What is the main benefit of using AgentDB with ReasoningBank?

AgentDB provides a massive performance boost, offering 150x faster pattern retrieval and sub-1ms memory access compared to standard database backends.

Can I migrate my existing ReasoningBank data?

Yes, the skill includes built-in CLI commands to automatically migrate and validate data from legacy .swarm/memory.db files into the optimized AgentDB format.

Is this compatible with existing agentic-flow projects?

Yes, it maintains 100% backward compatibility with the legacy ReasoningBank API while transparently upgrading the performance backend.

How does memory distillation work in this skill?

It automatically consolidates similar agent experiences into high-level patterns, pruning low-quality data and reducing redundancy to keep the agent's memory efficient.

What are the core reasoning modules included?

It includes PatternMatcher for diverse retrieval, ContextSynthesizer for narrative generation, MemoryOptimizer for pruning, and ExperienceCurator for quality filtering.

ReasoningBank Agent Memory & Adaptive Learning

Name: ReasoningBank Agent Memory & Adaptive Learning
Author: ricable

byricable

•

데이터 과학 및 ML

Enhances AI agents with high-performance adaptive learning and vector-based memory distillation using AgentDB.

소개

ReasoningBank with AgentDB provides a sophisticated framework for building self-learning AI agents that improve over time through experience. By leveraging AgentDB's high-performance vector backend, it enables sub-millisecond memory access and up to 150x faster pattern retrieval compared to legacy systems. The skill allows developers to implement trajectory tracking for agent actions, judge the success of outcomes, and distill complex experiences into reusable patterns. It is ideal for optimizing agent decision-making through experience replay and automated memory pruning, ensuring that AI models learn from past successes and failures in production environments while maintaining 100% backward compatibility.

주요 기능

Ultra-fast vector search with 150x faster pattern retrieval and sub-1ms access.
Verdict judgment system for evaluating and scoring the success of agent actions.
Trajectory tracking to record and analyze complex agent execution paths.
Advanced reasoning modules including PatternMatcher and ContextSynthesizer.
Automated memory distillation to consolidate experiences into high-level patterns.
1 GitHub stars

사용 사례

Implementing experience replay systems for reinforcement learning agents.
Building self-improving agents that learn from past debugging or coding tasks.
Optimizing decision-making by retrieving successful historical patterns in real-time.

소개

주요 기능

Ultra-fast vector search with 150x faster pattern retrieval and sub-1ms access.
Verdict judgment system for evaluating and scoring the success of agent actions.
Trajectory tracking to record and analyze complex agent execution paths.
Advanced reasoning modules including PatternMatcher and ContextSynthesizer.
Automated memory distillation to consolidate experiences into high-level patterns.
1 GitHub stars

사용 사례

Implementing experience replay systems for reinforcement learning agents.
Building self-improving agents that learn from past debugging or coding tasks.
Optimizing decision-making by retrieving successful historical patterns in real-time.