What problem does it solves?
This Skill provides AI agents with meta-cognitive capabilities, enabling them to learn from experience, recognize patterns, and continuously optimize their strategies, leading to smarter, more adaptive, and self-improving automated workflows.
Core Features & Use Cases
- Pattern Recognition: Automatically learn and match complex patterns from agent experiences and data, improving decision-making over time.
- Strategy Optimization: Compare and recommend the most effective strategies for various tasks based on past outcomes and performance metrics.
- Continuous Learning: Enable agents to automatically learn from all task outcomes, improving performance and adapting to new challenges without explicit programming.
- Use Case: Implement an adaptive code review agent that learns from every review, identifying optimal strategies for finding bugs, improving code quality, and reducing false positives based on historical data and learned patterns.
Quick Start
import { ReasoningBank } from 'agentic-flow/reasoningbank';
// Initialize ReasoningBank with persistence enabled via AgentDB
const rb = new ReasoningBank({
persist: true,
learningRate: 0.1,
adapter: 'agentdb' // Use AgentDB for storage
});
// Record a successful code review experience for continuous learning
await rb.recordExperience({
task: 'code_review',
approach: 'static_analysis_first',
outcome: { success: true, metrics: { bugs_found: 5, time_taken: 120 } }
});
