Enterprise-grade (40m+ LOC) codebase intelligence, zero-setup, private & local Plugin/Skill or MCP: hybrid semantic search, polyglot dependency graphs, symbol-level impact analysis & call-flow, interactive HTML viewer, cross-project & branch-aware search, DB/API/infra knowledge. 61% less tokens, 84% fewer calls, 37x faster. Cloud in private beta.

local-first semantic code search engine

Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift

🔎 SimilaritySearchKit is a Swift package providing on-device text embeddings and semantic search functionality for iOS and macOS applications.

Search images and videos offline using text or by reverse image search with on-device AI.

Browser-local AI code Q&A engine. Chat with your codebase, ensure privacy, and optimize LLM collaboration.

Memory for AI that works like yours—local, instant, persistent. 13x faster than Pinecone, 5x leaner than RAG. Finds what RAG misses. Zero cloud, zero cost.

Next-token prediction in JavaScript — build fast language and diffusion models.

Browse the top 10,000 packages on PyPI with the help of vector embeddings

YouTubeGPT • AI Chat with 100+ videos ft. YouTuber Marques Brownlee (@ MKBHD) ⚡️🔴🤖💬

⚡ Super fast clustering for high-dimensional vectors on CPUs (x86, ARM) and GPUs — for Python and C++. 100x faster clustering of vector embeddings than FAISS

AI chat over the US Constitution 📜 💬 🇺🇸

a vector embedding database with multiple storage engines and AI embedding integrations

YouTubeGPT • AI Chat with 100+ videos ft. YouTuber Matt Wolfe (@mreflow) 🐺🟣🤖💬

AI pipeline built with the honc and workers-ai. vector embeddings, web scraping and processing with Cloudflare Workflows (beta)

Automatos AI: Open-source platform for advanced context engineering and multi-agent orchestration in enterprise automation. Built on frontier research in RAG, vector embeddings, cognitive tools, emergent symbols, and neural field theory—powered by FastAPI, Next.js, and PostgreSQL.

Semantic QA with a markdown database: Query any markdown file using vector embedding, Pinecone vector database and GPT (langchain). A weaker version of privateGPT

UC Berkeley CS186 AI Chatbot 🤖 🖥️ 🐻

rudradb-opin-examples is for example implementations of the pip install rudradb-opin

.NET library for local embedding generation using ONNX and Microsoft.Extensions.AI