What problem does it solve?
AI agents often struggle with adaptive learning and efficient memory management, leading to slow decision-making and poor performance. This Skill integrates ReasoningBank's adaptive learning with AgentDB's ultra-fast vector database (150x-12,500x faster), enabling agents to learn from experiences, judge outcomes, distill memories, and recognize patterns with unprecedented speed and efficiency.
Core Features & Use Cases
- High-Performance Learning: Leverage AgentDB for 150x faster pattern retrieval and <1ms memory access, accelerating adaptive learning.
- Trajectory Tracking & Verdict Judgment: Record agent execution paths and outcomes, then judge success based on similarity to past successful patterns.
- Memory Distillation & Pattern Recognition: Consolidate similar experiences into high-level patterns and retrieve relevant knowledge with reasoning.
- Use Case: An agent is tasked with optimizing database queries. It records its
trajectory (steps taken, results). ReasoningBank, powered by AgentDB, quickly retrieves similar successful patterns from its memory, allowing the agent to judge its approach and distill new, optimized strategies for future tasks.
Quick Start
Initialize AgentDB for ReasoningBank
npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536
Start MCP server for Claude Code integration
npx agentdb@latest mcp
claude mcp add agentdb npx agentdb@latest mcp