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Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fruvnet\u002Freasoningbank-with-agentdb\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"with","ReasoningBank with AgentDB","self","reasoningbank adaptive learning with agentdb","distillation","trajectory","verdict","systems","judgment","decision-making","pattern","memory","replay","use","vector database","decision","learning","vector","database","implement","2026-11-10 10:12:30",false,"---\nname: reasoningbank-with-agentdb\ndescription: \"Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.\"\n---\n\n# ReasoningBank with AgentDB\n\n## What This Skill Does\n\nProvides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility.\n\n**Performance**: 150x faster pattern retrieval, 500x faster batch operations, \u003C1ms memory access.\n\n## Prerequisites\n\n- Node.js 18+\n- AgentDB v1.0.7+ (via agentic-flow)\n- Understanding of reinforcement learning concepts (optional)\n\n---\n\n## Quick Start with CLI\n\n### Initialize ReasoningBank Database\n\n```bash\n# Initialize AgentDB for ReasoningBank\nnpx agentdb@latest init .\u002F.agentdb\u002Freasoningbank.db --dimension 1536\n\n# Start MCP server for Claude Code integration\nnpx agentdb@latest mcp\nclaude mcp add agentdb npx agentdb@latest mcp\n```\n\n### Migrate from Legacy ReasoningBank\n\n```bash\n# Automatic migration with validation\nnpx agentdb@latest migrate --source .swarm\u002Fmemory.db\n\n# Verify migration\nnpx agentdb@latest stats .\u002F.agentdb\u002Freasoningbank.db\n```\n\n---\n\n## Quick Start with API\n\n```typescript\nimport { createAgentDBAdapter, computeEmbedding } from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Initialize ReasoningBank with AgentDB\nconst rb = await createAgentDBAdapter({\n  dbPath: '.agentdb\u002Freasoningbank.db',\n  enableLearning: true,      \u002F\u002F Enable learning plugins\n  enableReasoning: true,      \u002F\u002F Enable reasoning agents\n  cacheSize: 1000,            \u002F\u002F 1000 pattern cache\n});\n\n\u002F\u002F Store successful experience\nconst query = \"How to optimize database queries?\";\nconst embedding = await computeEmbedding(query);\n\nawait rb.insertPattern({\n  id: '',\n  type: 'experience',\n  domain: 'database-optimization',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: {\n      query,\n      approach: 'indexing + query optimization',\n      outcome: 'success',\n      metrics: { latency_reduction: 0.85 }\n    }\n  }),\n  confidence: 0.95,\n  usage_count: 1,\n  success_count: 1,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n\n\u002F\u002F Retrieve similar experiences with reasoning\nconst result = await rb.retrieveWithReasoning(embedding, {\n  domain: 'database-optimization',\n  k: 5,\n  useMMR: true,              \u002F\u002F Diverse results\n  synthesizeContext: true,    \u002F\u002F Rich context synthesis\n});\n\nconsole.log('Memories:', result.memories);\nconsole.log('Context:', result.context);\nconsole.log('Patterns:', result.patterns);\n```\n\n---\n\n## Core ReasoningBank Concepts\n\n### 1. Trajectory Tracking\n\nTrack agent execution paths and outcomes:\n\n```typescript\n\u002F\u002F Record trajectory (sequence of actions)\nconst trajectory = {\n  task: 'optimize-api-endpoint',\n  steps: [\n    { action: 'analyze-bottleneck', result: 'found N+1 query' },\n    { action: 'add-eager-loading', result: 'reduced queries' },\n    { action: 'add-caching', result: 'improved latency' }\n  ],\n  outcome: 'success',\n  metrics: { latency_before: 2500, latency_after: 150 }\n};\n\nconst embedding = await computeEmbedding(JSON.stringify(trajectory));\n\nawait rb.insertPattern({\n  id: '',\n  type: 'trajectory',\n  domain: 'api-optimization',\n  pattern_data: JSON.stringify({ embedding, pattern: trajectory }),\n  confidence: 0.9,\n  usage_count: 1,\n  success_count: 1,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n```\n\n### 2. Verdict Judgment\n\nJudge whether a trajectory was successful:\n\n```typescript\n\u002F\u002F Retrieve similar past trajectories\nconst similar = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'api-optimization',\n  k: 10,\n});\n\n\u002F\u002F Judge based on similarity to successful patterns\nconst verdict = similar.memories.filter(m =>\n  m.pattern.outcome === 'success' &&\n  m.similarity > 0.8\n).length > 5 ? 'likely_success' : 'needs_review';\n\nconsole.log('Verdict:', verdict);\nconsole.log('Confidence:', similar.memories[0]?.similarity || 0);\n```\n\n### 3. Memory Distillation\n\nConsolidate similar experiences into patterns:\n\n```typescript\n\u002F\u002F Get all experiences in domain\nconst experiences = await rb.retrieveWithReasoning(embedding, {\n  domain: 'api-optimization',\n  k: 100,\n  optimizeMemory: true,  \u002F\u002F Automatic consolidation\n});\n\n\u002F\u002F Distill into high-level pattern\nconst distilledPattern = {\n  domain: 'api-optimization',\n  pattern: 'For N+1 queries: add eager loading, then cache',\n  success_rate: 0.92,\n  sample_size: experiences.memories.length,\n  confidence: 0.95\n};\n\nawait rb.insertPattern({\n  id: '',\n  type: 'distilled-pattern',\n  domain: 'api-optimization',\n  pattern_data: JSON.stringify({\n    embedding: await computeEmbedding(JSON.stringify(distilledPattern)),\n    pattern: distilledPattern\n  }),\n  confidence: 0.95,\n  usage_count: 0,\n  success_count: 0,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n```\n\n---\n\n## Integration with Reasoning Agents\n\nAgentDB provides 4 reasoning modules that enhance ReasoningBank:\n\n### 1. PatternMatcher\n\nFind similar successful patterns:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'problem-solving',\n  k: 10,\n  useMMR: true,  \u002F\u002F Maximal Marginal Relevance for diversity\n});\n\n\u002F\u002F PatternMatcher returns diverse, relevant memories\nresult.memories.forEach(mem => {\n  console.log(`Pattern: ${mem.pattern.approach}`);\n  console.log(`Similarity: ${mem.similarity}`);\n  console.log(`Success Rate: ${mem.success_count \u002F mem.usage_count}`);\n});\n```\n\n### 2. ContextSynthesizer\n\nGenerate rich context from multiple memories:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'code-optimization',\n  synthesizeContext: true,  \u002F\u002F Enable context synthesis\n  k: 5,\n});\n\n\u002F\u002F ContextSynthesizer creates coherent narrative\nconsole.log('Synthesized Context:', result.context);\n\u002F\u002F \"Based on 5 similar optimizations, the most effective approach\n\u002F\u002F  involves profiling, identifying bottlenecks, and applying targeted\n\u002F\u002F  improvements. Success rate: 87%\"\n```\n\n### 3. MemoryOptimizer\n\nAutomatically consolidate and prune:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'testing',\n  optimizeMemory: true,  \u002F\u002F Enable automatic optimization\n});\n\n\u002F\u002F MemoryOptimizer consolidates similar patterns and prunes low-quality\nconsole.log('Optimizations:', result.optimizations);\n\u002F\u002F { consolidated: 15, pruned: 3, improved_quality: 0.12 }\n```\n\n### 4. ExperienceCurator\n\nFilter by quality and relevance:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'debugging',\n  k: 20,\n  minConfidence: 0.8,  \u002F\u002F Only high-confidence experiences\n});\n\n\u002F\u002F ExperienceCurator returns only quality experiences\nresult.memories.forEach(mem => {\n  console.log(`Confidence: ${mem.confidence}`);\n  console.log(`Success Rate: ${mem.success_count \u002F mem.usage_count}`);\n});\n```\n\n---\n\n## Legacy API Compatibility\n\nAgentDB maintains 100% backward compatibility with legacy ReasoningBank:\n\n```typescript\nimport {\n  retrieveMemories,\n  judgeTrajectory,\n  distillMemories\n} from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Legacy API works unchanged (uses AgentDB backend automatically)\nconst memories = await retrieveMemories(query, {\n  domain: 'code-generation',\n  agent: 'coder'\n});\n\nconst verdict = await judgeTrajectory(trajectory, query);\n\nconst newMemories = await distillMemories(\n  trajectory,\n  verdict,\n  query,\n  { domain: 'code-generation' }\n);\n```\n\n---\n\n## Performance Characteristics\n\n- **Pattern Search**: 150x faster (100µs vs 15ms)\n- **Memory Retrieval**: \u003C1ms (with cache)\n- **Batch Insert**: 500x faster (2ms vs 1s for 100 patterns)\n- **Trajectory Judgment**: \u003C5ms (including retrieval + analysis)\n- **Memory Distillation**: \u003C50ms (consolidate 100 patterns)\n\n---\n\n## Advanced Patterns\n\n### Hierarchical Memory\n\nOrganize memories by abstraction level:\n\n```typescript\n\u002F\u002F Low-level: Specific implementation\nawait rb.insertPattern({\n  type: 'concrete',\n  domain: 'debugging\u002Fnull-pointer',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: { bug: 'NPE in UserService.getUser()', fix: 'Add null check' }\n  }),\n  confidence: 0.9,\n  \u002F\u002F ...\n});\n\n\u002F\u002F Mid-level: Pattern across similar cases\nawait rb.insertPattern({\n  type: 'pattern',\n  domain: 'debugging',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: { category: 'null-pointer', approach: 'defensive-checks' }\n  }),\n  confidence: 0.85,\n  \u002F\u002F ...\n});\n\n\u002F\u002F High-level: General principle\nawait rb.insertPattern({\n  type: 'principle',\n  domain: 'software-engineering',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: { principle: 'fail-fast with clear errors' }\n  }),\n  confidence: 0.95,\n  \u002F\u002F ...\n});\n```\n\n### Multi-Domain Learning\n\nTransfer learning across domains:\n\n```typescript\n\u002F\u002F Learn from backend optimization\nconst backendExperience = await rb.retrieveWithReasoning(embedding, {\n  domain: 'backend-optimization',\n  k: 10,\n});\n\n\u002F\u002F Apply to frontend optimization\nconst transferredKnowledge = backendExperience.memories.map(mem => ({\n  ...mem,\n  domain: 'frontend-optimization',\n  adapted: true,\n}));\n```\n\n---\n\n## CLI Operations\n\n### Database Management\n\n```bash\n# Export trajectories and patterns\nnpx agentdb@latest export .\u002F.agentdb\u002Freasoningbank.db .\u002Fbackup.json\n\n# Import experiences\nnpx agentdb@latest import .\u002Fexperiences.json\n\n# Get statistics\nnpx agentdb@latest stats .\u002F.agentdb\u002Freasoningbank.db\n# Shows: total patterns, domains, confidence distribution\n```\n\n### Migration\n\n```bash\n# Migrate from legacy ReasoningBank\nnpx agentdb@latest migrate --source .swarm\u002Fmemory.db --target .agentdb\u002Freasoningbank.db\n\n# Validate migration\nnpx agentdb@latest stats .agentdb\u002Freasoningbank.db\n```\n\n---\n\n## Troubleshooting\n\n### Issue: Migration fails\n```bash\n# Check source database exists\nls -la .swarm\u002Fmemory.db\n\n# Run with verbose logging\nDEBUG=agentdb:* npx agentdb@latest migrate --source .swarm\u002Fmemory.db\n```\n\n### Issue: Low confidence scores\n```typescript\n\u002F\u002F Enable context synthesis for better quality\nconst result = await rb.retrieveWithReasoning(embedding, {\n  synthesizeContext: true,\n  useMMR: true,\n  k: 10,\n});\n```\n\n### Issue: Memory growing too large\n```typescript\n\u002F\u002F Enable automatic optimization\nconst result = await rb.retrieveWithReasoning(embedding, {\n  optimizeMemory: true,  \u002F\u002F Consolidates similar patterns\n});\n\n\u002F\u002F Or manually optimize\nawait rb.optimize();\n```\n\n---\n\n## Learn More\n\n- **AgentDB Integration**: node_modules\u002Fagentic-flow\u002Fdocs\u002FAGENTDB_INTEGRATION.md\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fruvnet\u002Fagentic-flow\u002Ftree\u002Fmain\u002Fpackages\u002Fagentdb\n- **MCP Integration**: `npx agentdb@latest mcp`\n- **Website**: https:\u002F\u002Fagentdb.ruv.io\n\n---\n\n**Category**: Machine Learning \u002F Reinforcement Learning\n**Difficulty**: Intermediate\n**Estimated Time**: 20-30 minutes\n","# ReasoningBank with AgentDB\n\n## What This Skill Does\n\nProvides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility.\n\n**Performance**: 150x faster pattern retrieval, 500x faster batch operations, \u003C1ms memory access.\n\n## Prerequisites\n\n- Node.js 18+\n- AgentDB v1.0.7+ (via agentic-flow)\n- Understanding of reinforcement learning concepts (optional)\n\n---\n\n## Quick Start with CLI\n\n### Initialize ReasoningBank Database\n\n```bash\n# Initialize AgentDB for ReasoningBank\nnpx agentdb@latest init .\u002F.agentdb\u002Freasoningbank.db --dimension 1536\n\n# Start MCP server for Claude Code integration\nnpx agentdb@latest mcp\nclaude mcp add agentdb npx agentdb@latest mcp\n```\n\n### Migrate from Legacy ReasoningBank\n\n```bash\n# Automatic migration with validation\nnpx agentdb@latest migrate --source .swarm\u002Fmemory.db\n\n# Verify migration\nnpx agentdb@latest stats .\u002F.agentdb\u002Freasoningbank.db\n```\n\n---\n\n## Quick Start with API\n\n```typescript\nimport { createAgentDBAdapter, computeEmbedding } from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Initialize ReasoningBank with AgentDB\nconst rb = await createAgentDBAdapter({\n  dbPath: '.agentdb\u002Freasoningbank.db',\n  enableLearning: true,      \u002F\u002F Enable learning plugins\n  enableReasoning: true,      \u002F\u002F Enable reasoning agents\n  cacheSize: 1000,            \u002F\u002F 1000 pattern cache\n});\n\n\u002F\u002F Store successful experience\nconst query = \"How to optimize database queries?\";\nconst embedding = await computeEmbedding(query);\n\nawait rb.insertPattern({\n  id: '',\n  type: 'experience',\n  domain: 'database-optimization',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: {\n      query,\n      approach: 'indexing + query optimization',\n      outcome: 'success',\n      metrics: { latency_reduction: 0.85 }\n    }\n  }),\n  confidence: 0.95,\n  usage_count: 1,\n  success_count: 1,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n\n\u002F\u002F Retrieve similar experiences with reasoning\nconst result = await rb.retrieveWithReasoning(embedding, {\n  domain: 'database-optimization',\n  k: 5,\n  useMMR: true,              \u002F\u002F Diverse results\n  synthesizeContext: true,    \u002F\u002F Rich context synthesis\n});\n\nconsole.log('Memories:', result.memories);\nconsole.log('Context:', result.context);\nconsole.log('Patterns:', result.patterns);\n```\n\n---\n\n## Core ReasoningBank Concepts\n\n### 1. Trajectory Tracking\n\nTrack agent execution paths and outcomes:\n\n```typescript\n\u002F\u002F Record trajectory (sequence of actions)\nconst trajectory = {\n  task: 'optimize-api-endpoint',\n  steps: [\n    { action: 'analyze-bottleneck', result: 'found N+1 query' },\n    { action: 'add-eager-loading', result: 'reduced queries' },\n    { action: 'add-caching', result: 'improved latency' }\n  ],\n  outcome: 'success',\n  metrics: { latency_before: 2500, latency_after: 150 }\n};\n\nconst embedding = await computeEmbedding(JSON.stringify(trajectory));\n\nawait rb.insertPattern({\n  id: '',\n  type: 'trajectory',\n  domain: 'api-optimization',\n  pattern_data: JSON.stringify({ embedding, pattern: trajectory }),\n  confidence: 0.9,\n  usage_count: 1,\n  success_count: 1,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n```\n\n### 2. Verdict Judgment\n\nJudge whether a trajectory was successful:\n\n```typescript\n\u002F\u002F Retrieve similar past trajectories\nconst similar = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'api-optimization',\n  k: 10,\n});\n\n\u002F\u002F Judge based on similarity to successful patterns\nconst verdict = similar.memories.filter(m =>\n  m.pattern.outcome === 'success' &&\n  m.similarity > 0.8\n).length > 5 ? 'likely_success' : 'needs_review';\n\nconsole.log('Verdict:', verdict);\nconsole.log('Confidence:', similar.memories[0]?.similarity || 0);\n```\n\n### 3. Memory Distillation\n\nConsolidate similar experiences into patterns:\n\n```typescript\n\u002F\u002F Get all experiences in domain\nconst experiences = await rb.retrieveWithReasoning(embedding, {\n  domain: 'api-optimization',\n  k: 100,\n  optimizeMemory: true,  \u002F\u002F Automatic consolidation\n});\n\n\u002F\u002F Distill into high-level pattern\nconst distilledPattern = {\n  domain: 'api-optimization',\n  pattern: 'For N+1 queries: add eager loading, then cache',\n  success_rate: 0.92,\n  sample_size: experiences.memories.length,\n  confidence: 0.95\n};\n\nawait rb.insertPattern({\n  id: '',\n  type: 'distilled-pattern',\n  domain: 'api-optimization',\n  pattern_data: JSON.stringify({\n    embedding: await computeEmbedding(JSON.stringify(distilledPattern)),\n    pattern: distilledPattern\n  }),\n  confidence: 0.95,\n  usage_count: 0,\n  success_count: 0,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n```\n\n---\n\n## Integration with Reasoning Agents\n\nAgentDB provides 4 reasoning modules that enhance ReasoningBank:\n\n### 1. PatternMatcher\n\nFind similar successful patterns:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'problem-solving',\n  k: 10,\n  useMMR: true,  \u002F\u002F Maximal Marginal Relevance for diversity\n});\n\n\u002F\u002F PatternMatcher returns diverse, relevant memories\nresult.memories.forEach(mem => {\n  console.log(`Pattern: ${mem.pattern.approach}`);\n  console.log(`Similarity: ${mem.similarity}`);\n  console.log(`Success Rate: ${mem.success_count \u002F mem.usage_count}`);\n});\n```\n\n### 2. ContextSynthesizer\n\nGenerate rich context from multiple memories:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'code-optimization',\n  synthesizeContext: true,  \u002F\u002F Enable context synthesis\n  k: 5,\n});\n\n\u002F\u002F ContextSynthesizer creates coherent narrative\nconsole.log('Synthesized Context:', result.context);\n\u002F\u002F \"Based on 5 similar optimizations, the most effective approach\n\u002F\u002F  involves profiling, identifying bottlenecks, and applying targeted\n\u002F\u002F  improvements. Success rate: 87%\"\n```\n\n### 3. MemoryOptimizer\n\nAutomatically consolidate and prune:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'testing',\n  optimizeMemory: true,  \u002F\u002F Enable automatic optimization\n});\n\n\u002F\u002F MemoryOptimizer consolidates similar patterns and prunes low-quality\nconsole.log('Optimizations:', result.optimizations);\n\u002F\u002F { consolidated: 15, pruned: 3, improved_quality: 0.12 }\n```\n\n### 4. ExperienceCurator\n\nFilter by quality and relevance:\n\n```typescript\nconst result = await rb.retrieveWithReasoning(queryEmbedding, {\n  domain: 'debugging',\n  k: 20,\n  minConfidence: 0.8,  \u002F\u002F Only high-confidence experiences\n});\n\n\u002F\u002F ExperienceCurator returns only quality experiences\nresult.memories.forEach(mem => {\n  console.log(`Confidence: ${mem.confidence}`);\n  console.log(`Success Rate: ${mem.success_count \u002F mem.usage_count}`);\n});\n```\n\n---\n\n## Legacy API Compatibility\n\nAgentDB maintains 100% backward compatibility with legacy ReasoningBank:\n\n```typescript\nimport {\n  retrieveMemories,\n  judgeTrajectory,\n  distillMemories\n} from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Legacy API works unchanged (uses AgentDB backend automatically)\nconst memories = await retrieveMemories(query, {\n  domain: 'code-generation',\n  agent: 'coder'\n});\n\nconst verdict = await judgeTrajectory(trajectory, query);\n\nconst newMemories = await distillMemories(\n  trajectory,\n  verdict,\n  query,\n  { domain: 'code-generation' }\n);\n```\n\n---\n\n## Performance Characteristics\n\n- **Pattern Search**: 150x faster (100µs vs 15ms)\n- **Memory Retrieval**: \u003C1ms (with cache)\n- **Batch Insert**: 500x faster (2ms vs 1s for 100 patterns)\n- **Trajectory Judgment**: \u003C5ms (including retrieval + analysis)\n- **Memory Distillation**: \u003C50ms (consolidate 100 patterns)\n\n---\n\n## Advanced Patterns\n\n### Hierarchical Memory\n\nOrganize memories by abstraction level:\n\n```typescript\n\u002F\u002F Low-level: Specific implementation\nawait rb.insertPattern({\n  type: 'concrete',\n  domain: 'debugging\u002Fnull-pointer',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: { bug: 'NPE in UserService.getUser()', fix: 'Add null check' }\n  }),\n  confidence: 0.9,\n  \u002F\u002F ...\n});\n\n\u002F\u002F Mid-level: Pattern across similar cases\nawait rb.insertPattern({\n  type: 'pattern',\n  domain: 'debugging',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: { category: 'null-pointer', approach: 'defensive-checks' }\n  }),\n  confidence: 0.85,\n  \u002F\u002F ...\n});\n\n\u002F\u002F High-level: General principle\nawait rb.insertPattern({\n  type: 'principle',\n  domain: 'software-engineering',\n  pattern_data: JSON.stringify({\n    embedding,\n    pattern: { principle: 'fail-fast with clear errors' }\n  }),\n  confidence: 0.95,\n  \u002F\u002F ...\n});\n```\n\n### Multi-Domain Learning\n\nTransfer learning across domains:\n\n```typescript\n\u002F\u002F Learn from backend optimization\nconst backendExperience = await rb.retrieveWithReasoning(embedding, {\n  domain: 'backend-optimization',\n  k: 10,\n});\n\n\u002F\u002F Apply to frontend optimization\nconst transferredKnowledge = backendExperience.memories.map(mem => ({\n  ...mem,\n  domain: 'frontend-optimization',\n  adapted: true,\n}));\n```\n\n---\n\n## CLI Operations\n\n### Database Management\n\n```bash\n# Export trajectories and patterns\nnpx agentdb@latest export .\u002F.agentdb\u002Freasoningbank.db .\u002Fbackup.json\n\n# Import experiences\nnpx agentdb@latest import .\u002Fexperiences.json\n\n# Get statistics\nnpx agentdb@latest stats .\u002F.agentdb\u002Freasoningbank.db\n# Shows: total patterns, domains, confidence distribution\n```\n\n### Migration\n\n```bash\n# Migrate from legacy ReasoningBank\nnpx agentdb@latest migrate --source .swarm\u002Fmemory.db --target .agentdb\u002Freasoningbank.db\n\n# Validate migration\nnpx agentdb@latest stats .agentdb\u002Freasoningbank.db\n```\n\n---\n\n## Troubleshooting\n\n### Issue: Migration fails\n```bash\n# Check source database exists\nls -la .swarm\u002Fmemory.db\n\n# Run with verbose logging\nDEBUG=agentdb:* npx agentdb@latest migrate --source .swarm\u002Fmemory.db\n```\n\n### Issue: Low confidence scores\n```typescript\n\u002F\u002F Enable context synthesis for better quality\nconst result = await rb.retrieveWithReasoning(embedding, {\n  synthesizeContext: true,\n  useMMR: true,\n  k: 10,\n});\n```\n\n### Issue: Memory growing too large\n```typescript\n\u002F\u002F Enable automatic optimization\nconst result = await rb.retrieveWithReasoning(embedding, {\n  optimizeMemory: true,  \u002F\u002F Consolidates similar patterns\n});\n\n\u002F\u002F Or manually optimize\nawait rb.optimize();\n```\n\n---\n\n## Learn More\n\n- **AgentDB Integration**: node_modules\u002Fagentic-flow\u002Fdocs\u002FAGENTDB_INTEGRATION.md\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fruvnet\u002Fagentic-flow\u002Ftree\u002Fmain\u002Fpackages\u002Fagentdb\n- **MCP Integration**: `npx agentdb@latest mcp`\n- **Website**: https:\u002F\u002Fagentdb.ruv.io\n\n---\n\n**Category**: Machine Learning \u002F Reinforcement Learning\n**Difficulty**: Intermediate\n**Estimated Time**: 20-30 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