What problem does it solve?
This Skill empowers AI agents with adaptive learning capabilities, enabling them to learn from experience, recognize patterns, and continuously optimize their strategies. It provides meta-cognitive functions for self-improving agents, leading to more efficient workflows and better decision-making over time, reducing the need for constant human oversight.
Core Features & Use Cases
- Pattern Recognition: Automatically identify recurring patterns and relationships from agent experiences.
- Strategy Optimization: Compare and recommend the most effective strategies for various tasks.
- Continuous Learning: Enable agents to automatically learn and update their models from all task outcomes.
- Use Case: An agent performing code reviews can use ReasoningBank to record each review's outcome (bugs found, time taken). Over time, it learns which review approaches (e.g., "static analysis first") are most effective for different code complexities and languages, then recommends and applies these optimal strategies automatically.
Quick Start
import { ReasoningBank } from 'agentic-flow/reasoningbank';
// Initialize ReasoningBank
const rb = new ReasoningBank({
persist: true,
learningRate: 0.1,
adapter: 'agentdb' // Use AgentDB for storage
});
// Record a task outcome
await rb.recordExperience({
task: 'code_review',
approach: 'static_analysis_first',
outcome: { success: true, metrics: { bugs_found: 5 } }
});
