A sophisticated LangGraph-based agent that automates financial options analysis with real-time data from Polygon.io, smart caching, persistent memory, and professional-grade analysis. Built for traders, analysts, and developers who need intelligent options data processing

One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.

A RAG pipeline implementation built on the 'Epstein Files 20K' dataset from Hugging Face (Teyler).

Ask questions across your Markdown notes using a fully local Graph RAG engine. Built for Obsidian vaults, works with any folder of Markdown files. Extracts entity-relation triples from wikilinks & YAML frontmatter, retrieves answers via hybrid search (vector + BM25 + temporal). Multilingual. No cloud. Runs on Ollama.

Local RAG MCP server for Claude Code — hybrid search (semantic + BM25), cross-encoder reranking, 13 MCP tools, 20 format parsers. Zero external servers, zero API keys.

pdfLLM is a completely open source, proof of concept RAG app.

CrawlAI RAG is an AI-powered website intelligence platform that allows users to crawl entire websites, index their content, and ask natural-language questions using Retrieval-Augmented Generation (RAG). It transforms static websites into queryable knowledge bases.

An open‑source desktop RAG application that enables semantic search across your Zotero library. Easily discover conceptually related papers and ideas within your PDF collection using local or cloud‑based LLMs. The app provides source attribution, metadata filtering, and seamless integration with Zotero. macOS and Linux. Windows support limited.

Open-source toolkit to extract structured knowledge graphs from documents and tables — power analytics, digital twins, and AI-driven assistants.

A RAG agent using Google's ADK & Vertex AI that lets set up semantic search across documents in under 2 minutes. Features GCS integration and natural language querying

Template for AI chatbots & document management using Retrieval-Augmented Generation with vector search and FastAPI.

Django BM25 full-text search using PostgreSQL - a lightweight Elasticsearch alternative

Predictive memory layer for AI agents. MongoDB + Qdrant + Neo4j with multi-tier caching, custom schema support & GraphQL. 91% Stanford STARK accuracy, <100ms on-device retrieval

Chat with your knowledge base — a conversational RAG agent (LangGraph + OpenRouter) built on Fast Dash's native chat mode. A modern rebuild of the original Embedchain demo.

FinchBot is an AI Agent framework that empowers agents with true autonomy, built on LangChain v1.2 and LangGraph v1.0. With fully async architecture, agents gain the ability to self-decide, self-extend, and self-evolve

Build and deploy a full-stack RAG app on AWS with Terraform, using free tier Gemini Pro, real-time web search using Remote MCP server and Streamlit UI with token based authentication.

Multi-Agent Systems with Google's Agent Development Kit + A2A + MCP

ChatBot, show how to implement a RAG based on OceanBase or OceanBase seekdb AI capabilities escpecailly hybrid search and AI embedding.

A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI capabilities for answering questions about Ultimate Frisbee rules and strategies. This project showcases how to build a production-ready RAG system using cutting-edge technologies.