What is the main benefit of using AgentDB with ReasoningBank?

It provides a massive performance boost, ranging from 150x to 12,500x faster vector searches and memory retrieval compared to legacy storage methods.

What reasoning modules are included in this skill?

It features four core modules: PatternMatcher for diversity-aware retrieval, ContextSynthesizer for narrative generation, MemoryOptimizer for pruning, and ExperienceCurator for quality filtering.

Can I migrate my existing ReasoningBank data?

Yes, the skill includes a built-in CLI migration tool that automatically transfers data from legacy .swarm/memory.db files into the optimized AgentDB format with full validation.

Is this skill suitable for production environments?

Yes, with features like sub-1ms memory access, batch operation support, and automatic memory consolidation, it is designed for high-performance production AI agent systems.

ReasoningBank with AgentDB

Name: ReasoningBank with AgentDB
Author: ellisapotheosis

byellisapotheosis

Data Science & ML

Implements high-performance adaptive learning and memory distillation for AI agents using the ultra-fast AgentDB vector engine.

About

This skill integrates ReasoningBank adaptive learning patterns with AgentDB's high-performance vector database, delivering up to 150x faster pattern retrieval and sub-millisecond memory access. It enables developers to build self-improving agents capable of tracking execution trajectories, judging outcomes, and distilling complex experiences into reusable logic patterns. By providing specialized reasoning modules like PatternMatcher and ContextSynthesizer, it simplifies the implementation of sophisticated experience replay systems and autonomous decision-making loops while maintaining full backward compatibility with legacy ReasoningBank implementations.

Key Features

Automated trajectory tracking to record and analyze agent execution sequences
High-speed pattern retrieval (150x faster) using AgentDB's optimized vector backend
Advanced memory distillation to consolidate raw experiences into high-level patterns
Seamless CLI-based migration tools for legacy ReasoningBank databases
0 GitHub stars
Built-in reasoning modules for pattern matching, context synthesis, and memory optimization

Use Cases

Implementing efficient experience replay systems for reinforcement learning applications
Building autonomous agents that learn and improve from past successes and failures
Optimizing complex multi-step decision-making processes in agentic workflows

About

Key Features

Automated trajectory tracking to record and analyze agent execution sequences
High-speed pattern retrieval (150x faster) using AgentDB's optimized vector backend
Advanced memory distillation to consolidate raw experiences into high-level patterns
Seamless CLI-based migration tools for legacy ReasoningBank databases
0 GitHub stars
Built-in reasoning modules for pattern matching, context synthesis, and memory optimization

Use Cases

Implementing efficient experience replay systems for reinforcement learning applications
Building autonomous agents that learn and improve from past successes and failures
Optimizing complex multi-step decision-making processes in agentic workflows