Ragbits provides a modular toolkit for building reliable and scalable Generative AI applications, targeting developers who need to rapidly prototype and deploy RAG and multi-agent systems. It simplifies LLM integration, data ingestion from various formats, and agent coordination, offering features for observability and testing.

How It Works

Ragbits employs a component-based architecture, abstracting core functionalities like LLM interactions (via LiteLLM for 100+ model support), vector store integration (Qdrant, PgVector, in-memory), and data processing (20+ formats with optional VLM support). Its RAG pipeline enables efficient document ingestion and retrieval, while its agent framework facilitates multi-agent coordination using the A2A protocol and real-time data integration via MCP.

Quick Start & Requirements

• Primary install / run command: pip install ragbits
• Prerequisites: Python 3.x. Supports various LLMs and vector stores; specific models/stores may have their own requirements.
• Links: Homepage, Documentation

Highlighted Details

• Supports 100+ LLMs via LiteLLM and local models.
• Ingests 20+ data formats, including tables and images with VLM support.
• Enables multi-agent workflows with role-based collaboration and conversation state management.
• Integrates observability with OpenTelemetry and provides built-in testing via promptfoo.

Maintenance & Community

• Actively maintained by Deepset AI.
• Contributions are welcomed; see CONTRIBUTING.md.

Licensing & Compatibility

• License: MIT License.
• Compatible with commercial and closed-source applications.

Limitations & Caveats

The project is presented as building blocks, implying that assembling a full-fledged application requires integrating multiple components. Specific performance benchmarks or detailed scalability limits are not explicitly provided in the README.