Do I need an external database to use this skill?

No, AgentDB initializes a local high-performance vector database (typically .db file) within your project directory, requiring no complex external infrastructure.

What is ReasoningBank?

ReasoningBank is a framework for adaptive learning that allows AI agents to store, retrieve, and learn from past experiences and trajectories to improve future decision-making.

How much faster is the AgentDB implementation?

AgentDB provides up to 150x faster pattern retrieval and 500x faster batch operations compared to standard database backends, enabling sub-millisecond memory access.

What is memory distillation in this context?

Memory distillation is the process of consolidating multiple similar experiences into single high-level patterns, which reduces noise and improves the agent's reasoning quality.

Is this skill compatible with existing ReasoningBank setups?

Yes, it offers 100% backward compatibility with legacy ReasoningBank APIs while automatically upgrading the backend to the high-performance AgentDB engine.

ReasoningBank AgentDB Adaptive Learning

Name: ReasoningBank AgentDB Adaptive Learning
Author: zellycloud

byzellycloud

데이터 과학 및 ML

Implements high-performance adaptive learning and memory distillation for self-improving AI agents using AgentDB.

소개

ReasoningBank with AgentDB provides a sophisticated framework for building self-learning agents that improve through experience. By leveraging AgentDB's high-speed vector database architecture, it enables real-time trajectory tracking, outcome judgment, and memory consolidation. This skill allows agents to distill raw execution data into actionable patterns, effectively creating an experience replay system that optimizes decision-making processes over time with significantly reduced latency (150x to 12,500x faster) compared to legacy systems.

주요 기능

0 GitHub stars
Context synthesis for rich, evidence-based reasoning
Real-time trajectory tracking and success verdict judgment
100% backward compatibility with legacy ReasoningBank systems
Automated memory distillation and pattern consolidation
High-speed vector retrieval with 150x faster pattern matching

사용 사례

Reducing latency in RAG-based agent workflows using high-performance vector memory
Building self-learning agents that optimize their own code generation strategies
Implementing experience replay systems for complex decision-making tasks

소개

주요 기능

0 GitHub stars
Context synthesis for rich, evidence-based reasoning
Real-time trajectory tracking and success verdict judgment
100% backward compatibility with legacy ReasoningBank systems
Automated memory distillation and pattern consolidation
High-speed vector retrieval with 150x faster pattern matching

사용 사례

Reducing latency in RAG-based agent workflows using high-performance vector memory
Building self-learning agents that optimize their own code generation strategies
Implementing experience replay systems for complex decision-making tasks