GraphBit

High Performance AI Agent Framework

GraphBit Documentation¶

Welcome to the comprehensive documentation for GraphBit - a high-performance AI agent framework that combines Rust's performance with Python's ease of use.

Quick Navigation¶

🚀 Getting Started¶

• Installation Guide (Python) - Install GraphBit for Python
• Installation Guide (JS/TS) - Install GraphBit for Node.js
• Dependency Installation - Install dependencies for different scenarios
• Quick Start (Python) - Build your first workflow in 5 minutes
• Quick Start (JS/TS) - Build your first agent in 5 minutes
• Basic Examples - Simple examples to get you started

📚 User Guide¶

• Core Concepts - Understand workflows, agents, and nodes
• Workflow Builder - Creating and connecting workflow nodes
• Agent Configuration (Python) - Setting up AI agents with different capabilities
• Agent Configuration (JS/TS) - Agent configuration for JavaScript/TypeScript
• Document Loader (Python) - Loading and processing documents (PDF, DOCX, TXT, etc.)
• Document Loader (JS/TS) - Document loading for JavaScript/TypeScript
• Text Splitters (Python) - Processing large documents with various splitting strategies
• Text Splitters (JS/TS) - Text splitting for JavaScript/TypeScript
• LLM Providers (Python) - Working with OpenAI, Anthropic, Ollama, and more
• LLM Providers (JS/TS) - LLM configuration for JavaScript/TypeScript
• Embeddings (Python) - Text embeddings and similarity search
• Embeddings (JS/TS) - Embeddings for JavaScript/TypeScript
• Data Validation (Python) - Input validation and data quality checks
• Data Validation (JS/TS) - JSON validation for JavaScript/TypeScript
• Dynamic Graph Generation - Auto-generating workflow structures
• Performance Optimization - Tuning for speed and efficiency
• Monitoring & Observability - Metrics collection and debugging
• Reliability & Fault Tolerance - Circuit breakers, retries, and error handling

🔧 API Reference¶

• Python API - Complete Python API documentation
• Configuration Options - All configuration parameters
• Node Types - Agent nodes

🔗 Connectors & Integrations¶

• AWS Boto3 - Amazon Web Services integration
• Azure - Microsoft Azure services integration
• Google Cloud Platform - Google Cloud services integration
• Vector Databases - Pinecone, Qdrant, ChromaDB, and more

🛠️ Development¶

• Architecture Overview - System design and components
• Contributing Guide - How to contribute to GraphBit
• Python Bindings - Python-Rust integration details
• Debugging - Troubleshooting and debugging workflows

📋 Examples & Use Cases¶

• Content Generation Pipeline - Multi-agent content creation
• Data Processing Workflow - ETL pipelines with AI agents
• LLM Integration - Working with different language models
• Semantic Search - Building intelligent search systems
• Comprehensive Pipeline - End-to-end workflow examples

What is GraphBit?¶

GraphBit is a declarative framework for building reliable AI agent workflows with strong type safety, comprehensive error handling, and predictable performance. It features:

• 🔒 Type Safety - Strong typing throughout the execution pipeline
• 🛡️ Reliability - Circuit breakers, retry policies, and error handling
• 🤖 Multi-LLM Support - OpenAI, Anthropic, Ollama
• ⚡ Performance - Rust core with Python bindings for optimal speed
• 📊 Observability - Built-in metrics and execution tracing
• 🔧 Resource Management - Concurrency controls and memory optimization

Architecture¶

GraphBit uses a three-tier architecture:

Community & Support¶

• GitHub: github.com/InfinitiBit/graphbit
• Issues: Report bugs and request features
• Discussions: Ask Questions and Share ideas
• Contributing: See Our contributing guide

Ready to build your first AI workflow? Start with GraphBit's Quick Start Tutorial!