Component: AgentDB Integration Phase: Core Production Status: Canonical Implementation Dependencies: AgentDB (npm) Version: 2.0 (Migration Complete) Date: January 4, 2026
| ADR | Relationship |
|---|---|
| ADR-003: Memory Persistence | Authorizes this spec |
AgentDB Integration is an optional Phase 2 enhancement that evaluates next-generation memory capabilities for the Cognitive Puzzle Solver POC. This component runs in parallel to the primary ReasoningBank implementation (Days 6-10) without blocking the main POC timeline.
Core Responsibilities:
- Implement stable AgentDB persistent memory
- Provide ReasoningBank, Reflexion, and SkillLibrary capabilities
- Execute Decision Transformer RL learning for strategy optimization (optional plugin)
- Enable automatic skill library consolidation
Non-Responsibilities:
- Replace ReasoningBank as primary memory (Phase 1 guaranteed demo)
- Block POC timeline if testing fails (fallback to ReasoningBank)
- Introduce dependencies that compromise POC completion
┌─────────────────────────────────────────────────────────────┐
│ MEMORY SYSTEM ARCHITECTURE │
│ │
│ ┌──────────────────────┐ │
│ │ AGENTDB (Core) │ │
│ │ │ │
│ │ • ReasoningBank │ │
│ │ • Reflexion Memory │ │
│ │ • Skill Library │ │
│ │ • Causal Graph │ │
│ └──────────┬───────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────┐ │
│ │ Local Storage │ │
│ │ (Vector DB) │ │
│ └────────────────┘ │
└─────────────────────────────────────────────────────────────┘
Upstream Dependencies:
- ReasoningBank Adapter (Phase 1) - 100% API compatibility required
- GRASP Loop - Provides trajectories for RL training
- Puzzle Engine - Generates state vectors for Decision Transformer
- Memory System Interface - Shared abstraction layer
- LLM Sudoku Player (Spec 11) - Provides LLM experiences for storage and consolidation
Note: When using LLM mode (Spec 11), AgentDB stores
LLMExperiencerecords including move proposals, validation outcomes, and reasoning chains. These are consolidated during dreaming to generate improved few-shot examples.
Downstream Consumers:
- Benchmarking Framework - Performance comparison metrics
- Dreaming Pipeline - Enhanced consolidation (if AgentDB adopted)
- Demo System - Dual-system demonstration (if AgentDB succeeds)
Integration Points:
interface MemorySystemFactory {
create(config: POCConfig): MemorySystem {
if (config.memorySystem === 'agentdb' && config.enableRL) {
return new AgentDBAdapter({
rlPlugin: 'decision-transformer',
reflexionEnabled: config.enableReflexion,
skillLibraryEnabled: config.enableSkillLibrary
});
}
// Fallback to ReasoningBank
return new ReasoningBankAdapter();
}
}FR-2.1.1: Decision Transformer Plugin Initialization
# REQUIRED: Create Decision Transformer learning plugin
npx agentdb@latest create-plugin \
-t decision-transformer \
-n sudoku-solver \
--state-dim 81 \ # 9x9 grid
--action-dim 729 \ # 81 cells × 9 values
--sequence-length 128 # Token context windowFR-2.1.2: RL Training Protocol
interface RLTrainingConfig {
algorithm: 'decision-transformer'; // REQUIRED: Best for sequence modeling
epochs: 50; // REQUIRED: Target convergence
batchSize: 32; // REQUIRED: Memory-efficient
learningRate: 0.0001; // OPTIONAL: Auto-tuned
validationSplit: 0.2; // REQUIRED: Hold-out set
earlyStoppingPatience: 5; // REQUIRED: Prevent overfitting
}
async function trainRL(adapter: AgentDBAdapter): Promise<TrainingResult> {
const result = await adapter.trainRL({
epochs: 50,
batchSize: 32,
dataSource: 'easy-puzzles', // Start with easy
curriculum: ['easy', 'medium'], // Progressive difficulty
saveCheckpoints: true
});
// REQUIRED: Convergence validation
if (result.finalLoss > 0.1) {
throw new Error('RL failed to converge');
}
return result;
}Acceptance Criteria:
- ✅ Plugin creates successfully in <5 minutes
- ✅ Training converges in <50 epochs (validation loss <0.1)
- ✅ Checkpoint saves enabled for recovery
- ✅ Validation accuracy >80% on held-out puzzles
FR-2.2.1: Error + Correction Storage
interface ReflexionMemory {
// Store failed trajectory with correction
storeReflexion(data: {
trajectory: Move[]; // Sequence leading to error
error: Error; // What went wrong
correction: Move; // What should have been done
context: PuzzleState; // State at error
timestamp: number;
}): Promise<void>;
// Retrieve similar errors for learning
getCorrections(query: {
error: Error; // Current error
context: PuzzleState; // Current state
k: number; // Top-k similar
}): Promise<ReflexionEntry[]>;
}FR-2.2.2: Repeat Error Detection
async function measureReflexionLearning(): Promise<ReflexionMetrics> {
const metrics = {
errorsDetected: 0,
correctionsApplied: 0,
repeatErrorRate: 0, // CRITICAL: Must decrease over time
improvementCurve: [] as number[]
};
// Track repeat errors across sessions
for (const session of sessions) {
const sessionErrors = detectErrors(session);
const repeatErrors = sessionErrors.filter(e =>
previousErrors.some(prev => isSimilar(e, prev))
);
metrics.repeatErrorRate = repeatErrors.length / sessionErrors.length;
metrics.improvementCurve.push(1 - metrics.repeatErrorRate);
}
return metrics;
}Acceptance Criteria:
- ✅ Reflexion storage writes <10ms per error
- ✅ Repeat error rate decreases >30% over 20 puzzles
- ✅ Correction retrieval <100ms per query
- ✅ No memory leaks after 100 reflexion entries
FR-2.3.1: Skill Extraction from Patterns
interface SkillConsolidation {
consolidateSkills(filter: {
minSuccessRate: 0.7; // Only successful patterns
domain: 'sudoku-solving';
minApplications: 5; // Must be reusable
maxSkills: 50; // Cap for POC
}): Promise<Skill[]>;
}
// Skill structure
interface Skill {
id: string; // Auto-generated
name: string; // E.g., "Naked Single Detection"
pattern: Pattern; // Extracted pattern
successRate: number; // 0.0-1.0
applicationCount: number; // How often applied
transferability: number; // Cross-puzzle reuse rate
examples: Experience[]; // Top 3 examples
}FR-2.3.2: Automatic Skill Application
async function applySkillLibrary(
state: PuzzleState,
skills: Skill[]
): Promise<Move | null> {
// Rank skills by applicability
const ranked = skills
.map(skill => ({
skill,
score: calculateApplicability(skill, state)
}))
.sort((a, b) => b.score - a.score);
// Try top-ranked skill
const topSkill = ranked[0];
if (topSkill.score > 0.5) {
return generateMoveFromSkill(topSkill.skill, state);
}
return null; // No applicable skill
}Acceptance Criteria:
- ✅ Extracts >10 reusable skills from 50 puzzles
- ✅ Skills have >70% success rate on application
- ✅ Skill consolidation completes in <5 minutes
- ✅ Transferability metric >0.6 for top skills
FR-2.4.1: Query Performance Validation
interface PerformanceBenchmark {
// Vector search speed (claimed: 150x faster)
vectorSearch: {
testSize: 1000; // Query count
targetP95: 100; // <100µs 95th percentile
compareBaseline: 'reasoningbank';
expectedSpeedup: 150; // Validate claim
};
// Batch operations (claimed: 500x faster)
batchInsert: {
batchSize: 100;
targetTime: 2; // <2ms for 100 inserts
expectedSpeedup: 500;
};
// Large-scale query (claimed: 12,500x faster)
largeScale: {
vectorCount: 1000000; // 1M vectors
queryTime: 10; // <10ms target
expectedSpeedup: 12500;
};
}FR-2.4.2: Memory Efficiency Testing
interface MemoryBenchmark {
// Quantization (claimed: 4-32x reduction)
quantization: {
mode: 'scalar'; // 4x reduction
originalSize: number; // Baseline
compressedSize: number; // After quantization
compressionRatio: number; // Must be >4x
accuracyLoss: number; // Must be <5%
};
}Acceptance Criteria:
- ✅ Vector search achieves >50x speedup (50% of claim acceptable)
- ✅ Batch operations >100x faster (20% of claim)
- ✅ Large-scale queries >1000x faster (10% of claim)
- ✅ Quantization achieves >3x compression with <5% accuracy loss
- ✅ No performance degradation after 1000 operations
FR-2.5.1: Data Migration Procedure
# REQUIRED: Migrate existing ReasoningBank data to AgentDB
npx agentdb@latest migrate \
--source .swarm/memory.db \
--target .agentdb/memory.db \
--preserve-structure \
--validate-integrityFR-2.5.2: Migration Validation
async function validateMigration(): Promise<MigrationReport> {
const report = {
recordsMigrated: 0,
recordsFailed: 0,
integrityChecks: [] as CheckResult[],
apiCompatibility: false
};
// Check record count
const rbCount = await reasoningBank.count();
const agentDbCount = await agentDB.count();
report.recordsMigrated = agentDbCount;
report.recordsFailed = rbCount - agentDbCount;
// Validate API compatibility (CRITICAL)
report.apiCompatibility = await testAPICompatibility();
// Integrity checks
report.integrityChecks = await runIntegrityChecks([
'trajectory-continuity',
'pattern-completeness',
'timestamp-ordering'
]);
return report;
}Acceptance Criteria:
- ✅ Migration completes in <10 minutes
- ✅ 100% of records migrated successfully
- ✅ All integrity checks pass
- ✅ API compatibility test passes (100% compatible)
- ✅ Rollback to ReasoningBank possible within 1 minute
| Metric | Target | Critical Threshold |
|---|---|---|
| RL Training Time | <5 min (50 epochs) | <10 min |
| Inference Latency | <50ms | <100ms |
| Vector Search P95 | <100µs | <200µs |
| Skill Consolidation | <5 min | <10 min |
| Memory Footprint | <500MB | <1GB |
| Startup Time | <30s | <60s |
NFR-3.2.1: Alpha Stability Criteria (CRITICAL for Day 10 Decision)
interface StabilityMetrics {
// No crashes during testing period
crashes: 0; // MUST be 0
corruptionEvents: 0; // MUST be 0
errorRate: number; // <1% acceptable
recoveryTime: number; // <10s if failure
// Data integrity
dataLossEvents: 0; // MUST be 0
checksumFailures: 0; // MUST be 0
// Operational metrics
uptimePct: number; // >99.9% required
meanTimeBetweenFailures: number; // >24 hours
}Acceptance Criteria (ALL must pass for Day 10 adoption):
- ✅ Zero crashes during Days 6-10 testing
- ✅ Zero data corruption events
- ✅ Error rate <1% on all operations
- ✅ All checksums validate successfully
- ✅ Graceful degradation on errors (fallback to ReasoningBank)
NFR-3.3.1: RL Convergence Criteria
interface ConvergenceMetrics {
finalLoss: number; // <0.1 required
validationAccuracy: number; // >80% required
epochsToConvergence: number; // <50 required
convergenceStability: boolean; // No divergence after convergence
// Learning quality
strategyDiversity: number; // >5 strategies learned
transferability: number; // >0.6 cross-puzzle
}Acceptance Criteria:
- ✅ Training loss <0.1 by epoch 50
- ✅ Validation accuracy >80%
- ✅ No divergence in final 10 epochs
- ✅ Learns >5 distinct strategies
- ✅ Transfer performance >60% of source task
/**
* AgentDB Adapter - 100% Compatible with ReasoningBank + Enhanced Features
*/
export class AgentDBAdapter implements MemorySystem {
// ========================================
// TIER 1: ReasoningBank Compatible API
// ========================================
async logMove(move: Move, outcome: ValidationResult): Promise<void> {
// Store trajectory with vector embedding
await this.db.storeTrajectory({
move,
outcome,
embedding: await this.generateEmbedding(move, outcome),
timestamp: Date.now()
});
}
async querySimilar(context: PuzzleState): Promise<Experience[]> {
// HNSW vector search (150x faster)
const embedding = await this.generateEmbedding(context);
return await this.db.vectorSearch({
query: embedding,
k: 10,
algorithm: 'hnsw'
});
}
async distillPatterns(sessionId: string): Promise<Pattern[]> {
// Leverage reasoning agents for pattern extraction
const experiences = await this.db.getSession(sessionId);
return await this.reasoningAgents.patternMatcher.extract(experiences);
}
async consolidate(experiences: Experience[]): Promise<ConsolidatedKnowledge> {
// Use MemoryOptimizer for compression
return await this.reasoningAgents.memoryOptimizer.consolidate({
experiences,
compressionRatio: 10,
quantization: 'scalar'
});
}
// ========================================
// TIER 2: RL Learning Enhancements
// ========================================
async trainRL(config: RLTrainingConfig): Promise<TrainingResult> {
return await this.rlPlugin.train({
algorithm: 'decision-transformer',
epochs: config.epochs,
batchSize: config.batchSize,
dataSource: await this.getTrainingData()
});
}
async selectActionRL(
state: PuzzleState,
availableActions: Move[]
): Promise<RLAction> {
const stateVector = this.puzzleStateToVector(state);
const actionProbs = await this.rlPlugin.predict({
state: stateVector,
availableActions: availableActions.map(m => this.moveToVector(m))
});
return {
cell: actionProbs[0].cell,
value: actionProbs[0].value,
confidence: actionProbs[0].probability
};
}
// ========================================
// TIER 3: Reflexion Memory
// ========================================
async storeReflexion(error: ReflexionError): Promise<void> {
await this.db.storeReflexion({
trajectory: error.trajectory,
errorType: error.error.name,
errorMessage: error.error.message,
correction: error.correction,
embedding: await this.generateErrorEmbedding(error),
timestamp: Date.now()
});
}
async getCorrections(similarError: Error): Promise<Move[]> {
const errorEmbedding = await this.generateErrorEmbedding(similarError);
const similarReflexions = await this.db.vectorSearch({
query: errorEmbedding,
k: 5,
collection: 'reflexions'
});
return similarReflexions.map(r => r.correction);
}
// ========================================
// TIER 4: Skill Library
// ========================================
async consolidateSkills(filter: {
minSuccessRate: number;
}): Promise<Skill[]> {
const patterns = await this.db.getPatterns();
const skills = await this.reasoningAgents.experienceCurator.extractSkills({
patterns,
minSuccessRate: filter.minSuccessRate,
domain: 'sudoku-solving'
});
return skills.map(s => ({
...s,
transferability: this.calculateTransferability(s)
}));
}
async applySkill(state: PuzzleState): Promise<Move | null> {
const skills = await this.getSkills();
const applicableSkills = skills.filter(s =>
this.isApplicable(s, state)
);
if (applicableSkills.length === 0) return null;
const topSkill = applicableSkills[0];
return this.generateMoveFromSkill(topSkill, state);
}
// ========================================
// TIER 5: Advanced Reasoning
// ========================================
async synthesizeContext(
state: PuzzleState,
k: number
): Promise<RichContext> {
// Use ContextSynthesizer reasoning agent
const embedding = await this.generateEmbedding(state);
return await this.reasoningAgents.contextSynthesizer.synthesize({
query: embedding,
k,
useMMR: true, // Maximal Marginal Relevance
includeCrossPatterns: true
});
}
async optimizeMemory(): Promise<{ patternsConsolidated: number }> {
return await this.reasoningAgents.memoryOptimizer.optimize({
compressionRatio: 10,
quantization: 'scalar',
pruneRedundancy: true,
similarityThreshold: 0.95
});
}
}interface AgentDBConfig {
// Database
dbPath: string; // '.agentdb/memory.db'
preset: 'large'; // Optimized for POC scale
// RL Plugin
rlPlugin: {
type: 'decision-transformer';
name: 'sudoku-solver';
stateDim: 81; // 9x9 grid
actionDim: 729; // 81 cells × 9 values
sequenceLength: 128;
};
// Reflexion Memory
reflexion: {
enabled: boolean;
maxEntries: 1000;
similarityThreshold: 0.8;
};
// Skill Library
skillLibrary: {
enabled: boolean;
minSuccessRate: 0.7;
maxSkills: 50;
autoConsolidate: boolean;
};
// Performance
quantization: 'scalar' | 'binary' | 'product';
indexing: 'hnsw'; // Fast vector search
cacheEnabled: boolean;
}async function trainDecisionTransformer(
plugin: DecisionTransformerPlugin,
experiences: Experience[]
): Promise<void> {
// Convert experiences to (state, action, reward) sequences
const sequences = experiences.map(exp => ({
states: exp.trajectory.map(m => puzzleStateToVector(m.state)),
actions: exp.trajectory.map(m => moveToVector(m)),
rewards: exp.trajectory.map(m => calculateReward(m, exp.outcome))
}));
// Train with auto-regressive loss
await plugin.train({
sequences,
epochs: 50,
batchSize: 32,
optimizer: 'adam',
learningRate: 0.0001,
scheduler: 'cosine-annealing'
});
}
function calculateReward(move: Move, outcome: Outcome): number {
if (outcome === 'success') return 1.0;
if (outcome === 'progress') return 0.5;
if (outcome === 'failure') return -0.5;
return 0.0;
}async function clusterReflexionErrors(
reflexions: ReflexionEntry[]
): Promise<ErrorCluster[]> {
// Generate error embeddings
const embeddings = await Promise.all(
reflexions.map(r => generateErrorEmbedding(r.error))
);
// HNSW-based clustering
const clusters = await agentDB.query(`
MATCH (e:Error)
WITH e, e.embedding AS embedding
CALL agentdb.cluster.hnsw(embedding, {k: 5, minClusterSize: 3})
YIELD cluster, members
RETURN cluster, members
`);
// Extract common correction patterns
return clusters.map(c => ({
errorType: identifyErrorType(c.members),
correction: extractCommonCorrection(c.members),
frequency: c.members.length
}));
}async function autoConsolidateSkills(
patterns: Pattern[]
): Promise<Skill[]> {
// Filter by success rate
const successful = patterns.filter(p => p.successRate > 0.7);
// Group by similarity
const skillGroups = await groupBySimilarity(successful, {
similarityThreshold: 0.8,
useHNSW: true
});
// Extract representative skill from each group
const skills = skillGroups.map(group => {
const representative = selectRepresentative(group);
return {
id: generateSkillId(),
name: generateSkillName(representative),
pattern: representative,
successRate: averageSuccessRate(group),
applicationCount: sumApplications(group),
transferability: calculateTransferability(group),
examples: selectTopExamples(group, 3)
};
});
return skills;
}async function handleTrainingDivergence(
plugin: DecisionTransformerPlugin,
metrics: TrainingMetrics
): Promise<void> {
if (metrics.lossIncreasing && metrics.consecutiveEpochs > 5) {
// Divergence detected - reduce learning rate
await plugin.reduceLearningRate(0.5);
// Restore best checkpoint
await plugin.loadCheckpoint('best');
// Resume training with reduced LR
await plugin.resume();
}
}async function detectMemoryCorruption(
db: AgentDB
): Promise<CorruptionReport> {
const checks = [
// Checksum validation
async () => await db.validateChecksums(),
// Trajectory continuity
async () => await db.validateTrajectories(),
// Embedding consistency
async () => await db.validateEmbeddings(),
// Reference integrity
async () => await db.validateReferences()
];
const results = await Promise.all(checks.map(c => c()));
if (results.some(r => !r.valid)) {
// Corruption detected - trigger rollback
await rollbackToReasoningBank();
throw new Error('Memory corruption detected');
}
return { valid: true, checks: results };
}class MemorySystemWithFallback {
private primary: AgentDBAdapter;
private fallback: ReasoningBankAdapter;
private useFallback = false;
async executeWithFallback<T>(
operation: (adapter: MemorySystem) => Promise<T>
): Promise<T> {
try {
if (this.useFallback) {
return await operation(this.fallback);
}
return await operation(this.primary);
} catch (error) {
console.error('AgentDB operation failed, falling back to ReasoningBank');
this.useFallback = true;
return await operation(this.fallback);
}
}
}describe('AgentDB RL Learning', () => {
test('Decision Transformer converges in <50 epochs', async () => {
const result = await trainRL(agentDB, easyPuzzles);
expect(result.epochs).toBeLessThan(50);
expect(result.finalLoss).toBeLessThan(0.1);
});
test('Reflexion memory reduces repeat errors', async () => {
const before = await measureRepeatErrorRate(session1);
await storeReflexions(session1Errors);
const after = await measureRepeatErrorRate(session2);
expect((before - after) / before).toBeGreaterThan(0.3); // 30% reduction
});
test('Skill consolidation extracts >10 skills', async () => {
const skills = await agentDB.consolidateSkills({ minSuccessRate: 0.7 });
expect(skills.length).toBeGreaterThan(10);
expect(skills.every(s => s.successRate > 0.7)).toBe(true);
});
});describe('AgentDB Performance', () => {
test('Vector search <100µs P95', async () => {
const latencies = await benchmarkVectorSearch(1000);
const p95 = percentile(latencies, 0.95);
expect(p95).toBeLessThan(100); // microseconds
});
test('Batch insert >100x faster than sequential', async () => {
const sequentialTime = await benchmarkSequentialInsert(100);
const batchTime = await benchmarkBatchInsert(100);
expect(sequentialTime / batchTime).toBeGreaterThan(100);
});
});describe('AgentDB Stability', () => {
test('Zero crashes in 100 hour stress test', async () => {
const crashCount = await stressTest({ duration: 100 * 3600 * 1000 });
expect(crashCount).toBe(0);
});
test('Data integrity maintained across operations', async () => {
await performRandomOperations(10000);
const integrity = await validateDataIntegrity();
expect(integrity.valid).toBe(true);
});
});Day 10 Decision Matrix:
| Criterion | Target | Critical Threshold | Status |
|---|---|---|---|
| Alpha Stability | 0 crashes | 0 crashes | ✅/❌ |
| RL Convergence | <50 epochs | <75 epochs | ✅/❌ |
| Reflexion Learning | >30% error reduction | >20% reduction | ✅/❌ |
| Skill Extraction | >10 skills | >5 skills | ✅/❌ |
| Performance Gain | >50x faster | >10x faster | ✅/❌ |
| Data Integrity | 0 corruptions | 0 corruptions | ✅/❌ |
Decision Logic:
function makeDay10Decision(metrics: Day10Metrics): 'adopt' | 'fallback' {
const criticalChecks = [
metrics.crashes === 0,
metrics.dataCorruptions === 0,
metrics.rlConvergenceEpochs < 75,
metrics.reflexionImprovement > 0.2,
metrics.skillsExtracted > 5,
metrics.performanceSpeedup > 10
];
const allPassed = criticalChecks.every(check => check);
if (allPassed) {
console.log('✅ AgentDB adoption approved for Phase 3');
return 'adopt';
} else {
console.log('❌ AgentDB failed criteria - fallback to ReasoningBank');
return 'fallback';
}
}Enhanced Benchmarking:
- ✅ Dual-system performance comparison
- ✅ RL learning curves and convergence analysis
- ✅ Reflexion memory effectiveness demonstration
- ✅ Skill library transfer learning metrics
- ✅ Production migration path recommendation
Enhanced Demo:
- ✅ Side-by-side ReasoningBank vs AgentDB solving
- ✅ Live RL action selection visualization
- ✅ Reflexion memory error correction showcase
- ✅ Skill library auto-consolidation demo
No Impact Guarantee:
- ✅ ReasoningBank demo ready (guaranteed)
- ✅ No delay to Day 15 presentation
- ✅ AgentDB evaluation documented for future reference
- ✅ Lessons learned captured for production planning
#!/bin/bash
# AgentDB Migration Script (Day 7)
set -e # Exit on error
echo "Starting ReasoningBank → AgentDB migration..."
# Step 1: Initialize AgentDB
npx agentdb@latest init ./.agentdb/memory.db --preset large
echo "✅ AgentDB initialized"
# Step 2: Create Decision Transformer plugin
npx agentdb@latest create-plugin \
-t decision-transformer \
-n sudoku-solver \
--state-dim 81 \
--action-dim 729 \
--sequence-length 128
echo "✅ RL plugin created"
# Step 3: Migrate data
npx agentdb@latest migrate \
--source .swarm/memory.db \
--target .agentdb/memory.db \
--preserve-structure \
--validate-integrity
echo "✅ Data migrated"
# Step 4: Validate migration
node scripts/validate-migration.js
echo "✅ Migration validated"
# Step 5: Start MCP server
npx agentdb@latest mcp &
MCP_PID=$!
echo "✅ MCP server started (PID: $MCP_PID)"
# Step 6: Run smoke tests
npm run test:agentdb-smoke
echo "✅ Smoke tests passed"
echo "Migration complete! AgentDB ready for evaluation."// scripts/validate-migration.js
import { ReasoningBankAdapter } from '../src/memory/reasoningbank-adapter';
import { AgentDBAdapter } from '../src/memory/agentdb-adapter';
async function validateMigration() {
const rb = new ReasoningBankAdapter();
const adb = new AgentDBAdapter();
// Count records
const rbCount = await rb.count();
const adbCount = await adb.count();
console.assert(rbCount === adbCount, 'Record count mismatch');
// Sample queries
const testStates = await loadTestStates();
for (const state of testStates) {
const rbResults = await rb.querySimilar(state);
const adbResults = await adb.querySimilar(state);
// Should return similar results (order may differ due to HNSW)
console.assert(
jaccardSimilarity(rbResults, adbResults) > 0.8,
'Query result mismatch'
);
}
console.log('✅ Migration validation passed');
}
validateMigration().catch(console.error);interface BenchmarkSuite {
// Category 1: Vector Search Performance
vectorSearch: {
queries: 1000;
vectorSize: 768;
k: 10;
expectedP50: 50; // µs
expectedP95: 100; // µs
expectedP99: 200; // µs
};
// Category 2: Batch Operations
batchOperations: {
batchSizes: [10, 100, 1000];
operations: ['insert', 'update', 'delete'];
expectedSpeedup: 100; // vs sequential
};
// Category 3: RL Training
rlTraining: {
datasetSize: 100; // puzzles
epochs: 50;
expectedTime: 300; // seconds (5 min)
expectedConvergence: 0.1; // final loss
};
// Category 4: Memory Efficiency
memoryEfficiency: {
quantizationMode: 'scalar';
expectedCompressionRatio: 4;
maxAccuracyLoss: 0.05; // 5%
};
// Category 5: Skill Consolidation
skillConsolidation: {
inputPatterns: 100;
expectedSkills: 15;
expectedTime: 300; // seconds (5 min)
};
}#!/bin/bash
# run-agentdb-benchmarks.sh
echo "Running AgentDB Performance Benchmarks..."
# Vector search benchmark
npm run benchmark:vector-search
echo "✅ Vector search benchmark complete"
# Batch operations benchmark
npm run benchmark:batch-ops
echo "✅ Batch operations benchmark complete"
# RL training benchmark
npm run benchmark:rl-training
echo "✅ RL training benchmark complete"
# Memory efficiency benchmark
npm run benchmark:memory-efficiency
echo "✅ Memory efficiency benchmark complete"
# Skill consolidation benchmark
npm run benchmark:skill-consolidation
echo "✅ Skill consolidation benchmark complete"
# Generate comparison report
npm run benchmark:compare-with-reasoningbank
echo "✅ Comparison report generated: docs/memory-comparison-report.md"| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Crashes during testing | Medium | High | Fallback to ReasoningBank, no POC impact |
| RL fails to converge | Medium | Medium | Use baseline ReasoningBank patterns |
| Performance claims unverified | Low | Medium | Document actual performance, adjust expectations |
| Data corruption | Low | High | Continuous integrity checks, rollback capability |
| API incompatibility | Low | High | 100% compatibility testing before migration |
class AgentDBRollback {
async rollbackToReasoningBank(): Promise<void> {
console.log('🔄 Initiating rollback to ReasoningBank...');
// Step 1: Stop AgentDB MCP server
await this.stopAgentDBServer();
// Step 2: Verify ReasoningBank data integrity
const rbIntegrity = await this.verifyReasoningBankIntegrity();
if (!rbIntegrity.valid) {
throw new Error('ReasoningBank data corrupted - critical failure');
}
// Step 3: Switch memory adapter
config.memorySystem = 'reasoningbank';
await this.reinitializeMemoryAdapter();
// Step 4: Resume POC with ReasoningBank
console.log('✅ Rollback complete - POC resumed with ReasoningBank');
}
}# AgentDB Evaluation Report (Day 10)
**Date:** [Date]
**Evaluation Period:** Days 6-10
**Decision:** ✅ ADOPT / ❌ FALLBACK
## Executive Summary
[Brief summary of evaluation results]
## Stability Assessment
| Metric | Target | Actual | Status |
|--------|--------|--------|--------|
| Crashes | 0 | [X] | ✅/❌ |
| Data Corruptions | 0 | [X] | ✅/❌ |
| Error Rate | <1% | [X%] | ✅/❌ |
| Uptime | >99.9% | [X%] | ✅/❌ |
## Performance Results
| Benchmark | Target | Actual | Speedup |
|-----------|--------|--------|---------|
| Vector Search P95 | <100µs | [Xµs] | [Xx] |
| Batch Insert | >100x | [Xx] | ✅/❌ |
| Large Query | >1000x | [Xx] | ✅/❌ |
## RL Learning Results
- **Convergence:** [X] epochs (target: <50)
- **Final Loss:** [X] (target: <0.1)
- **Validation Accuracy:** [X%] (target: >80%)
- **Strategies Learned:** [X] (target: >5)
## Reflexion Memory Results
- **Repeat Error Reduction:** [X%] (target: >30%)
- **Corrections Applied:** [X]
- **Learning Curve:** [Chart/Graph]
## Skill Library Results
- **Skills Extracted:** [X] (target: >10)
- **Average Success Rate:** [X%] (target: >70%)
- **Transferability:** [X] (target: >0.6)
## Decision Rationale
[Explanation of why AgentDB was adopted or fallback decision was made]
## Phase 3 Recommendations
[If adopted: How to leverage AgentDB in final benchmarks]
[If fallback: Lessons learned for production consideration]See /workspaces/machine-dream/src/types.ts lines 197-254 for full AgentDB integration types.
When operating in LLM mode, AgentDB stores LLM-specific experience types:
interface LLMExperience {
id: string;
puzzleId: string;
puzzleHash: string; // For finding similar puzzles
moveNumber: number;
gridState: number[][];
move: {
row: number; // 1-9 (user-facing)
col: number; // 1-9 (user-facing)
value: number; // 1-9
reasoning: string; // LLM's explanation
confidence?: number; // Optional self-assessment
};
validation: {
isValid: boolean; // Doesn't violate Sudoku rules
isCorrect: boolean; // Matches the solution
outcome: 'correct' | 'invalid' | 'valid_but_wrong';
error?: string; // Human-readable error
};
timestamp: Date;
modelUsed: string;
memoryWasEnabled: boolean;
}
// Storage interface for LLM experiences
interface LLMExperienceStore {
save(experience: LLMExperience): Promise<void>;
getByPuzzle(puzzleId: string): Promise<LLMExperience[]>;
getUnconsolidated(): Promise<LLMExperience[]>;
markConsolidated(ids: string[]): Promise<void>;
// Few-shot example management
saveFewShots(examples: FewShotExample[]): Promise<void>;
getFewShots(limit?: number): Promise<FewShotExample[]>;
}Storage Location: LLM experiences are stored in the llm_experiences table with vector embeddings for similarity search.
| Feature | ReasoningBank | AgentDB | Improvement |
|---|---|---|---|
| Vector Search | ~2-3ms | <100µs (claimed) | 150x (claimed) |
| Batch Insert | ~1s/100 | 2ms/100 (claimed) | 500x (claimed) |
| Large Query (1M) | ~100s | 8ms (claimed) | 12,500x (claimed) |
| RL Algorithms | ❌ None | ✅ 9 algorithms | New capability |
| Reflexion Memory | ❌ None | ✅ Built-in | New capability |
| Skill Auto-Consolidation | ❌ Manual | ✅ Automatic | New capability |
| Reasoning Agents | ❌ Basic | ✅ 4 modules | Enhanced |
| Quantization | ❌ None | ✅ 4-32x | New capability |
| Production Status | ✅ Stable | Risk factor |
See:
docs/agentdb-analysis.md- Comprehensive AgentDB analysisdocs/poc-strategy-report.md- Phased adoption strategy (Section 0)src/types.ts- Type definitions (lines 197-254)
Document Status: Complete Next Steps:
- Day 6: Initialize AgentDB and create Decision Transformer plugin
- Day 7: Migrate ReasoningBank data and validate
- Day 8: RL training and reflexion memory testing
- Day 9: Performance benchmarking
- Day 10: Final decision (adopt or fallback)
Risk Level: Medium (mitigated by fallback strategy) Timeline Impact: Zero (runs in parallel to primary track) Success Probability: 70% (alpha stability unknown)