What is 'Memory Distillation' in this context?

Memory distillation is the process of consolidating multiple similar experiences into a single, high-level optimized pattern while pruning low-quality or redundant data.

Is this skill backward compatible with older ReasoningBank versions?

Yes, it maintains 100% backward compatibility with legacy APIs while automatically utilizing the faster AgentDB backend.

Does this skill require a specific database setup?

Yes, it requires AgentDB (v1.0.7+) which can be initialized via the CLI to handle the vector embeddings and trajectory data.

How do I migrate my existing agent memories?

You can use the provided CLI migration command: 'npx agentdb@latest migrate --source path/to/legacy.db' to move data to the new AgentDB format.

What is the primary benefit of using ReasoningBank with AgentDB?

It allows AI agents to learn from every interaction by storing experiences in a high-performance vector database, enabling them to retrieve successful patterns 150x faster than legacy solutions.

ReasoningBank with AgentDB

Name: ReasoningBank with AgentDB
Author: whrit

bywhrit

データサイエンスとML

Implements high-performance adaptive learning and experience replay for AI agents using the AgentDB vector engine.

概要

This skill integrates ReasoningBank's adaptive learning patterns with AgentDB's high-performance vector database to enable self-improving AI agents. It provides a comprehensive framework for tracking agent execution trajectories, judging outcomes, distilling memories into reusable patterns, and optimizing decision-making over time. With 100% backward compatibility and significant performance gains (up to 150x faster retrieval) over legacy systems, it is an essential tool for developers building sophisticated agentic workflows that require persistence, learning from success or failure, and ultra-fast memory access.

主な機能

0 GitHub stars
Trajectory tracking and verdict judgment for outcome analysis
Seamless migration tools for legacy ReasoningBank databases
Automatic memory distillation and pattern consolidation
High-speed vector search with 150x faster pattern retrieval
Context synthesis for generating rich narratives from past experiences

ユースケース

Implementing experience replay systems for LLM-based decision making
Optimizing complex workflows by retrieving and applying successful past patterns
Building self-learning agents that improve task success rates over time

概要

主な機能

0 GitHub stars
Trajectory tracking and verdict judgment for outcome analysis
Seamless migration tools for legacy ReasoningBank databases
Automatic memory distillation and pattern consolidation
High-speed vector search with 150x faster pattern retrieval
Context synthesis for generating rich narratives from past experiences

ユースケース

Implementing experience replay systems for LLM-based decision making
Optimizing complex workflows by retrieving and applying successful past patterns
Building self-learning agents that improve task success rates over time