ReasoningBank with AgentDB

关于

Enables Claude to leverage the ReasoningBank adaptive learning framework powered by AgentDB's high-performance vector backend. This skill allows agents to record execution trajectories, judge outcomes, and distill experiences into high-level patterns, significantly improving decision-making over time. It provides sophisticated memory management features like context synthesis, pattern matching, and automatic memory pruning, making it essential for developers building autonomous agents that need to learn from past successes and failures through efficient experience replay.

主要功能

• Fast vector retrieval with 150x faster pattern matching
• 0 GitHub stars
• High-performance trajectory tracking and outcome judgment
• Automated memory distillation and pattern consolidation
• Context synthesis for generating narrative-rich agent experiences
• Multi-domain learning and hierarchical memory organization

使用场景

• Building self-learning agents that improve through experience
• Optimizing decision-making systems using experience replay
• Implementing high-speed vector storage for complex agentic workflows