deepsense-ai/ragbits

Health v2 · maintenance only

Adoption not part of the score

1668 stars · 143 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Ragbits is a Python framework of modular building blocks for rapidly developing generative AI applications, covering LLM interaction, prompt management, RAG document ingestion and search, and multi-agent workflows. It includes developer CLI tools, type-safe LLM calls via LiteLLM, support for multiple vector stores, and Ray-based distributed ingestion.

Use cases

• build a RAG pipeline over PDFs and other documents
• swap between 100+ LLM providers without rewriting code
• ingest documents into Qdrant or PgVector vector stores
• create multi-agent workflows with the A2A protocol
• test and manage prompts from the terminal
• add guardrails and evaluation to GenAI apps
• run distributed document ingestion at scale

When to choose

• you want a batteries-included Python framework for GenAI/RAG development
• you need flexible document ingestion across many formats and cloud sources
• you want type-safe prompting and swappable LLM backends
• you're building multi-agent systems with interoperability needs

When to avoid

• you need a simple one-off LLM API call without framework overhead
• you're not working in Python
• you need a fully managed SaaS solution rather than a self-assembled toolkit

Facets

framework · maturity active

rag llm-inference agent-framework prompt-engineering vector-database etl cli sdk large-language-models machine-learning developer-tools python cross-platform genai llm document-ingestion vector-stores a2a-protocol guardrails prompt-management litellm retrieval-augmented-generation ai-agents natural-language-processing

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