The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities. It also supports seamless integration with the openai/langchain sdk.

The GPT-RAG Orchestrator service is an agentic orchestration layer built on Microsoft Foundry Agent Service and the Microsoft Agent framework. It enables agent-based RAG workflows by coordinating multiple specialized agents to collaboratively generate accurate, context-aware responses for complex user queries.

This repository provides the user interface for the GPT-RAG solution, enabling seamless interaction with the Orchestrator backend. Built with Chainlit, it supports customization, theming, and ensures a smooth end-user experience.

🤖 Production-ready multi-agent AI system orchestrating specialized agents (Planner, Coder, Reviewer, Debugger, Optimizer) with RAG, function calling, and multi-LLM support. Built with FastAPI, React, ChromaDB, GPT-4 & Claude.

A next-generation, zero-code, hyper-scalable autonomous AI agent swarm orchestration ecosystem. Leverage multi-modal LLM reasoning like Claude, GPT Codex, or Local Ollama, and asynchronous RAG memory layers to deploy an entire decentralized AI software agency, managed natively through enterprise chat platforms (Discord, Slack, Telegram, WhatsApp).

Modular RAG pipeline with GPT-4o, semantic search, enrichment, and multi-agent orchestration

Experimental AI agent playground with LangChain, LangGraph, RAG pipelines, multi-agent orchestration & OpenAI GPT-4 Turbo. Build intelligent agents, retrieval-augmented generation systems & autonomous AI workflows.

🎵 The World's First Intelligent Multi-AI Orchestration Platform — Claude, GPT-4, Grok, Mistral, DeepSeek + Ollama local. Smart routing, cross-check verification, RAG certified sources.

Multi-agent AI orchestration system with RAG, vector search, and multimodal processing. Built with GPT-4o, LangChain, Weaviate, FastAPI, and React. Template for internal AI assistants with JWT auth and WebSocket streaming.

Automated AI pipeline that scrapes, enriches and vectorizes 196+ industrial sewing machine products using dual-AI validation (GPT-4o + Gemini), Pinecone RAG storage, and weekly n8n orchestration. 95%+ data accuracy. Zero human intervention.

A RAG agent that explains financial market trends and concepts using LangChain, FAISS, and LangGraph, powered by GPT-4o. Serves responses via FastAPI and persists conversations with PostgreSQL. The system is containerized with Docker and orchestrated using local Kubernetes.

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This service provides an audio-enabled user interface that extends the GPT-RAG ecosystem with real-time voice interaction capabilities. It integrates with the GPT-RAG Orchestrator backend to enable natural, two-way communication, combining speech recognition, speech synthesis, and retrieval-augmented generation in a single experience.

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RAG + Agent architecture fused with GPT-5 intelligence and Agno orchestration

Dagster-orchestrated RAG pipeline: GitHub + docs ingestion, OpenAI embeddings, Pinecone storage, GPT-4 Q&A

RAG pipeline with FastAPI backend and n8n workflow orchestration — PDF ingestion, OpenAI embeddings, Qdrant vector search, GPT-4o-mini generation

Agentic AI diagnostic decision support system for chest X-ray analysis using CrewAI orchestration, GPT-4o Vision, and PubMed RAG.

Lightweight Python framework for building intelligent Telegram bots with Claude, GPT, Gemini & Groq — agent orchestration, multi-provider failover, RAG, memory, plugins

Complete AI Orchestration Platform - GPT-5, Grok-3, Claude Sonnet 4, Gemini 2.0 + 8 Azure Cognitive Services (Speech, Vision, Document Intelligence, RAG). Production-ready backend for SaintVision AI ecosystem.

A quiz bot created with RAG capability to use a doucument uploaded as its knowledge base. Uses Open AI gpt 3.5 model for chat generation and powered by Langchain for orchestration

A multi‑agent, agentic AI travel planner that uses LangGraph, CrewAI, FastAPI, and Streamlit to orchestrate GPT‑5.1/Gemini, MCP tools, and hybrid RAG (FAISS + Neo4j) for end‑to‑end smart itinerary planning.

Multi-agent adaptive learning system for Microsoft Agentic AI curriculum. 5 AI agents (Intake, Planner, RAG, Feedback, Progress Tracker) built with Azure AI Foundry Agent Service, GPT-4o, Azure AI Search, Semantic Kernel, and Copilot Studio. Python SDK orchestration with RAG-powered content retrieval from indexed training materials.

Autonomous AI agent for industrial compliance auditing using LangChain and GPT-4o. Orchestrates RAG (ChromaDB), equipment data, email, scheduling, and logging to execute end-to-end AER compliance audits autonomously, reducing workflows from hours to seconds.

RAG-based LLM tutor backend. Ingests PDF/DOCX/PPTX/TXT, chunks with LangChain, indexes in ChromaDB, and routes queries through dual LLM orchestration: LLaMA 3.2 (module naming) + GPT-4o (answers). Flask API with gunicorn, Firebase, AWS Polly TTS

Automated Video Compliance QA pipeline using LangGraph (orchestration) and RAG architecture. Features multimodal ingestion via Azure Video Indexer, vector retrieval via Azure AI Search/OpenAI Embeddings, and GPT-4o reasoning. Includes production-grade observability with LangSmith and Azure Application Insights.

MadPrice Mate is an agentic bargain-discovery system that predicts product prices from descriptions and flags true bargains. It orchestrates multiple agents using GPT-5.2, a QLoRA-tuned Llama 3.2, and RAG for grounded estimation, then sends time-sensitive Pushover alerts to mobile.

This project demonstrates a production-ready Retrieval-Augmented Generation (RAG) pipeline that delivers accurate, context-aware answers by combining: OpenAI (text-embedding-3-small & GPT) for embeddings and generation LlamaIndex for intelligent document processing and retrieval orchestration MongoDB Atlas as a high-performance vector database

This project implements a Retrieval-Augmented Generation (RAG) pipeline that allows users to upload documents (PDFs, text, or other formats) and interact with them via a conversational interface. It uses LangChain for orchestration, FAISS for vector storage, and OpenAI GPT models for natural language understanding.