What problem does it solve?
Creating AI agents that can continuously learn, adapt their strategies, and improve performance over time is a complex challenge. This Skill implements ReasoningBank's adaptive learning system, enabling agents to recognize patterns, optimize strategies, and develop meta-cognitive capabilities for continuous improvement.
Core Features & Use Cases
- Pattern Recognition: Learns patterns from task outcomes and data, allowing agents to match current situations to past experiences.
- Strategy Optimization: Compares different approaches and recommends the most effective strategy for a given task and context.
- Continuous Learning & Meta-Learning: Enables agents to automatically learn from all tasks (above a confidence threshold) and even learn about the learning process itself (meta-learning).
- Use Case: Develop an adaptive code review agent. The agent records each code review's outcome (bugs found, time taken), learns optimal strategies (e.g., "static analysis first"), and continuously refines its approach based on success rates, providing better recommendations over time.
Quick Start
Initialize ReasoningBank, record an experience from a code review task, and then get an optimal strategy recommendation for a new code review.
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' } });
const strategy = await rb.recommendStrategy('code_review', { language: 'typescript', complexity: 'high' });
