🚀 GoLangGraph

Build Intelligent AI Agent Workflows with Go

🚀 Quick Start

Get up and running with GoLangGraph in minutes. Build your first AI agent workflow with just a few lines of code.

Getting Started
📊 Graph Workflows

Design AI workflows as directed graphs. Each node represents a computational unit, edges define execution flow.

Learn More
💾 Persistence

Built-in support for PostgreSQL, Redis, and memory-based persistence. Perfect for stateful applications.

Persistence Guide
🔧 Examples

Comprehensive examples showing real-world usage patterns with Ollama and other LLM providers.

View Examples

🎯 What is GoLangGraph?¶

GoLangGraph is a Go framework for building AI agent workflows with graph-based execution. It provides a clean, type-safe API for creating intelligent agents that can reason, use tools, and execute complex workflows.

💡 Perfect for: Building AI applications with local LLMs like Ollama, creating RAG systems, and developing intelligent automation tools.

✨ Key Features¶

🚀 Graph-Based Execution

Design workflows as directed graphs with nodes and edges. Build complex logic with simple, composable components.
🔄 State Management

Thread-safe state containers with persistence options. Maintain workflow state across executions.
🤖 AI Agent Framework

Built-in support for Chat, ReAct, and Tool agents. Easy integration with multiple LLM providers.
🗄️ Database Integration

Native support for PostgreSQL, Redis, and memory-based persistence. Checkpointing and session management.
🔧 Built-in Tools

Comprehensive tool library including calculator, web search, file operations, and more.
⚡ High Performance

Optimized for production workloads with Go's native concurrency and comprehensive testing.
🔒 Type Safety

Full Go type safety with comprehensive error handling. Catch issues at compile time.
🐳 Production Ready

Docker support, comprehensive testing, and everything you need for production deployment.

🏃 Quick Example¶

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/piotrlaczkowski/GoLangGraph/pkg/agent"
    "github.com/piotrlaczkowski/GoLangGraph/pkg/llm"
    "github.com/piotrlaczkowski/GoLangGraph/pkg/tools"
)

func main() {
    // Create LLM provider manager
    llmManager := llm.NewProviderManager()

    // Add Ollama provider
    provider, err := llm.NewOllamaProvider(&llm.ProviderConfig{
        Endpoint: "http://localhost:11434",
        Model:    "gemma3:1b",
    })
    if err != nil {
        log.Fatal(err)
    }
    llmManager.RegisterProvider("ollama", provider)

    // Create tool registry
    toolRegistry := tools.NewToolRegistry()

    // Create agent
    config := &agent.AgentConfig{
        Name:         "chat-agent",
        Type:         agent.AgentTypeChat,
        Model:        "gemma3:1b",
        Provider:     "ollama",
        SystemPrompt: "You are a helpful AI assistant.",
        Temperature:  0.7,
    }

    chatAgent := agent.NewAgent(config, llmManager, toolRegistry)

    // Execute
    ctx := context.Background()
    execution, err := chatAgent.Execute(ctx, "Hello! Tell me about Go programming.")
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("🤖 Agent: %s\n", execution.Output)
}

🏗️ Architecture Overview¶

graph TB
    A[👤 User Input] --> B[🤖 Agent]
    B --> C[📊 Graph Execution]
    C --> D[🔄 Node Processing]
    D --> E[🌐 LLM Provider]
    D --> F[🔧 Tools]
    D --> G[💾 State Management]
    G --> H[🗄️ Persistence Layer]
    H --> I[📊 Database]
    E --> J[✨ Response]
    F --> J
    J --> K[👤 User Output]

    style A fill:#e1f5fe
    style K fill:#e8f5e8
    style B fill:#fff3e0
    style C fill:#f3e5f5
    style G fill:#fce4ec

🎯 Use Cases¶

🤖 AI Agents

Build intelligent agents that can reason, plan, and execute tasks using various LLM providers.
🔍 RAG Applications

Create Retrieval-Augmented Generation systems with database integration for knowledge retrieval.
🤝 Multi-Agent Systems

Design workflows where multiple specialized agents collaborate to solve complex problems.
📊 Data Processing

Build intelligent data processing workflows that can adapt and make decisions based on content.
🛠️ Automation

Create smart automation systems that can handle exceptions and make intelligent decisions.

🌟 Community & Support¶

⭐ GitHub

Star the project, report issues, and contribute to the codebase. Join our growing community!

GitHub Repository
📚 Documentation

Comprehensive guides, examples, and API reference to help you build AI workflows.

Browse Docs
🐛 Issues

Report bugs, request features, and get help from the community. We're here to help!

Report Issue
💬 Discussions

Join discussions about features, usage patterns, and best practices with other developers.

Join Discussions

🚀 Why Choose GoLangGraph?¶

⚡ Performance

Built with Go's performance and concurrency in mind. Optimized for production workloads.
👨‍💻 Developer Experience

Clean, intuitive API with excellent error handling and debugging tools. Comprehensive examples.
🏭 Production Ready

Comprehensive test coverage, CI/CD pipelines, and production deployment examples.
🔧 Extensible

Easy to extend with custom tools, LLM providers, and persistence backends.
🔒 Secure

Built-in security features including input validation and secure credential handling.
🌍 Open Source

MIT licensed with an active community. Contribute, customize, and build upon our foundation.

🚀 Ready to Build Your First AI Agent?

Get started with GoLangGraph today and build intelligent AI workflows with Go!

[Get Started Now!](getting-started/quick-start.md){ .md-button .md-button--primary } [View Examples](examples/ollama-integration.md){ .md-button } [GitHub Repository](https://github.com/piotrlaczkowski/GoLangGraph){ .md-button }

🌟 Built with ❤️ by the GoLangGraph Team

⭐ Star us on GitHub • 🐛 Report Bug • 💬 Request Feature