Wikori Documentation

What is Wikori?

Wikori is a desktop knowledge management system that turns raw information into structured, AI-enriched knowledge. Drop any file — a PDF, a web article, an email, a voice note transcript — into Wikori and it automatically extracts the content, runs it through an AI model, and stores a richly annotated version you (and your AI agents) can search and explore.

The core idea: you own the data, it stays on your machine, and it's always in plain readable Markdown. No cloud lock-in, no proprietary format. And unlike a static document archive, Wikori is a living memory system — AI agents can write their own observations back into your vault, decisions are preserved across sessions, and search results are ranked by trustworthiness, not just keyword frequency.

Key Concepts

Vaults

A vault is an isolated knowledge base — a folder on your disk that Wikori monitors and enriches. You can have many vaults: one per client, per project, per topic. Each vault has its own ingestion pipeline, knowledge graph, and optional email tag. Switching vaults in the sidebar instantly scopes all operations to that vault.

The INGEST Folder

Every vault has an INGEST/ subfolder. Drop any supported file here and Wikori picks it up automatically — extracting text from Office documents, analyzing images, scraping URLs — then passes the content to your AI endpoint for metadata enrichment. The built-in Web Crawler can also discover and queue entire sites for bulk ingestion.

AI Enrichment

Wikori sends each piece of content to any OpenAI-compatible API endpoint. The AI extracts and returns structured YAML frontmatter: title, summary, entities, tags, source type, confidence score, and source authority. This metadata is prepended to the Markdown file and drives both the Knowledge Map and search ranking.

Vault Schema

Each vault automatically maintains a SCHEMA.md file — a machine-readable overview of what's inside: entity types, dominant tags, memory tiers, and a curated Ground Truth list of the most reliable entries. AI agents read this first so they understand the vault's structure before searching, leading to far smarter queries from the very first interaction.

Agent Memory

AI agents connected via MCP can write observations back into your vault — decisions, errors, patterns, preferences, and lessons. These are stored as first-class Markdown entries with full metadata, indistinguishable from human-authored documents. The vault becomes a shared, accumulating memory that persists across every session.

MCP Server

Wikori runs a local Model Context Protocol server. Point any compatible AI agent at it and the agent can search, read, write observations, and navigate your entire knowledge base directly. Five purpose-built memory tools handle schema reading, memory saving, reliability-ranked search, session review, and memory consolidation.

Documentation Guides

Supported Formats at a Glance