Is it compatible with existing ReasoningBank projects?

Yes, it maintains 100% backward compatibility with legacy ReasoningBank APIs while automatically providing the performance benefits of the AgentDB backend.

How much faster is AgentDB compared to legacy systems?

AgentDB offers significantly improved performance, with up to 150x faster pattern retrieval and 500x faster batch operations compared to standard implementations.

What are trajectories in this context?

Trajectories are sequences of actions and their outcomes. Recording them allows the AI to analyze which steps lead to success or failure in various tasks.

What is ReasoningBank with AgentDB?

It is a skill for Claude Code that enables agents to learn from their past actions using a high-performance vector database to store and retrieve 'memories' or patterns.

Do I need experience in Reinforcement Learning to use this?

While helpful, it's not required. The skill provides high-level APIs like PatternMatcher and ContextSynthesizer that handle the complex logic for you.

ReasoningBank with AgentDB

Name: ReasoningBank with AgentDB
Author: while-basic

bywhile-basic

Data Science & ML

Implements high-performance adaptive learning and trajectory tracking for self-improving AI agents using AgentDB's ultra-fast vector backend.

About

ReasoningBank with AgentDB is a specialized skill for building autonomous agents that learn from experience. By leveraging AgentDB's high-performance vector storage, this skill enables agents to record execution trajectories, judge outcomes, and distill complex memories into reusable patterns. It provides a robust framework for implementing reinforcement learning concepts, allowing Claude to optimize its decision-making over time. With 100% backward compatibility and significantly faster retrieval times than legacy systems, it is ideal for developers creating sophisticated AI systems that require persistent memory and continuous performance improvement.

Key Features

0 GitHub stars
High-performance vector retrieval (up to 150x faster)
Automatic memory optimization and pruning for efficiency
Autonomous trajectory tracking and outcome judgment
Memory distillation for pattern recognition and consolidation
Context synthesis from multiple historical experiences

Use Cases

Implementing high-speed persistent memory for agentic workflows
Optimizing complex decision-making processes through experience replay
Building self-learning agents that improve task success rates over time

About

Key Features

0 GitHub stars
High-performance vector retrieval (up to 150x faster)
Automatic memory optimization and pruning for efficiency
Autonomous trajectory tracking and outcome judgment
Memory distillation for pattern recognition and consolidation
Context synthesis from multiple historical experiences

Use Cases

Implementing high-speed persistent memory for agentic workflows
Optimizing complex decision-making processes through experience replay
Building self-learning agents that improve task success rates over time