Lattice helps you build AI agents in Go with clean abstractions for LLMs, tool calling, retrieval-augmented memory, and multi-agent coordination. Focus on your domain logic while Lattice handles the orchestration plumbing.

Why Lattice?

Building production AI agents requires more than just LLM calls. You need:

• Pluggable LLM providers that swap without rewriting logic
• Tool calling that works across different model APIs
• Memory systems that remember context across conversations
• Multi-agent coordination for complex workflows
• Testing infrastructure that doesn't hit external APIs

Lattice provides all of this with idiomatic Go interfaces and minimal dependencies.

Features

• 🧩 Modular Architecture – Compose agents from reusable modules with declarative configuration
• 🤖 Multi-Agent Support – Coordinate specialist agents through a shared catalog and delegation system
• 🔧 Rich Tooling – Implement the Tool interface once, use everywhere automatically
• 🧠 Smart Memory – RAG-powered memory with importance scoring, MMR retrieval, and automatic pruning
• 🔌 Model Agnostic – Adapters for Gemini, Anthropic, Ollama, or bring your own
• 📡 UTCP Ready – First-class Universal Tool Calling Protocol support

Quick Start

Installation

git clone https://github.com/Protocol-Lattice/go-agent.git
cd lattice-agent
go mod download

Basic Usage

package main

import (
	"context"
	"flag"
	"log"

	"github.com/Protocol-Lattice/go-agent/src/adk"
	adkmodules "github.com/Protocol-Lattice/go-agent/src/adk/modules"
	"github.com/Protocol-Lattice/go-agent"
	"github.com/Protocol-Lattice/go-agent/src/subagents"

	"github.com/Protocol-Lattice/go-agent/src/memory"
	"github.com/Protocol-Lattice/go-agent/src/memory/engine"
	"github.com/Protocol-Lattice/go-agent/src/models"
	"github.com/Protocol-Lattice/go-agent/src/tools"
)

func main() {
	qdrantURL := flag.String("qdrant-url", "http://localhost:6333", "Qdrant base URL")
	qdrantCollection := flag.String("qdrant-collection", "adk_memories", "Qdrant collection name")
	flag.Parse()
	ctx := context.Background()

	// --- Shared runtime
	researcherModel, err := models.NewGeminiLLM(ctx, "gemini-2.5-pro", "Research summary:")
	if err != nil {
		log.Fatalf("create researcher model: %v", err)
	}
	memOpts := engine.DefaultOptions()

	adkAgent, err := adk.New(ctx,
		adk.WithDefaultSystemPrompt("You orchestrate a helpful assistant team."),
		adk.WithSubAgents(subagents.NewResearcher(researcherModel)),
		adk.WithModules(
			adkmodules.NewModelModule("gemini-model", func(_ context.Context) (models.Agent, error) {
				return models.NewGeminiLLM(ctx, "gemini-2.5-pro", "Swarm orchestration:")
			}),
			adkmodules.InQdrantMemory(100000, *qdrantURL, *qdrantCollection, memory.AutoEmbedder(), &memOpts),
		),
	)
	if err != nil {
		log.Fatal(err)
	}

	agent, err := adkAgent.BuildAgent(ctx)
	if err != nil {
		log.Fatal(err)
	}

	// Use the agent
	resp, err := agent.Generate(ctx, "SessionID", "What is pgvector")
	if err != nil {
		log.Fatal(err)
	}

	log.Println(resp)
}

Running Examples

# Interactive CLI demo
go run cmd/demo/main.go

# Multi-agent coordination
go run cmd/team/main.go

# Quick start example
go run cmd/quickstart/main.go

Project Structure

lattice-agent/
├── cmd/
│   ├── demo/          # Interactive CLI with tools, delegation, and memory
│   ├── quickstart/    # Minimal getting-started example
│   └── team/          # Multi-agent coordination demos
├── pkg/
│   ├── adk/           # Agent Development Kit and module system
│   ├── memory/        # Memory engine and vector store adapters
│   ├── models/        # LLM provider adapters (Gemini, Ollama, Anthropic)
│   ├── subagents/     # Pre-built specialist agent personas
├── └── tools/         # Built-in tools (echo, calculator, time, etc.)

Configuration

Environment Variables

Variable	Description	Required
GOOGLE_API_KEY	Gemini API credentials	For Gemini models
GEMINI_API_KEY	Alternative to GOOGLE_API_KEY	For Gemini models
DATABASE_URL	PostgreSQL connection string	For persistent memory
ADK_EMBED_PROVIDER	Embedding provider override	No (defaults to Gemini)

Example Configuration

export GOOGLE_API_KEY="your-api-key-here"
export DATABASE_URL="postgres://user:pass@localhost:5432/lattice?sslmode=disable"
export ADK_EMBED_PROVIDER="gemini"

Core Concepts

Memory Engine

Lattice includes a sophisticated memory system with retrieval-augmented generation (RAG):

store := memory.NewInMemoryStore() // or PostgreSQL/Qdrant
engine := memory.NewEngine(store, memory.Options{}).
    WithEmbedder(yourEmbedder)

sessionMemory := memory.NewSessionMemory(
    memory.NewMemoryBankWithStore(store), 
    8, // context window size
).WithEngine(engine)

Features:

• Importance Scoring – Automatically weights memories by relevance
• MMR Retrieval – Maximal Marginal Relevance for diverse results
• Auto-Pruning – Removes stale or low-value memories
• Multiple Backends – In-memory, PostgreSQL+pgvector,mongodb, neo4j or Qdrant

Tool System

Create custom tools by implementing a simple interface:

package tools

import (
        "context"
        "fmt"
        "strings"

        "github.com/Protocol-Lattice/go-agent"
)

// EchoTool repeats the provided input. Useful for testing tool wiring.
type EchoTool struct{}

func (e *EchoTool) Spec() agent.ToolSpec {
        return agent.ToolSpec{
                Name:        "echo",
                Description: "Echoes the provided text back to the caller.",
                InputSchema: map[string]any{
                        "type": "object",
                        "properties": map[string]any{
                                "input": map[string]any{
                                        "type":        "string",
                                        "description": "Text to echo back.",
                                },
                        },
                        "required": []any{"input"},
                },
        }
}

func (e *EchoTool) Invoke(_ context.Context, req agent.ToolRequest) (agent.ToolResponse, error) {
        raw := req.Arguments["input"]
        if raw == nil {
                return agent.ToolResponse{Content: ""}, nil
        }
        return agent.ToolResponse{Content: strings.TrimSpace(fmt.Sprint(raw))}, nil
}

Register tools with the module system and they're automatically available to all agents.

Multi-Agent Coordination

Use Shared Spaces to coordinate multiple agents with shared memory

Perfect for:

• Team-based workflows where agents need shared context
• Complex tasks requiring specialist coordination
• Projects with explicit access control requirements

Why Use TOON?

Token-Oriented Object Notation (TOON) is integrated into Lattice to dramatically reduce token consumption when passing structured data to and from LLMs. This is especially critical for AI agent workflows where context windows are precious and API costs scale with token usage.

The Problem with JSON

Traditional JSON is verbose and wastes tokens on repetitive syntax. Consider passing agent memory or tool responses:

{
  "memories": [
    { "id": 1, "content": "User prefers Python", "importance": 0.9, "timestamp": "2025-01-15" },
    { "id": 2, "content": "User is building CLI tools", "importance": 0.85, "timestamp": "2025-01-14" },
    { "id": 3, "content": "User works with PostgreSQL", "importance": 0.8, "timestamp": "2025-01-13" }
  ]
}

Token count: ~180 tokens

The TOON Solution

TOON compresses the same data by eliminating redundancy:

memories[3]{id,content,importance,timestamp}:
1,User prefers Python,0.9,2025-01-15
2,User is building CLI tools,0.85,2025-01-14
3,User works with PostgreSQL,0.8,2025-01-13

Token count: ~85 tokens

Savings: ~53% fewer tokens

Why This Matters for AI Agents

1. Larger Context Windows – Fit more memories, tool results, and conversation history into the same context limit
2. Lower API Costs – Reduce your LLM API bills by up to 50% on structured data
3. Faster Processing – Fewer tokens mean faster inference times and lower latency
4. Better Memory Systems – Store and retrieve more historical context without hitting token limits
5. Multi-Agent Communication – Pass more information between coordinating agents efficiently

When TOON Shines

TOON is particularly effective for:

• Agent Memory Banks – Retrieving and formatting conversation history
• Tool Responses – Returning structured data from database queries or API calls
• Multi-Agent Coordination – Sharing state between specialist agents
• Batch Operations – Processing multiple similar records (users, tasks, logs)
• RAG Contexts – Injecting retrieved documents with metadata

Example: Memory Retrieval

When your agent queries its memory system, TOON can encode dozens of memories in the space where JSON would fit only a handful:

// Retrieve memories
memories := sessionMemory.Retrieve(ctx, "user preferences", 20)

// Encode with TOON for LLM context
encoded, _ := toon.Marshal(memories, toon.WithLengthMarkers(true))

// Pass to LLM with 40-60% fewer tokens than JSON
prompt := fmt.Sprintf("Based on these memories:\n%s\n\nAnswer the user's question.", encoded)

Human-Readable Format

Despite its compactness, TOON remains readable for debugging and development. The format explicitly declares its schema, making it self-documenting:

users[2]{id,name,role}:
1,Alice,admin
2,Bob,user

You can immediately see: 2 users, with fields id/name/role, followed by their values.

Getting Started with TOON

Lattice automatically uses TOON for internal data serialization. To use it in your custom tools or memory adapters:

import "github.com/toon-format/toon-go"

// Encode your structs
encoded, err := toon.Marshal(data, toon.WithLengthMarkers(true))

// Decode back to structs
var result MyStruct
err = toon.Unmarshal(encoded, &result)

// Or decode to dynamic maps
var doc map[string]any
err = toon.Unmarshal(encoded, &doc)

For more details, see the TOON specification.

---

Bottom Line: TOON helps your agents do more with less, turning token budgets into a competitive advantage rather than a constraint.

🧠 Tool Orchestrator (LLM-Driven UTCP Tool Selection)

The Tool Orchestrator is an intelligent decision engine that lets the LLM choose when and how to call UTCP tools. It analyzes user input, evaluates available tools, and returns a structured JSON plan describing the next action.

This brings go-agent to the same capability tier as OpenAI’s tool choice, but with fully pluggable UTCP backends and Go-native execution.

---

🎯 What It Does

• Interprets the user’s request

• Loads and renders all available UTCP tools

• Allows the LLM to reason using TOON-Go

• Produces a strict JSON decision object:

  {
    "use_tool": true,
    "tool_name": "search.files",
    "arguments": { "query": "config" },
    "reason": "User asked to look for configuration files"
  }

• Executes the chosen tool deterministically

---

⚙️ How It Works

1. Collect Tool Definitions

   rendered := a.renderUtcpToolsForPrompt()

2. Build the Orchestration Prompt

   choicePrompt := fmt.Sprintf(`
     You are a UTCP tool selection engine.
   
     A user asked:
     %q
   
     You have access to these UTCP tools:
     %s
   
     You can also discover tools dynamically using:
     search_tools("<query>", <limit>)
   
     Return ONLY JSON:
     { "use_tool": ..., "tool_name": "...", "arguments": { }, "reason": "..." }
   `, userInput, rendered)

3. LLM Makes a Decision (via TOON)

   • Coordinator executes reasoning
   • Assistant returns the final JSON only

4. Agent Executes the Tool

   • CallTool
   • SearchTools
   • CallToolStream

5. The result becomes the agent’s final response

---

🧩 Why TOON-Go Improves Tool Selection

The orchestrator uses TOON as its structured reasoning layer:

• Coordinator → analyzes tool options
• Assistant → returns the strict JSON
• No hallucinated formatting
• Easy to debug via TOON traces
• Session memory stores the entire reasoning trajectory

This yields stable, deterministic tool choice behavior.

---

🚀 Example

User

find all files containing “db connection” in the workspace

LLM Output

{
  "use_tool": true,
  "tool_name": "search.files",
  "arguments": {
    "query": "db connection",
    "limit": 20
  },
  "reason": "User wants to search through the workspace files"
}

Agent Execution

The search.files UTCP tool is invoked, and its direct output is returned to the user.

---

---

🧵 Works Seamlessly with CodeMode + Chain

CodeMode

UTCP tool calls can run inside the Go DSL:

r, _ := codemode.CallTool("echo", map[string]any{"input": "hi"})

ChainStep

The orchestrator can:

• call a tool
• run a chain step
• discover tools dynamically
• combine reasoning + execution

This makes go-agent one of the first Go frameworks with multi-step, LLM-driven tool-routing.

Development

Running Tests

# Run all tests
go test ./...

# Run with coverage
go test -cover ./...

# Run specific package tests
go test ./pkg/memory/...

Code Style

We follow standard Go conventions:

• Use gofmt for formatting
• Follow Effective Go guidelines
• Add tests for new features
• Update documentation when adding capabilities

Adding New Components

New LLM Provider:

1. Implement the models.LLM interface in pkg/models/
2. Add provider-specific configuration
3. Update documentation and examples

New Tool:

1. Implement agent.Tool interface in pkg/tools/
2. Register with the tool module system
3. Add tests and usage examples

New Memory Backend:

1. Implement memory.VectorStore interface
2. Add migration scripts if needed
3. Update configuration documentation

Prerequisites

• Go 1.22+ (1.25 recommended)
• PostgreSQL 15+ with pgvector extension (optional, for persistent memory)
• API Keys for your chosen LLM provider

PostgreSQL Setup (Optional)

For persistent memory with vector search:

CREATE EXTENSION IF NOT EXISTS vector;

The memory module handles schema migrations automatically.

Troubleshooting

Common Issues

Missing pgvector extension

ERROR: type "vector" does not exist

Solution: Run CREATE EXTENSION vector; in your PostgreSQL database.

API key errors

ERROR: authentication failed

Solution: Verify your API key is correctly set in the environment where you run the application.

Tool not found

ERROR: tool "xyz" not registered

Solution: Ensure tool names are unique and properly registered in your tool catalog.

Getting Help

• Check existing GitHub Issues
• Review the examples for common patterns
• Join discussions in GitHub Discussions

Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Update documentation
5. Submit a pull request

Please ensure:

• Tests pass (go test ./...)
• Code is formatted (gofmt)
• Documentation is updated
• Commit messages are clear

License

This project is licensed under the Apache 2.0 License.

Acknowledgments

• Inspired by Google's Agent Development Kit (Python)

---

Star us on GitHub if you find Lattice useful! ⭐