What is memory distillation in this context?

Memory distillation is the process of consolidating multiple similar experiences into high-level patterns, pruning low-quality data, and increasing the overall efficiency of the agent's knowledge base.

What is ReasoningBank?

ReasoningBank is an adaptive learning framework that allows AI agents to store, retrieve, and learn from past experiences and execution trajectories to improve future performance.

How much faster is the AgentDB integration?

AgentDB provides up to 150x faster pattern retrieval and 500x faster batch operations compared to legacy ReasoningBank implementations, with memory access under 1ms.

Do I need a separate database server?

No, AgentDB can be initialized locally via the CLI and integrated directly into your workflow using the Model Context Protocol (MCP) for Claude Code.

Is it backward compatible with my existing ReasoningBank projects?

Yes, it maintains 100% backward compatibility with legacy APIs while automatically upgrading the backend performance to AgentDB.

ReasoningBank Adaptive Learning

Name: ReasoningBank Adaptive Learning
Author: ellisapotheosis

byellisapotheosis

Data Science & ML

Implements high-performance adaptive learning and memory distillation for AI agents using AgentDB’s ultra-fast vector database.

About

ReasoningBank with AgentDB empowers Claude to build self-learning agents that evolve through experience. By leveraging AgentDB’s high-performance backend—capable of 150x faster pattern retrieval—it provides a structured framework for trajectory tracking, outcome judgment, and hierarchical memory management. This skill is essential for developers building autonomous systems that need to optimize decision-making over time, perform experience replay, or maintain persistent, searchable knowledge of past successes and failures with 100% backward compatibility.

Key Features

Full MCP integration for seamless Claude Code interaction
High-speed vector memory with <1ms access times
Intelligent memory distillation and pattern consolidation
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Automated trajectory tracking and success judgment
Multi-domain learning for knowledge transfer across tasks

Use Cases

Building autonomous agents that learn from past debugging or optimization successes
Managing large-scale hierarchical knowledge bases for complex engineering tasks
Implementing experience replay systems for reinforcement learning workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ellisapotheosis/project-nyra reasoningbank-agentdb

For use in Claude.ai and ChatGPT

Download Skill

GitHub