ChainFury is an open-source, production-grade chaining engine designed for building and deploying complex AI workflows, particularly for Software 2.0 applications. It targets developers and researchers looking to orchestrate multiple AI models and data sources into cohesive pipelines, offering a self-hostable server with a GUI for easier management and experimentation.

How It Works

ChainFury utilizes a Directed Acyclic Graph (DAG) execution engine to define and run AI chains. This approach allows for flexible and efficient orchestration of various components, such as retrieval-augmented generation (RAG), image generation, and private data storage. The engine processes these chains, enabling complex interactions between different AI models and data sources.

Quick Start & Requirements

• Install via pip: pip install chainfury and pip install chainfury_server.
• Launch server: python3 -m chainfury_server.
• Docker: docker build . -f Dockerfile -t chainfury:latest and docker run -p 8000:8000 chainfury:latest.
• Building from source requires yarn for frontend compilation.
• Default GUI access: localhost:8000 with credentials admin:admin.
• Documentation: https://docs.chainfury.io/

Highlighted Details

• Supports Retrieval Augmented Generation (RAG) with PDF inputs.
• Integrates with Stability AI for image generation.
• Offers private data storage capabilities via AWS S3.
• Provides a self-hosted server with a GUI for workflow management.

Maintenance & Community

• Open to contributions for features, infrastructure, and documentation.
• Community support available via Discord.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

• The README does not specify a license, which may impact commercial use or closed-source integration.
• Building from source requires yarn for frontend compilation, adding a dependency.