When NOT to Use This Skill

• Local-only operations with no vector search needs
• Simple key-value storage without semantic similarity
• Real-time streaming data without persistence requirements
• Operations that do not require embedding-based retrieval

Success Criteria

• Vector search query latency: <10ms for 99th percentile
• Embedding generation: <100ms per document
• Index build time: <1s per 1000 vectors
• Recall@10: >0.95 for similar documents
• Database connection success rate: >99.9%
• Memory footprint: <2GB for 1M vectors with quantization

Edge Cases & Error Handling

• Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
• Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
• Index Corruption: Maintain backup indices; rebuild from source if corrupted
• Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
• Stale Embeddings: Implement TTL-based refresh for dynamic content
• Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion

Guardrails & Safety

• NEVER expose database connection strings in logs or error messages
• ALWAYS validate vector dimensions before insertion
• ALWAYS sanitize metadata to prevent injection attacks
• NEVER store PII in vector metadata without encryption
• ALWAYS implement access control for multi-tenant deployments
• ALWAYS validate search results before returning to users

Evidence-Based Validation

• Verify database health: Check connection status and index integrity
• Validate search quality: Measure recall/precision on test queries
• Monitor performance: Track query latency, throughput, and memory usage
• Test failure recovery: Simulate connection drops and index corruption
• Benchmark improvements: Compare against baseline metrics (e.g., 150x speedup claim)

AgentDB Learning Plugins

What This Skill Does

Use this skill to create, train, and deploy learning plugins for autonomous agents using AgentDB's 9 reinforcement learning algorithms. Implement offline RL (Decision Transformer) for safe learning from logged experiences. Apply value-based learning (Q-Learning) for discrete actions. Deploy policy gradients (Actor-Critic) for continuous control. Enable agents to improve through experience with WASM-accelerated neural inference.

Performance: Train models 10-100x faster with WASM-accelerated neural inference.

Prerequisites

• Node.js 18+
• AgentDB v1.0.7+ (via agentic-flow)
• Basic understanding of reinforcement learning (recommended)

---

Quick Start with CLI

Create Learning Plugin

# Interactive wizard
npx agentdb@latest create-plugin

# Use specific template
npx agentdb@latest create-plugin -t decision-transformer -n my-agent

# Preview without creating
npx agentdb@latest create-plugin -t q-learning --dry-run

# Custom output directory
npx agentdb@latest create-plugin -t actor-critic -o ./plugins

List Available Templates

# Show all plugin templates
npx agentdb@latest list-templates

# Available templates:
# - decision-transformer (sequence modeling RL - recommended)
# - q-learning (value-based learning)
# - sarsa (on-policy TD learning)
# - actor-critic (policy gradient with baseline)
# - curiosity-driven (exploration-based)

Manage Plugins

# List installed plugins
npx agentdb@latest list-plugins

# Get plugin information
npx agentdb@latest plugin-info my-agent

# Shows: algorithm, configuration, training status

---

Quick Start with API

import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';

// Initialize with learning enabled
const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/learning.db',
  enableLearning: true,       // Enable learning plugins
  enableReasoning: true,
  cacheSize: 1000,
});

// Store training experience
await adapter.insertPattern({
  id: '',
  type: 'experience',
  domain: 'game-playing',
  pattern_data: JSON.stringify({
    embedding: await computeEmbedding('state-action-reward'),
    pattern: {
      state: [0.1, 0.2, 0.3],
      action: 2,
      reward: 1.0,
      next_state: [0.15, 0.25, 0.35],
      done: false
    }
  }),
  confidence: 0.9,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
});

// Train learning model
const metrics = await adapter.train({
  epochs: 50,
  batchSize: 32,
});

console.log('Training Loss:', metrics.loss);
console.log('Duration:', metrics.duration, 'ms');

---

Available Learning Algorithms (9 Total)

1. Decision Transformer (Recommended)

Type: Offline Reinforcement Learning Best For: Learning from logged experiences, imitation learning Strengths: No online interaction needed, stable training

npx agentdb@latest create-plugin -t decision-transformer -n dt-agent

Use Cases:

• Learn from historical data
• Imitation learning from expert demonstrations
• Safe learning without environment interaction
• Sequence modeling tasks

Configuration:

{
  "algorithm": "decision-transformer",
  "model_size": "base",
  "context_length": 20,
  "embed_dim": 128,
  "n_heads": 8,
  "n_layers": 6
}

2. Q-Learning

Type: Value-Based RL (Off-Policy) Best For: Discrete action spaces, sample efficiency Strengths: Proven, simple, works well for small/medium problems

npx agentdb@latest create-plugin -t q-learning -n q-agent

Use Cases:

• Grid worlds, board games
• Navigation tasks
• Resource allocation
• Discrete decision-making

Configuration:

{
  "algorithm": "q-learning",
  "learning_rate": 0.001,
  "gamma": 0.99,
  "epsilon": 0.1,
  "epsilon_decay": 0.995
}

3. SARSA

Type: Value-Based RL (On-Policy) Best For: Safe exploration, risk-sensitive tasks Strengths: More conservative than Q-Learning, better for safety

npx agentdb@latest create-plugin -t sarsa -n sarsa-agent

Use Cases:

• Safety-critical applications
• Risk-sensitive decision-making
• Online learning with exploration

Configuration:

{
  "algorithm": "sarsa",
  "learning_rate": 0.001,
  "gamma": 0.99,
  "epsilon": 0.1
}

4. Actor-Critic

Type: Policy Gradient with Value Baseline Best For: Continuous actions, variance reduction Strengths: Stable, works for continuous/discrete actions

npx agentdb@latest create-plugin -t actor-critic -n ac-agent

Use Cases:

• Continuous control (robotics, simulations)
• Complex action spaces
• Multi-agent coordination

Configuration:

{
  "algorithm": "actor-critic",
  "actor_lr": 0.001,
  "critic_lr": 0.002,
  "gamma": 0.99,
  "entropy_coef": 0.01
}

5. Active Learning

Type: Query-Based Learning Best For: Label-efficient learning, human-in-the-loop Strengths: Minimizes labeling cost, focuses on uncertain samples

Use Cases:

• Human feedback incorporation
• Label-efficient training
• Uncertainty sampling
• Annotation cost reduction

6. Adversarial Training

Type: Robustness Enhancement Best For: Safety, robustness to perturbations Strengths: Improves model robustness, adversarial defense

Use Cases:

• Security applications
• Robust decision-making
• Adversarial defense
• Safety testing

7. Curriculum Learning

Type: Progressive Difficulty Training Best For: Complex tasks, faster convergence Strengths: Stable learning, faster convergence on hard tasks

Use Cases:

• Complex multi-stage tasks
• Hard exploration problems
• Skill composition
• Transfer learning

8. Federated Learning

Type: Distributed Learning Best For: Privacy, distributed data Strengths: Privacy-preserving, scalable

Use Cases:

• Multi-agent systems
• Privacy-sensitive data
• Distributed training
• Collaborative learning

9. Multi-Task Learning

Type: Transfer Learning Best For: Related tasks, knowledge sharing Strengths: Faster learning on new tasks, better generalization

Use Cases:

• Task families
• Transfer learning
• Domain adaptation
• Meta-learning

---

Training Workflow

1. Collect Experiences

// Store experiences during agent execution
for (let i = 0; i < numEpisodes; i++) {
  const episode = runEpisode();

  for (const step of episode.steps) {
    await adapter.insertPattern({
      id: '',
      type: 'experience',
      domain: 'task-domain',
      pattern_data: JSON.stringify({
        embedding: await computeEmbedding(JSON.stringify(step)),
        pattern: {
          state: step.state,
          action: step.action,
          reward: step.reward,
          next_state: step.next_state,
          done: step.done
        }
      }),
      confidence: step.reward > 0 ? 0.9 : 0.5,
      usage_count: 1,
      success_count: step.reward > 0 ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }
}

2. Train Model

// Train on collected experiences
const trainingMetrics = await adapter.train({
  epochs: 100,
  batchSize: 64,
  learningRate: 0.001,
  validationSplit: 0.2,
});

console.log('Training Metrics:', trainingMetrics);
// {
//   loss: 0.023,
//   valLoss: 0.028,
//   duration: 1523,
//   epochs: 100
// }

3. Evaluate Performance

// Retrieve similar successful experiences
const testQuery = await computeEmbedding(JSON.stringify(testState));
const result = await adapter.retrieveWithReasoning(testQuery, {
  domain: 'task-domain',
  k: 10,
  synthesizeContext: true,
});

// Evaluate action quality
const suggestedAction = result.memories[0].pattern.action;
const confidence = result.memories[0].similarity;

console.log('Suggested Action:', suggestedAction);
console.log('Confidence:', confidence);

---

Advanced Training Techniques

Experience Replay

// Store experiences in buffer
const replayBuffer = [];

// Sample random batch for training
const batch = sampleRandomBatch(replayBuffer, batchSize: 32);

// Train on batch
await adapter.train({
  data: batch,
  epochs: 1,
  batchSize: 32,
});

Prioritized Experience Replay

// Store experiences with priority (TD error)
await adapter.insertPattern({
  // ... standard fields
  confidence: tdError,  // Use TD error as confidence/priority
  // ...
});

// Retrieve high-priority experiences
const highPriority = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'task-domain',
  k: 32,
  minConfidence: 0.7,  // Only high TD-error experiences
});

Multi-Agent Training

// Collect experiences from multiple agents
for (const agent of agents) {
  const experience = await agent.step();

  await adapter.insertPattern({
    // ... store experience with agent ID
    domain: `multi-agent/${agent.id}`,
  });
}

// Train shared model
await adapter.train({
  epochs: 50,
  batchSize: 64,
});

---

Performance Optimization

Batch Training

// Collect batch of experiences
const experiences = collectBatch(size: 1000);

// Batch insert (500x faster)
for (const exp of experiences) {
  await adapter.insertPattern({ /* ... */ });
}

// Train on batch
await adapter.train({
  epochs: 10,
  batchSize: 128,  // Larger batch for efficiency
});

Incremental Learning

// Train incrementally as new data arrives
setInterval(async () => {
  const newExperiences = getNewExperiences();

  if (newExperiences.length > 100) {
    await adapter.train({
      epochs: 5,
      batchSize: 32,
    });
  }
}, 60000);  // Every minute

---

Integration with Reasoning Agents

Combine learning with reasoning for better performance:

// Train learning model
await adapter.train({ epochs: 50, batchSize: 32 });

// Use reasoning agents for inference
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'decision-making',
  k: 10,
  useMMR: true,              // Diverse experiences
  synthesizeContext: true,    // Rich context
  optimizeMemory: true,       // Consolidate patterns
});

// Make decision based on learned experiences + reasoning
const decision = result.context.suggestedAction;
const confidence = result.memories[0].similarity;

---

CLI Operations

# Create plugin
npx agentdb@latest create-plugin -t decision-transformer -n my-plugin

# List plugins
npx agentdb@latest list-plugins

# Get plugin info
npx agentdb@latest plugin-info my-plugin

# List templates
npx agentdb@latest list-templates

---

Troubleshooting

Issue: Training not converging

// Reduce learning rate
await adapter.train({
  epochs: 100,
  batchSize: 32,
  learningRate: 0.0001,  // Lower learning rate
});

Issue: Overfitting

// Use validation split
await adapter.train({
  epochs: 50,
  batchSize: 64,
  validationSplit: 0.2,  // 20% validation
});

// Enable memory optimization
await adapter.retrieveWithReasoning(queryEmbedding, {
  optimizeMemory: true,  // Consolidate, reduce overfitting
});

Issue: Slow training

# Enable quantization for faster inference
# Use binary quantization (32x faster)

---

Learn More

• Algorithm Papers: See docs/algorithms/ for detailed papers
• GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
• MCP Integration: npx agentdb@latest mcp
• Website: https://agentdb.ruv.io

---

Category: Machine Learning / Reinforcement Learning Difficulty: Intermediate to Advanced Estimated Time: 30-60 minutes