An AI Coding Agent Powered by LangGraph

Chrome MCP Server is a Chrome extension-based Model Context Protocol (MCP) server that exposes your Chrome browser functionality to AI assistants like Claude, enabling complex browser automation, c…

Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams.（Flock 是一个基于workflow工作流的低代码平台，用于快速构建聊天机器人、RAG、Agent和Muti-Agent应用。）

🪐 ✨ Model Context Protocol (MCP) Server for Jupyter.

An open-source implementaion for Gemma3 series by Google.

All-in-one infrastructure for search, recommendations, RAG, and analytics offered via API

A Practical Course on Embeddings, RAG, Multimodal Models, and Agents with Amazon Nova.

Build and query dynamic, temporally-aware Knowledge Graphs

"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework"

Qdrant Model Context Protocol (MCP) server

A Python framework for multi-modal document understanding with Amazon Bedrock

It shows case studies of the LangGraph agent.

Naive barcode reader

Multiple agents with LangGraph and MCP

Set of use cases demonstrating how to build AI-featured applications.

A Python-based QR/Barcode scanner app with a GUI built using Tkinter. It supports scanning via webcam or image file, detecting and opening hyperlinks, copying results to the clipboard, and opening …

Document (PDF) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown

Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models, clip, clap and colpali

OCR, layout analysis, reading order, table recognition in 90+ languages

Extract tables from PDFs using LLMWhisperer and extract structured information from those tables using Langchain

Leveraging LLM to build Conversational UIs

This project demonstrates how to build and automate an ETL pipeline using DAGs in Airflow and load the transformed data to Bigquery. There are different tools that have been used in this project su…

Automatically evaluate your LLMs in Google Colab

Use BQML (k-means) to generate customer segments (from BigQuery public data) and then calls Gemini in Vertex AI through SQL to create human readable headlines and summaries for each segment.

BigQuery Column Lineage parser