graphbit

GraphBit is the world’s first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.

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GraphBit is an industry-grade agentic AI framework built for developers and AI teams that demand stability, scalability, and low resource usage. It is written in Rust for maximum performance and safety, delivering significantly lower CPU usage and memory footprint compared to leading alternatives. The framework is designed to run multi-agent workflows in parallel, persist memory across steps, recover from failures, and ensure 100% task success under load. With lightweight architecture, observability, and concurrency support, GraphBit is suitable for deployment in high-scale enterprise environments and low-resource edge scenarios.

README:

GraphBit - High Performance Agentic Framework

Website | Docs | Discord

Type-Safe AI Agent Workflows with Rust Performance

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

Grant Thornton Germany adopted GraphBit to move AI from "permanent pilot" to production without regulatory risk as a core component of their tech stack.

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

Watch the GraphBit Benchmark Demo

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

Watch the Install GraphBit via PyPI | Full Example & Run Guide tutorial

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"))

Building Your First Agent Workflow by GraphBit

Watch the Making Agent Workflow by GraphBit tutorial

Observability & Tracing

GraphBit Tracer captures and monitors LLM calls and AI workflows with minimal configuration. It wraps GraphBit LLM clients and workflow executors to trace prompts, responses, token usage, latency, and errors without changing your code.

Watch the GraphBit Observability & Tracing tutorial

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:

LLM Clients - Multi-provider LLM integrations (OpenAI, Anthropic, Azure, and more)
Workflows - Define and manage multi-agent workflow graphs with state management
Nodes - Agent nodes, tool nodes, and custom workflow components
Executors - Workflow execution engine with configuration management
Tool System - Function decorators, registry, and execution framework for agent tools
Workflow Results - Execution results with metadata, timing, and output access
Embeddings - Vector embeddings for semantic search and retrieval
Workflow Context - Shared state and variables across workflow execution
Document Loaders - Load and parse documents from multiple formats (PDF, DOCX, TXT, JSON, CSV, XML, HTML)
Text Splitters - Split documents into chunks (character, token, sentence, recursive)

For the complete list of classes, methods, and usage examples, see the Python API Reference.

Ecosystem & Extensions

GraphBit's modular architecture supports external integrations:

Category	Examples
LLM Providers	OpenAI, Anthropic, Azure OpenAI, DeepSeek, Together, Ollama, OpenRouter, Fireworks, Mistral AI, Replicate, Perplexity, HuggingFace, AI21, Bytedance, xAI, and more
Vector Stores	Pinecone, Qdrant, Chroma, Milvus, Weaviate, FAISS, Elasticsearch, AstraDB, Redis, and more
Databases	PostgreSQL (PGVector), MongoDB, MariaDB, IBM DB2, Redis, and more
Cloud Platforms	AWS (Boto3), Azure, Google Cloud Platform, and more
Search APIs	Serper, Google Search, GitHub Search, GitLab Search, and more
Embedding Models	OpenAI Embeddings, Voyage AI, and more

Extensions are developed and maintained by the community.

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.

Security

GraphBit is committed to maintaining security standards for our agentic framework. We recommend using environment variables for API keys, keeping GraphBit updated, and using proper secret management for production environments. If you discover a security vulnerability, please report it responsibly through GitHub Security or via email rather than creating a public issue.

For detailed reporting procedures and response timelines, see our Security Policy.

License

GraphBit project is licensed under the Apache License, Version 2.0.

For complete terms and conditions, see the Full License.

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