What problem does it solves?
AI agents often struggle with continuous improvement and adapting their strategies based on past experiences. This skill provides ReasoningBank's adaptive learning system, enabling agents to recognize patterns, optimize their approaches, and continuously improve their performance over time, fostering meta-cognitive capabilities.
Core Features & Use Cases
- Pattern Recognition: Learns and matches successful operational patterns from past experiences.
- Strategy Optimization: Compares and recommends the most effective strategies for specific tasks.
- Continuous Learning: Automatically learns from all task outcomes, improving models over time.
- Use Case: An AI agent performing code reviews can use this skill to learn which review approaches (e.g., static analysis first, then security scan) are most effective for different code complexities or languages. Over time, the agent will automatically adopt the optimal strategy, leading to faster and more accurate code reviews.
Quick Start
Initialize ReasoningBank and record a successful code review experience with a "static_analysis_first" approach.
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' }
});
