@577-industries/agent-memory

Bio-inspired memory for AI agents with 5 memory types, logarithmic reinforcement, exponential decay, and composite recall scoring. Mimics biological memory consolidation. Zero runtime dependencies.

Implements the core algorithm described in the "Autonomous Memory Evolution" patent (December 2025) by 577 Industries.

How It Works

  Input ──► Store ──► [Duplicate?] ──yes──► Reinforce
                          │                    │
                          no             confidence +=
                          │              0.1/ln(count+2)
                          ▼
                    New Memory (0.5)

  Recall ──► Score = similarity × 0.7 + confidence × 0.3 ──► Ranked Results

  Decay  ──► confidence *= 0.95 (per 30d unreinforced) ──► delete if < 0.1

Quick Start

npm install @577-industries/agent-memory

import { MemoryStore } from "@577-industries/agent-memory";

const store = new MemoryStore({ agentId: "my-agent" });

// Store memories (auto-deduplicates)
await store.store("pattern", "Users ask about pricing first");
await store.store("preference", "Prefers bullet-point summaries");

// Reinforce when pattern repeats
await store.store("pattern", "Users ask about pricing first");
// → { reinforced: true } — confidence increases logarithmically

// Recall top memories
const memories = await store.recall(undefined, 5);

// Format for LLM system prompt
const prompt = store.format(memories);
// → "## Agent Memory\n- [pattern] Users ask about..."

// Simulate time passing and decay
store.advanceTime(35); // 35 days
store.decay(); // → { decayed: N, deleted: M }

Memory Types

| Type | Purpose | |------|---------| | pattern | Recurring workflow or behavior | | preference | User preference or style | | baseline | Metric or normal value | | entity | Key entity or relationship | | insight | Strategic observation |

API Reference

MemoryStore

| Method | Description | |--------|-------------| | new MemoryStore(config) | Create a store with optional embedding provider | | store(type, content) | Store or reinforce a memory | | recall(query?, limit?) | Recall ranked memories | | reinforce(id) | Manually reinforce a memory | | decay() | Run a decay cycle | | format(memories?) | Format for LLM prompt injection | | getAll() | Get all stored memories | | advanceTime(days) | Simulate time passing |

Pluggable Embeddings

interface EmbeddingProvider {
  embed(text: string): Promise<number[]>;
}

Without an embedding provider, the store falls back to substring matching for deduplication and confidence-only ranking for recall.

Standalone Functions

| Function | Description | |----------|-------------| | computeReinforcement(confidence, count) | Logarithmic reinforcement formula | | applyDecay(memories, config) | Exponential decay with cleanup | | scoreMemories(memories, embedding?, options?) | Composite recall scoring | | cosineSimilarity(a, b) | Vector cosine similarity | | formatMemoriesForPrompt(memories) | Format for LLM injection |

Architecture

Three bio-inspired mechanisms:

1. Reinforcement — confidence += 0.1 / ln(count + 2) — logarithmic growth with diminishing returns
2. Decay — confidence *= 0.95 per 30-day unreinforced cycle — exponential fade
3. Recall — score = similarity × 0.7 + confidence × 0.3 — composite ranking

Based on the "Autonomous Memory Evolution" patent by 577 Industries.

Extracted from FORGE OS by 577 Industries.