trusty-memory

Memory palace MCP server (stdio + HTTP/SSE) backed by usearch vector store, SQLite metadata, and fastembed embeddings. Stores and retrieves natural-language memories organized into named "palaces" (namespaces), with an optional knowledge-graph layer for structured triples.

Integrates with Claude Code and any other MCP-aware client as a first-class long-term memory backend.

System Requirements

RAM: 512 MB minimum; 1 GB+ recommended (ONNX embedding model loads ~22 MB, usearch index scales with corpus size)
Disk: ~100 MB for the model cache on first run (~/Library/Application Support/trusty-memory/ on macOS, ~/.local/share/trusty-memory/ on Linux)
Rust: 1.88+ (if building from source)

Installation

cargo install trusty-memory

The installed binary is named trusty-memory.

Quick Start

stdio mode (Claude Code integration)

trusty-memory serve

Reads JSON-RPC 2.0 from stdin, writes responses to stdout. This is the mode Claude Code uses.

HTTP mode (browser dashboard + REST API)

trusty-memory serve --port 7880

Binds an HTTP server with the embedded Svelte admin UI at http://127.0.0.1:7880 and a REST API under /api/v1/.

Bind to a named palace

When all tool calls should default to one palace namespace, use --palace:

trusty-memory serve --palace my-project

With a default set, the palace argument becomes optional in every MCP tool call.

Claude Code Integration

Add to your project's .mcp.json (or ~/.claude/mcp.json for machine-wide):

{
  "mcpServers": {
    "trusty-memory": {
      "command": "trusty-memory",
      "args": ["serve"],
      "env": {}
    }
  }
}

To pre-bind a palace so Claude Code never needs to pass palace explicitly:

{
  "mcpServers": {
    "trusty-memory": {
      "command": "trusty-memory",
      "args": ["serve", "--palace", "my-project"],
      "env": {}
    }
  }
}

Claude Code auto-discovers .mcp.json on project open. The trusty-memory binary must be on PATH.

Available MCP Tools

All 12 tools are exposed via both the stdio MCP server and the HTTP API (/api/v1/). The palace argument is required unless the server was started with --palace <name>.

Memory tools

Tool	Arguments	Description
`memory_remember`	`palace, text, room?, tags?`	Store a memory. Returns the `drawer_id`.
`memory_recall`	`palace, query, top_k?`	Hybrid BM25+vector recall (L0/L1/L2 layers).
`memory_recall_deep`	`palace, query, top_k?`	Deep recall (L3 — slower, higher recall).
`memory_list`	`palace, room?, tag?, limit?`	List stored memories, optionally filtered.
`memory_forget`	`palace, drawer_id`	Delete a specific memory by ID.

Palace management tools

Tool	Arguments	Description
`palace_create`	`name, description?`	Create a new palace namespace.
`palace_list`	—	List all palaces and their IDs.
`palace_info`	`palace`	Palace metadata and statistics.

Knowledge graph tools

Tool	Arguments	Description
`kg_assert`	`palace, subject, predicate, object, confidence?, provenance?`	Assert a knowledge triple.
`kg_query`	`palace, subject`	Query triples by subject.

Dream and status tools

Tool	Arguments	Description
`memory_dream`	`palace?`	Run a consolidation cycle (merge near-duplicates, prune, compact).
`memory_status`	—	Global statistics (total drawers, vectors, KG triples).

Web UI

When running in HTTP mode, the embedded Svelte admin dashboard is available at:

http://127.0.0.1:<port>/

The dashboard provides:

Real-time palace overview (drawer counts, vector counts, KG triple counts)
Live event stream (palace created, drawer added/deleted, dream completed)
Manual dream (consolidation) trigger
Palace-scoped memory browsing

Configuration

Environment variables

Variable	Default	Description
`RUST_LOG`	`warn`	Tracing filter. E.g. `RUST_LOG=info` or `RUST_LOG=trusty_memory=debug`.
`OPENROUTER_API_KEY`	—	Enables chat completions via OpenRouter (`/api/v1/chat`).
`TRUSTY_DATA_DIR_OVERRIDE`	—	Override the data directory (intended for tests).

Config file

~/.trusty-memory/config.toml (created on first run if absent):

[openrouter]
api_key = ""
model = "anthropic/claude-3.5-sonnet"

[local_model]
enabled = false
base_url = "http://127.0.0.1:11434"
model = "llama3"

Data directory

Memories and vector indexes persist under the OS-standard data directory:

macOS: ~/Library/Application Support/trusty-memory/<palace-id>/
Linux: ~/.local/share/trusty-memory/<palace-id>/

Each palace directory contains:

drawers.db — SQLite metadata store
vectors.usearch — usearch vector index
kg.db — knowledge-graph triples (SQLite)
chat_sessions.db — chat session history

Architecture

trusty-memory (this crate)          trusty-memory-core
  stdio JSON-RPC 2.0 loop  ──────►  PalaceRegistry
  axum HTTP/SSE server     ──────►  usearch vector index
  embedded Svelte UI               SQLite metadata + KG
  12 MCP tools                     fastembed (AllMiniLML6V2Q)

trusty-memory-core owns the storage engine: usearch for approximate nearest-neighbor search, SQLite for metadata and knowledge-graph triples, and fastembed for 384-dim text embeddings. The MCP server (trusty-memory) is a thin protocol layer on top.

The embedded Svelte UI is compiled at build time and served via rust-embed — no separate web server or Node.js installation is needed at runtime.

Development

# Build and run
cargo run -p trusty-memory -- serve

# Run in HTTP mode
cargo run -p trusty-memory -- serve --port 7880

# Tests
cargo test -p trusty-memory
cargo test -p trusty-memory-core

# Check only (faster)
cargo check -p trusty-memory

License

Licensed under the MIT License.

Repository

https://github.com/bobmatnyc/trusty-tools

trusty-memory 0.1.50