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πŸš€ GoLangGraph

Build Intelligent AI Agent Workflows with Go


  • πŸš€ 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

  • πŸ“Š Graph Workflows


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

    Learn More

  • πŸ’Ύ Persistence & RAG


    Built-in support for PostgreSQL, Redis, and vector databases. Perfect for RAG applications and long-running workflows.

    Persistence Guide

  • πŸ”§ Rich Tooling


    Comprehensive debugging, visualization, and monitoring tools. Built-in support for popular LLM providers.

    Tools & Extensions

🎯 What is GoLangGraph?

GoLangGraph is a powerful Go framework for building AI agent workflows with graph-based execution. It provides a clean, type-safe API for creating complex multi-agent systems, RAG applications, and intelligent workflows.

πŸ’‘ Perfect for: Building production-ready AI applications that require reliability, performance, and scalability.

✨ Key Features

  • πŸš€ Graph-Based Execution


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

  • πŸ”„ State Management


    Thread-safe state containers with automatic persistence. Never lose your workflow progress.

  • πŸ€– Multi-Agent Support


    Build complex multi-agent systems with ease. Coordinate multiple AI agents working together.

  • πŸ—„οΈ Database Integration


    Native support for PostgreSQL, Redis, and vector databases. Perfect for RAG applications.

  • πŸ”§ Rich Tooling


    Comprehensive debugging, visualization, and monitoring tools built right in.

  • ⚑ High Performance


    Optimized for production workloads with comprehensive benchmarking and Go's native concurrency.

  • πŸ”’ Type Safety


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

  • 🐳 Production Ready


    Docker support, CI/CD pipelines, monitoring, and everything you need for production.

πŸƒ Quick Example

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/piotrlaczkowski/GoLangGraph/pkg/builder"
    "github.com/piotrlaczkowski/GoLangGraph/pkg/llm"
)

func main() {
    // πŸ€– Create a simple chat agent
    agent := builder.OneLineChat("MyAgent")

    // 🌐 Configure with OpenAI
    provider, err := llm.NewOpenAIProvider(llm.OpenAIConfig{
        APIKey: "your-api-key",
        Model:  "gpt-4",
    })
    if err != nil {
        log.Fatal(err)
    }

    agent.SetLLMProvider(provider)

    // πŸš€ Execute the agent
    ctx := context.Background()
    response, err := agent.Execute(ctx, "Hello, world! πŸ‘‹")
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("πŸ€– Agent Response: %s\n", response.Content)
}

πŸ—οΈ Architecture Overview

graph TB
    A[πŸ‘€ User Input] --> B[πŸ€– Agent]
    B --> C[πŸ“Š Graph Execution]
    C --> D[πŸ”„ Node Processing]
    D --> E[🌐 LLM Provider]
    D --> F[πŸ”§ Tools]
    D --> G[πŸ’Ύ State Management]
    G --> H[πŸ—„οΈ Persistence Layer]
    H --> I[πŸ“Š Database]
    E --> J[✨ Response]
    F --> J
    J --> K[πŸ‘€ User Output]

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

🎯 Use Cases

  • πŸ€– AI Agents


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

  • πŸ” RAG Applications


    Create sophisticated Retrieval-Augmented Generation systems with vector database integration.

  • 🀝 Multi-Agent Systems


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

  • πŸ“Š Data Processing Pipelines


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

  • πŸ› οΈ Automation Workflows


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

  • 🌐 API Orchestration


    Coordinate complex API interactions with intelligent error handling and retry logic.

🌟 Community & Support

  • ⭐ GitHub


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

    GitHub Repository

  • πŸ’¬ Discord


    Join our community for real-time discussions, support, and collaboration with other developers.

    Join Discord

  • πŸ“š Documentation


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

    Browse Docs

  • πŸ› Issues


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

    Report Issue

πŸš€ Why Choose GoLangGraph?

  • ⚑ Performance First


    Built with Go's performance and concurrency in mind. Optimized for production workloads with comprehensive benchmarking.

  • πŸ‘¨β€πŸ’» Developer Experience


    Clean, intuitive API with excellent error handling and debugging tools. Comprehensive documentation and examples.

  • 🏭 Production Ready


    Battle-tested with comprehensive test coverage, CI/CD pipelines, and production deployment guides.

  • πŸ”§ Extensible


    Plugin architecture allows easy extension with custom tools, LLM providers, and persistence backends.

  • πŸ”’ Secure


    Built-in security features including input validation, SQL injection prevention, and secure credential handling.

  • 🌍 Open Source


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

πŸ“Š Performance Highlights

  • πŸƒ Fast Execution


    1.2ms average graph execution time 120ns state operations Concurrent node processing

  • πŸ’Ύ Memory Efficient


    512B per operation 8 allocs per execution Optimized state management

  • πŸ”— Scalable


    Connection pooling Streaming execution Distributed processing ready

  • πŸ“ˆ Benchmarked


    Comprehensive performance testing Production-validated metrics Continuous optimization

πŸ—ΊοΈ Roadmap

  • πŸš€ v1.1 - Enhanced RAG


    • πŸ” Advanced vector search
    • πŸ“Š Multi-modal embeddings
    • 🧠 Improved retrieval strategies
  • 🎭 v1.2 - Multi-Modal


    • πŸ–ΌοΈ Image processing
    • 🎡 Audio support
    • πŸ“Ή Video analysis
  • 🌐 v1.3 - Distributed


    • ☁️ Cloud deployment
    • πŸ”„ Horizontal scaling
    • 🌍 Multi-region support
  • 🎨 v1.4 - Visual Editor


    • πŸ–₯️ Web-based editor
    • πŸ“Š Real-time visualization
    • 🎯 Drag-and-drop workflows

πŸš€ Ready to Build Your First AI Agent?

Get started with GoLangGraph today and join the future of AI workflow development!

[Get Started Now!](getting-started/quick-start.md){ .md-button .md-button--primary } [View Examples](examples/ollama-integration.md){ .md-button } [Join Community](https://discord.gg/golanggraph){ .md-button }

🌟 Built with ❀️ by the GoLangGraph Team

⭐ Star us on GitHub β€’ πŸ› Report Bug β€’ πŸ’¬ Request Feature