skills.rest
justSteve
justSteveCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

ReasoningBank with AgentDB

Store, retrieve, and reason over experiential memories using AgentDB.

Updated Oct 22, 2025
Search Tags:#vector database#adaptive learning#self-learning#reasoningbank#agentdb#pattern recognition#AI memory
One-click install
npx skills add https://github.com/justSteve/myOrchestration --skill reasoningbank-with-agentdb-juststeve
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/justSteve/myOrchestration/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/justSteve/myOrchestration --skill reasoningbank-with-agentdb-juststeve

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Node.js, agentdb, agentic-flow/reasoningbank, claude-flow, and includes references (resource) components.

What problem does it solve?

Building self-learning AI agents that can quickly recall past experiences, judge outcomes, and distill memories for improved decision-making is often hampered by slow data retrieval and inefficient memory management.

Core Features & Use Cases

  • High-Performance Learning: Integrates ReasoningBank adaptive learning with AgentDB's vector database, achieving 150x faster pattern retrieval and sub-millisecond memory access.
  • Trajectory Tracking & Verdict Judgment: Records agent execution paths (trajectories), judges their success based on similarity to successful patterns, and provides confidence scores.
  • Memory Distillation: Consolidates similar experiences into high-level, generalized patterns, and automatically prunes low-quality memories for efficient knowledge retention.
  • Use Case: Optimize an AI agent's approach to database query optimization. The agent records successful query optimization trajectories, distills these into general best practices (e.g., "For N+1 queries, add eager loading, then cache"), and uses these patterns for faster, more reliable future optimizations.

Quick Start

Initialize an AgentDB database for ReasoningBank with a dimension of 1536. Start the MCP server for Claude Code integration. Store a successful experience in the database for "How to optimize database queries?".

Frequently Asked Questions about ReasoningBank with AgentDB

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I speed up pattern recall in self-learning AI agents?▼

ReasoningBank with AgentDB enables 150x faster pattern recall by integrating adaptive learning with vector database storage, delivering sub-millisecond memory access for autonomous agents that need to retrieve and reason over past experiences quickly.

Can I track agent decision trajectories and judge outcomes automatically?▼

Yes. ReasoningBank records agent execution paths, judges success by comparing against similar patterns with confidence scores, and distills trajectories into generalized best practices for repeated optimization.

How do I set up high-performance memory for database query optimization agents?▼

Initialize an AgentDB vector database with dimension 1536, integrate ReasoningBank via Node.js 18+, store successful query optimization trajectories, and retrieve patterns with context-rich reasoning for faster, more reliable agent decisions.

What's the difference between ReasoningBank and standard agent memory systems?▼

ReasoningBank combines trajectory tracking, memory distillation, and verdict judgment on vector-backed storage, achieving 150x faster recall and sub-millisecond access versus traditional inefficient memory retrieval in autonomous agent frameworks.

Do I need to manage memory pruning manually in self-learning agents?▼

No. ReasoningBank automatically consolidates similar experiences into high-level patterns and prunes low-quality memories, eliminating manual memory management while maintaining efficient knowledge retention for long-running agents.

What dependencies and environment does ReasoningBank require?▼

ReasoningBank requires Node.js 18+, AgentDB v1.0.7+ (via agentic-flow), claude-flow integration, and embedding-based APIs (computeEmbedding, insertPattern, retrieveWithReasoning) for vector-backed memory and programmable integration.

Related Skills

Explore More Software Engineering Skills→
adeboldadebold

ReasoningBank with AgentDB

Integrate ReasoningBank with AgentDB for adaptive learning and experience replay.

Community
Advanced
Geralt1983Geralt1983
1

ReasoningBank with AgentDB

Implement adaptive learning for AI agents using AgentDB vector database.

Community
Intermediate
BURHANDEV-ENTERPRISEBURHANDEV-ENTERPRISE

ReasoningBank with AgentDB

Implement adaptive learning and experience replay with AgentDB vector database integration.

Official
Intermediate
coreyhulencoreyhulen

ReasoningBank with AgentDB

Implement ReasoningBank adaptive learning with AgentDB vector database.

Community
Intermediate