Agent Harness: Long-Term Memory for Adaptive AI Agents
Published:
Overview
Agent Harness is a memory-augmented framework that equips AI agents with long-term episodic and semantic memory capabilities. The system enables agents to retrieve, update, and utilize past experiences for improved decision-making and continual learning in dynamic environments.
Key Features
- Episodic Memory: Storage and retrieval of agent experiences for learning from past interactions
- Semantic Memory: Structured knowledge representation for efficient reasoning
- Adaptive Retrieval: Context-aware memory retrieval mechanisms for relevant experience recall
- Continual Learning: Online adaptation without catastrophic forgetting
- Memory Consolidation: Mechanisms for integrating new experiences into long-term knowledge
Research Directions
- Long-term memory architectures for AI agents
- Memory-augmented decision-making
- Continual learning with experience replay
- Adaptive retrieval and memory management
- Cross-task knowledge transfer
Technologies
- Reinforcement Learning
- Memory-Augmented Neural Networks
- Long-Term Memory Systems
- Continual Learning
- PyTorch
Status
Ongoing research project exploring memory-augmented architectures for adaptive AI agents.