A Python library providing key functional primitives for research in multimodal language models, agentic AI, and knowledge graphs, featuring unified model invocation, multi-agent collaboration and debate, knowledge graph lifecycle management, and multimodal generation.
An AI research assistant plugin that deeply integrates LLMs into Zotero, featuring paper chat, multi-paper comparison, Agent Mode for automated literature management, and note export to Obsidian/Logseq.
A self-hostable AI research workspace for grounded chat, paper study, 206+ scientific skills, and 13-stage deep research execution.
An enterprise-grade research agent built on NVIDIA NeMo Agent Toolkit, supporting shallow fast Q&A and deep multi-step research with auto-generated long-form reports with citations.
A teaching-oriented multi-agent framework (PicoAgents) with a companion book, covering the full path from building LLM agents from scratch to production deployment, with 50+ examples, DAG workflow engine, autonomous orchestration, Computer Use Agent, and evaluation framework.
A local-first AI research assistant featuring multi-LLM support, 20+ research strategies, multi-search-engine integration, and automated quality scoring for 212K+ academic sources, producing citation-backed PDF/Markdown reports via CLI, Web UI, REST API, or MCP Server.
An AI agent-driven multi-platform aggregated search engine for the last 30 days, ranked by social engagement signals across 15+ sources including Reddit, X, YouTube, HN, and Polymarket.
A modular Python toolkit developed by the University of Innsbruck that integrates information retrieval, re-ranking, and RAG generation, featuring 40+ pre-processed datasets and single-line pipeline construction.
An open-source collection of tutorials and runnable scripts for over 30 advanced Retrieval-Augmented Generation (RAG) techniques, including Graph RAG, Agentic RAG, and various retrieval optimization strategies, implemented primarily with LangChain.
Official code repository for the O'Reilly book "Hands-On Large Language Models". Features 12 core chapters and bonus content covering Tokens, Transformers, RAG, and Fine-tuning. Includes 300+ illustrations and runnable Jupyter Notebooks optimized for Colab and local environments.
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