docker-agent
AI Agent Builder and Runtime by Docker Engineering
Retrieval-augmented generation (RAG) makes a language model answer from your own documents. The text is split into chunks, turned into vectors and stored in an index, and the most relevant chunks are added to the prompt when a question comes in. The projects here cover the whole pipeline or one part of it: document parsing, vector search, or a complete question-answering app.
Answer quality in RAG depends mostly on retrieval, not on the model. Compare how each project handles difficult documents such as PDFs, tables and scans, whether it supports hybrid search that combines keywords with vectors and re-ranking, and whether it shows which sources an answer came from.
AI Agent Builder and Runtime by Docker Engineering
🚀 EvoAgentX: Building a Self-Evolving Ecosystem of AI Agents
Open-source inference server and production cluster for all the models your agent needs.
A native macOS app that allows users to chat with a local LLM that can respond with information from files, folders and websites on your Mac without installing any other software. Powered by llama.cpp.
RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology.
基于 Spring Boot 4.1、Java 25、Spring AI 2.0、React、PostgreSQL/pgvector、Redis 和 RustFS 构建的开源 AI 面试平台,支持简历智能分析、模拟面试、语音面试和知识库 RAG。
Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active Hybrid GraphRAG, DreamService, and self-healing loops.
Your Cheat Sheet for AI Engineering Interview – Questions and Answers.
All-in-One Native Local Development Environment for Windows, macOS & Linux. Docker alternative for PHP, Node.js, Python and more. Faster alternative to XAMPP, Laragon, MAMP and Laravel Herd with databases, Cron Jobs and runtime management.
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
An open-source research agent system for your Zotero library.
Improved file parsing for LLM’s
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
面向长篇小说创作的 AI Native 开源系统,用 Agent、世界观、写法引擎、RAG 和整本生产工作流,帮助新手从一句灵感走到完整小说。AI-native engine for end-to-end novel creation — from idea to full chapters, with structured planning, worldbuilding, and agent-driven workflows.
AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months.
This project helps teams deliver faster with open-source tooling and practical workflows.
Jupyter Notebooks to help you get hands-on with Pinecone vector databases
The AI-Native Search Database. Best for agent storage, it unifies vector, text, structured, and semi-structured data into a single engine. This all-in-one database makes agents smarter, easier to run, and more stable.
pingcap/autoflow is a Graph RAG based and conversational knowledge base tool built with TiDB Serverless Vector Storage. Demo: https://tidb.ai
AI Agent 面试全攻略:从零到Offer,包含200+面试题、企业级项目(Python/Java/Go)、简历模板、STAR面试稿、哆啦A梦漫画图解
Multi-agent systems, memory, planning, reasoning loops
Examples demonstrating usage of Spring AI & Spring AI Alibaba 📜
A distributed SQLite server with MySQL wire compatible interface
The official .NET library for the OpenAI API