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Vestige

Memory traces that fade like yours do

The only AI memory system built on real cognitive science.
FSRS-6 spaced repetition. Retroactive importance. Context-dependent recall.
All local. All free.

Installation | Quick Start | Features | The Science

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Why Vestige?

The only AI memory built on real cognitive science.

Feature	What It Does
FSRS-6 Spaced Repetition	Full 21-parameter algorithm - nobody else in AI memory has this
Retroactive Importance	Mark something important, past 9 hours of memories strengthen too
Context-Dependent Recall	Retrieval matches encoding context (Tulving 1973)
Memory States	See if memories are Active, Dormant, Silent, or Unavailable
100% Local	No API keys, no cloud, your data stays yours

Other tools store memories. Vestige understands how memory actually works.

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Installation

From Source (Recommended)

git clone https://github.com/samvallad33/vestige
cd vestige
cargo build --release --package vestige-mcp

The binary will be at ./target/release/vestige-mcp

Homebrew (macOS/Linux)

brew install samvallad33/tap/vestige

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Quick Start

1. Build Vestige

cargo build --release --package vestige-mcp

2. Configure Claude Desktop

Add Vestige to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "vestige": {
      "command": "/path/to/vestige-mcp",
      "args": [],
      "env": {
        "VESTIGE_DATA_DIR": "~/.vestige"
      }
    }
  }
}

3. Restart Claude Desktop

Claude will now have access to persistent, biologically-inspired memory.

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Features

Core

Feature	Description
FSRS-6 Algorithm	Full 21-parameter spaced repetition (20-30% better than SM-2)
Dual-Strength Memory	Bjork & Bjork (1992) - Storage + Retrieval strength model
Hybrid Search	BM25 + Semantic + RRF fusion for best retrieval
Local Embeddings	768-dim BGE embeddings, no API required
SQLite + FTS5	Fast full-text search with persistence

Neuroscience-Inspired

Feature	Description
Synaptic Tagging	Retroactive importance (Frey & Morris 1997)
Memory States	Active/Dormant/Silent/Unavailable continuum
Context-Dependent Memory	Encoding specificity principle (Tulving 1973)
Prospective Memory	Future intentions with time/context triggers
Basic Consolidation	Decay + prune cycles

MCP Tools (25 Total)

Core Memory (7):

• ingest - Store new memories
• recall - Semantic retrieval
• semantic_search - Pure embedding search
• hybrid_search - BM25 + semantic fusion
• get_knowledge - Get memory by ID
• delete_knowledge - Remove memory
• mark_reviewed - FSRS review (1-4 rating)

Stats & Maintenance (3):

• get_stats - Memory statistics
• health_check - System health
• run_consolidation - Trigger consolidation

Codebase Memory (3):

• remember_pattern - Store code patterns
• remember_decision - Store architectural decisions
• get_codebase_context - Retrieve project context

Prospective Memory (5):

• set_intention - Remember to do something
• check_intentions - Check triggered intentions
• complete_intention - Mark intention done
• snooze_intention - Delay intention
• list_intentions - List all intentions

Neuroscience (7):

• get_memory_state - Check cognitive state
• list_by_state - Filter by state
• state_stats - State distribution
• trigger_importance - Retroactive strengthening
• find_tagged - Find strengthened memories
• tagging_stats - Tagging system statistics
• match_context - Context-dependent retrieval

---

The Science

Ebbinghaus Forgetting Curve (1885)

Memory retention decays exponentially over time:

R = e^(-t/S)

Where:

• R = Retrievability (probability of recall)
• t = Time since last review
• S = Stability (strength of memory)

Bjork & Bjork Dual-Strength Model (1992)

Memories have two independent strengths:

• Storage Strength: How well encoded (never decreases)
• Retrieval Strength: How accessible now (decays with time)

Key insight: difficult retrievals increase storage strength more than easy ones.

FSRS-6 Algorithm (2024)

Free Spaced Repetition Scheduler version 6. Trained on millions of reviews:

const FSRS_WEIGHTS: [f64; 21] = [
    0.40255, 1.18385, 3.173, 15.69105, 7.1949,
    0.5345, 1.4604, 0.0046, 1.54575, 0.1192,
    1.01925, 1.9395, 0.11, 0.29605, 2.2698,
    0.2315, 2.9898, 0.51655, 0.6621, 0.1, 0.5
];

Synaptic Tagging & Capture (Frey & Morris 1997)

When something important happens, it can retroactively strengthen memories from the past several hours. Vestige implements this with a 9-hour capture window.

Encoding Specificity Principle (Tulving 1973)

Memory retrieval is most effective when the retrieval context matches the encoding context. Vestige scores memories by context match.

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Comparison

Feature	Vestige	Mem0	Zep	Letta
FSRS-6 spaced repetition	Yes	No	No	No
Dual-strength memory	Yes	No	No	No
Retroactive importance	Yes	No	No	No
Memory states	Yes	No	No	No
Local embeddings	Yes	No	No	No
100% local	Yes	No	No	No
Free & open source	Yes	Freemium	Freemium	Yes

---

Environment Variables

Variable	Description	Default
VESTIGE_DATA_DIR	Data storage directory	~/.vestige
VESTIGE_LOG_LEVEL	Log verbosity	info

---

Development

Prerequisites

• Rust 1.75+

Building

git clone https://github.com/samvallad33/vestige
cd vestige
cargo build --release --package vestige-mcp

Testing

cargo test --workspace

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Contributing

Contributions are welcome! Please open an issue or submit a pull request.

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License

MIT OR Apache-2.0

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_{Built with cognitive science and Rust.}