RAG · Search

zg indexes your workspace so both terminal users and AI agents can query by meaning or exact text without sending code to the cloud.

It exists because most RAG tutorials end at 'hello vector DB,' while production requires query routing, evidence budgets, and circuit breakers.

LLM-Wiki turns AI coding assistants into collaborative researchers that ingest sources, investigate topics, and compile persistent markdown knowledge bases.

OpenKB compiles raw documents into a persistent, interlinked wiki so knowledge accumulates instead of being re-derived on every query.

ArcadeDB crams graph, document, key-value, time-series, search, and vector models into a single engine so you can stop duct-taping databases together.

Gortex indexes your code into a queryable knowledge graph so AI agents can ask for exactly the context they need instead of wasting tokens on full-file reads.

It builds an agent-ready knowledge base over your local files without dragging them into a proprietary workspace.

A hands-on Node.js tutorial series that makes you implement embeddings, vector stores, and retrieval yourself so RAG stops feeling like magic.

A project-based course that teaches LangChain v1 and LangGraph by building real agents with real APIs.

ToolUniverse standardizes how any LLM discovers and executes over 1,000 scientific tools, from literature search to molecular simulation, so AI agents can perform actual research rather than just summarizing it.

BiliSum exists to convert Bilibili, YouTube, and local videos into structured, offline notes and a personal knowledge base that keeps data off the cloud.

It packages Karpathy's LLM wiki concept as an Agent Skill so your coding agent can maintain a durable knowledge base with citations instead of re-deriving answers from raw sources on every query.

It gives coding agents a persistent SQLite-backed knowledge graph so they stop forgetting how the project works between sessions.

It turns unstructured text into an interactive knowledge graph by chunking documents, extracting Subject-Predicate-Object triplets with an LLM, and stitching the results together across chunks.

A multi-agent pipeline that automates the full math-modeling competition workflow—from problem analysis to code execution to formatted paper generation.

DDGS is a metasearch library that lets you query Google, Bing, DuckDuckGo, and others without signing up for anything.

It exists because RAG shouldn't be the only way to turn a file dump into usable knowledge.

Because choosing between nineteen agent frameworks shouldn't require nineteen browser tabs.

wigolo gives AI coding agents a local, keyless web research layer that runs entirely on your machine and bills nothing per query.

RocketRide exists because wiring LLM workflows shouldn't require leaving your IDE or surrendering your data to a cloud dashboard.