Deploy Agentic RAG Pipelines in Minutes with ragbits
ragbits GenAI Framework
ragbits delivers modular, open-source building blocks for fast GenAI app development, enabling you to deploy RAG and agentic workflows in minutes, not hours. Easily build autonomous AI agents that think, reason, and act with minimal code. Use the full stack with pre-built APIs and UI or integrate modules into existing projects for maximum flexibility.
Benefits of Using ragbits
Our stack of LLM and agentic building blocks accelerates your AI solution development.
Accelerate Project Kickoff
Leverage pre-built components to develop and deploy an MVP-level LLM or agentic solution in under 2 weeks.
Future-Proof Your AI Stack
Easily adapt to evolving technologies by seamlessly replacing base models, vector databases, and enterprise integrations.
Enhance Security and Monitoring
Mitigate hallucinations, evaluate and optimize prompts, build agent-based workflows, and ensure complete observability for reliable AI performance.
Key Features
With ragbits, you get a modular, high-performance framework designed to make building and deploying RAG and agentic applications faster and more efficient than ever.
Agentic AI
Build AI Agents in 20 Lines of Code
Define agents using simple Python functions as tools, enabling LLMs to reason and act autonomously.
Streaming Support for Agents
Deliver partial outputs instantly to improve user experience.
A2A Protocol for Multi-Agent Systems
Enable agents to communicate with each other via the built-in A2A protocol.
MCP Server Integrations
Connect to hundreds of tools and APIs instantly using MCP server support.
Built-in Tracing and Monitoring
Full observability with Logfire, OpenTelemetry, and Grafana integrations.
Flexible, Scalable, and Future-Proof GenAI
Hot-swappable LLMs
Integrate with over 100 LLMs through LiteLLM or run local models without modifying your application code.
Type-safe LLM calls
Use Python generics to enforce strict type safety in LLM interactions, reducing runtime errors.
VectorStore Compatibility
Connect to Qdrant, PgVector, and other vector databases with built-in support for easy storage and retrieval.
Developer Tools Included
Execute ingestion, query vector stores, and test RAG pipelines directly from the command line.
Modular Installation
Install only the required components, minimizing dependencies and reducing resource overhead.
Rapid RAG Implementation
Ingest 20+ Document Formats
Process PDFs, HTML, spreadsheets, presentations, and other structured or unstructured formats.
Structured Data Processing
Extract and interpret tables, images, and complex layouts with built-in Vision-Language Model (VLM) support.
Native Data Source Connectors
Integrate with S3, GCS, Azure, and other storage systems using prebuilt connectors.
Scalable Ingestion Pipeline
Use Ray-based parallel processing to handle large datasets efficiently.
Reliable Deployment & Monitoring
Built-in Observability
Track application performance with OpenTelemetry integration and detailed CLI insights.
Built-in Testing & Evaluation
Use promptfoo to test, validate, and refine prompts before deployment.
Automatic Optimization
Continuously assess and enhance model performance with integrated evaluation tools.
Intuitive Testing UI
Interactively test and optimize applications through a user-friendly UI (coming soon!).
Watch the Technical Demo
In this session, we break down what it really takes to move from a quick RAG demo to a scalable, reliable system. Without duct tape and fragile hacks.
Explore Our Latest Implementations with ragbits
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Revolutionizing Geospatial Tech with AI-Powered Assistant
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Accelerating GenAI Development with ragbits
ragbits enables developers to bypass repetitive tasks, jumpstarting projects with pre-built components.
The Components of ragbits
ragbits provides ready-to-use building blocks for GenAI applications. Here’s what we cover:
Agents
Define autonomous agents with tools and reasoning capabilities.
Flow Controls
Orchestrate multi-stage workflows for advanced interactions – now fully agentic.
Document Search
Perform vector searches to retrieve relevant documents with a benchmarking framework.
Structured Querying
Query structured data sources precisely, powered by db-ally.
Response Caching
Reduce costs and improve response times with built-in caching.
Observability & Audit
Track queries, traces, and system events for efficient debugging and monitoring.
Guardrails
Ensure response safety and relevance, protecting your applications.
… and more
All modules are designed for seamless integration and synergy. New agentic and RAG components can be easily introduced into the ecosystem.
Using Modules Independently
ragbits modules can be used independently in existing projects.
Frequently Asked Questions
What is ragbits?
ragbits is an open-source framework for building GenAI apps, RAG apps, and agentic apps quickly and efficiently. It provides modular building blocks for ingestion, vector stores, chat UIs, and now agentic workflows, so developers can focus on business logic instead of infrastructure.
How does ragbits help build agentic GenAI apps?
With ragbits v1.1, you can turn any RAG application into an agentic app. Define simple Python functions as tools, combine them with LLMs and prompts, and build AI agents that reason, act autonomously, and call external tools – all with minimal code.
Can I use ragbits to implement an open-source RAG app easily?
Yes. ragbits was designed to help developers implement open-source RAG apps in hours instead of days, providing ready-to-use modules for document ingestion, retrieval, and chat pipelines.
Is ragbits suitable for production GenAI deployments?
Absolutely. ragbits has been used in production across multiple client projects, with teams reporting faster deployment, stable performance, and simplified agentic workflows. It also has over 1.4k stars on GitHub, confirming its reliability and developer adoption.
Which vector stores does ragbits support for my RAG app?
ragbits integrates with Weaviate, Qdrant, PgVector, and other popular vector databases, allowing flexible storage and retrieval solutions for your RAG pipelines.
How do I implement an open-source agentic app with ragbits?
You can start by installing the ragbits-agents package, defining your agent tools as Python functions, and using the built-in Agent API to create agentic workflows. The documentation provides step-by-step guidance for building agentic apps easily.
Does ragbits support streaming outputs in GenAI apps?
Yes, ragbits supports streaming outputs by default. This feature improves user experience by delivering partial results instantly instead of waiting for full completion.
Is ragbits free to use?
Yes, ragbits is fully open source under the MIT license, allowing integration, modification, and commercial deployment without restrictions.
Where can I see a demo of ragbits in action?
Check out our demo section for a walkthrough of building agentic and RAG apps using ragbits, or read our case studies to see how teams are using ragbits in production.