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.
Compresses any context sent to LLMs (tool outputs, DB reads, RAG results, files, logs) to cut tokens by ~70–95% while preserving reversible originals; runs as a proxy or Python/TypeScript SDK with integrations for common agent frameworks.
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.
Searches raw files with no vector DB or embedding step — drop documents in and query instantly, firing LLM calls only when a match needs reasoning. Adds Monte Carlo evidence sampling and self-evolving clusters as a low-overhead RAG alternative.
Provides a systematic, project-driven tutorial and runnable codebase for building AI agents, RAG pipelines, and multi-agent systems—focused on Python, LangChain/LangGraph, tooling, deployment, and an interview question bank for engineers aiming to ship production agent applications.
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.
Self-hosted personal AI agent runtime that runs chats, tools, automations and long-term memory for persistent workflows. Small, readable core with a bundled WebUI, multi-chat integrations, an OpenAI-compatible API and a Python SDK for easy extension and deployment.
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.
Runs a local-first, full AI stack—LLM inference, chat UI, voice, agents, workflows, RAG, and image generation—deployable with one command. Auto-detects hardware and bootstraps a small model for instant chat while larger models download; supports Linux, Windows, macOS and optional cloud/hybrid modes.
Turns a PC, Mac, or Linux machine into a private AI server with one-command installers: local LLM inference, a ChatGPT-style web UI, voice, agents, RAG, workflows, image generation, hardware-aware model selection, and optional cloud/hybrid modes.
Desktop app for managing markdown-based knowledge bases with a files-first, git-first workflow. Works offline, uses plain markdown + YAML frontmatter for portability, and includes AI-agent integrations and agent configuration to organize context, memory, and procedures for assistants.
Desktop-first personal agent that compresses your connected accounts into a local memory tree and runs agentic workflows. Key features include 118+ one‑click integrations, TokenJuice token compression into an Obsidian‑style vault, model routing with optional local models (Ollama).