What problem does it solve?
AI agents often repeat mistakes or fail to adapt their strategies effectively. This Skill provides ReasoningBank's adaptive learning system, enabling agents to continuously learn from experience, recognize patterns, and optimize their strategies over time, leading to self-improving, meta-cognitive capabilities.
Core Features & Use Cases
- Pattern Recognition: Learn and match patterns from task outcomes and contexts, identifying recurring situations and effective responses.
- Strategy Optimization: Compare and recommend optimal strategies for specific tasks based on past performance and learned patterns.
- Continuous Learning & Meta-Learning: Automatically learn from all task outcomes, transfer knowledge across domains, and even learn about the learning process itself.
- Use Case: An agent performs code reviews. ReasoningBank records each review's
approach (e.g., static analysis first), outcome (bugs found, time taken), and context (language, complexity). Over time, it optimizes its strategy, recommending the most effective approach for a given code review scenario, leading to faster and more accurate bug detection.
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 task outcome
await rb.recordExperience({
task: 'code_review',
approach: 'static_analysis_first',
outcome: { success: true, metrics: { bugs_found: 5 } },
context: { language: 'typescript' }
});
// Get optimal strategy
const strategy = await rb.recommendStrategy('code_review', {
language: 'typescript',
complexity: 'high'
});
