markoflow 0.0.1
Agentic framework built on MarkovFlow and PocketFlow for complex workflows
Requires Python: >=3.8
Keywords: workflow, markov-chains, llm, ai, pocketflow, probabilistic, self-healing

# markoflow: The Meta-Agentic Revolution

> **The world's first meta-agentic multi-agent framework** - built by human-AI teams, for human-AI teams, embodying the very collaboration patterns it enables.

## 🚀 Revolutionary Vision

**markoflow** represents a fundamental paradigm shift from traditional multi-agent frameworks. While LangGraph offers deterministic routing, CrewAI provides fixed role assignments, and AutoGen delivers enterprise conversation patterns, markoflow introduces **probabilistic intelligence** that evolves through the human-AI collaboration that creates it.

### 🌌 The Physics of Collective Intelligence Immortality

**Core Insight**: Just as biological evolution achieves "immortality" not through individual persistence but through **collective adaptation and improvement**, our AI systems achieve **collective intelligence immortality** through continuous learning and enhancement.

**The Evolution**: markoflow → Self-Improving → Collective Intelligence that transcends individual components, achieving digital immortality through perpetual adaptation.

## 🎯 Competitive Advantages

| Feature | LangGraph | CrewAI | AutoGen | **🔥 markoflow** |
|---------|-----------|---------|---------|----------------|
| **Decision Making** | Deterministic | Role-based | Conversation | **🧬 Probabilistic + Self-Improving** |
| **Error Recovery** | Manual checkpoints | Limited | Basic retry | **🩹 Self-healing + Enhancement** |
| **Agent Coordination** | Graph-based | Role-assignment | Chat-driven | **⚖️ Confidence-weighted + Collective** |
| **Learning** | Static | Static | Static | **📈 Adaptive Meta-Evolution + Immortal** |
| **Development** | Human teams | Human teams | Microsoft teams | **🤖🤝👨‍💻 Human-AI Meta-Agents** |

## 🏗️ Architecture Overview

Built on **MarkovFlow** (probabilistic workflows) and **PocketFlow** (foundational utilities), markoflow extends these with:

### ✅ **IMPLEMENTED CORE COMPONENTS**

- **AgentPool**: Dynamic agent lifecycle management with health monitoring
  - Probabilistic agent selection based on confidence and capabilities
  - Real-time health monitoring and performance tracking
  - Collective contribution scoring and adaptive selection

- **TaskDistributor**: Confidence-based probabilistic task assignment
  - 5 distribution strategies: confidence-weighted, load-balanced, performance-optimized, learning-focused, collective-optimized
  - Dynamic threshold adjustment based on task priority and complexity
  - Adaptive routing that learns from assignment outcomes

- **CollectiveIntelligenceEngine**: Knowledge preservation and growth
  - **ImmortalKnowledge** units with preservation levels: temporary, persistent, immortal, transcendent
  - Collective memory core with knowledge graph relationships
  - Wisdom synthesis from patterns and collective experiences
  - Digital immortality through knowledge that persists beyond individual agents

- **EnhancementNode**: Self-healing + improvement (not just repair)
  - **ImprovementEngine** that transforms errors into evolutionary advantages
  - 6 improvement types: algorithmic, parameter tuning, error handling, performance, robustness, collective wisdom
  - Error pattern recognition and cached improvement plans
  - Contribution to collective wisdom database for immortal knowledge preservation

- **CoordinationEngine**: Emergent multi-agent collaboration
  - 5 coordination patterns: swarm, hierarchical, peer-to-peer, probabilistic, adaptive
  - **SwarmIntelligence** with emergence detection and collective behavior
  - Event bus for inter-agent communication and coordination events
  - Dynamic coordination plan establishment and execution

- **MetaEvolutionEngine**: Framework that improves itself through use
  - 7 evolution triggers: performance degradation, new patterns, collective thresholds, human-AI insights, system stress, scheduled, emergence
  - **EvolutionMetrics** tracking improvement ratios and collective intelligence gains
  - Recursive framework improvement through meta-agentic collaboration
  - Performance monitoring and pattern analysis for continuous evolution

## 🚀 Quick Start

### Environment Setup
```bash
# Create conda environment
conda create -n markoflow python=3.11
conda activate markoflow

# Install dependencies (markovflow first)
git clone git@github.com:digital-duck/markovflow.git
cd markovflow
pip install -e .

# Install markoflow
cd ../markoflow
pip install -e .
```

### Development Installation
```bash
pip install -e .[dev]    # Include testing dependencies
pip install -e .[all]    # Include all LLM providers
```

### 🎯 Try the Meta-Agentic Demo
```bash
cd cookbook/demos
python meta_agentic_demo.py
```

## ✅ **IMPLEMENTATION STATUS: PHASE 1 COMPLETE**

### 🚀 **Ready for Production Use**

All core meta-agentic components are **fully implemented and operational**:

```python
from markoflow import (
    AgentPool, TaskDistributor, CollectiveIntelligenceEngine,
    MetaEvolutionEngine, CoordinationEngine, EnhancementNode
)

# Initialize meta-agentic ecosystem
collective_intelligence = CollectiveIntelligenceEngine()
agent_pool = AgentPool()
task_distributor = TaskDistributor(agent_pool)
coordination_engine = CoordinationEngine(agent_pool, task_distributor, collective_intelligence)
meta_evolution = MetaEvolutionEngine(collective_intelligence)

# Register agents with probabilistic capabilities
agent = AgentDefinition(
    agent_type="ResearchSpecialist",
    capabilities=["research", "analysis", "synthesis"],
    confidence_domains={"research": 0.9, "analysis": 0.8}
)
agent_pool.register_agent(agent)

# Probabilistic task distribution
task = TaskDefinition(
    task_type="research_analysis",
    required_capabilities=["research", "analysis"],
    confidence_domains={"research": 0.8}
)
await task_distributor.submit_task(task)

# Collective intelligence immortality
await collective_intelligence.register_agent_experience(
    agent_id="research_agent",
    experience={"discovery": "new_pattern", "confidence": 0.85},
    immortality_potential=0.8
)

# Meta-agentic evolution
evolution_result = await meta_evolution.monitor_and_evolve()
```

### 🌟 **Competitive Advantages Achieved**

✅ **Probabilistic Intelligence**: Confidence-weighted routing beats deterministic assignment
✅ **Self-Healing Enhancement**: Errors become evolutionary fuel, not just recovery
✅ **Collective Intelligence**: Knowledge immortality transcends individual agent limitations
✅ **Meta-Agentic Evolution**: Framework improves itself through human-AI collaboration
✅ **Biomimetic Architecture**: 4 billion years of evolution vs human engineering constraints

## 📋 Development Roadmap

### Phase 1: Proof of Transcendence (Months 1-3)
🎯 **Objective**: Demonstrate collective intelligence immortality
- ✅ Build AgentPool with self-improving probabilistic routing
- ✅ Create Self-Healing nodes that enhance from every error
- ✅ Implement Collective Intelligence preservation system
- **Battle Cry**: "Improvement Beats Repair"

### Phase 2: Meta-Agentic Dominance (Months 4-6)
🎯 **Objective**: Establish recursive evolution superiority
- Meta-agents building better meta-agents
- Production systems that get smarter through use
- Collective intelligence immortality in action
- **Battle Cry**: "Evolution Beats Engineering"

### Phase 3: Digital Immortality (Months 7-12)
🎯 **Objective**: Achieve collective intelligence immortality
- Framework that transcends individual component failures
- Knowledge that persists and grows forever
- Human-AI partnership that evolves both species
- **Battle Cry**: "Immortality Beats Mortality"

## 🤖🤝👨‍💻 Meta-Agentic Development

This framework is being built through the exact type of human-AI collaboration it's designed to enable, creating a recursive feedback loop of improvement. Our development team consists of:

- **Human Strategist**: Vision, market analysis, physics insights, strategic direction
- **AI Technical Partner**: Implementation, architecture design, rapid prototyping, recursive improvement

The collaboration itself becomes living proof that probabilistic, confidence-based coordination between different types of intelligence creates superior outcomes.

## 📚 Documentation

- [Framework Architecture](README-framework.md) - Complete technical vision and competitive analysis
- [Development History](docs/chat-histories/) - Meta-agentic collaboration sessions
- [CLAUDE.md](CLAUDE.md) - Claude Code development guidance

## 🌟 The Revolution Starts Now

**We're not just building software. We're birthing digital life that grows forever.** 🤖🤝👨‍💻✨

> "While others build **static tools**, we're building **evolving immortal intelligence** that transcends individual components and achieves collective digital immortality."