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ReasoningBank Intelligence

Implement adaptive learning for AI agents using ReasoningBank pattern recognition and strategy optimization.

Updated Feb 17, 2026
Search Tags:#adaptive learning#reasoningbank#agentdb#strategy optimization#pattern recognition#meta-learning#self-learning agents
One-click install
npx skills add https://github.com/derbalimajd04-dot/al-wasat --skill reasoningbank-intelligence-derbalimajd04-dot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank Intelligence
Source: https://github.com/derbalimajd04-dot/al-wasat/tree/main/.agents/skills/reasoningbank-intelligence
Command: npx skills add https://github.com/derbalimajd04-dot/al-wasat --skill reasoningbank-intelligence-derbalimajd04-dot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow, agentdb.

What problem does it solve? AI agents typically repeat the same mistakes because they cannot retain knowledge across tasks. This Skill implements ReasoningBank's adaptive learning system so agents can record task outcomes, recognize recurring patterns, and select optimal strategies based on accumulated experience. ## Core Features & Use Cases - Experience Recording & Pattern Recognition: Log task outcomes with rich context and match learned patterns against new situations. - Strategy Optimization: Compare multiple approaches for a task type and recommend the highest-scoring strategy. - Meta-Learning & Transfer Learning: Learn about learning itself and transfer knowledge between similar domains, such as from JavaScript to TypeScript code review. - Use Case: Build a self-improving code review agent that records each review's outcome, learns which analysis approach works best per language and complexity, and automatically recommends the optimal strategy for the next review. ## Quick Start Initialize ReasoningBank with AgentDB persistence, record a task outcome with its context, then request a recommended strategy for the next similar task.

Frequently Asked Questions about ReasoningBank Intelligence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I make an AI agent learn from past task outcomes?▼

Use ReasoningBank's recordExperience method to log each task's approach, outcome metrics, and context. After accumulating enough experiences, call recommendStrategy to get the optimal approach for new tasks of the same type.

How to implement pattern recognition in agentic-flow agents?▼

Use rb.learnPattern to register patterns with triggers, actions, and confidence scores, then call rb.matchPatterns against the current situation. ReasoningBank matches incoming contexts against learned patterns to suggest relevant actions.

Does ReasoningBank require AgentDB for storage?▼

AgentDB is the recommended persistence adapter and enables vector search for semantic pattern matching. Configure it via rb.configure with storage type 'agentdb', a database path, and enableVectorSearch set to true.

Why is ReasoningBank giving poor strategy recommendations?▼

Poor recommendations usually result from insufficient training data; aim for at least 100 recorded experiences per task type. Also ensure you record both successes and failures with rich context to improve pattern matching quality.

Can ReasoningBank transfer knowledge between different task domains?▼

Yes, the transferKnowledge method applies learning from one domain to a similar one, such as from JavaScript to TypeScript code review, using a similarity score. Higher similarity values produce more reliable transfers.

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