adk-utils-go

ADK Utils Go

Utilities and implementations for Google's Agent Development Kit (ADK) in Go.

This repository provides production-ready implementations for:

• LLM Clients: OpenAI and Anthropic clients compatible with ADK
• Session Management: Redis-based session persistence
• Long-term Memory: PostgreSQL + pgvector for semantic search
• Memory Tools: Toolsets for agent-controlled memory operations
• Artifact Storage: Filesystem-based artifact persistence with versioning
• Context Guard: Automatic context window management with LLM-powered summarization

Structure

├── genai/            # LLM client implementations
│   ├── openai/       # OpenAI client (works with Ollama, OpenRouter, etc.)
│   └── anthropic/    # Anthropic Claude client
├── session/          # Session service implementations
│   └── redis/        # Redis session service
├── memory/           # Memory service implementations
│   └── postgres/     # PostgreSQL + pgvector memory service
├── tools/            # Tool and toolset implementations
│   └── memory/       # Memory toolset for agents
├── artifact/         # Artifact service implementations
│   └── filesystem/   # Filesystem artifact service (versioned, user-scoped)
├── plugin/           # ADK plugin implementations
│   └── contextguard/ # Context window management plugin + CrushRegistry
└── examples/         # Working examples

Installation

go get github.com/achetronic/adk-utils-go

LLM Clients

OpenAI Client

Works with OpenAI API and any OpenAI-compatible API (Ollama, OpenRouter, Azure OpenAI, etc.):

import genaiopenai "github.com/achetronic/adk-utils-go/genai/openai"

llmModel := genaiopenai.New(genaiopenai.Config{
    APIKey:    os.Getenv("OPENAI_API_KEY"),
    BaseURL:   "http://localhost:11434/v1", // For Ollama
    ModelName: "gpt-4o",                     // Or "qwen3:8b" for Ollama
})

agent, _ := llmagent.New(llmagent.Config{
    Name:  "assistant",
    Model: llmModel,
})

Anthropic Client

Native Anthropic Claude support:

import genaianthropic "github.com/achetronic/adk-utils-go/genai/anthropic"

llmModel := genaianthropic.New(genaianthropic.Config{
    APIKey:    os.Getenv("ANTHROPIC_API_KEY"),
    ModelName: "claude-sonnet-4-5-20250929",
})

agent, _ := llmagent.New(llmagent.Config{
    Name:  "assistant",
    Model: llmModel,
})

Custom HTTP Headers

Both clients support custom HTTP headers via HTTPOptions, useful for beta features, auth proxies, or provider-specific flags:

import "net/http"

llmModel := genaianthropic.New(genaianthropic.Config{
    APIKey:    os.Getenv("ANTHROPIC_API_KEY"),
    ModelName: "claude-sonnet-4-6-20250929",
    HTTPOptions: genaianthropic.HTTPOptions{
        Headers: http.Header{
            "anthropic-beta": []string{"context-1m-2025-08-07"},
        },
    },
})

Supported Features

Both clients support:

• Streaming and non-streaming responses
• System instructions
• Tool/function calling
• Image inputs (base64)
• Temperature, TopP, MaxTokens, StopSequences
• Usage metadata
• Custom HTTP headers (multi-value)

Session Service (Redis)

Persistent session storage with Redis:

import sessionredis "github.com/achetronic/adk-utils-go/session/redis"

sessionService, _ := sessionredis.NewRedisSessionService(sessionredis.RedisSessionServiceConfig{
    Addr:     "localhost:6379",
    Password: "",
    DB:       0,
    TTL:      24 * time.Hour,
})
defer sessionService.Close()

runner, _ := runner.New(runner.Config{
    SessionService: sessionService,
})

Memory Service (PostgreSQL + pgvector)

Long-term memory with semantic search:

import memorypostgres "github.com/achetronic/adk-utils-go/memory/postgres"

memoryService, _ := memorypostgres.NewPostgresMemoryService(ctx, memorypostgres.PostgresMemoryServiceConfig{
    ConnString: "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable",
    EmbeddingModel: memorypostgres.NewOpenAICompatibleEmbedding(memorypostgres.OpenAICompatibleEmbeddingConfig{
        BaseURL: "http://localhost:11434/v1",
        Model:   "nomic-embed-text",
    }),
})
defer memoryService.Close()

runner, _ := runner.New(runner.Config{
    MemoryService: memoryService,
})

Memory Toolset

Give agents explicit control over long-term memory:

import memorytools "github.com/achetronic/adk-utils-go/tools/memory"

memoryToolset, _ := memorytools.NewToolset(memorytools.ToolsetConfig{
    MemoryService: memoryService,
    AppName:       "my_app",
})

agent, _ := llmagent.New(llmagent.Config{
    Toolsets: []tool.Toolset{memoryToolset},
})

The toolset provides:

• search_memory: Semantic search across stored memories
• save_to_memory: Save information for future recall

Artifact Service (Filesystem)

Versioned artifact storage backed by the local filesystem. Agents can save, load, list, and delete files (code, documents, data) that are delivered to the user as downloadable content.

import artifactfs "github.com/achetronic/adk-utils-go/artifact/filesystem"

artifactService, _ := artifactfs.NewFilesystemService(artifactfs.FilesystemServiceConfig{
    BasePath: "data/artifacts",
})

// Use with ADK launcher
launcherCfg := &launcher.Config{
    SessionService:  sessionService,
    AgentLoader:     agentLoader,
    ArtifactService: artifactService,
}

Artifacts are stored at {BasePath}/{appName}/{userID}/{sessionID}/{fileName}/{version}.json. Filenames prefixed with user: are scoped to the user across all sessions, making them accessible from any conversation.

Context Guard Plugin

Automatic context window management that prevents conversations from exceeding the LLM's token limit. It works as an ADK BeforeModelCallback plugin — before every LLM call, it checks whether the conversation is approaching the limit and summarizes older messages to make room.

Strategies

Setup

The plugin requires a ModelRegistry to look up context window sizes. A built-in CrushRegistry is provided that fetches model metadata from Crush's provider.json and refreshes every 6 hours:

import "github.com/achetronic/adk-utils-go/plugin/contextguard"

// 1. Start the registry (built-in, fetches from Crush)
registry := contextguard.NewCrushRegistry()
registry.Start(ctx)
defer registry.Stop()

// 2. Create the guard and add agents
guard := contextguard.New(registry)
guard.Add("assistant", llmModel)

// 3. Pass to ADK runner
runnr, _ := runner.New(runner.Config{
    Agent:        myAgent,
    PluginConfig: guard.PluginConfig(),
})

Per-agent options are available via functional options:

guard := contextguard.New(registry)

// Threshold strategy (default) — summarizes when tokens approach the limit
guard.Add("assistant", llmModel)

// Sliding window — summarizes after N turns regardless of token count
guard.Add("researcher", llmResearcher, contextguard.WithSlidingWindow(30))

// Manual context window override — bypasses the registry for this agent
guard.Add("writer", llmWriter, contextguard.WithMaxTokens(1_000_000))

Multi-agent setup is the same API — just call Add multiple times:

guard := contextguard.New(registry)
for _, agentDef := range agents {
    guard.Add(agentDef.ID, llmMap[agentDef.ID], optsFromDef(agentDef)...)
}

Custom Model Registry

You can implement your own ModelRegistry instead of using CrushRegistry:

type myRegistry struct{}

func (r *myRegistry) ContextWindow(modelID string) int {
    windows := map[string]int{
        "claude-sonnet-4-5-20250929": 200000,
        "gpt-4o":                     128000,
    }
    if w, ok := windows[modelID]; ok {
        return w
    }
    return 128000
}

func (r *myRegistry) DefaultMaxTokens(modelID string) int {
    return 4096
}

guard := contextguard.New(&myRegistry{})
guard.Add("assistant", llmModel)

How it works

1. Before every LLM call, the plugin checks the configured strategy for the agent
2. Threshold: estimates total tokens and triggers summarization when remaining capacity drops below a safety buffer (fixed 20k for windows >200k, 20% for smaller ones)
3. Sliding window: counts Content entries since the last compaction and triggers when the limit is exceeded
4. When triggered, the conversation is split into "old" (summarized by the agent's own LLM) and "recent" (kept verbatim)
5. The summary is persisted in session state and injected on subsequent requests until the next compaction
6. Tool call chains (tool_use + tool_result) are never split mid-chain to prevent provider errors

Examples

Complete working examples in the examples/ directory:

Quick Start

# Start services
docker run -d --name postgres -e POSTGRES_PASSWORD=postgres -p 5432:5432 pgvector/pgvector:pg16
docker run -d --name redis -p 6379:6379 redis:alpine
ollama pull qwen3:8b
ollama pull nomic-embed-text

# Run an example
go run ./examples/openai-client

Environment Variables

Requirements

• Go 1.24+
• Google ADK v0.4.0+

License

Apache 2.0