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.
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查看安装方法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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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
中文简介正在准备中。当前显示原文说明。