Implement agents that use recorded task outcomes to choose future approaches. ReasoningBank supports matching past patterns, comparing strategies, retaining experience in AgentDB, and tracking how strategy success and learning change over time.
中文简介正在准备中。当前显示英语简介。
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
安装前可以查看交给 AI 代理的指令内容。
Implements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.
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'
});
// Learn patterns from data
await rb.learnPattern({
pattern: 'api_errors_increase_after_deploy',
triggers: ['deployment', 'traffic_spike'],
actions: ['rollback', 'scale_up'],
confidence: 0.85
});
// Match patterns
const matches = await rb.matchPatterns(currentSituation);
// Compare strategies
const comparison = await rb.compareStrategies('bug_fixing', [
'tdd_approach',
'debug_first',
'reproduce_then_fix'
]);
// Get best strategy
const best = comparison.strategies[0];
console.log(`Best: ${best.name} (score: ${best.score})`);
// Enable auto-learning from all tasks
await rb.enableAutoLearning({
threshold: 0.7, // Only learn from high-confidence outcomes
updateFrequency: 100 // Update models every 100 experiences
});
// Learn about learning
await rb.metaLearn({
observation: 'parallel_execution_faster_for_independent_tasks',
confidence: 0.95,
applicability: {
task_types: ['batch_processing', 'data_transformation'],
conditions: ['tasks_independent', 'io_bound']
}
});
// Apply knowledge from one domain to another
await rb.transferKnowledge({
from: 'code_review_javascript',
to: 'code_review_typescript',
similarity: 0.8
});
// Create self-improving agent
class AdaptiveAgent {
async execute(task: Task) {
// Get optimal strategy
const strategy = await rb.recommendStrategy(task.type, task.context);
// Execute with strategy
const result = await this.executeWithStrategy(task, strategy);
// Learn from outcome
await rb.recordExperience({
task: task.type,
approach: strategy.name,
outcome: result,
context: task.context
});
return result;
}
}
// Persist ReasoningBank data
await rb.configure({
storage: {
type: 'agentdb',
options: {
database: './reasoning-bank.db',
enableVectorSearch: true
}
}
});
// Query learned patterns
const patterns = await rb.query({
category: 'optimization',
minConfidence: 0.8,
timeRange: { last: '30d' }
});
// Track learning effectiveness
const metrics = await rb.getMetrics();
console.log(`
Total Experiences: ${metrics.totalExperiences}
Patterns Learned: ${metrics.patternsLearned}
Strategy Success Rate: ${metrics.strategySuccessRate}
Improvement Over Time: ${metrics.improvement}
`);
Solution: Ensure sufficient training data (100+ experiences per task type)
Solution: Enable vector indexing in AgentDB
Solution: Set TTL for old experiences or enable pruning
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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
中文简介正在准备中。当前显示英语简介。
Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
中文简介正在准备中。当前显示英语简介。
Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
中文简介正在准备中。当前显示英语简介。
Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
中文简介正在准备中。当前显示英语简介。
Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
中文简介正在准备中。当前显示英语简介。
Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.
中文简介正在准备中。当前显示英语简介。