CortexGraph: Temporal Memory for AI

A Model Context Protocol (MCP) server providing human-like memory dynamics for AI assistants. Memories naturally fade over time unless reinforced through use, mimicking the Ebbinghaus forgetting curve.

> [!NOTE]

> About the Name & Version

>

> This project was originally developed as mnemex (published to PyPI up to v0.6.0). In November 2025, it was transferred to Prefrontal Systems and renamed to CortexGraph to better reflect its role within a broader cognitive architecture for AI systems.

>

> Version numbering starts at 0.1.0 for the cortexgraph package to signal a fresh start under the new name, while acknowledging the mature, well-tested codebase (791 tests, 98%+ coverage) inherited from mnemex. The mnemex package remains frozen at v0.6.0 on PyPI.

>

> This versioning approach:

> - Signals "new package" to PyPI users discovering cortexgraph

> - Gives room to evolve the brand, API, and organizational integration before 1.0

> - Maintains continuity: users can migrate from `pip install mnemex` → `pip install cortexgraph`

> - Reflects that while the code is mature, the cortexgraph identity is just beginning

> [!IMPORTANT]

> 🔬 RESEARCH ARTIFACT - NOT FOR PRODUCTION

>

> This software is a Proof of Concept (PoC) and reference implementation for research purposes. It exists to validate theoretical frameworks in cognitive architecture and AI safety (specifically the STOPPER Protocol and CortexGraph).

>

> It is NOT a commercial product. It is not maintained for general production use, may contain breaking changes, and offers no guarantees of stability or support. Use it to study the concepts, but build your own production implementations.

> 📖 New to this project? Start with the ELI5 Guide for a simple explanation of what this does and how to use it.

What is CortexGraph?

CortexGraph gives AI assistants like Claude a human-like memory system.

The Problem

When you chat with Claude, it forgets everything between conversations. You tell it "I prefer TypeScript" or "I'm allergic to peanuts," and three days later, you have to repeat yourself. This is frustrating and wastes time.

What CortexGraph Does

CortexGraph makes AI assistants remember things naturally, just like human memory:

• 🧠 Remembers what matters - Your preferences, decisions, and important facts
• ⏰ Forgets naturally - Old, unused information fades away over time (like the Ebbinghaus forgetting curve)
• 💪 Gets stronger with use - The more you reference something, the longer it's remembered
• 📦 Saves important things permanently - Frequently used memories get promoted to long-term storage

How It Works (Simple Version)

1. You talk naturally - "I prefer dark mode in all my apps"

2. Memory is saved automatically - No special commands needed

3. Time passes - Memory gradually fades if not used

4. You reference it again - "Make this app dark mode"

5. Memory gets stronger - Now it lasts even longer

6. Important memories promoted - Used 5+ times? Saved permanently to your Obsidian vault

No flashcards. No explicit review. Just natural conversation.

Why It's Different

Most memory systems are dumb:

• ❌ "Delete after 7 days" (doesn't care if you used it 100 times)
• ❌ "Keep last 100 items" (throws away important stuff just because it's old)

CortexGraph is smart:

• ✅ Combines recency (when?), frequency (how often?), and importance (how critical?)
• ✅ Memories fade naturally like human memory
• ✅ Frequently used memories stick around longer
• ✅ You can mark critical things to "never forget"

Technical Overview

This repository contains research, design, and a complete implementation of a short-term memory system that combines:

• Novel temporal decay algorithm based on cognitive science
• Reinforcement learning through usage patterns
• Two-layer architecture (STM + LTM) for working and permanent memory
• Smart prompting patterns for natural LLM integration
• Git-friendly storage with human-readable JSONL
• Knowledge graph with entities and relations

Module Organization

CortexGraph follows a modular architecture:

• `cortexgraph.core`: Foundational algorithms (decay, similarity, clustering, consolidation, search validation)
• `cortexgraph.agents`: Multi-agent consolidation pipeline and storage utilities
• `cortexgraph.storage`: JSONL and SQLite storage backends with batch operations
• `cortexgraph.tools`: MCP tool implementations

Why CortexGraph?

🔒 Privacy & Transparency

All data stored locally on your machine - no cloud services, no tracking, no data sharing.

• Short-term memory:
  • JSONL (default): Human-readable, git-friendly files (`~/.config/cortexgraph/jsonl/`)
  • SQLite: Robust database storage for larger datasets (`~/.config/cortexgraph/cortexgraph.db`)

• Long-term memory: Markdown files optimized for Obsidian
  • YAML frontmatter with metadata
  • Wikilinks for connections
  • Permanent storage you control

• Export: Built-in utility to export memories to Markdown for portability.

You own your data. You can read it, edit it, delete it, or version control it - all without any special tools.

Core Algorithm

The temporal decay scoring function:

$$

\Large \text{score}(t) = (n_{\text{use}})^\beta \cdot e^{-\lambda \cdot \Delta t} \cdot s

$$

Where:

• $\large n_{\text{use}}$ - Use count (number of accesses)
• $\large \beta$ (beta) - Sub-linear use count weighting (default: 0.6)
• $\large \lambda = \frac{\ln(2)}{t_{1/2}}$ (lambda) - Decay constant; set via half-life (default: 3-day)
• $\large \Delta t$ - Time since last access (seconds)
• $\large s$ - Strength parameter $\in [0, 2]$ (importance multiplier)

Thresholds:

• $\large \tau_{\text{forget}}$ (default 0.05) — if score Short-Term Memory- JSONL storage- Temporal decay- Hours to weeks retention"]

LTM["LTM (Long-Term Memory)- Markdown files Obsidian- Permanent storage- Git version control"]

STM -->|Automatic promotion| LTM

style STM fill:#e1f5ff,stroke:#01579b,stroke-width:2px

style LTM fill:#f3e5f5,stroke:#4a148c,stroke-width:2px

### 5. Multi-Agent Consolidation Pipeline

Automated memory maintenance through five specialized agents:

graph LR

decay["DecayAnalyzerFind at-riskmemories"]

cluster["ClusterDetectorFind similargroups"]

merge["SemanticMergeCombinesimilar groups"]

promote["LTMPromoterPromoteto LTM"]

relations["RelationshipDiscoveryDiscover cross-domain links"]

decay --> cluster

cluster --> merge

merge --> promote

promote --> relations

relations -.->|feedback| decay

style decay fill:#ffebee,stroke:#b71c1c,stroke-width:2px

style cluster fill:#fff3e0,stroke:#e65100,stroke-width:2px

style merge fill:#f3e5f5,stroke:#4a148c,stroke-width:2px

style promote fill:#e8f5e9,stroke:#1b5e20,stroke-width:2px

style relations fill:#e1f5fe,stroke:#01579b,stroke-width:2px

**The Five Agents:**

| Agent | Purpose |
|-------|---------|
| **DecayAnalyzer** | Find memories at risk of being forgotten (danger zone: 0.15-0.35) |
| **ClusterDetector** | Group similar memories using embedding similarity |
| **SemanticMerge** | Intelligently combine clustered memories, preserving unique info |
| **LTMPromoter** | Move high-value memories to permanent Obsidian storage |
| **RelationshipDiscovery** | Find cross-domain connections via shared entities |

**Key Features:**

- **Dry-run mode**: Preview changes without modifying data
- **Rate limiting**: Configurable operations per minute (default: 60)
- **Audit trail**: Every decision tracked via beads issue tracking
- **Human override**: Review and approve decisions before execution

**Usage:**

from cortexgraph.agents import Scheduler

Preview what would change (dry run)

scheduler = Scheduler(dry_run=True)

preview = scheduler.run_pipeline()

Run full pipeline

scheduler = Scheduler(dry_run=False)

results = scheduler.run_pipeline()

Run single agent

decay_results = scheduler.run_agent("decay")

**CLI:**

Dry run (preview)

cortexgraph-consolidate --dry-run

Run specific agent

cortexgraph-consolidate --agent decay --dry-run

Scheduled execution (with interval)

cortexgraph-consolidate --scheduled --interval-hours 1

See [docs/agents.md](docs/agents.md) for complete documentation including configuration, beads integration, and troubleshooting.

## Quick Start

### Installation

**Recommended: UV Tool Install (from PyPI)**

Install from PyPI (recommended - fast, isolated, includes all 7 CLI commands)

uv tool install cortexgraph

This installs `cortexgraph` and all 7 CLI commands in an isolated environment.

**Alternative Installation Methods**

Using pipx (similar isolation to uv)

pipx install cortexgraph

Using pip (traditional, installs in current environment)

pip install cortexgraph

From GitHub (latest development version)

uv tool install git+https://github.com/simplemindedbot/cortexgraph.git

**For Development (Editable Install)**

Clone and install in editable mode

git clone https://github.com/simplemindedbot/cortexgraph.git

cd cortexgraph

uv pip install -e ".[dev]"

### Configuration

**IMPORTANT**: Configuration location depends on installation method:

**Method 1: .env file (Works for all installation methods)**

Create `~/.config/cortexgraph/.env`:

Create config directory

mkdir -p ~/.config/cortexgraph

Option A: Copy from cloned repo

cp .env.example ~/.config/cortexgraph/.env

Option B: Download directly

curl -o ~/.config/cortexgraph/.env https://raw.githubusercontent.com/simplemindedbot/cortexgraph/main/.env.example

Edit `~/.config/cortexgraph/.env` with your settings:

Storage

CORTEXGRAPH_STORAGE_PATH=~/.config/cortexgraph/jsonl

Decay model (power_law | exponential | two_component)

CORTEXGRAPH_DECAY_MODEL=power_law

Power-law parameters (default model)

CORTEXGRAPH_PL_ALPHA=1.1

CORTEXGRAPH_PL_HALFLIFE_DAYS=3.0

Exponential (if selected)

CORTEXGRAPH_DECAY_LAMBDA=2.673e-6 # 3-day half-life

Two-component (if selected)

CORTEXGRAPH_TC_LAMBDA_FAST=1.603e-5 # ~12h

CORTEXGRAPH_TC_LAMBDA_SLOW=1.147e-6 # ~7d

CORTEXGRAPH_TC_WEIGHT_FAST=0.7

Common parameters

CORTEXGRAPH_DECAY_LAMBDA=2.673e-6

CORTEXGRAPH_DECAY_BETA=0.6

Thresholds

CORTEXGRAPH_FORGET_THRESHOLD=0.05

CORTEXGRAPH_PROMOTE_THRESHOLD=0.65

Long-term memory (optional)

LTM_VAULT_PATH=~/Documents/Obsidian/Vault

**Where cortexgraph looks for .env files:**
1. **Primary**: `~/.config/cortexgraph/.env` ← Use this for `uv tool install` / `uvx`
2. **Fallback**: `./.env` (current directory) ← Only works for editable installs

### MCP Configuration

**Recommended: Use absolute path (works everywhere)**

Add to `~/Library/Application Support/Claude/claude_desktop_config.json`:

{

"mcpServers": {

"cortexgraph": {

"command": "/Users/yourusername/.local/bin/cortexgraph"

}

}

}

**Find your actual path:**

which cortexgraph

Example output: /Users/yourusername/.local/bin/cortexgraph

Use that path in your config. Replace `yourusername` with your actual username.

**Why absolute path?** GUI apps like Claude Desktop don't inherit your shell's PATH configuration (`.zshrc`, `.bashrc`). Using the full path ensures it always works.

**For development (editable install):**

{

"mcpServers": {

"cortexgraph": {

"command": "uv",

"args": ["--directory", "/path/to/cortexgraph", "run", "cortexgraph"],

"env": {"PYTHONPATH": "/path/to/cortexgraph/src"}

}

}

}

Configuration can be loaded from `./.env` in the project directory OR `~/.config/cortexgraph/.env`.

#### Troubleshooting: Command Not Found

If Claude Desktop shows `spawn cortexgraph ENOENT` errors, the `cortexgraph` command isn't in Claude Desktop's PATH.

**macOS/Linux: GUI apps don't inherit shell PATH**

GUI applications on macOS and Linux don't see your shell's PATH configuration (`.zshrc`, `.bashrc`, etc.). Claude Desktop only searches:
- `/usr/local/bin`
- `/opt/homebrew/bin` (macOS)
- `/usr/bin`
- `/bin`
- `/usr/sbin`
- `/sbin`

If `uv tool install` placed `cortexgraph` in `~/.local/bin/` or another custom location, Claude Desktop can't find it.

**Solution: Use absolute path**

Find where cortexgraph is installed

which cortexgraph

Example output: /Users/username/.local/bin/cortexgraph

Update your Claude config with the absolute path:

{

"mcpServers": {

"cortexgraph": {

"command": "/Users/username/.local/bin/cortexgraph"

}

}

}

Replace `/Users/username/.local/bin/cortexgraph` with your actual path from `which cortexgraph`.
### Maintenance

Use the maintenance CLI to inspect and compact JSONL storage:

Show storage stats (active counts, file sizes, compaction hints)

cortexgraph-maintenance stats

Compact JSONL (rewrite without tombstones/duplicates)

cortexgraph-maintenance compact

### Migrating to UV Tool Install

If you're currently using an editable install (`uv pip install -e .`), you can switch to the simpler UV tool install:

1. Uninstall editable version

uv pip uninstall cortexgraph

2. Install as UV tool

uv tool install git+https://github.com/simplemindedbot/cortexgraph.git

3. Update Claude Desktop config to just:

{"command": "cortexgraph"}

Remove the --directory, run, and PYTHONPATH settings

**Your data is safe!** This only changes how the command is installed. Your memories in `~/.config/cortexgraph/` are untouched.

## CLI Commands

The server includes 7 command-line tools:

cortexgraph # Run MCP server

cortexgraph-migrate # Migrate from old STM setup

cortexgraph-index-ltm # Index Obsidian vault

cortexgraph-backup # Git backup operations

cortexgraph-vault # Vault markdown operations

cortexgraph-search # Unified STM+LTM search

cortexgraph-maintenance # JSONL storage stats and compaction

## Visualization

Interactive graph visualization using PyVis:

Install visualization dependencies

pip install "cortexgraph[visualization]"

or with uv

uv pip install "cortexgraph[visualization]"

Or install dependencies manually

pip install pyvis networkx

Generate interactive HTML visualization

python scripts/visualize_graph.py

Custom output location

python scripts/visualize_graph.py --output ~/Desktop/memory_graph.html

Custom data paths

python scripts/visualize_graph.py --memories ~/data/memories.jsonl --relations ~/data/relations.jsonl

**Features:**
- Interactive network graph with pan/zoom
- Node colors by status (active=blue, promoted=green, archived=gray)
- Node size based on use count
- Edge colors by relation type
- Hover tooltips showing full content, tags, and entities
- Physics controls for layout adjustment

The visualization reads directly from your JSONL files and creates a standalone HTML file you can open in any browser.

## MCP Tools

13 tools for AI assistants to manage memories:

| Tool | Purpose |
|------|---------|
| `save_memory` | Save new memory with tags, entities (auto-enrichment in v0.6.0+) |
| `search_memory` | Search with filters and scoring (includes review candidates) |
| `search_unified` | Unified search across STM + LTM |
| `touch_memory` | Reinforce memory (boost strength) |
| `observe_memory_usage` | Record memory usage for natural spaced repetition |
| `analyze_message` | ✨ **NEW v0.6.0** - Detect memory-worthy content, suggest entities/strength |
| `analyze_for_recall` | ✨ **NEW v0.6.0** - Detect recall intent, suggest search queries |
| `gc` | Garbage collect low-scoring memories |
| `promote_memory` | Move to long-term storage |
| `cluster_memories` | Find similar memories |
| `consolidate_memories` | Merge similar memories (algorithmic) |
| `read_graph` | Get entire knowledge graph |
| `open_memories` | Retrieve specific memories |
| `create_relation` | Link memories explicitly |

### Example: Unified Search

Search across STM and LTM with the CLI:

cortexgraph-search "typescript preferences" --tags preferences --limit 5 --verbose

### Example: Reinforce (Touch) Memory

Boost a memory's recency/use count to slow decay:

{

"memory_id": "mem-123",

"boost_strength": true

}

Sample response:

{

"success": true,

"memory_id": "mem-123",

"old_score": 0.41,

"new_score": 0.78,

"use_count": 5,

"strength": 1.1

}

### Example: Promote Memory

Suggest and promote high-value memories to the Obsidian vault.

Auto-detect (dry run):

{

"auto_detect": true,

"dry_run": true

}

Promote a specific memory:

{

"memory_id": "mem-123",

"dry_run": false,

"target": "obsidian"

}

As an MCP tool (request body):

{

"query": "typescript preferences",

"tags": ["preferences"],

"limit": 5,

"verbose": true

}

### Example: Consolidate Similar Memories

Find and merge duplicate or highly similar memories to reduce clutter:

Auto-detect candidates (preview):

{

"auto_detect": true,

"mode": "preview",

"cohesion_threshold": 0.75

}

Apply consolidation to detected clusters:

{

"auto_detect": true,

"mode": "apply",

"cohesion_threshold": 0.80

}

The tool will:
- Merge content intelligently (preserving unique information)
- Combine tags and entities (union)
- Calculate strength based on cluster cohesion
- Preserve earliest `created_at` and latest `last_used` timestamps
- Create tracking relations showing consolidation history

## Mathematical Details

### Decay Curves

For a memory with $n_{\text{use}}=1$, $s=1.0$, and $\lambda = 2.673 \times 10^{-6}$ (3-day half-life):

| Time | Score | Status |
|------|-------|--------|
| 0 hours | 1.000 | Fresh |
| 12 hours | 0.917 | Active |
| 1 day | 0.841 | Active |
| 3 days | 0.500 | Half-life |
| 7 days | 0.210 | Decaying |
| 14 days | 0.044 | Near forget |
| 30 days | 0.001 | **Forgotten** |

### Use Count Impact

With $\beta = 0.6$ (sub-linear weighting):

| Use Count | Boost Factor |
|-----------|--------------|
| 1 | 1.0× |
| 5 | 2.6× |
| 10 | 4.0× |
| 50 | 11.4× |

Frequent access significantly extends retention.

## Documentation

- **[MCP Tools Reference](docs/mcp-tools.md)** - Comprehensive documentation for all 18 MCP tools
- **[API Quick Reference](docs/api.md)** - Minimal tool signatures and usage examples
- **[Scoring Algorithm](docs/scoring_algorithm.md)** - Complete mathematical model with LaTeX formulas
- **[Smart Prompting](docs/prompts/memory_system_prompt.md)** - Patterns for natural LLM integration
- **[Architecture](docs/architecture.md)** - System design and implementation
- **[Multi-Agent System](docs/agents.md)** - Consolidation agents and pipeline architecture
- **[Bear Integration](docs/bear-integration.md)** - Guide to using Bear app as an LTM store
- **[Graph Features](docs/graph_features.md)** - Knowledge graph usage

## Use Cases

### Personal Assistant (Balanced)

- 3-day half-life
- Remember preferences and decisions
- Auto-promote frequently referenced information

### Development Environment (Aggressive)

- 1-day half-life
- Fast context switching
- Aggressive forgetting of old context

### Research / Archival (Conservative)

- 14-day half-life
- Long retention
- Comprehensive knowledge preservation

## License

AGPL-3.0 License - See [LICENSE](LICENSE) for details.

This project uses the GNU Affero General Public License v3.0, which requires that modifications to this software be made available as source code when used to provide a network service.

## Related Work

- [Model Context Protocol](https://github.com/modelcontextprotocol) - MCP specification
- [Ebbinghaus Forgetting Curve](https://en.wikipedia.org/wiki/Forgetting_curve) - Cognitive science foundation
- [Basic Memory](https://github.com/basicmachines-co/basic-memory) - Primary inspiration for the integration layer. CortexGraph extends this concept by adding the Ebbinghaus forgetting curve, temporal decay algorithms, short-term memory in JSONL storage, and natural spaced repetition.
- Additional research inspired by: mem0, Neo4j Graph Memory

## Citation

If you use this work in research, please cite:

@software{cortexgraph_2025,

title = {Mnemex: Temporal Memory for AI},

author = {simplemindedbot},

year = {2025},

url = {https://github.com/simplemindedbot/cortexgraph},

version = {0.5.3}

}

## Contributing

Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed instructions.

### 🚨 **Help Needed: Windows & Linux Testers!**

I develop on macOS and need help testing on Windows and Linux. If you have access to these platforms, please:

- Try the installation instructions
- Run the test suite
- Report what works and what doesn't

See the [**Help Needed section**](CONTRIBUTING.md#-help-needed-windows--linux-testers) in CONTRIBUTING.md for details.

### General Contributions

For all contributors, see [CONTRIBUTING.md](CONTRIBUTING.md) for:

- Platform-specific setup (Windows, Linux, macOS)
- Development workflow
- Testing guidelines
- Code style requirements
- Pull request process

Quick start:

1. Read [CONTRIBUTING.md](CONTRIBUTING.md) for platform-specific setup
2. Understand the [Architecture docs](docs/architecture.md)
3. Review the [Scoring Algorithm](docs/scoring_algorithm.md)
4. Follow existing code patterns
5. Add tests for new features
6. Update documentation

## Status

**Version:** 1.0.0
**Status:** Research implementation - functional but evolving

### Phase 1 (Complete) ✅

- 14 MCP tools
- Temporal decay algorithm
- Knowledge graph

### Phase 2 (Complete) ✅

- JSONL storage
- LTM index
- Git integration
- Smart prompting documentation
- Maintenance CLI
- Memory consolidation (algorithmic merging)

### Phase 3 (Complete) ✅

- **Multi-Agent Consolidation Pipeline**
  - DecayAnalyzer, ClusterDetector, SemanticMerge, LTMPromoter, RelationshipDiscovery
  - Scheduler for orchestration
  - Beads issue tracking integration
  - Dry-run and rate limiting support
- Natural language activation (v0.6.0+)
- Auto-enrichment for entity extraction

### Future Work

- Adaptive decay parameters
- Performance benchmarks
- LLM-assisted consolidation (optional enhancement)

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