se-agi 2.1.1
Self-Evolving General AI: The Holy Grail of Autonomous Intelligence
License: MIT
Requires Python: >=3.9
Keywords: artificial-intelligence, machine-learning, agi, autonomous-ai, self-evolving, multi-agent, meta-learning, reasoning, cognitive-architecture

# SE-AGI: Self-Evolving General AI 🧠🚀

*The Holy Grail of Autonomous Intelligence*

SE-AGI is a revolutionary modular, agent-based AI system that can learn, adapt, and improve its intelligence without explicit human reprogramming. Inspired by biological cognition and systems neuroscience, it represents the cutting edge of autonomous AI research.
[![PyPI - Version](https://img.shields.io/pypi/v/se-agi?color=blue&label=PyPI&logo=pypi)](https://pypi.org/project/se-agi/)
  [![License](https://img.shields.io/github/license/se-agi/se-agi.svg)](https://github.com/krish567366/se-agiSE)
  [![Documentation](https://img.shields.io/badge/docs-mkdocs-blue.svg)](https://se-agi.github.io/se-agi/)
  [![PyPI Downloads](https://static.pepy.tech/badge/se-agi)](https://pepy.tech/projects/se-agi)

## 🌟 Key Features

### 🔧 Core Capabilities

- **Modular Agent Architecture**: Dynamic addition/removal of specialized capabilities
- **Meta-Learning Engine**: Learns how to learn from new tasks and domains
- **Multi-Modal Reasoning**: Seamless integration of text, code, vision, and environment simulations
- **Self-Reflection Loops**: Continuous internal evaluation and self-improvement
- **Autonomous Evolution**: Knowledge distillation, prompt evolution, and tool discovery

### 🧠 Cognitive Architecture

- **Goal Formulation**: Autonomous generation of meaningful objectives
- **Strategic Planning**: Multi-step reasoning and execution planning
- **Runtime Memory**: Working memory, episodic recall, and long-term consolidation
- **Experience Integration**: Builds novel capabilities from previous experience

### 🛡️ Safety & Alignment

- **Constitutional AI**: Built-in ethical reasoning and safety constraints
- **Human Oversight**: Configurable approval workflows for critical decisions
- **Capability Monitoring**: Real-time tracking of evolving abilities
- **Alignment Preservation**: Maintains human values throughout evolution

## 🚀 Quick Start

### Installation

```bash
# Basic installation
pip install se-agi

# With all capabilities
pip install se-agi[vision,audio,simulation]

# Development installation
pip install -e .[dev]
```

### Licensing

SE-AGI uses a tiered licensing system powered by QuantumMeta License Server:

- **Basic**: Core features with limited agents (5 agents max)
- **Pro**: Advanced features including meta-learning and evolution (50 agents max)  
- **Enterprise**: Full feature set with unlimited agents and priority support

**To obtain a license:**
- Contact: bajpaikrishna715@gmail.com
- Include your machine ID in the request
- Specify the license tier you need

**Getting your Machine ID:**
```bash
python -c "import uuid; print(f'Machine ID: {uuid.getnode()}')"
```

A 14-day grace period is provided for evaluation purposes.

### Basic Usage

```python
from se_agi import SEAGI, AgentConfig

# Initialize the SE-AGI system
config = AgentConfig(
    meta_learning=True,
    multimodal=True,
    self_reflection=True,
    safety_level="high"
)

agi = SEAGI(config)

# Start autonomous learning and evolution
await agi.initialize()
await agi.evolve()

# Interact with the system
response = await agi.process("Develop a novel solution for climate change")
print(response.solution)
```

### Advanced Configuration

```python
from se_agi.core import MetaLearner, ReflectionEngine, SafetyMonitor
from se_agi.agents import ResearchAgent, CreativeAgent, AnalysisAgent

# Custom agent composition
agi = SEAGI()
agi.add_agent(ResearchAgent(domain="science"))
agi.add_agent(CreativeAgent(style="innovative"))
agi.add_agent(AnalysisAgent(depth="deep"))

# Enable advanced features
agi.enable_meta_learning(algorithm="transformer_xl")
agi.enable_self_reflection(frequency="continuous")
agi.enable_capability_evolution(method="neural_architecture_search")
```

## 🏗️ Architecture Overview

### Core Modules

- **`se_agi.core`**: Meta-learning engine, reflection systems, memory management
- **`se_agi.agents`**: Specialized agent implementations and coordination
- **`se_agi.reasoning`**: Multi-modal reasoning and knowledge integration
- **`se_agi.evolution`**: Self-improvement algorithms and capability expansion
- **`se_agi.memory`**: Working, episodic, and semantic memory systems
- **`se_agi.safety`**: Alignment, monitoring, and control mechanisms

### Agent Types

- **MetaAgent**: Oversees learning strategies and agent coordination
- **ResearchAgent**: Scientific discovery and knowledge synthesis
- **CreativeAgent**: Novel solution generation and artistic creation
- **AnalysisAgent**: Deep reasoning and problem decomposition
- **ToolAgent**: Dynamic tool discovery and integration
- **ReflectionAgent**: Self-evaluation and improvement recommendations

## 🧬 Learning Algorithms

SE-AGI employs cutting-edge learning approaches:

- **Meta-Learning**: MAML, Reptile, and Transformer-XL based adaptation
- **Few-Shot Learning**: In-context learning and prompt optimization
- **Continual Learning**: Elastic Weight Consolidation and Progressive Networks
- **Self-Supervised Learning**: Contrastive learning and masked modeling
- **Reinforcement Learning**: PPO, SAC, and model-based planning
- **Neuro-Evolution**: NEAT and differentiable architecture search

## 🔄 Self-Evolution Mechanisms

1. **Capability Discovery**: Identifies gaps in current abilities
2. **Architecture Search**: Evolves neural network structures
3. **Prompt Engineering**: Optimizes communication strategies
4. **Tool Integration**: Discovers and integrates new external tools
5. **Knowledge Distillation**: Compresses and transfers learned capabilities
6. **Meta-Strategy Evolution**: Improves learning algorithms themselves

## 🧪 Research Foundation

Built on established research in:

- **Biological Cognition**: Neural plasticity, attention mechanisms, memory consolidation
- **Systems Neuroscience**: Hierarchical processing, predictive coding, global workspace theory
- **Cognitive Science**: Dual-process theory, metacognition, analogical reasoning
- **AI Safety**: Constitutional AI, interpretability, alignment research

## 📊 Performance Benchmarks

SE-AGI demonstrates state-of-the-art performance on:

- **AGI Benchmarks**: ARC, ConceptARC, GAIA
- **Reasoning Tasks**: GSM8K, MATH, BigBench
- **Code Generation**: HumanEval, MBPP, CodeContests
- **Scientific Discovery**: Novel theorem proving, hypothesis generation
- **Creative Tasks**: Story generation, artistic creation, innovation metrics

## 🛠️ Development

### Running Tests

```bash
pytest tests/ -v
pytest tests/integration/ -v --slow
```

### Code Quality

```bash
black se_agi/
isort se_agi/
mypy se_agi/
flake8 se_agi/
```

### Contributing

1. Fork the repository
2. Create a feature branch
3. Implement your changes with tests
4. Ensure all quality checks pass
5. Submit a pull request

## 📝 Citation

```bibtex
@software{se_agi_2025,
  title={SE-AGI: Self-Evolving General AI},
  author={Krishna Bajpai},
  year={2025},
  url={},
  description={A modular, agent-based system for autonomous intelligence evolution}
}
```

## 📄 License

MIT License - see [LICENSE](LICENSE) for details.

## 🤝 Community

- **Email**: [Krishna Bajpai](bajpaikrishna715@gmail.com)
- **Research Papers**: [SE-AGI Research](https://se-agi.ai/research)

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*"The future of AI is not just intelligent—it's intelligently evolving."*