Persistent memory
Knowledge stored as plain markdown files with SQLite metadata. Survives across sessions, human-readable, editable by hand. Every edit is versioned — nothing is lost.
Wadachi (轍): the tracks wheels leave in a road. Your AI forgets everything between sessions — re-reading files, re-deriving architecture, re-debating solved problems. Wadachi gives it tracks that stay.
The model re-reads the same files, re-discovers the same patterns — and you type the same reminders, again:
get_context loads project, memories, decisions and what
needs review — ~400 tokens, then it compounds:
Most memory tools make knowledge searchable. Wadachi makes it active: it connects, revises, reflects, and learns how you work.
Knowledge stored as plain markdown files with SQLite metadata. Survives across sessions, human-readable, editable by hand. Every edit is versioned — nothing is lost.
Finds memories by meaning, not keywords. Powered by local embeddings via
fastembed — no API calls, no cloud, no cost. Runs entirely on your machine.
The killer tool. get_context detects your project, gathers the relevant
memories and recent decisions, and hands your AI everything it needs in one call.
Not just what you know — what you decided and why. Each decision keeps its rationale and the rejected alternatives, so no choice gets re-debated.
An entity knowledge graph over your brain. recall_associative spreads
activation along citation, semantic and shared-entity edges — surfacing connected
memories even when the words don't match.
Memories are beliefs with an epistemic envelope. Wadachi flags what's gone stale or been superseded, so a session opens knowing what it can no longer trust.
Thinks across memories: surfaces cross-project analogies and non-obvious connections recall can't reach, proposed as insights you accept or reject.
Notices recurring incidents and proposes always-on rules for review — your brain learning not just facts, but how to act. Read-only: it suggests, never edits.
One command wires Wadachi into Claude Code, Claude Desktop, Cursor — any MCP client.
Map filesystem paths to a project name. Wadachi auto-detects which one you're in and scopes memories accordingly.
Every session opens with get_context — the relevant brain is loaded before
the first file is touched.
Bugs, decisions, configs, patterns — saved the moment they're figured out. The brain grows; each session starts faster.
The memory space is crowded and full of good tools. Here is where wadachi actually differs — and where the alternatives are genuinely strong.
| wadachi 轍 | Mem0 / Zep | Supermemory | |
|---|---|---|---|
| Where your memories live | Markdown files on your disk — an Obsidian vault, an OKF bundle | Their cloud (self-host possible) | Their cloud |
| Telemetry | None. Ever. | Product analytics | Product analytics |
| Cost to run | $0 — local embeddings, no API calls | API-metered | Subscription |
| Memory model | Beliefs that go stale, decisions with provenance (why), time-travel (as_of), sleep consolidation |
Facts + graph (Zep: temporal graph, excellent) | Documents + recall |
| Scale & teams | Single-brain, one human — by design | Multi-tenant, production-scale | Team sharing |
| Managed convenience | You run it (one pipx install) |
Hosted, zero ops | Hosted, zero ops |
Honest summary: if you need multi-tenant scale or a managed service, use the alternatives — they are good at it. If you want a brain that is yours, readable on disk, free to run, and that reasons about what it believes — that is what wadachi is for.
Three commands, and you never re-explain your codebase again.
# install (semantic search included, runs locally)
pipx install "wadachi[semantic]"
# guided setup: brain dir, database, Claude Code registration
wadachi init
# restart Claude Code — your AI now remembers