An intelligent lobster with critical thinking and self-examination capabilities
SmartClaw is an advanced AI agent creation platform that goes beyond traditional agent frameworks by incorporating self-reflection, knowledge mapping, and adaptive learning capabilities. Inspired by the lobster's remarkable sensory systems and adaptability, SmartClaw creates intelligent agents with structured reasoning and continuous improvement mechanisms.
When creating AI agents, SmartClaw automatically generates a comprehensive knowledge graph that visualizes:
- Agent structure and decision-making flow
- Integrated skills and capabilities
- MCP (Multi-Cloud Platform) interfaces used by each skill
- Dependency relationships between components
- Knowledge sources and data flows
This knowledge graph provides transparency into how agents operate and enables better debugging, optimization, and extension.
After agent creation:
- Automated test suites verify each skill and interface connection
- Comprehensive functionality tests simulate real-world scenarios
- Performance benchmarks ensure efficiency and reliability
- Self-diagnosis algorithms identify and isolate issues
- Auto-repair mechanisms fix common problems without human intervention
- Voice notification system announces agent capabilities and readiness
For time-based or recurring tasks:
- Task scheduling with flexible time constraints
- Persistent storage in dedicated long-term memory blocks
- Database-backed state management
- Automatic recovery from system failures
- Task prioritization and resource allocation
- Memory block retention until explicitly removed by the user
- Continuous learning from task execution data
- Self-examination of decision-making processes
- Critical thinking about past performance
- Adaptation to changing environmental conditions
- Knowledge refinement through experience
- Performance optimization over time
- Agent communication protocols for teamwork
- Task distribution and load balancing
- Knowledge sharing between agents
- Collaborative problem-solving capabilities
- Hierarchical agent organization
- Modular skill architecture
- Easy integration of new capabilities
- Third-party skill marketplace
- Version control for skills
- Skill compatibility verification
┌─────────────────────────────────────────────────────────┐
│ SmartClaw Core │
├─────────┬─────────┬─────────┬─────────┬─────────┬───────┤
│ Agent │ Skill │ MCP │ Memory │ Test │ Voice │
│ Builder │ Library │ Handler │ System │ Engine │ API │
└─────────┴─────────┴─────────┴─────────┴─────────┴───────┘
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
┌─────────────────────────────────────────────────────────┐
│ Knowledge Graph Visualizer │
└─────────────────────────────────────────────────────────┘
- Python 3.8+
- Docker (optional, for containerized deployment)
- Access to MCP interfaces
- Voice synthesis API key (optional)
git clone https://github.com/devshilei/SmartClaw.git
cd SmartClaw
pip install -r requirements.txtfrom smartclaw import AgentBuilder
# Create a new agent
builder = AgentBuilder()
agent = builder.create_agent(
name="FinancialAdvisor",
skills=["market_analysis", "investment_recommendation", "risk_assessment"],
mcp_interfaces=["stock_data", "news_api", "portfolio_manager"]
)
# Get agent knowledge graph
knowledge_graph = agent.get_knowledge_graph()
knowledge_graph.visualize("financial_advisor_graph.png")
# Run agent tests
test_results = agent.run_self_tests()
print(f"Test Results: {test_results}")
# Schedule a task
agent.schedule_task(
task_name="daily_market_update",
frequency="daily",
time="09:00",
parameters={"market": "NASDAQ"}
)
smartclaw/
├── agent_builder/ # Agent creation and configuration
├── skill_library/ # Pre-built skills and capabilities
├── mcp_handler/ # MCP interface management
├── knowledge_graph/ # Knowledge representation and visualization
├── test_engine/ # Self-testing and validation
├── memory_system/ # Persistent storage and task management
├── voice_api/ # Voice notification system
├── examples/ # Usage examples and tutorials
├── docs/ # Documentation
└── tests/ # Unit and integration tests
SmartClaw can be configured through environment variables or a config.yml file:
# config.yml
smartclaw:
agent_builder:
default_language: "en"
auto_test: true
auto_repair: true
memory_system:
database_url: "sqlite:///smartclaw.db"
retention_policy: "until_removed"
voice_api:
provider: "google"
language: "en-US"
knowledge_graph:
visualization_format: "png"
include_dependencies: trueWe welcome contributions to SmartClaw! Please refer to our Contributing Guide for more information.
SmartClaw is licensed under the MIT License.
For questions, suggestions, or collaborations, please contact:
- Email: contact@smartclaw.ai
- GitHub Issues: https://github.com/devshilei/SmartClaw/issues
- Discord: Join our community
- ✅ Core agent creation framework
- ✅ Knowledge graph generation
- ✅ Basic self-testing capabilities
- ✅ Persistent task management
- ✅ Voice notification system
- 🚧 Advanced self-repair mechanisms
- 🚧 Multi-agent collaboration features
- 🚧 Skill marketplace integration
- 🚧 Enhanced visualization tools
- 📅 Reinforcement learning-based improvement
- 📅 Natural language agent configuration
- 📅 Cross-platform deployment options
- 📅 Real-time performance monitoring
"Adapt, Reflect, Excel" - The SmartClaw Philosophy