Enterprise policy AI Q&A system — policy management + RAG Q&A + action guidance, deployable on-premises
-
Updated
Aug 15, 2026 - TypeScript
Enterprise policy AI Q&A system — policy management + RAG Q&A + action guidance, deployable on-premises
Production multi-agent research system — LangGraph, Groq, Tavily, Redis, Celery, FastAPI, Docker
PIXIE — An OpenEnv-compatible Dual Rover RL environment. Contains two distinct environments: Mars Rover (deep autonomy, communication delay, long-horizon planning) and Moon Rover (short-horizon, survival under extreme day/night cycle). Both are controlled via natural language by an LLM agent
An intelligent chat message processing agent built with Spring Boot, Spring AI, Harness, and LangSmith.一个基于 Spring Boot、Spring AI、Harness 和 LangSmith 构建的智能聊天消息处理代理。
Agentic RAG using FastAPI, FAISS, LangChain & Groq — with real-time web validation via Tavily — to answer your PDF-based queries intelligently. Android app using Java.
60 deep-dive markdown notes on Retrieval-Augmented Generation — from embeddings and vector search to agentic RAG, GraphRAG, evaluation, security, and enterprise-scale architecture.
AL/ML learning Gym including traditional ML, deep learning, fine-tuning, Gen AI, Agentic AI. Everything from experiments to production with evaluation and observability.
complete learning path for mastering LangGraph, from building agents from scratch to implementing sophisticated multi-agent systems.
A AI Work Agent Platform
To associate your repository with the agnetic-rag topic, visit your repo's landing page and select "manage topics."