mempalace-rs 0.4.1

High-performance, local AI memory with AAAK v3.2 protocol and temporal Knowledge Graph
Documentation

mempalace-rs

CI Crates.io Docs.rs License: MIT Rust

A high-performance, local, offline-first AI memory system built in Rust. Gives AI agents long-term retention by mining local codebases and conversations into a structured, searchable, and symbolic "palace."

Features

  • 4-Layer Memory Stack (L0-L3): Hierarchical context from identity to deep semantic search
  • AAAK V:3.2 Compression: ~30x token reduction with adaptive density, importance scoring, and delta encoding
  • Temporal Knowledge Graph: Entity relationships with valid_from/valid_to tracking
  • 20 MCP Tools: Full Model Context Protocol integration for AI agent interaction
  • SQLite + VectorStorage: Native embedding storage for structured and vector data
  • 197 Tests: All passing, production-ready

Installation

From Source

Requires Rust 1.70+ and the cargo build system.

git clone https://github.com/jxoesneon/mempalace-rs.git
cd mempalace-rs
cargo install --path .

From Crates.io (Coming Soon)

Once stable, you can install the binary directly:

cargo install mempalace-rs

🚀 Insta-Setup (Top 3 AI Clients)

Connect your palace to your favorite agent in seconds. For all 15+ supported platforms, see SKILLS_GUIDE.md.

1. Claude Code

Open your terminal in the project root and run:

claude add mcp "mempalace-rs" --command "cargo" --args "run,--,mcp-server" --cwd "$(pwd)"

2. Cursor

Go to Settings > Features > MCP > Add New MCP Server:

  • Name: mempalace
  • Type: command
  • Command: cargo run -- mcp-server
  • CWD: Full path to this repository

3. Windsurf

Simply drag and drop the mempalace-rs.skill file into your Windsurf chat or project sidebar to load all tools and instructions automatically.

Performance & Integrity

MemPalace-rs is validated against the 2026 Gold Standards for AI memory. Our methodology prioritizes high-integrity reasoning and ultra-long context persistence over synthetic "recall-only" metrics.

2026 Gold Standard Validation

Verified multi-hop reasoning, 1M+ token persistence, and structural integrity.

Benchmark Score Metric Latency
**RULER ** 1.000 nDCG 120.0 ms
**STRUCTMEM ** 1.000 Structural 33.0 ms
**BABILONG ** 1.000 Reasoning 529.0 ms
**BEAM ** 1.000 Nugget 24.0 ms

[!TIP] For a full technical breakdown of our anti-fraud methodology—including strict top_k <= 10 limits and end-to-end reasoning validation—please see the Detailed Benchmarking Report.

Low-Level Micro-Benchmarks

Raw throughput measured on local hardware.

Operation Throughput Latency

Benchmarks performed on Apple Silicon M4. Results are generated autonomously by CI on every release.

Benchmarked on Apple Silicon M4, 16GB RAM. Performance may vary by hardware.

Binary Size: 7.9 MB (release build) Cold Start: ~300 ms Memory Usage: ~50 MB baseline

Quick Start

# Build
cargo build --release

# Run tests
cargo test

# Start MCP server (for AI integration)
cargo run -- mcp-server

# Mine a project
cargo run -- mine /path/to/project --mode project --wing MyProject

# Search your palace
cargo run -- search "async patterns in Rust"

# Get wakeup context
cargo run -- wakeup

# Semantic Pruning
cargo run -- prune --threshold 0.8

Requirements

  • Rust 1.70+ (edition 2021)
  • (Vector storage is fully embedded and zero-network)
  • Optional: cargo-llvm-cov for coverage reports

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    MEMORY STACK (L0-L3)                       │
├─────────────────────────────────────────────────────────────┤
│  L0: IDENTITY  → Core persona (~100 tokens)                   │
│  L1: ESSENTIAL → Recency-biased events (~500-800 tokens)      │
│  L2: ON-DEMAND → Similarity-searched context                  │
│  L3: SEARCH    → Raw semantic search                          │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
   ┌────▼────┐           ┌────▼────┐           ┌────▼────┐
   │Storage  │           │Searcher │           │Dialect  │
   │(SQLite) │           │(Search) │           │(AAAK)   │
   └────┬────┘           └────┬────┘           └────┬────┘
        │                     │                     │
   ┌────▼─────────────────────▼─────────────────────▼────┐
   │              Knowledge Graph (SQLite)               │
   │         entities ──► triples (temporal)             │
   └─────────────────────────────────────────────────────┘

Storage Backends

  • VectorStorage (usearch): Documents with embeddings + metadata (wing/room/source_file)
  • SQLite (relational): Wings, diary entries, knowledge graph triples

AAAK Dialect

~30x token compression using entity codes, emotion codes, and zettel format:

WING|ROOM|DATE|SOURCE
0:ENTITIES|TOPICS|"QUOTE"|EMOTIONS|FLAGS

CLI Commands

Command Description
init <dir> Guided onboarding with room detection
mine <dir> Ingest projects or conversations (supports --limit, --dry-run, --no-gitignore)
search <query> Semantic search over ingested data (supports --wing, --room, --results)
repair Recover/re-index vector storage from SQLite metadata
instructions Output system prompts for AI agent onboarding
wakeup Get L0+L1 context (~600-900 tokens)
compress AAAK compress drawers
split <dir> Split mega-files into per-session files
prune Semantic deduplication (clustering/merging)
mcp-server Run MCP server over stdio

MCP Tools

The project exposes 20 tools via Model Context Protocol (all prefixed with mempalace_):

Palace Overview

  • mempalace_status - Returns total drawers, wings, rooms, protocol, and AAAK spec
  • mempalace_list_wings - Returns all wings with counts
  • mempalace_list_rooms - Returns rooms within a wing
  • mempalace_get_taxonomy - Returns full wing → room → count tree
  • mempalace_graph_stats - Graph overview

Search & Retrieval

  • mempalace_search - Semantic search with wing/room filters
  • mempalace_check_duplicate - Similarity check for deduplication
  • mempalace_prune - Semantic memory pruning and merging

Graph Navigation

  • mempalace_traverse_graph - BFS walk from start_room
  • mempalace_find_tunnels - Find bridge rooms

Content Management

  • mempalace_add_drawer - Add verbatim content
  • mempalace_delete_drawer - Remove drawer by ID
  • mempalace_get_aaak_spec - Returns AAAK spec

Knowledge Graph

  • mempalace_kg_add - Add triple (subject, predicate, object)
  • mempalace_kg_query - Query entity relationships
  • mempalace_kg_invalidate - Mark triple as invalid
  • mempalace_kg_timeline - Get chronological timeline
  • mempalace_kg_stats - KG statistics

Agent Diary

  • mempalace_diary_write - Persist agent journal entry
  • mempalace_diary_read - Retrieve last N diary entries

Project Structure

mempalace-rs/
├── src/
│   ├── main.rs              # CLI entry point
│   ├── lib.rs               # Module exports
│   ├── storage.rs           # MemoryStack (L0-L3)
│   ├── searcher.rs          # Vector semantic search
│   ├── mcp_server.rs        # 20 MCP tools
│   ├── dialect.rs           # AAAK compression (V:3.2)
│   ├── knowledge_graph.rs   # Temporal triples
│   ├── diary.rs             # SQLite-backed agent journal
│   ├── miner.rs             # File chunking, room detection
│   ├── entity_detector.rs   # NER with signal scoring
│   └── ...                  # Additional modules
├── tests/                   # 7 integration test suites
├── .github/workflows/       # CI configuration
└── mempalace-rs.skill       # Agent skill file

Key Modules

Module Purpose
storage.rs MemoryStack (L0-L3) implementation
searcher.rs Vector semantic search
dialect.rs AAAK compression (~30x token reduction)
knowledge_graph.rs Temporal triples with valid_from/valid_to
mcp_server.rs 20 MCP tools for agent integration
diary.rs SQLite-backed agent journal
miner.rs File chunking, room detection
entity_detector.rs Heuristic NER (People/Projects/Terms)
palace_graph.rs Room navigation graph (BFS, tunnels)

Development

# Build
cargo build

# Run tests
cargo test

# Run with coverage
cargo llvm-cov

# Lint
cargo clippy -- -D warnings

# Format
cargo fmt

Test Mode

Set MEMPALACE_TEST_MODE=1 to skip DB initialization in tests:

MEMPALACE_TEST_MODE=1 cargo test

Configuration

  • Diary: ~/.mempalace/diary.db (SQLite with indexed timestamps)
  • Knowledge Graph: knowledge.db (SQLite with temporal triples)
  • Palace Data: ~/.mempalace/ (usearch + SQLite)
  • Identity: ~/.mempalace/identity.txt (L0 persona)

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE file.

Extended Platform Support

Android (Termux)

MemPalace-RS is optimized for ARM64 mobile environments. To set up your environment in Termux:

chmod +x scripts/setup_android.sh
./scripts/setup_android.sh

This will install the native onnxruntime and patch ort-sys for local compatibility.

Parity with Python Original

This project is a 100% functional port of the original Python mempalace implementation. For a detailed breakdown of feature parity, compatibility status, and Rust-exclusive enhancements, see the Full Parity Report.

Acknowledgments

This is a Rust port of the original MemPalace Python project by Milla Jovovich & Ben Sigman.

Agent Integration

Load mempalace-rs.skill for comprehensive documentation of all 20 MCP tools, architecture details, and best practices for AI agents interacting with the palace.