What problem does it solve?
ReasoningBank Intelligence enables autonomous agents to learn from experience and optimize strategies over time. This approach supports meta-cognitive systems and continuous improvement by turning past outcomes into refined decision policies.
Core Features & Use Cases
- Pattern Recognition: Learn patterns from prior tasks and propose improved approaches.
- Strategy Optimization: Compare and select best strategies for given task contexts.
- Continuous Learning: Persist experiences and automatically adapt models over time.
- Use Case: Apply to self-learning agents in code reviews, debugging, and workflow automation to reduce cycle times and improve quality.
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,
time_taken: 120,
false_positives: 1
}
},
context: {
language: 'typescript',
complexity: 'medium'
}
});
// Get optimal strategy
const strategy = await rb.recommendStrategy('code_review', {
language: 'typescript',
complexity: 'high'
});
