GraphBit - High Performance Agentic Framework

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Type-Safe AI Agent Workflows with Rust Performance

Graphbit is an industry-grade agentic AI framework built for developers and AI teams that demand stability, scalability, and low resource usage.

Written in Rust for maximum performance and safety, it delivers up to 68× lower CPU usage and 140× lower memory footprint than certain leading alternatives while consistently using far fewer resources than the rest, all while maintaining comparable throughput and execution speed. See benchmarks for more details.

Designed to run multi-agent workflows in parallel, Graphbit persists memory across steps, recovers from failures, and ensures 100% task success under load. Its lightweight, resource-efficient architecture enables deployment in both high-scale enterprise environments and low-resource edge scenarios. With built-in observability and concurrency support, Graphbit eliminates the bottlenecks that slow decision-making and erode ROI.

Key Features

• Tool Selection - LLMs intelligently select tools based on descriptions
• Type Safety - Strong typing throughout the execution pipeline
• Reliability - Circuit breakers, retry policies, and error handling
• Multi-LLM Support - OpenAI, Azure OpenAI, Anthropic, OpenRouter, DeepSeek, Replicate, Ollama, TogetherAI
• Resource Management - Concurrency controls and memory optimization
• Observability - Built-in metrics and execution tracing

Quick Start

Installation

Recommended to use virtual environment.

pip install graphbit

Environment Setup

Set up API keys you want to use in your project:

# OpenAI (optional – required if using OpenAI models)
export OPENAI_API_KEY=your_openai_api_key_here

# Anthropic (optional – required if using Anthropic models)
export ANTHROPIC_API_KEY=your_anthropic_api_key_here

Security Note: Never commit API keys to version control. Always use environment variables or secure secret management.

Basic Usage

import os

from graphbit import LlmConfig, Executor, Workflow, Node, tool

# Initialize and configure
config = LlmConfig.openai(os.getenv("OPENAI_API_KEY"), "gpt-4o-mini")

# Create executor
executor = Executor(config)

# Create tools with clear descriptions for LLM selection
@tool(_description="Get current weather information for any city")
def get_weather(location: str) -> dict:
    return {"location": location, "temperature": 22, "condition": "sunny"}

@tool(_description="Perform mathematical calculations and return results")
def calculate(expression: str) -> str:
    return f"Result: {eval(expression)}"

# Build workflow
workflow = Workflow("Analysis Pipeline")

# Create agent nodes
smart_agent = Node.agent(
    name="Smart Agent",
    prompt="What's the weather in Paris and calculate 15 + 27?",
    system_prompt="You are an assistant skilled in weather lookup and math calculations. Use tools to answer queries accurately.",
    tools=[get_weather, calculate]
)

processor = Node.agent(
    name="Data Processor",
    prompt="Process the results obtained from Smart Agent.",
    system_prompt="""You process and organize results from other agents.

    - Summarize and clarify key points
    - Structure your output for easy reading
    - Focus on actionable insights
    """
)

# Connect and execute
id1 = workflow.add_node(smart_agent)
id2 = workflow.add_node(processor)
workflow.connect(id1, id2)

result = executor.execute(workflow)
print(f"Workflow completed: {result.is_success()}")
print("\nSmart Agent Output: \n", result.get_node_output("Smart Agent"))
print("\nData Processor Output: \n", result.get_node_output("Data Processor"))

High-Level Architecture

Three-tier design for reliability and performance:

• Rust Core - Workflow engine, agents, and LLM providers
• Orchestration Layer - Project management and execution
• Python API - PyO3 bindings with async support

Python API Integrations

GraphBit provides a rich Python API for building and integrating agentic workflows, including executors, nodes, LLM clients, and embeddings. For the complete list of classes, methods, and usage examples, see the Python API Reference.

Contributing to GraphBit

We welcome contributions. To get started, please see the Contributing file for development setup and guidelines.

GraphBit is built by a wonderful community of researchers and engineers.