Adaptive Memory

An associative memory system using spreading activation. Memories are stored in SQLite with FTS5 full-text search, and retrieved using BM25 text matching combined with graph-based activation spreading through explicit relationships.

Core Concepts

Memories

Entries with id, datetime, text, and optional source. Stored in SQLite with FTS5 full-text indexing. IDs are sequential integers assigned on insertion.

Relationships

Symmetric connections between memories. Created only via explicit strengthen calls - no auto-generated relationships. Multiple strengthen events accumulate; effective strength is the sum of all events (with optional decay).

Relationships are stored canonically (from_mem < to_mem) as an event log. This allows strength to build up over time through repeated strengthening.

Spreading Activation

Search works by:

1. FTS5 Search: Find memories matching query using BM25 ranking
2. Seed Selection: Top BM25 results become seeds with energy 0.1-1.0 (proportional to relevance)
3. Energy Propagation: Energy spreads through relationship graph
   • Energy is distributed across neighbors (PageRank-style normalization)
   • Each hop multiplies by energy_decay (default 0.5)
   • Propagation stops when energy < 0.01 threshold
4. Results: Memories sorted by ID (timeline order) with accumulated energy scores

Context Expansion

Instead of pre-computed temporal relationships, use --context N to fetch N memories before/after each result by ID. This is like grep -B/-A for temporal context.

Installation

From crates.io

cargo install adaptive_memory

From source

git clone https://github.com/spoj/adaptive_memory
cd adaptive_memory
cargo build --release
# Binary at: target/release/adaptive-memory

CLI Usage

adaptive-memory [OPTIONS] <COMMAND>

Commands:
  init        Initialize the database
  add         Add a new memory
  amend       Amend (update) an existing memory's text
  search      Search for memories
  strengthen  Strengthen relationships between memories
  connect     Connect memories (only if no existing relationship)
  tail        Show the latest N memories
  list        List memories by ID range
  stats       Show database statistics
  stray       Sample unconnected (stray) memories

Global Options:
  --db <PATH>  Database path (default: ~/.adaptive_memory.db)

Initialize Database

adaptive-memory init

Add Memory

adaptive-memory add [OPTIONS] <TEXT>

Options:
  -s, --source <SOURCE>      Source identifier (e.g., "journal", "slack")
  -d, --datetime <DATETIME>  Override datetime (RFC3339 format)

Examples:

# Simple memory
adaptive-memory add "Had coffee with Sarah, discussed the new project"

# With source
adaptive-memory add "Reviewed PR #123" -s "github"

# Historical entry
adaptive-memory add "Started learning Rust" -d "2023-06-15T10:00:00Z"

Output:

{
  "memory": {
    "id": 42,
    "datetime": "2026-01-05T12:30:00Z",
    "text": "Had coffee with Sarah, discussed the new project",
    "source": null
  }
}

Search Memories

adaptive-memory search [OPTIONS] <QUERY>

Options:
  -l, --limit <N>          Maximum results (default: 100)
  -c, --context <N>        Fetch N memories before/after each result (default: 0)
  --decay <FACTOR>         Relationship decay over memory distance (default: 0)
  --energy-decay <FACTOR>  Energy multiplier per hop (default: 0.5)

Examples:

# Basic search
adaptive-memory search "project meeting"

# With temporal context (like grep -B2 -A2)
adaptive-memory search "rust" --context 2

# Limit results
adaptive-memory search "database" --limit 10

# Deeper activation spread (reach more distant associations)
adaptive-memory search "ideas" --energy-decay 0.7

Output:

{
  "query": "project meeting",
  "seed_count": 15,
  "total_activated": 47,
  "iterations": 234,
  "memories": [
    {
      "id": 38,
      "datetime": "2026-01-04T09:00:00Z",
      "text": "Project kickoff meeting with the team",
      "source": "calendar",
      "energy": 1.87
    },
    {
      "id": 42,
      "datetime": "2026-01-05T12:30:00Z",
      "text": "Had coffee with Sarah, discussed the new project",
      "source": null,
      "energy": 2.45
    }
  ]
}

Results are sorted by memory ID (timeline order). The energy field indicates relevance:

• ~1.0 = direct BM25 match
• ~0.5 = one hop from a seed
• < 0.1 = reached via multi-hop spreading

Context items (from --context) have energy: 0.0 and is_context: true.

Strengthen Relationships

Create explicit associations between memories.

adaptive-memory strengthen <IDS>

Arguments:
  <IDS>  Comma-separated memory IDs (max 10)

Examples:

# Link two related memories (adds 1.0 strength)
adaptive-memory strengthen 42,38

# Link multiple (creates all pairs, 1.0 each)
# 4 IDs = 6 pairs, each gets 1.0 strength
adaptive-memory strengthen 1,5,12,34

Output:

{
  "relationships": [
    {
      "from_mem": 38,
      "to_mem": 42,
      "effective_strength": 2.0,
      "event_count": 2
    }
  ],
  "event_count": 1
}

Connect Memories

Like strengthen, but only creates relationships if none exist between the pair.

adaptive-memory connect <IDS>

Arguments:
  <IDS>  Comma-separated memory IDs (max 10)

Example:

# Connect memories only if not already related
adaptive-memory connect 42,38,15

Amend Memory

Update the text of an existing memory. Only allowed if the memory has no relationships to later memories (preserves integrity of memories that later entries depend on).

adaptive-memory amend <ID> <TEXT>

Arguments:
  <ID>    Memory ID to amend
  <TEXT>  New text for the memory

Example:

# Fix a typo in memory 42
adaptive-memory amend 42 "Had coffee with Sarah, discussed the new project timeline"

List Memories

List memories by ID range.

adaptive-memory list [OPTIONS]

Options:
  --from <FROM>    Start ID (inclusive)
  --to <TO>        End ID (inclusive)
  -l, --limit <N>  Maximum number of results

Examples:

# List memories 10-20
adaptive-memory list --from 10 --to 20

# List last 50 memories
adaptive-memory list --limit 50

Tail

Show the latest N memories (shorthand for list --limit N).

adaptive-memory tail [N]

Arguments:
  [N]  Number of memories to show (default: 10)

Example:

# Show last 5 memories
adaptive-memory tail 5

Stats

Show database statistics including memory count, relationship count, and graph metrics.

adaptive-memory stats

Output:

{
  "memory_count": 1234,
  "relationship_count": 567,
  "connected_memories": 890,
  "stray_memories": 344,
  "avg_connections": 1.27
}

Stray

Sample unconnected (stray) memories - useful for finding memories that could benefit from being linked to others.

adaptive-memory stray [N]

Arguments:
  [N]  Number of stray memories to sample (default: 10)

Example:

# Find 5 unconnected memories to review
adaptive-memory stray 5

FTS5 Query Syntax

The search query uses SQLite FTS5 syntax, which supports powerful search operators:

Special characters: Characters like +, -, @ have special meaning in FTS5. To search for literal special characters, quote them: "2024-01-15" or "email@example.com".

Examples:

# All memories with "rust" AND "async"
adaptive-memory search "rust async"

# Either term
adaptive-memory search "rust OR python"

# Exact phrase
adaptive-memory search '"weekly standup"'

# Prefix matching
adaptive-memory search "meet*"

# Exclude term
adaptive-memory search "project NOT cancelled"

# Words near each other
adaptive-memory search "NEAR(database migration, 10)"

Library Usage

use adaptive_memory::{MemoryStore, MemoryError, SearchParams};

fn main() -> Result<(), MemoryError> {
    let mut store = MemoryStore::open("~/.adaptive_memory.db")?;

    // Add memories
    let result = store.add("Learning about spreading activation", Some("research"))?;
    println!("Added memory {}", result.memory.id);

    // Search with default params
    let results = store.search("activation", &SearchParams::default())?;
    for mem in results.memories {
        println!("{}: {} (energy: {:.2})", mem.memory.id, mem.memory.text, mem.energy);
    }

    // Search with context expansion
    let params = SearchParams {
        limit: 50,
        context: 2,
        ..SearchParams::default()
    };
    let results = store.search("activation", &params)?;

    // Strengthen relationships
    store.strengthen(&[1, 2, 3])?;

    Ok(())
}

Configuration

Compile-time Constants (src/lib.rs)

Runtime Parameters (SearchParams)

Tuning energy_decay

Controls how far activation spreads through the graph:

Energy at each hop (starting from seed with energy 1.0):

Hop:    0     1      2       3        4
0.5:   1.0   0.50   0.25    0.125    0.0625
0.7:   1.0   0.70   0.49    0.343    0.240

Tuning decay_factor

Controls how relationship strength fades over memory distance:

effective_strength = stored_strength × exp(-distance × decay_factor)

Default is 0.0 (no decay). The ln_1p compression and PageRank-style normalization already prevent old relationships from dominating.

Database Schema

CREATE TABLE memories (
    id INTEGER PRIMARY KEY,
    datetime TEXT NOT NULL,
    text TEXT NOT NULL,
    source TEXT
);

CREATE VIRTUAL TABLE memories_fts USING fts5(text, content=memories, content_rowid=id);

CREATE TABLE relationships (
    id INTEGER PRIMARY KEY,
    from_mem INTEGER NOT NULL,
    to_mem INTEGER NOT NULL,
    created_at_mem INTEGER NOT NULL,
    strength REAL NOT NULL,
    CHECK (from_mem < to_mem)
);

How It Works

Adding a Memory

1. Insert into memories table
2. FTS5 trigger auto-indexes the text
3. No relationships created (use strengthen or --context for associations)

Searching

1. FTS5: BM25-ranked text matches become seeds
2. Spreading Activation:
   • Seeds get energy proportional to BM25 score
   • Energy spreads through relationships (delta propagation)
   • Neighbors' strengths are normalized (sum to 1.0) - energy is distributed, not amplified
   • Raw strength is compressed via ln(1+x) for diminishing returns
3. Context Expansion: Optionally fetch surrounding memories by ID
4. Results: Sorted by memory ID (timeline order)

Strengthening

1. For each pair of IDs, add relationship event with strength 1.0
2. Events accumulate - the pair's effective strength grows with repeated strengthening
3. ln_1p compression means: 1st event → 0.69 effective, 10 events → 2.40, 100 events → 4.62

Tips

• Source field: Tag memories for filtering/identification (e.g., "slack", "journal", "calendar")
• Strengthen after retrieval: If a search surfaces related memories, strengthen them to reinforce the association
• Context for temporal: Use --context N instead of pre-computed temporal links
• Batch import: Use -d to preserve original timestamps when importing historical data
• Quote special chars: FTS5 special characters (+, -, *, etc.) should be quoted for literal matching

License

MIT