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# GraphBit - High Performance Agentic Framework

**Type-Safe AI Agent Workflows with Rust Performance**

Installation¶

pip install graphbit

Quick Start¶

import os
from graphbit import init, LlmClient, LlmConfig

# Initialize GraphBit
init()

# Configure LLM provider
config = LlmConfig.openai(
    os.getenv("OPENAI_API_KEY"),
    "gpt-4o-mini"
)

# Create client and generate completion
client = LlmClient(config)
response = client.complete("What is AI?", 100)
print(response)

Features¶

🤖 Multi-LLM Support - OpenAI, Anthropic, Ollama
🔄 Workflow Automation - Build complex agentic workflows
🛠️ Tool Integration - Function calling and tool execution
📄 Document Processing - PDF, DOCX, text splitting
🎯 Type-Safe - Full Type Hints support
⚡ High Performance - Native Rust implementation via PyO3

Core APIs¶

LLM Client¶

client = LlmClient(config)
client.complete(prompt, max_tokens)
client.complete_batch(prompts, max_tokens, temperature, concurrency)

Workflows¶

workflow = Workflow("My Workflow")
    .description("Description")
    .build()

executor = Executor(llm_config)
result = executor.execute(workflow)

Embeddings¶

emb_client = EmbeddingClient(emb_config)
# Embedding logic...

Document Processing¶

loader = DocumentLoader()
doc = loader.load_file("./document.pdf", "pdf")

splitter = TextSplitter.recursive(500, 50)
chunks = splitter.split(doc.content)

Tool Calling (with Async Support)¶

from graphbit import ToolRegistry

registry = ToolRegistry()
# Sync/Async tool registration...

Documentation¶

Requirements¶

Python ≥ 3.10
Supported platforms: Windows, macOS, Linux (x64, ARM64)

License¶

Apache License, Version 2.0.

Links¶

🚀 Getting Started¶

Installation Guide (Python) - Install GraphBit for Python
Dependency Installation - Install dependencies for different scenarios
Quick Start (Python) - Build your first workflow 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
Document Loader (Python) - Loading and processing documents (PDF, DOCX, TXT, etc.)
Text Splitters (Python) - Processing large documents with various splitting strategies
LLM Providers (Python) - Working with OpenAI, Anthropic, Ollama, and more
Embeddings (Python) - Text embeddings and similarity search
Data Validation (Python) - Input validation and data quality checks
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:

GraphBit 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!