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π Quick Start
Get up and running with GoLangGraph in minutes. Build your first AI agent workflow with just a few lines of code.
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π Graph Workflows
Design complex AI workflows as directed graphs. Each node represents a computational unit, edges define execution flow.
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πΎ Persistence & RAG
Built-in support for PostgreSQL, Redis, and vector databases. Perfect for RAG applications and long-running workflows.
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π§ Rich Tooling
Comprehensive debugging, visualization, and monitoring tools. Built-in support for popular LLM providers.
π― 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.
π‘ Perfect for: Building production-ready AI applications that require reliability, performance, and scalability.
β¨ Key Features¶
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π Graph-Based Execution
Design workflows as directed graphs with nodes and edges. Build complex logic with simple, composable components.
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π State Management
Thread-safe state containers with automatic persistence. Never lose your workflow progress.
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π€ Multi-Agent Support
Build complex multi-agent systems with ease. Coordinate multiple AI agents working together.
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ποΈ Database Integration
Native support for PostgreSQL, Redis, and vector databases. Perfect for RAG applications.
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π§ Rich Tooling
Comprehensive debugging, visualization, and monitoring tools built right in.
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β‘ High Performance
Optimized for production workloads with comprehensive benchmarking and Go's native concurrency.
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π Type Safety
Full Go type safety with comprehensive error handling. Catch issues at compile time.
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π³ Production Ready
Docker support, CI/CD pipelines, monitoring, and everything you need for production.
π Quick Example¶
package main
import (
"context"
"fmt"
"log"
"github.com/piotrlaczkowski/GoLangGraph/pkg/builder"
"github.com/piotrlaczkowski/GoLangGraph/pkg/llm"
)
func main() {
// π€ Create a simple chat agent
agent := builder.OneLineChat("MyAgent")
// π 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)
// π Execute the agent
ctx := context.Background()
response, err := agent.Execute(ctx, "Hello, world! π")
if err != nil {
log.Fatal(err)
}
fmt.Printf("π€ Agent Response: %s\n", response.Content)
}
ποΈ Architecture Overview¶
graph TB
A[π€ User Input] --> B[π€ Agent]
B --> C[π Graph Execution]
C --> D[π Node Processing]
D --> E[π LLM Provider]
D --> F[π§ Tools]
D --> G[πΎ State Management]
G --> H[ποΈ Persistence Layer]
H --> I[π Database]
E --> J[β¨ Response]
F --> J
J --> K[π€ User Output]
style A fill:#e1f5fe
style K fill:#e8f5e8
style B fill:#fff3e0
style C fill:#f3e5f5
style G fill:#fce4ec
π― Use Cases¶
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π€ AI Agents
Build intelligent agents that can reason, plan, and execute complex tasks using various LLM providers.
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π RAG Applications
Create sophisticated Retrieval-Augmented Generation systems with vector database integration.
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π€ Multi-Agent Systems
Design workflows where multiple specialized agents collaborate to solve complex problems.
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π Data Processing Pipelines
Build intelligent data processing workflows that can adapt and make decisions based on content.
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π οΈ Automation Workflows
Create smart automation systems that can handle exceptions and make intelligent decisions.
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π API Orchestration
Coordinate complex API interactions with intelligent error handling and retry logic.
π Community & Support¶
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β GitHub
Star the project, report issues, and contribute to the codebase. Join our growing community!
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π¬ Discord
Join our community for real-time discussions, support, and collaboration with other developers.
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π Documentation
Comprehensive guides, examples, and API reference to help you build amazing AI workflows.
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π Issues
Report bugs, request features, and get help from the community. We're here to help!
π Why Choose GoLangGraph?¶
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β‘ Performance First
Built with Go's performance and concurrency in mind. Optimized for production workloads with comprehensive benchmarking.
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π¨βπ» Developer Experience
Clean, intuitive API with excellent error handling and debugging tools. Comprehensive documentation and examples.
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π Production Ready
Battle-tested with comprehensive test coverage, CI/CD pipelines, and production deployment guides.
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π§ Extensible
Plugin architecture allows easy extension with custom tools, LLM providers, and persistence backends.
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π Secure
Built-in security features including input validation, SQL injection prevention, and secure credential handling.
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π Open Source
MIT licensed with an active community. Contribute, customize, and build upon our foundation.
π Performance Highlights¶
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π Fast Execution
1.2ms average graph execution time 120ns state operations Concurrent node processing
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πΎ Memory Efficient
512B per operation 8 allocs per execution Optimized state management
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π Scalable
Connection pooling Streaming execution Distributed processing ready
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π Benchmarked
Comprehensive performance testing Production-validated metrics Continuous optimization
πΊοΈ Roadmap¶
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π v1.1 - Enhanced RAG
- π Advanced vector search
- π Multi-modal embeddings
- π§ Improved retrieval strategies
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π v1.2 - Multi-Modal
- πΌοΈ Image processing
- π΅ Audio support
- πΉ Video analysis
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π v1.3 - Distributed
- βοΈ Cloud deployment
- π Horizontal scaling
- π Multi-region support
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π¨ v1.4 - Visual Editor
- π₯οΈ Web-based editor
- π Real-time visualization
- π― Drag-and-drop workflows
π 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 }π Built with β€οΈ by the GoLangGraph Team
β Star us on GitHub β’ π Report Bug β’ π¬ Request Feature