Is it compatible with the standard Claude Code MCP?

Yes, it integrates seamlessly via the AgentDB MCP server, allowing Claude Code to interact with the learning database directly through a standard interface.

What is the main benefit of using AgentDB with ReasoningBank?

It provides a 150x to 12,500x performance boost in vector operations and pattern retrieval compared to legacy storage methods, allowing for sub-millisecond memory access.

Can I migrate my existing ReasoningBank data?

Yes, the skill includes built-in migration commands to move data from legacy .swarm$memory.db files to the optimized AgentDB format with full validation.

Does this skill require deep knowledge of reinforcement learning?

While helpful, it is not required; the skill provides high-level APIs and CLI tools that handle the underlying learning patterns and trajectory judgments automatically.

What are reasoning modules in this context?

Reasoning modules are specialized components like PatternMatcher and ContextSynthesizer that filter, group, and summarize agent experiences for better decision-making.

ReasoningBank with AgentDB

Name: ReasoningBank with AgentDB
Author: nbossn

bynbossn

0•

数据科学与机器学习

Implements high-performance adaptive learning and memory distillation for AI agents using AgentDB's optimized vector database.

ReasoningBank with AgentDB empowers Claude Code and other AI agents with advanced reinforcement learning patterns and a lightning-fast vector backend. It enables agents to record execution trajectories, judge outcomes, and distill complex experiences into reusable patterns, resulting in significant performance gains with up to 150x faster retrieval. This skill is essential for developers building self-evolving agentic systems that need to improve their decision-making logic over time through experience replay, automated memory management, and high-performance pattern recognition.

主要功能

01High-speed pattern retrieval (150x faster than legacy systems)

02Trajectory tracking for recording and analyzing agent execution paths

03Automated memory distillation to consolidate experiences into actionable patterns

04Context synthesis for generating rich, coherent narratives from multiple memories

05Full backward compatibility with legacy ReasoningBank implementations

060 GitHub stars

使用场景

01Building self-learning agents that improve through experience replay

02Optimizing complex decision-making processes in autonomous workflows

03Implementing persistent memory layers for long-running AI tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base reasoningbank-agentdb

For use in Claude.ai and ChatGPT

主要功能

01High-speed pattern retrieval (150x faster than legacy systems)

02Trajectory tracking for recording and analyzing agent execution paths

03Automated memory distillation to consolidate experiences into actionable patterns

04Context synthesis for generating rich, coherent narratives from multiple memories

05Full backward compatibility with legacy ReasoningBank implementations

060 GitHub stars

使用场景

01Building self-learning agents that improve through experience replay

02Optimizing complex decision-making processes in autonomous workflows

03Implementing persistent memory layers for long-running AI tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base reasoningbank-agentdb

For use in Claude.ai and ChatGPT