What problem does it solve?
This Skill empowers AI agents with meta-cognitive capabilities, allowing them to learn from experience, recognize patterns, and continuously optimize their strategies. It addresses the challenge of building truly adaptive and self-improving AI systems, leading to more efficient workflows and better decision-making over time.
Core Features & Use Cases
- Pattern Recognition: Automatically learn and match patterns from agent experiences and data.
- Strategy Optimization: Compare and recommend the most effective strategies for specific tasks.
- Continuous Learning: Enable agents to automatically update their models based on new, high-confidence outcomes.
- Use Case: Develop an adaptive code review agent that learns from past code review outcomes. It identifies successful review patterns, optimizes its approach based on project context (language, complexity), and continuously improves its bug-finding efficiency.
Quick Start
Initialize ReasoningBank and record a task outcome:
import { ReasoningBank } from 'agentic-flow/reasoningbank';
const rb = new ReasoningBank({
persist: true,
learningRate: 0.1,
adapter: 'agentdb'
});
await rb.recordExperience({
task: 'code_review',
approach: 'static_analysis_first',
outcome: { success: true, metrics: { bugs_found: 5 } },
context: { language: 'typescript' }
});
