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features.txt
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- Dynamic decision-making: The agent can decide between responding with information or executing code based on the user's input.
- Contextual understanding: The agent uses procedural, conceptual, contextual, meta-cognitive, and semantic knowledge for decision-making.
- Multi-turn conversation handling: The agent can handle multi-turn conversations, maintaining context across interactions.
- Knowledge integration from various sources: The agent integrates knowledge from various sources, including procedural, conceptual, contextual, meta-cognitive, and semantic knowledge.
- Real-time response generation: The agent generates responses in real-time, using asynchronous methods to handle requests efficiently.
- Adaptive learning capabilities: The agent updates its knowledge base and decision-making process based on user interactions and feedback.
- Error handling and retries: The agent includes robust error handling and retry mechanisms to ensure reliable operation.
- JSON response validation: The agent validates JSON responses against predefined schemas to ensure data integrity.
- Code execution: The agent can execute Python, JavaScript, or Bash code to perform tasks, such as copying directories or generating code snippets.
- Summarization: The agent can chunk and summarize large texts to fit within token limits, preserving key information.
- Logging and monitoring: The agent uses structured logging to monitor its operations and debug issues effectively.
- Knowledge graph integration: The agent uses a Neo4j-based knowledge graph to store and retrieve information, enabling complex queries and relationships.
- Embedding management: The agent uses embeddings to represent and retrieve knowledge, enhancing its ability to understand and process information.
- Task complexity analysis: The agent can analyze tasks to determine their complexity and decide the best approach to handle them.
- Episodic memory: The agent maintains an episodic memory system to log and recall past interactions and experiences.
- Procedural knowledge: The agent uses procedural knowledge to execute tasks and improve its performance over time.
- Conceptual knowledge: The agent integrates conceptual knowledge to understand and relate different concepts.
- Contextual knowledge: The agent uses contextual knowledge to provide relevant information and responses based on the current context.
- Meta-cognitive knowledge: The agent employs meta-cognitive strategies to monitor and improve its own performance.
- Semantic knowledge: The agent uses semantic knowledge to understand and process natural language inputs.
- Community knowledge: The agent leverages community knowledge to enhance its understanding and decision-making capabilities.
- Virtual environment management: The agent can create, manage, and destroy virtual environments to execute code in isolated settings.
- Tool usage tracking: The agent tracks the usage of various tools and updates its knowledge base with insights from these interactions.
- Performance monitoring: The agent monitors its performance and logs metrics to improve over time.
- Error analysis and correction: The agent can analyze errors in code execution and suggest fixes to improve task outcomes.
- Temporal knowledge: The agent manages temporal knowledge, including storing and retrieving temporal data, analyzing temporal relationships, and enhancing temporal reasoning.
- Spatial knowledge: The agent manages spatial knowledge, including storing and retrieving spatial data, analyzing spatial relationships, and enhancing spatial reasoning.