GraphAI is a minimalistic “AI framework” that aims to not be an AI framework at all. Instead, it provides a simple, flexible graph-based architecture that engineers can use to develop their own AI frameworks and projects.
GraphAI is a lightweight library built around the concept of a computational graph. It provides:
A graph-based architecture for connecting various components in a workflow
An async-first design to handle API calls efficiently
Minimal abstractions to avoid boxing developers into a specific AI implementation
Flexible callback mechanisms for streaming and communication between components
Unlike other AI libraries, GraphAI doesn’t ship with predefined concepts of “LLMs”, “Agents”, or other high-level AI abstractions. Instead, it gives you the tools to build these concepts yourself, exactly how you want them.
Many existing AI frameworks impose their view of what AI applications should look like, creating a “local minimum” that constrains innovation. GraphAI takes a different approach:
Bring your own components: Use any LLM provider, agent methodology, or telemetry system
Create your perfect workflow: Build exactly the AI application architecture you need
Escape the box: Don’t be limited by someone else’s conception of AI
Focus on flow, not frameworks: Think about how data and processing should flow through your application
AI applications frequently rely on API calls that involve significant waiting time. GraphAI is built from the ground up to be async-first, allowing your Python code to efficiently handle these operations rather than wasting compute cycles while waiting for responses.