GraphBit - High Performance Agentic Framework

<p align="center"> <img src="assets/GraphBit_Final_GB_Github_GIF.gif" style="max-width: 600px; height: auto;" alt="Logo" /> </p> <p align="center"> <img alt="GraphBit - Developer-first, enterprise-grade LLM framework. | Product Hunt" loading="lazy" width="250" height="54" decoding="async" data-nimg="1" class="w-auto h-[54px] max-w-[250px]" style="color:transparent" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=1004951&theme=light&t=1757340621693"> <img alt="GraphBit - Developer-first, enterprise-grade LLM framework. | Product Hunt" loading="lazy" width="250" height="54" decoding="async" data-nimg="1" class="w-auto h-[54px] max-w-[250px]" style="color:transparent" src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=1004951&theme=light&period=daily&t=1757933101511"> </p>  <p align="center"> <a href="https://graphbit.ai/">Website</a> | <a href="https://docs.graphbit.ai/">Docs</a> | <a href="https://discord.com/invite/FMhgB3paMD">Discord</a> <br /><br /> </p> <p align="center"> <a href="https://trendshift.io/repositories/14884" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14884" alt="InfinitiBit%2Fgraphbit | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> <br> <a href="https://pepy.tech/projects/graphbit"><img src="https://static.pepy.tech/personalized-badge/graphbit?period=total&units=INTERNATIONAL_SYSTEM&left_color=GREY&right_color=GREEN&left_text=Downloads" alt="PyPI Downloads"/></a> </p> <p align="center"> <a href="https://pypi.org/project/graphbit/"><img src="https://img.shields.io/pypi/v/graphbit?color=blue&label=PyPI" alt="PyPI"></a>  <a href="https://github.com/InfinitiBit/graphbit/actions/workflows/update-docs.yml"><img src="https://img.shields.io/github/actions/workflow/status/InfinitiBit/graphbit/update-docs.yml?branch=main&label=Build" alt="Build Status"></a> <a href="https://github.com/InfinitiBit/graphbit/blob/main/CONTRIBUTING.md"><img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" alt="PRs Welcome"></a> <br> <a href="https://www.rust-lang.org"><img src="https://img.shields.io/badge/rust-1.70+-orange.svg?logo=rust" alt="Rust Version"></a> <a href="https://www.python.org"><img src="https://img.shields.io/badge/python-3.9--3.13-blue.svg?logo=python&logoColor=white" alt="Python Version"></a> <a href="https://github.com/InfinitiBit/graphbit/blob/main/LICENSE.md"><img src="https://img.shields.io/badge/license-Custom-lightgrey.svg" alt="License"></a> </p> <p align="center"> <a href="https://www.youtube.com/@graphbitAI"><img src="https://img.shields.io/badge/YouTube-FF0000?logo=youtube&logoColor=white" alt="YouTube"></a> <a href="https://x.com/graphbit_ai"><img src="https://img.shields.io/badge/X-000000?logo=x&logoColor=white" alt="X"></a> <a href="https://discord.com/invite/FMhgB3paMD"><img src="https://img.shields.io/badge/Discord-7289da?logo=discord&logoColor=white" alt="Discord"></a> <a href="https://www.linkedin.com/showcase/graphbitai/"><img src="https://img.shields.io/badge/LinkedIn-0077B5?logo=linkedin&logoColor=white" alt="LinkedIn"></a> </p>

Type-Safe AI Agent Workflows with Rust Performance

</div>

GraphBit is an open-source agentic AI framework for deterministic, concurrent, low-overhead execution.

Why GraphBit?

Efficiency decides who scales. GraphBit is built for developers who need deterministic, concurrent, and ultra-efficient AI execution without the overhead.

Built with a Rust core and a minimal Python layer, GraphBit delivers up to 68× lower CPU usage and 140× lower memory footprint than other frameworks, while maintaining equal or greater throughput.

It powers multi-agent workflows that run in parallel, persist memory across steps, self-recover from failures, and ensure 100% task reliability. GraphBit is built for production workloads, from enterprise AI systems to low-resource edge deployments.

Used in Production

<div align="center"> <a href="https://www.grantthornton.de/"> <img src="assets/Grant Thornton_idgV8Wo5Id_1.svg" alt="Grant Thornton Logo" height="80"> </a> <br><br> <strong>Grant Thornton Germany</strong> adopted GraphBit to move AI from "permanent pilot" to production without regulatory risk as a core component of their tech stack. </div>

Key Features

Tool Selection - LLMs intelligently choose tools based on descriptions
Type Safety - Strong typing through every execution layer
Reliability - Circuit breakers, retry policies, and error handling and fault recovery
Multi-LLM Support - OpenAI, Azure OpenAI, Anthropic, OpenRouter, DeepSeek, Replicate, Ollama, TogetherAI and more
Resource Management - Concurrency controls and memory optimization
Observability - Built-in tracing, structured logs, and performance metrics

Benchmark

GraphBit was built for efficiency at scale, not theoretical claims, but measured results.

Our internal benchmark suite compared GraphBit to leading Python-based agent frameworks across identical workloads.

| Metric | GraphBit | Other Frameworks | Gain | |:--------------------|:---------------:|:----------------:|:-------------------------| | CPU Usage | 1.0× baseline | 68.3× higher | ~68× CPU | | Memory Footprint | 1.0× baseline | 140× higher | ~140× Memory | | Execution Speed | ≈ equal / faster| — | Consistent throughput | | Determinism | 100% success | Variable | Guaranteed reliability |

GraphBit consistently delivers production-grade efficiency across LLM calls, tool invocations, and multi-agent chains.

Benchmark Demo

<div align="center"> <a href="https://www.youtube.com/watch?v=MaCl5oENeAY"> <img src="https://img.youtube.com/vi/MaCl5oENeAY/maxresdefault.jpg" alt="GraphBit Benchmark Demo" style="max-width: 600px; height: auto;"> </a> <p><em>Watch the GraphBit Benchmark Demo</em></p> </div>

When to Use GraphBit

Choose GraphBit if you need:

Production-grade multi-agent systems that won't collapse under load
Type-safe execution and reproducible outputs
Real-time orchestration for hybrid or streaming AI applications
Rust-level efficiency with Python-level ergonomics

If you're scaling beyond prototypes or care about runtime determinism, GraphBit is for you.

Quick Start

Installation

Recommended to use virtual environment.

pip install graphbit

Quick Start Video Tutorial

<div align="center"> <a href="https://youtu.be/ti0wbHFKKFM?si=hnxi-1W823z5I_zs"> <img src="https://img.youtube.com/vi/ti0wbHFKKFM/maxresdefault.jpg" alt="GraphBit Quick Start Tutorial" style="max-width: 600px; height: auto;"> </a> <p><em>Watch the Install GraphBit via PyPI | Full Example & Run Guide tutorial</em></p> </div>

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, GuardRailPolicyConfig

# 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 looku

Graphbit

Install / Use

README