EPH-MCP: Emergent Pattern Hunter

🕸️ EPH-MCP: Emergent Pattern Hunter

A revolutionary thinking architecture for LLMs via MCP (Model Context Protocol)

EPH-MCP transforms how AI systems reason by simulating the emergence of insights from interacting thought fragments, similar to how patterns arise in complex physical systems.

Key Features

Bottom-up Insight Emergence: Instead of forcing conclusions, the insights just show up once all the pieces bounce around enough.
Quantum-like Thought Dynamics: Ideas overlap, collide, and stick together—sometimes they’re in two states at once until the picture clears.
Multi-scale Pattern Detection: We can spot the small stuff and the big picture at the same time—like zooming from street level to skyline.
Contradiction as Feature: Tension isn’t a bug, it’s fuel. Conflicts push the thinking somewhere new.
Field-based Reasoning: Everything plays out in this high-dimensional “idea space,” where concepts pull, push, and interact like a living grid.

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/eph-mcp.git
cd eph-mcp

# Install dependencies
pip install -r requirements.txt
python -m spacy download en_core_web_sm

# Quick test
python quickstart.py

Basic Usage

Start MCP Server

python -m eph_mcp.server

The server will start on localhost:3333 by default.

How It Works

EPH uses a 5-phase process:

Phase 1: Thought Explosion

First we blow up the question into a bunch of little sparks—50 to 150 fragments, each one a different angle or half-formed idea.
We mix in every trick we’ve got: free association, “what if” games, parallel universes, quantum superposition vibes.
Each fragment lands in some wild high-dimensional space, like confetti drifting around a cosmic dance floor.

Phase 2: Interaction Dynamics

Now those fragments start bumping into each other like charged particles.

Similar ones pull together.
Opposites push apart.
Some bind tightly, others spin off.

It’s basically like running a mini-universe simulation where ideas collide until the system chills into something stable (simulated annealing).

Phase 3: Pattern Detection

From the chaos, we spot emergent shapes—like finding constellations in the stars:

Crystalline lattices → clean, regular structures
Strange attractors → looping chaos
Phase transitions → that “sudden click” when ideas reorganize
Soliton waves → insights that keep traveling without losing shape
…plus more funky forms

Phase 4: Pattern Crystallization

Here, the raw patterns solidify into actual insights.
We check each one for:

Confidence (does it hold up?)
Novelty (is it fresh?)
Clarity (can you actually explain it to a friend?)

We don’t force everything to agree—contradictions are saved too, like tension in a good story.

Phase 5: Pattern Weaving

Finally, we stitch the insights together into something you can actually use.
Different ways to weave:

Convergent synthesis → pull it all into one neat answer
Dialectical → thesis + antithesis → synthesis
Narrative threading → tell it like a story, connecting the dots naturally

📊 Configuration

Create a config.json file to customize behavior:

{
  "explosion": {
    "n_fragments": 100,
    "temperature": 1.5,
    "embedding_model": "all-MiniLM-L6-v2"
  },
  "interaction": {
    "iterations": 150,
    "initial_temperature": 1.0,
    "cooling_rate": 0.995
  },
  "detection": {
    "min_pattern_size": 3,
    "pattern_threshold": 0.5
  },
  "crystallization": {
    "confidence_threshold": 0.5,
    "novelty_threshold": 0.3
  },
  "weaving": {
    "max_insights": 5,
    "coherence_threshold": 0.6
  }
}

🛠️ MCP Tools

The server exposes 4 main tools via MCP:

`think_emergently`

Main reasoning tool - applies full EPH process

{
  "query": "Your question here",
  "return_intermediate": false,
  "visualize": true
}

`analyze_patterns`

Analyze text for emergent patterns without full reasoning

{
  "text": "Text to analyze",
  "pattern_types": ["contradiction", "harmony"],
  "min_confidence": 0.5
}

`compare_thoughts`

Compare multiple ideas for relationships

{
  "thoughts": ["idea 1", "idea 2", "idea 3"],
  "find_contradictions": true,
  "find_harmonies": true
}

`reasoning_history`

Access and analyze past reasoning sessions

{
  "last_n": 5,
  "analyze": true
}

Enable with visualization.enabled: true in config.

Testing

Run the test suite:

# Basic tests
python tests/test_basic.py

# Full test suite (if available)
pytest tests/

📚 Examples

Explore different reasoning scenarios:

python examples/usage_examples.py

Contributing

Contributions are welcome! Areas of interest:

New generation strategies for thought explosion
Alternative pattern detection algorithms
Visualization improvements
Performance optimization
Integration with other MCP tools

Acknowledgments

Inspired by physics and emergent systems

"In the dance of fragments, meaning emerges" - EPH Philosophy

EPH-MCP: Emergent Pattern Hunter