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An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw integrations.

A production-ready FastAPI platform with modular components and a built-in control plane.

A python library for creating AI assistants with Vectara, using Agentic RAG

MCP server for AI agent for cybersecurity: automate assessment of documents, questionnaires & reports. Multi-format parsing, RAG knowledge base,Risks, compliance gaps, remediations.

AI-powered text compression library for RAG systems and API calls. Reduce token usage by up to 50-60% while preserving semantic meaning with advanced compression strategies.

Local-first AI daemon for Logseq OG: background semantic indexing, link hygiene, and agent-ready CLI/MCP — edits Markdown on disk (no cloud, no Logseq API). Karpathy LLM-Wiki inspired.

CLI + Local MCP - A shared structured memory store across Claude Code, Cursor, Windsurf, Antigravity, and every MCP client. Semantically queryable.

Django BM25 full-text search using PostgreSQL - a lightweight Elasticsearch alternative

A High-Efficiency System of Large Language Model Based Search Agents

Scientific paper evidence search for ChatGPT, MCP, REST, Python, and AI agents.

One memory, three terminals. Shared memory layer for Claude Code, Codex, and Gemini CLI — hybrid retrieval (vector + BM25 + KG), session continuity, 41 MCP tools. Local-first, LanceDB-backed.

Open-source, self-hosted knowledge backend for AI agents — hybrid search (vector + keyword), MCP server, 5 connectors, Docker-ready

Self-hosted MCP server for AI agent memory — 14 core tools, profile-based tiering (86+ available). 88.2% LoCoMo. Works with Claude, Cursor, Windsurf.

MCP server for Apple Notes. Semantic search, full CRUD, works with MCP clients.

Hardware-Accelerated Cryptographic RAG System — GPU simulation of SHA-256 hash engine with AES-256-GCM encryption and Merkle tree integrity. NOT real ASIC hardware.

LLM readiness linter for websites. Audits robots.txt, llms.txt, Schema.org, and content density on a 0-100 scale. Includes MCP server. Published on PyPI: pip install context-cli.

Fast Python web crawler for RAG and AI ingestion. Extracts clean Markdown from any site for LLMs and vector stores.

MCP server that adds RAG-powered AI chat to any website. One command from Claude Code. Local vector store, multi-provider LLM (OpenAI/Anthropic/Gemini). Zero cloud dependency.