Graphbit: Build and coordinate AI-agent workflows

Understand the project

Practical overview

Use GraphBit when you need to implement a structured LLM workflow in Python, particularly one that connects agents or gives them callable tools.

Before you choose

The repository identifies the project as open source under the Apache-2.0 license. The README lists Python 3.9–3.13 and Rust 1.70 or later.

The documented workflow model uses an LLM configuration, an executor, agent nodes, tools and explicit workflow connections.
The README lists multiple model providers, but its concrete quick-start configuration uses OpenAI.
The repository describes concurrency controls, tracing, structured logs, performance metrics, retries and fault recovery.
Performance and reliability figures in the README are project claims rather than independently verified results.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the Python package and prepare provider credentials for a GraphBit workflow.

Python 3.9–3.13, as listed in the repository README.
A virtual environment is recommended.
An API key for the selected provider; the README shows OpenAI and Anthropic environment-variable examples.

Install the package with the command below.
Documented command
```
pip install graphbit
```
Set the provider key needed by your project as an environment variable, such as OPENAI_API_KEY or ANTHROPIC_API_KEY.

The Python package is installed and the selected provider credential is available to a project.

This covers installation and credential setup, not building or executing a workflow.
Provider credentials are only needed for the provider being used.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 15 October 2026
Original website title: InfinitiBit/graphbit · Official homepage title · retained in its source language