ragbits and agentic-rag
These are competitors: ragbits offers a mature, production-ready framework for building RAG systems with established adoption, while agentic-rag provides an alternative approach focused specifically on agentic reasoning patterns with minimal adoption, making them substitute solutions rather than complementary tools.
About ragbits
deepsense-ai/ragbits
Building blocks for rapid development of GenAI applications
Provides modular Python packages for LLM integration (100+ models via LiteLLM), RAG pipelines with 20+ document formats, and multi-agent coordination using the A2A protocol and Model Context Protocol. Features type-safe prompt execution with Python generics, support for Qdrant/PgVector and other vector stores, Ray-based distributed document ingestion, and OpenTelemetry observability—installable as granular components (core, agents, document-search, evaluate, guardrails, chat, CLI) rather than monolithic framework.
About agentic-rag
FareedKhan-dev/agentic-rag
Agentic RAG to achieve human like reasoning
Implements a multi-stage agentic pipeline with specialized tools (Librarian, Analyst, Scout) coordinated through deliberate reasoning nodes—Gatekeeper for validation, Planner for orchestration, Auditor for self-correction, and Strategist for causal inference. Builds knowledge from structure-aware document parsing, LLM-generated metadata, and hybrid vector/relational stores, then stress-tests robustness through adversarial Red Team challenges and evaluation across retrieval quality, reasoning correctness, and cost metrics.
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