
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:
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.
- 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
- Resource Management - Concurrency controls and memory optimization
- Observability - Built-in metrics and execution tracing
Clone the repository
git clone https://github.com/InfinitiBit/graphbit.git
cd graphbit
Install Rust
- Linux/macOS:
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && source $HOME/.cargo/env
- Windows: Download & run rustup-init.exe
Install Poetry
curl -sSL https://install.python-poetry.org | python3 -
Install dependencies (recommended to use a Python virtual environment)
poetry install --no-root
Build from source
cargo build --release
Build Python bindings
cd python/
maturin develop --release
First, set up your API keys:
export OPENAI_API_KEY=your_openai_api_key_here
export ANTHROPIC_API_KEY=your_anthropic_api_key_here
export REPLICATE_API_KEY=your_replicate_api_token_here
Security Note: Never commit API keys to version control. Always use environment variables or secure secret management.
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"))
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
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.
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.
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