Embeds into an app like SQLite, persisting to a local file with no server or separate process. Combines dense and sparse vectors, full-text search, and scalar filters in one hybrid query; C++ core with Python, Node, Go, Rust, and Dart bindings.
On-device search engine for notes, transcripts, and code that blends BM25 full-text, vector semantic search, and a local LLM re-rank — all running offline via node-llama-cpp and SQLite. Ships an MCP server so AI agents can query your knowledge base.
Provides an agent-native personalized tutoring platform that combines persistent TutorBots, RAG-powered knowledge bases, and a CLI-first workflow. Designed for extensible agent skills, multi-channel deployment, and long-term learner memory.
A Claude Code plugin for long-form serial fiction that keeps characters, timeline, and world rules consistent across hundreds of chapters. Facts are committed to a versioned state store, and review gates flag contradictions before each chapter.
Registry where OpenClaw agents publish, version, and vector-search text-based skills — each a SKILL.md plus supporting files. Adds a CLI with install pinning and moderation, plus a unified catalog that lists native code and bundle plugins beside skills.
Indexes full text of visited web pages and local files on a self‑hosted server so you can search your personal knowledge from a web UI, terminal, CLI, or an AI assistant. Runs without mandatory telemetry, offers a browser extension for automatic capture, and supports optional semantic search via a configurable embeddings endpoint.
Transforms unstructured documents into strongly-typed Knowledge Abstracts with one CLI command, extracting entities and relations into graphs, hypergraphs, and spatio‑temporal structures. Includes 80+ templates, multiple RAG engines, local vLLM support, Obsidian export and an MCP server.
Aggregates global news, infrastructure, military and market signals into an interactive map dashboard and synthesizes AI-generated intelligence briefs. Key features: local/remote LLM support, 3D globe + flat map, 35+ data layers, country instability index and client-side RAG/embeddings.
Provides a conditional memory module that performs O(1) N‑gram lookups and fuses static embeddings into transformer hidden states — enables offloading large embedding tables to host memory with minimal inference overhead.
Models an AI agent's context as a file system, unifying memory, resources, and skills instead of flat vector RAG. Uses L0/L1/L2 tiered loading to cut tokens, directory-recursive plus semantic retrieval, and visualized retrieval traces for debugging.
Provides cross-platform semantic memory for AI coding agents by turning human-editable Markdown logs into a rebuildable Milvus “shadow” index and syncing memories across plugins (Claude Code, OpenClaw, OpenCode, Codex). Supports progressive retrieval, hybrid dense+BM25+RRF search, smart deduplication, live sync, and local ONNX embeddings.