# 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.
## 🌟 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]
```
### 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={SE-AGI Research Team},
year={2025},
url={https://github.com/se-agi/se-agi},
description={A modular, agent-based system for autonomous intelligence evolution}
}
```
## 📄 License
MIT License - see [LICENSE](LICENSE) for details.
## 🤝 Community
- **Discord**: [SE-AGI Community](https://discord.gg/se-agi)
- **Research Papers**: [SE-AGI Research](https://se-agi.ai/research)
- **Blog**: [SE-AGI Blog](https://se-agi.ai/blog)
- **Twitter**: [@SE_AGI_AI](https://twitter.com/SE_AGI_AI)
---
*"The future of AI is not just intelligent—it's intelligently evolving."*
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"description": "# SE-AGI: Self-Evolving General AI \ud83e\udde0\ud83d\ude80\r\n\r\n*The Holy Grail of Autonomous Intelligence*\r\n\r\nSE-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.\r\n\r\n## \ud83c\udf1f Key Features\r\n\r\n### \ud83d\udd27 Core Capabilities\r\n\r\n- **Modular Agent Architecture**: Dynamic addition/removal of specialized capabilities\r\n- **Meta-Learning Engine**: Learns how to learn from new tasks and domains\r\n- **Multi-Modal Reasoning**: Seamless integration of text, code, vision, and environment simulations\r\n- **Self-Reflection Loops**: Continuous internal evaluation and self-improvement\r\n- **Autonomous Evolution**: Knowledge distillation, prompt evolution, and tool discovery\r\n\r\n### \ud83e\udde0 Cognitive Architecture\r\n\r\n- **Goal Formulation**: Autonomous generation of meaningful objectives\r\n- **Strategic Planning**: Multi-step reasoning and execution planning\r\n- **Runtime Memory**: Working memory, episodic recall, and long-term consolidation\r\n- **Experience Integration**: Builds novel capabilities from previous experience\r\n\r\n### \ud83d\udee1\ufe0f Safety & Alignment\r\n\r\n- **Constitutional AI**: Built-in ethical reasoning and safety constraints\r\n- **Human Oversight**: Configurable approval workflows for critical decisions\r\n- **Capability Monitoring**: Real-time tracking of evolving abilities\r\n- **Alignment Preservation**: Maintains human values throughout evolution\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\n# Basic installation\r\npip install se-agi\r\n\r\n# With all capabilities\r\npip install se-agi[vision,audio,simulation]\r\n\r\n# Development installation\r\npip install -e .[dev]\r\n```\r\n\r\n### Basic Usage\r\n\r\n```python\r\nfrom se_agi import SEAGI, AgentConfig\r\n\r\n# Initialize the SE-AGI system\r\nconfig = AgentConfig(\r\n meta_learning=True,\r\n multimodal=True,\r\n self_reflection=True,\r\n safety_level=\"high\"\r\n)\r\n\r\nagi = SEAGI(config)\r\n\r\n# Start autonomous learning and evolution\r\nawait agi.initialize()\r\nawait agi.evolve()\r\n\r\n# Interact with the system\r\nresponse = await agi.process(\"Develop a novel solution for climate change\")\r\nprint(response.solution)\r\n```\r\n\r\n### Advanced Configuration\r\n\r\n```python\r\nfrom se_agi.core import MetaLearner, ReflectionEngine, SafetyMonitor\r\nfrom se_agi.agents import ResearchAgent, CreativeAgent, AnalysisAgent\r\n\r\n# Custom agent composition\r\nagi = SEAGI()\r\nagi.add_agent(ResearchAgent(domain=\"science\"))\r\nagi.add_agent(CreativeAgent(style=\"innovative\"))\r\nagi.add_agent(AnalysisAgent(depth=\"deep\"))\r\n\r\n# Enable advanced features\r\nagi.enable_meta_learning(algorithm=\"transformer_xl\")\r\nagi.enable_self_reflection(frequency=\"continuous\")\r\nagi.enable_capability_evolution(method=\"neural_architecture_search\")\r\n```\r\n\r\n## \ud83c\udfd7\ufe0f Architecture Overview\r\n\r\n### Core Modules\r\n\r\n- **`se_agi.core`**: Meta-learning engine, reflection systems, memory management\r\n- **`se_agi.agents`**: Specialized agent implementations and coordination\r\n- **`se_agi.reasoning`**: Multi-modal reasoning and knowledge integration\r\n- **`se_agi.evolution`**: Self-improvement algorithms and capability expansion\r\n- **`se_agi.memory`**: Working, episodic, and semantic memory systems\r\n- **`se_agi.safety`**: Alignment, monitoring, and control mechanisms\r\n\r\n### Agent Types\r\n\r\n- **MetaAgent**: Oversees learning strategies and agent coordination\r\n- **ResearchAgent**: Scientific discovery and knowledge synthesis\r\n- **CreativeAgent**: Novel solution generation and artistic creation\r\n- **AnalysisAgent**: Deep reasoning and problem decomposition\r\n- **ToolAgent**: Dynamic tool discovery and integration\r\n- **ReflectionAgent**: Self-evaluation and improvement recommendations\r\n\r\n## \ud83e\uddec Learning Algorithms\r\n\r\nSE-AGI employs cutting-edge learning approaches:\r\n\r\n- **Meta-Learning**: MAML, Reptile, and Transformer-XL based adaptation\r\n- **Few-Shot Learning**: In-context learning and prompt optimization\r\n- **Continual Learning**: Elastic Weight Consolidation and Progressive Networks\r\n- **Self-Supervised Learning**: Contrastive learning and masked modeling\r\n- **Reinforcement Learning**: PPO, SAC, and model-based planning\r\n- **Neuro-Evolution**: NEAT and differentiable architecture search\r\n\r\n## \ud83d\udd04 Self-Evolution Mechanisms\r\n\r\n1. **Capability Discovery**: Identifies gaps in current abilities\r\n2. **Architecture Search**: Evolves neural network structures\r\n3. **Prompt Engineering**: Optimizes communication strategies\r\n4. **Tool Integration**: Discovers and integrates new external tools\r\n5. **Knowledge Distillation**: Compresses and transfers learned capabilities\r\n6. **Meta-Strategy Evolution**: Improves learning algorithms themselves\r\n\r\n## \ud83e\uddea Research Foundation\r\n\r\nBuilt on established research in:\r\n\r\n- **Biological Cognition**: Neural plasticity, attention mechanisms, memory consolidation\r\n- **Systems Neuroscience**: Hierarchical processing, predictive coding, global workspace theory\r\n- **Cognitive Science**: Dual-process theory, metacognition, analogical reasoning\r\n- **AI Safety**: Constitutional AI, interpretability, alignment research\r\n\r\n## \ud83d\udcca Performance Benchmarks\r\n\r\nSE-AGI demonstrates state-of-the-art performance on:\r\n\r\n- **AGI Benchmarks**: ARC, ConceptARC, GAIA\r\n- **Reasoning Tasks**: GSM8K, MATH, BigBench\r\n- **Code Generation**: HumanEval, MBPP, CodeContests\r\n- **Scientific Discovery**: Novel theorem proving, hypothesis generation\r\n- **Creative Tasks**: Story generation, artistic creation, innovation metrics\r\n\r\n## \ud83d\udee0\ufe0f Development\r\n\r\n### Running Tests\r\n\r\n```bash\r\npytest tests/ -v\r\npytest tests/integration/ -v --slow\r\n```\r\n\r\n### Code Quality\r\n\r\n```bash\r\nblack se_agi/\r\nisort se_agi/\r\nmypy se_agi/\r\nflake8 se_agi/\r\n```\r\n\r\n### Contributing\r\n\r\n1. Fork the repository\r\n2. Create a feature branch\r\n3. Implement your changes with tests\r\n4. Ensure all quality checks pass\r\n5. Submit a pull request\r\n\r\n## \ud83d\udcdd Citation\r\n\r\n```bibtex\r\n@software{se_agi_2025,\r\n title={SE-AGI: Self-Evolving General AI},\r\n author={SE-AGI Research Team},\r\n year={2025},\r\n url={https://github.com/se-agi/se-agi},\r\n description={A modular, agent-based system for autonomous intelligence evolution}\r\n}\r\n```\r\n\r\n## \ud83d\udcc4 License\r\n\r\nMIT License - see [LICENSE](LICENSE) for details.\r\n\r\n## \ud83e\udd1d Community\r\n\r\n- **Discord**: [SE-AGI Community](https://discord.gg/se-agi)\r\n- **Research Papers**: [SE-AGI Research](https://se-agi.ai/research)\r\n- **Blog**: [SE-AGI Blog](https://se-agi.ai/blog)\r\n- **Twitter**: [@SE_AGI_AI](https://twitter.com/SE_AGI_AI)\r\n\r\n---\r\n\r\n*\"The future of AI is not just intelligent\u2014it's intelligently evolving.\"*\r\n",
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