappers

Using Different Providers

Session Options

Error Handling

Complete Example

Headless Embedded Chat (`pkg/embeddedchat`)