RAG · Search

It stores AI agent memory as plain Markdown inside your repository, replacing expensive vector databases with sub-300-microsecond local search and cutting token bloat by 80%.

agent-memory persists what an agent learns across sessions as plain Markdown files with local ranked retrieval, so Claude Code, Codex, and any shell-capable host can share one store without API keys.

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 to replace a tangle of single-modal encoders with one model that puts text, images, videos, and documents into the same vector space.

It demonstrates how to wire an LLM into delivery workflows while keeping compensation authority locked in a deterministic rule engine.

Runnable Colab notebooks that teach backend engineers the applied-LLM stack through raw API calls, because understanding the loop is the actual job skill.

An open-source pipeline that curates, licenses, and retrieves task-specific instructions so agents don't have to wing it.

Utopia ingests your documents into a self-hosted, time-aware knowledge graph so you can ask what was true last quarter—and prove it with citations.

It gives AI agents a fully local, keyless web research stack built from scratch in Rust, down to the TLS fingerprint.

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

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

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

Jonex unifies multimodal ingestion, ontology compilation, and source-grounded retrieval into a single governed enterprise platform.

Freehire crawls 3.3 million live IT postings directly from company ATS pages, deduplicates the reposts, and serves them through a faceted search API — no recruiters, no dead links.

Documentation and SDK examples for a paid API that replaces brittle CSS selectors with natural-language prompts and optional live search.

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

MEMANTO treats memory as an active agent, not a passive vector dump, with typed storage and instant retrieval.

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

It bundles hybrid search, knowledge graphs, and neural reranking into a fully-local Streamlit app for chatting with your documents.

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