What is trajectory tracking?

It records the specific sequence of actions an agent takes and the resulting outcome, allowing the system to learn which paths lead to success.

How does memory distillation work?

It automatically consolidates multiple similar experiences into a single high-level pattern, reducing noise and improving the quality of retrieved context.

Is this skill compatible with existing AI agent frameworks?

It is 100% backward compatible with legacy ReasoningBank APIs while utilizing the high-performance AgentDB backend automatically.

What is the main benefit of using AgentDB with ReasoningBank?

AgentDB provides a massive performance boost, offering up to 150x faster pattern retrieval and 500x faster batch operations compared to legacy backends.

Can I migrate my existing ReasoningBank data?

Yes, the skill includes built-in migration tools to automatically transfer data from legacy SQLite-based memory systems with full validation.

ReasoningBank with AgentDB

Name: ReasoningBank with AgentDB
Author: ricable

byricable

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Data Science & ML

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

About

This skill integrates ReasoningBank's adaptive learning patterns with AgentDB's high-performance vector database, offering retrieval speeds up to 150x faster than legacy implementations. It enables developers to build self-improving agents that track execution trajectories, judge outcomes, and distill complex experiences into reusable patterns. By providing tools for memory optimization and context synthesis, it allows Claude to maintain a highly efficient, searchable memory of domain-specific experiences, making it ideal for building autonomous systems that learn from past successes and failures to optimize decision-making over time.

Key Features

Trajectory tracking for recording and analyzing agent action sequences
Context synthesis for generating rich narratives from retrieved memories
High-speed vector retrieval (150x-12,500x faster than legacy systems)
Verdict judgment for automated outcome assessment and confidence scoring
2 GitHub stars
Memory distillation to consolidate experiences into high-level patterns

Use Cases

Implementing experience replay systems for reinforcement learning workflows
Building self-learning agents that improve performance based on historical execution data
Optimizing agent decision-making in complex domains like code refactoring or database tuning

About

Key Features

Trajectory tracking for recording and analyzing agent action sequences
Context synthesis for generating rich narratives from retrieved memories
High-speed vector retrieval (150x-12,500x faster than legacy systems)
Verdict judgment for automated outcome assessment and confidence scoring
2 GitHub stars
Memory distillation to consolidate experiences into high-level patterns

Use Cases

Implementing experience replay systems for reinforcement learning workflows
Building self-learning agents that improve performance based on historical execution data
Optimizing agent decision-making in complex domains like code refactoring or database tuning