Inkwell — Persistent Knowledge Base MCP Server

Inkwell is a self-hosted Model Context Protocol server that gives AI agents persistent memory across sessions and projects. Three things set it apart from the other memory servers: plain Markdown files are the source of truth (the index is a throwaway cache you can delete and rebuild), entries are connected by a typed kb:// graph rather than dumped into a flat pile, and updates are bi-temporal — superseding a fact keeps the old version readable instead of overwriting it.

Docker Hub: foreigndmitryi/inkwell-memory · Website: veronchenko.github.io/inkwell-memory

Formerly Engram. Renamed in 0.14.0 — there are a dozen unrelated projects called "Engram" and the name had stopped being findable. Not affiliated with any of them, nor with the Engram keyboard layout. See the changelog for what the rename breaks.

Table of Contents

• Concept
• Features
• Comparison
• Measured Against a Plain Markdown Wiki
• Design Patterns
• Architecture
• Quick Start
• Search
• Tools
• Dashboard
• Graph Relations
• Usage Examples
• Prompt Your Agent
• Configuration
• Storage Format
• Development
• License

Concept

Agent conversations end and take their context with them. Inkwell is the piece that survives: a knowledge base an agent searches before acting and writes to after resolving something non-obvious, so the next session — same project or a different one — starts with what was already learned instead of re-deriving it.

It deliberately stores zero discoverable information. If a fact can be pulled from code, git history, config files, or existing docs, it does not belong in Inkwell — that's what greps and re-reads are for. What belongs is the kind of knowledge a conversation would otherwise lose: a decision and the alternatives it ruled out, a bug's root cause and fix, a procedure learned the hard way, a preference stated once that should hold from then on.

Two things keep the base usable as it grows:

• Atomicity — one entry, one fact. remember warns (non-blocking) on Markdown headers, more than 3 paragraphs, or content past 512 B/1 KB, pushing multi-fact dumps back into separate linked entries instead of a wall of text no search will rank well.
• The graph, not a pile — entries link to each other via kb://uuid#type references, so related facts (a hub project, its features, a diagnostic tied to one of them) stay navigable both ways instead of living as isolated rows.

Features

• Hybrid search — SQLite FTS5 (BM25, Porter stemming) fused with cosine similarity over local Model2Vec embeddings and an IDF-weighted exact-match channel over title/tags via Reciprocal Rank Fusion; finds entries by meaning or by a literal proper noun BM25/embeddings alone would dilute among lexically-similar distractors, with zero cloud dependency
• Typed graph relations — kb://uuid#type links between entries, resolved both directions (outgoing + backlinks) on every recall, with an optional second hop (hops=2) to see how two entries connect through an intermediate one
• Schema-enforced entry types — hub, decision, diagnostic, feature, procedure, integration, pattern, snippet, `pr

... View full README on GitHub