What problem does it solve?
This Skill provides AI agents with adaptive learning capabilities, enabling them to continuously learn from experience, recognize patterns, and optimize their strategies over time. It helps users build self-improving agents that can adapt to new situations, make better decisions, and achieve higher success rates, reducing the need for constant human intervention.
Core Features & Use Cases
- Pattern Recognition: Automatically learns and matches patterns from agent experiences and task outcomes, enabling agents to identify recurring situations and apply learned solutions.
- Strategy Optimization: Compares and recommends the most effective strategies for various tasks and contexts, guiding agents toward optimal decision-making.
- Continuous Learning: Enables agents to automatically update their knowledge and improve performance over time, fostering true meta-cognitive capabilities.
- Use Case: Deploy an adaptive code review agent. It records each review's outcome, learns which approaches (e.g., static analysis first, TDD approach) are most effective for different code complexities and languages, and then recommends the optimal strategy for future code reviews.
Quick Start
import { ReasoningBank } from 'agentic-flow/reasoningbank';
// Initialize ReasoningBank with persistence
const rb = new ReasoningBank({
persist: true,
learningRate: 0.1,
adapter: 'agentdb' // Use AgentDB for storage
});
// Record a successful code review experience
await rb.recordExperience({
task: 'code_review',
approach: 'static_analysis_first',
outcome: { success: true, metrics: { bugs_found: 5 } },
context: { language: 'typescript', complexity: 'medium' }
});
