ReasoningBank with AgentDB

Retrieve adaptive learning patterns from AgentDB vector databases.

Updated Feb 4, 2026

Search Tags:#machine learning #adaptive learning #self-learning agents #decision-making #pattern retrieval

Ready to run

npx skills add https://github.com/Marcus-Mok-GH/Chess.com-app --skill reasoningbank-with-agentdb-marcus-mok-gh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/Marcus-Mok-GH/Chess.com-app/tree/main/.migration-backup/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/Marcus-Mok-GH/Chess.com-app --skill reasoningbank-with-agentdb-marcus-mok-gh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires node, agentdb, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides adaptive learning patterns for self-learning agents using AgentDB's high-performance backend, solving the problem of slow pattern retrieval and enabling more efficient learning and decision-making.

Core Features & Use Cases

Fast Pattern Retrieval: 150x faster pattern retrieval, 500x faster batch operations.
Memory Distillation: Consolidate similar experiences into patterns.
Trajectory Tracking: Track agent execution paths and outcomes.
Verdict Judgment: Judge whether a trajectory was successful.
Use Case: Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

Quick Start

Initialize ReasoningBank Database: npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536

Frequently Asked Questions about ReasoningBank with AgentDB

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

FAQPage Schema

How do I accelerate pattern retrieval for self-learning agents?▼

Accelerate pattern retrieval for self-learning agents by using ReasoningBank with AgentDB's vector database, achieving 150x faster access to adaptive learning patterns and memories to improve decision-making.

How does memory distillation work for adaptive learning patterns?▼

Memory distillation for adaptive learning patterns consolidates similar agent experiences into reusable patterns, enabling self-learning agents to optimize decision-making through experience replay systems and trajectory tracking.

What are the prerequisites for using AgentDB to track agent execution trajectories?▼

Tracking agent execution trajectories with AgentDB requires Node.js 18+ and AgentDB v1.0.7+, initialized via the command line with a specified vector dimension to store adaptive learning patterns.

How do I initialize a vector database for self-learning agent memory?▼

Initialize a vector database for self-learning agent memory by running `npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536` to configure pattern retrieval and trajectory tracking storage.

When should I use specialized vector databases for experience replay instead of standard storage?▼

Use specialized vector databases for experience replay when self-learning agents require rapid batch operations, verdict judgment on execution paths, and memory distillation that standard storage cannot efficiently provide.

Can I track and judge whether an agent execution trajectory was successful?▼

Track and judge agent execution trajectory success using ReasoningBank's trajectory tracking and verdict judgment features, which evaluate execution paths and consolidate outcomes into adaptive learning patterns.