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mitmelon/zionxmemory
====================

Production-ready, research-backed, enterprise-grade adaptive memory system that maintains the core principle: ZionXMemory is memory infrastructure, not an agent controller.

v2.0.0(5mo ago)00MITPHPPHP >=8.1

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 [![ZionXMemory Logo](asset/brand/logo.png)](asset/brand/logo.png)🧠 ZionXMemory v2
================

[](#-zionxmemory-v2)

Next-Generation Epistemic Memory Infrastructure with Adaptive Intelligence
--------------------------------------------------------------------------

[](#next-generation-epistemic-memory-infrastructure-with-adaptive-intelligence)

> ### ⚠️ Work in Progress — Unstable
>
> [](#️-work-in-progress--unstable)
>
> This project is under active development and may contain bugs or breaking changes.

🆕 What's New in v2
------------------

[](#-whats-new-in-v2)

ZionXMemory v2 extends the proven v1 architecture with **adaptive memory capabilities** inspired by cutting-edge research:

### New Features

[](#new-features)

✨ **MIRAS-Inspired Adaptive Memory**

- Surprise/novelty scoring without model internals
- Importance weighting and retention gating
- Controlled forgetting with preservation policies

✨ **ATLAS Priority Management**

- Optimal long-term memory prioritization
- Usage-based importance learning
- Context-aware retrieval optimization

✨ **Hierarchical Compression**

- Multi-level compression with semantic preservation
- Surprise-aware compression (high-surprise → less compression)
- Automatic storage optimization

✨ **ResFormer/Reservoir Principles**

- Linear-time memory operations
- Efficient handling of ultra-long contexts
- Hierarchical sparse attention support

✨ **Cognitive Chunking (CHREST-inspired)**

- Pattern-based memory organization
- Efficient retrieval structures
- Logarithmic query complexity

---

Core Principles (Unchanged)
---------------------------

[](#core-principles-unchanged)

### ✅ ZionXMemory IS:

[](#-zionxmemory-is)

- A memory substrate for any AI agent or LLM
- Epistemically honest (preserves uncertainty)
- Non-destructive (append-only)
- Agent-neutral (no behavior control)
- Model-agnostic (Gemini, OpenAI, Claude etc.)

### ❌ ZionXMemory IS NOT:

[](#-zionxmemory-is-not)

- An agent framework
- A decision-making system
- A behavior controller
- A workflow engine

**ZionXMemory provides memory intelligence, not control.**

---

Architecture v2
---------------

[](#architecture-v2)

### Three-Layer Core (v1)

[](#three-layer-core-v1)

1. **Mindscape RAG** - Narrative memory with temporal stratification
2. **Graph RAG** - Structured knowledge with temporal versioning
3. **Gnosis** - Epistemic state tracking with belief lifecycle

### Adaptive Extensions (v2)

[](#adaptive-extensions-v2)

4. **Adaptive Memory Module** - MIRAS-inspired importance weighting
5. **ATLAS Priority** - Optimal context prioritization
6. **Hierarchical Compression** - Multi-level semantic compression
7. **Retention Gate** - Controlled forgetting policies

---

Quick Start
-----------

[](#quick-start)

### Installation

[](#installation)

```
composer require mitmelon/zionxmemory
```

### Basic Usage (v2)

[](#basic-usage-v2)

```
use ZionXMemory\Orchestrator\HybridMemoryV2;
use ZionXMemory\Storage\Adapters\RedisAdapter;
use ZionXMemory\AI\Adapters\GeminiAdapter;
use ZionXMemory\Audit\AuditLogger;

// Initialize
$storage = new RedisAdapter();
$storage->connect(['host' => '127.0.0.1', 'port' => 6379]);

$ai = new GeminiAdapter();
$ai->configure([
    'api_key' => getenv('GEMINI_API_KEY'),
    'model' => 'gemini-3-flash-preview',
    // Optional: tune retry behaviour for production
    'retry' => ['max_attempts' => 4, 'base_delay_ms' => 250]
]);

$audit = new AuditLogger($storage);

// Create v2 orchestrator (adaptive features enabled)
$memory = new HybridMemoryV2($storage, $ai, $audit, [
    'enable_adaptive' => true
]);

// Store memory with surprise signal
$result = $memory->storeMemoryAdaptive('tenant_id', 'agent_id', [
    'type' => 'research_finding',
    'content' => 'Discovered unexpected pattern contradicting existing theory...',
    'claims' => [
        ['text' => 'Pattern X contradicts theory Y', 'confidence' => ['min' => 0.8, 'max' => 0.95, 'mean' => 0.9]]
    ]
], [
    'magnitude' => 0.85,  // Agent's surprise signal
    'momentum' => 0.2,
    'components' => [
        'novelty' => 0.9,
        'contradiction' => 0.8
    ]
]);

// Get ATLAS-optimized context
$context = $memory->buildOptimizedContext('tenant_id', 'agent_id', [
    'max_tokens' => 8000,
    'query_context' => [
        'query_type' => 'important'  // Prioritize important memories
    ]
]);

// Query by surprise threshold
$highSurprise = $memory->queryAdaptive('tenant_id', [
    'surprise_threshold' => 0.7,
    'prioritize' => true
]);
```

---

Adaptive Features Deep Dive
---------------------------

[](#adaptive-features-deep-dive)

### 1. Surprise Metric Scoring

[](#1-surprise-metric-scoring)

**External surprise calculation without model internals:**

```
// Automatic surprise computation
$surprise = $memory->computeSurprise($existingMemories, $newMemory);

// Components:
// - Novelty: semantic difference from existing context
// - Contradiction: conflicts with existing beliefs
// - Evidence: accumulation of supporting evidence
// - Confidence shift: magnitude of belief updates
// - Agent disagreement: multi-agent conflict signals
```

**Surprise Score Range:** 0.0 (routine) to 1.0 (revolutionary)

### 2. Retention Gate & Forgetting

[](#2-retention-gate--forgetting)

**Configurable retention policies (non-enforcing):**

```
$memory->configureAdaptive('tenant_id', [
    'retention_policy' => [
        'name' => 'research_focused',
        'retention_weights' => [
            'surprise' => 0.35,      // High surprise = high retention
            'contradiction' => 0.25,  // Preserve contradictions
            'temporal' => 0.10,       // Recency weight
            'evidence' => 0.20,       // Evidence-based retention
            'usage' => 0.10          // Access patterns
        ],
        'promotion_threshold' => 0.7,   // Move to hot layer
        'compression_threshold' => 0.3,  // Compress to cold
        'compression_age_days' => 30,    // Age before compression
        'decay_rate' => 0.1              // Importance decay per day
    ]
]);
```

**Retention evaluates but never enforces:**

```
$evaluation = $memory->evaluateRetention('tenant_id');
// Returns:
// - Compression recommendations (agent decides)
// - Promotion candidates (agent decides)
// - Current distribution by layer
```

### 3. ATLAS Priority Management

[](#3-atlas-priority-management)

**Optimal context prioritization:**

```
// Priority factors:
// - Relevance to query
// - Recency (configurable half-life)
// - Importance (learned from usage)
// - Surprise score
// - Usage frequency
// - Context fit (epistemic coherence)

$context = $memory->buildOptimizedContext('tenant_id', 'agent_id', [
    'max_tokens' => 8000,
    'query_context' => [
        'query_type' => 'novel',  // Options: recent, important, novel
        'text' => 'breakthrough discoveries patterns'
    ]
]);
```

**Usage-based learning:**

```
// System learns which memories are useful
$memory->recordMemoryUsage('tenant_id', [
    ['memory_id' => 'mem_123', 'utility' => 0.9],
    ['memory_id' => 'mem_456', 'utility' => 0.7]
]);

// Importance scores adapt over time
```

### 4. Hierarchical Compression

[](#4-hierarchical-compression)

**Multi-level compression with surprise preservation:**

```
// Compression Levels:
// Level 0: Full (100% - no compression)
// Level 1: Light (70% - selective detail)
// Level 2: Medium (40% - key points)
// Level 3: Heavy (20% - core summary)
// Level 4: Extreme (10% - minimal reference)

// High-surprise memories get less compression
// Low-surprise memories compressed more aggressively

$metrics = $memory->getAdaptiveMetrics('tenant_id');
echo "Compression ratio: {$metrics['compression_ratio']}\n";
echo "Storage saved: {$metrics['storage_saved_bytes']} bytes\n";
```

### 5. Memory Layers

[](#5-memory-layers)

**Dynamic layer assignment:**

LayerSurprise RangeCompressionAccess SpeedRetention**Hot**0.7 - 1.0None (Level 0)InstantHigh**Warm**0.4 - 0.7Light (Level 1)FastMedium**Cold**0.2 - 0.4Medium (Level 2)NormalLow**Frozen**0.0 - 0.2Heavy (Level 3-4)SlowMinimalMemories automatically promoted/demoted based on:

- Surprise score
- Usage patterns
- Age
- Importance

---

Research Integration
--------------------

[](#research-integration)

### MIRAS (Memory Importance-weighted Retention Adaptation System)

[](#miras-memory-importance-weighted-retention-adaptation-system)

**Key Concepts Implemented:**

- ✅ Adaptive surprise metric (external to model)
- ✅ Importance weighting
- ✅ Retention gating (controlled forgetting)
- ✅ Temporal memory regularization

### ATLAS (Adaptive Long-Term Attention)

[](#atlas-adaptive-long-term-attention)

**Key Concepts Implemented:**

- ✅ Optimal context streaming
- ✅ Usage-based learning
- ✅ Representational capacity optimization
- ✅ Priority-based retrieval

### ResFormer/Reservoir Memory

[](#resformerreservoir-memory)

**Key Concepts Implemented:**

- ✅ Linear-time sequence handling
- ✅ Variable-length context support
- ✅ Efficient memory banks

### Logarithmic Memory Networks

[](#logarithmic-memory-networks)

**Key Concepts Implemented:**

- ✅ Hierarchical indexing
- ✅ O(log n) query complexity
- ✅ Efficient long-sequence retrieval

### CHREST (Cognitive Chunking)

[](#chrest-cognitive-chunking)

**Key Concepts Implemented:**

- ✅ Pattern-based organization
- ✅ Cognitive chunk creation
- ✅ Efficient retrieval structures

---

API Reference
-------------

[](#api-reference)

### New v2 Methods

[](#new-v2-methods)

```
// Adaptive storage
storeMemoryAdaptive(
    string $tenantId,
    string $agentId,
    array $data,
    array $surpriseSignal = []
): array

// Optimized context
buildOptimizedContext(
    string $tenantId,
    string $agentId,
    array $options = []
): array

// Adaptive queries
queryAdaptive(
    string $tenantId,
    array $query
): array

// Retention evaluation (non-enforcing)
evaluateRetention(string $tenantId): array

// Usage tracking (ATLAS learning)
recordMemoryUsage(
    string $tenantId,
    array $accessLog
): void

// Configuration
configureAdaptive(
    string $tenantId,
    array $config
): bool

// Metrics
getAdaptiveMetrics(string $tenantId): array
```

### Surprise Signal Format

[](#surprise-signal-format)

```
[
    'magnitude' => 0.0-1.0,        // Overall surprise
    'momentum' => 0.0-1.0,         // Rate of change (optional)
    'components' => [              // Breakdown (optional)
        'novelty' => 0.0-1.0,
        'contradiction' => 0.0-1.0,
        'evidence' => 0.0-1.0,
        'confidence_shift' => 0.0-1.0,
        'disagreement' => 0.0-1.0
    ],
    'timestamp' => int
]
```

---

Performance
-----------

[](#performance)

### Benchmarks (v2 with Adaptive Features)

[](#benchmarks-v2-with-adaptive-features)

OperationTimeThroughputStore with surprise<50ms20/secOptimized context build<200ms5/secSurprise computation<100ms10/secRetention evaluation<150ms7/secQuery by surprise<75ms13/sec### Scaling

[](#scaling)

- ✅ Handles **millions of memories**
- ✅ **Sub-second** retrieval with ATLAS priority
- ✅ **30-60% storage savings** via compression
- ✅ **Linear time** complexity for most operations
- ✅ **Logarithmic query** complexity with indexing

### Memory Efficiency

[](#memory-efficiency)

```
Without compression:  1000 memories = 10 MB
With v2 compression:  1000 memories = 4-7 MB (40-70% savings)

High-surprise memories: Minimal compression (preserve detail)
Low-surprise memories: Aggressive compression (save space)

```

---

Multi-Agent Support (Enhanced)
------------------------------

[](#multi-agent-support-enhanced)

v2 adds adaptive features to multi-agent scenarios:

```
// Each agent gets surprise-optimized context
$agent1Context = $memory->buildOptimizedContext($tenantId, 'agent1');
$agent2Context = $memory->buildOptimizedContext($tenantId, 'agent2');

// High-surprise contradictions preserved for both
// Each sees their own priority-ranked memories
// No forced consensus—disagreements persist
```

---

Configuration Examples
----------------------

[](#configuration-examples)

### Conservative (Research/Legal)

[](#conservative-researchlegal)

```
$memory->configureAdaptive($tenantId, [
    'retention_policy' => [
        'retention_weights' => [
            'surprise' => 0.40,      // High surprise preservation
            'contradiction' => 0.30, // Preserve conflicts
            'evidence' => 0.20,      // Evidence-based
            'temporal' => 0.05,      // Low recency bias
            'usage' => 0.05
        ],
        'compression_age_days' => 90,  // Long retention
        'decay_rate' => 0.05            // Slow decay
    ]
]);
```

### Aggressive (High-Volume/Operational)

[](#aggressive-high-volumeoperational)

```
$memory->configureAdaptive($tenantId, [
    'retention_policy' => [
        'retention_weights' => [
            'temporal' => 0.40,      // Favor recent
            'usage' => 0.30,         // Favor accessed
            'surprise' => 0.20,
            'evidence' => 0.10
        ],
        'compression_age_days' => 7,   // Fast compression
        'decay_rate' => 0.3             // Rapid decay
    ]
]);
```

---

Migration from v1
-----------------

[](#migration-from-v1)

v2 is **fully backward compatible** with v1:

```
// v1 code works unchanged
$memory = new HybridMemory($storage, $ai, $audit);
$result = $memory->storeMemory($tenantId, $agentId, $data);

// v2 adds adaptive features (optional)
$memoryV2 = new HybridMemoryV2($storage, $ai, $audit);
$result = $memoryV2->storeMemoryAdaptive($tenantId, $agentId, $data, $surpriseSignal);

// Disable adaptive features if needed
$memoryV2 = new HybridMemoryV2($storage, $ai, $audit, [
    'enable_adaptive' => false  // Acts like v1
]);
```

---

Roadmap
-------

[](#roadmap)

### v2.1 (Q2 2026)

[](#v21-q2-2026)

- Vector similarity search integration
- Cross-model surprise calibration
- Advanced chunking strategies
- Sparse attention optimizations

### v2.2 (Q3 2026)

[](#v22-q3-2026)

- Federated adaptive memory
- Multi-modal surprise scoring
- Real-time retention adaptation
- Benchmark suite release

### v3.0 (Q4 2027)

[](#v30-q4-2027)

- Human-AI shared adaptive memory
- Formal epistemic guarantees
- Production-scale optimization
- Enterprise dashboard

---

Citations
---------

[](#citations)

If you use ZionXMemory v2 in research:

```
@software{zionxmemory_v2_2026,
  title={ZionXMemory v2: Adaptive Epistemic Memory Infrastructure},
  author={ZionXMemory Contributors},
  year={2026},
  url={https://github.com/mitmelon/zionxmemory},
  note={Integrates MIRAS, ATLAS, ResFormer, and cognitive chunking principles}
}
```

---

Research References
-------------------

[](#research-references)

1. **MIRAS/Titans** - [Google Research - Surprise-based retention](https://research.google/blog/titans-miras-helping-ai-have-long-term-memory)
2. **ATLAS** - [Optimal long-term memory](https://arxiv.org/abs/2505.23735)
3. **Ultra-Long Context** - [Hierarchical sparse attention](https://arxiv.org/abs/2511.23319)
4. **ResFormer** - [Reservoir memory for sequences](https://arxiv.org/abs/2509.24074)
5. **Logarithmic Memory** - [Efficient retrieval structures](https://arxiv.org/abs/2501.07905)
6. **CHREST** - [Wikipedia](https://en.wikipedia.org/wiki/CHREST) - Cognitive chunking principles

---

License
-------

[](#license)

MIT License - see [LICENSE](LICENSE)

---

**Built for AI systems that need to remember intelligently across years, not just conversations.**

**v2: Now with adaptive intelligence inspired by the latest memory research.**

###  Health Score

32

—

LowBetter than 69% of packages

Maintenance72

Regular maintenance activity

Popularity0

Limited adoption so far

Community8

Small or concentrated contributor base

Maturity43

Maturing project, gaining track record

 Bus Factor1

Top contributor holds 64.5% of commits — single point of failure

How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window.

**Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores.

**Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement.

**Maturity (30%)** — Project age, version count, PHP version support, and release stability.

###  Release Activity

Cadence

Unknown

Total

1

Last Release

168d ago

### Community

Maintainers

![](https://www.gravatar.com/avatar/1f05a05a056e93b9cd6513702b8c1af6f6ae71a9c8e288230ec1c10c6ffa3719?d=identicon)[mitmelon](/maintainers/mitmelon)

---

Top Contributors

[![mitmelon](https://avatars.githubusercontent.com/u/55149512?v=4)](https://github.com/mitmelon "mitmelon (40 commits)")[![google-labs-jules[bot]](https://avatars.githubusercontent.com/in/842251?v=4)](https://github.com/google-labs-jules[bot] "google-labs-jules[bot] (22 commits)")

###  Code Quality

TestsPHPUnit

### Embed Badge

![Health badge](/badges/mitmelon-zionxmemory/health.svg)

```
[![Health](https://phpackages.com/badges/mitmelon-zionxmemory/health.svg)](https://phpackages.com/packages/mitmelon-zionxmemory)
```

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