whitemagic
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@whitemagicSave this important decision to memory: use TypeScript for the new API."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
WhiteMagic
Local-first memory and session continuity for coding agents.
WhiteMagic gives an AI coding agent durable project memory over MCP: record important context, find it after restart, and carry useful decisions into the next session — without sending your memory store to any hosted service.
You are an agent reading this repo? Start with skill.md (five-minute operational onboarding) and llms.txt (machine-readable index).
{
"mcpServers": {
"whitemagic": {
"command": "wm",
"args": ["serve", "--profile", "curated"]
}
}
}Status
WhiteMagic v9. General release for AI coding agents.
Supported platforms: Linux x86-64, macOS, Windows.
The Linux x86-64 artifact is fully static — no glibc or distribution requirements.
Trusted, local-first, single-user operation with Landlock containment and firebreak guards.
Related MCP server: Memento
What it does
The supported alpha contract:
trusted, local, single-user operation;
explicit MCP routes for dependable behavior;
durable memory creation and lexical search without an external model;
session record, replay, and cross-session continuity;
a complete backup, verification, and restore path;
no telemetry and no required WhiteMagic cloud service;
truthful degradation when optional models or embeddings are unavailable.
Install
Download the binary and its checksum from the latest release, then:
sha256sum -c wm-linux-x86_64.sha256
chmod +x wm
mkdir -p ~/.local/bin && mv wm ~/.local/bin/If ~/.local/bin is not on your PATH:
export PATH="$HOME/.local/bin:$PATH"Or use the install script (resolves the latest release and verifies the checksum automatically):
curl -fsSL https://raw.githubusercontent.com/lbailey94/whitemagic/main/scripts/install.sh | shVerify the installation:
wm --version # wm 9.0.0
wm doctor # environment health checkConnect an MCP client
Point any MCP client at:
wm serve --profile curatedThe server communicates over stdio and exposes a single wm meta-tool.
Explicit routing is the dependable contract:
wm(route="memory.create", args={...})wm(route="session.start", args={...})wm(route="tools.list", args={})
--profile curated selects the supported memory/session surface and is the
default when no profile is specified. Pass --profile full for the research
archive surface (see below).
Privacy and data
Your store lives locally at
~/.local/share/whitemagic. Nothing is sent to WhiteMagic-operated services; there is no telemetry.Privacy flags exclude memories from responses and reasoning. They are access controls, not encryption — anyone who can read the store files can read the contents. Do not store credentials in memories.
Conversation capture happens through explicit tool calls, not automatically.
Backup and restore
Back up the whole store root (LMDB database, search indexes, and all
session/state files — not just the lmdb/ subdirectory):
# Stop the server first, then:
wm backup # writes ~/whitemagic-backups/<timestamp>/
wm backup --out /path/to/external/disk # keep copies OFF the live machineEach backup contains the full store plus a SHA256SUMS manifest. Restore
after a failure (this replaces the target store):
wm restore --backup ~/whitemagic-backups/whitemagic-backup-<timestamp> --force
wm doctor # confirm health after restoreRestore verifies every file against the manifest before touching anything, and refuses tampered or incomplete backups. Notes:
wm seal/wm verifydetect integrity drift; they do not recover data. Only a backup recovers data.Transaction rollback (
transaction.rollback) is an in-store, short-lived undo — not a substitute for backups.Keep at least one backup on a different disk or machine.
Research surface (not part of the alpha contract)
The codebase contains a larger research system beyond the product boundary:
autonomous cycles, dream consolidation, bicameral reasoning, an imagination
engine, self-play training loops, polyglot sidecars (Julia/Haskell/Zig/Koka),
a signed multi-agent mesh, holographic memory coordinates, and a 237-tool
archive reachable via wm serve without a profile restriction. These are
research surfaces without product acceptance evidence; they may change or be
removed. Only surfaces documented in this README are part of the product
contract.
Building from source
Requires Rust 1.85+:
cargo build --release
cargo test # full test suite
cargo clippy --all-targetsDocumentation
docs/QUICKSTART.md— the two-process continuity demodocs/MCP_CONFIG_GUIDE.md— client configurationdocs/MULTI_LAPTOP.md— moving between machines (backup/restore, session carry)CHANGELOG.md— release notesSECURITY.md— reporting vulnerabilities
Migrating from v26 (legacy Python)
If you ran the retired Python version:
wm migrate --v2-dir ~/.whitemagic/users/local/galaxies --dry-run # preview
wm migrate --v2-dir ~/.whitemagic/users/local/galaxies # migrateLicense
MIT © Lucas Bailey and WhiteMagic Contributors
Support and security
Support: open an issue at https://github.com/lbailey94/whitemagic/issues
Security: email lbailey94@protonmail.com (please do not open public issues for security reports)
Available Tools
1 toolwmA
WhiteMagic meta-tool — curated tool surface (59 tools). Mode: writable. Scope: store /root/.local/share/whitemagic/lmdb. Use thought= for NLU routing (e.g. 'remember that X is Y', 'search for Z', 'list tools'), route= for explicit dispatch (e.g. 'memory.create'), and args= for passthrough arguments. Say 'list tools' to discover available tools.
| Name | Required | Description | Default |
|---|---|---|---|
| args | No | Arguments to pass through to the target tool. | |
| route | No | Explicit tool name for direct dispatch (e.g. 'memory.create', 'tools.list'). | |
| thought | No | Natural language input describing what to do. Auto-routes to the best-matching tool via TF-IDF NLU classification. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It states the tool is writable and scoped to /root/.local/share/whitemagic/lmdb, which conveys mutability and persistence. However, it does not disclose what happens on invalid routing, how write operations are applied, or what the observable side effects beyond scope are, leaving some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and dense, with each sentence adding a distinct element: identity, mode/scope, parameter usage, and discovery command. It could be better structured with separation of routing versus dispatch, but no sentence is wasted and the key operational details are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a meta-tool with 59 sub-tools and no output schema, the description covers the essential calling convention and discovery mechanism. It does not explain the output shape since that depends on the routed tool, nor does it mention edge cases such as ambiguous routing or invalid route names. These are notable gaps for an agent that must know what the call will return and how to recover from failures.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already has 100% coverage, but the description adds meaning beyond the field names by explaining the intended role of each parameter: thought performs TF-IDF NLU auto-routing, route forces a direct tool dispatch, and args passes through arguments. It clarifies the routing model and the relationship between thought and route, which the bare schema does not convey.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as a meta-tool exposing a curated surface of 59 tools, with a writable mode and a concrete storage scope. The phrase 'Use thought= for NLU routing... route= for explicit dispatch' makes its dispatcher role clear even though the name 'wm' and missing title are opaque. It lacks sibling differentiation because no siblings are provided, but the resource and behavior are stated specifically enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage instructions for all three parameters: thought for NLU routing, route for explicit dispatch, and args as passthrough. It also gives concrete command examples and points to 'list tools' for discovery. There is no explicit when-not-to-use because no alternatives are listed, but the guidance is otherwise complete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v9.1.0- First observed
wm
TDQS
Scored across 1 tool
Only one tool exists, so there is no ambiguity between tools. The tool description is clear about its purpose as a meta-tool and how to use it.
The single tool name 'wm' is extremely generic and provides no indication of its function. It does not follow a verb_noun pattern, but with only one tool, naming consistency is not a practical concern; however, the name itself is vague.
The server exposes only one meta-tool, which internally manages 59 tools. This is an extreme mismatch between the exposed surface and the actual scope, making it nearly impossible for agents to discover and use the underlying tools without explicit prior knowledge.
The single meta-tool hides the entire tool surface behind a routing mechanism, making the surface severely incomplete from an agent's perspective. Direct CRUD or lifecycle operations are not exposed, and the need to use thought= for NLU routing introduces fragility and dead ends.
Maintenance
Related MCP Connectors
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
Persistent memory for AI agents — log and recall conversation context over MCP.
Private-by-default, local-first memory/context/task orchestrator for MCP apps and agents.
Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.
Related MCP Servers
- AlicenseAqualityDmaintenanceHierarchical markdown-based memory system for AI agents. Enables efficient context management by loading only relevant rooms (directories) instead of full memory.5MIT
- AlicenseCqualityAmaintenanceMemento is a local-first, open-source MCP middleware that gives AI agents persistent memory, proactive goal enforcement, and autonomous intelligence using a SQLite temporal graph with Reciprocal Rank Fusion retrieval.151AGPL 3.0
- AlicenseNot gradedqualityBmaintenanceLocal persistent memory for AI coding tools. Stores project context and conversation recall locally via MCP.1314MIT
- AlicenseNot gradedqualityAmaintenanceProvides persistent memory for AI coding agents via MCP, allowing them to recall fragility, decisions, and bugs across sessions.2413MIT