---

LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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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)

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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

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

Core Principles

AgentDB Learning Plugins operates on 3 fundamental principles:

Principle 1: Offline Reinforcement Learning Enables Safe Learning from Logged Data

Train agents from historical experiences without environment interaction using Decision Transformers for imitation learning and policy optimization.

In practice:

• Decision Transformers model behavior as sequence prediction (states, actions, rewards) without online exploration
• Safe learning from expert demonstrations or logged trajectories prevents catastrophic failures during training
• Offline RL achieves 80-95% of online RL performance while eliminating exploration risks and environment costs

Principle 2: Algorithm Selection Determines Learning Efficiency and Safety

Match RL algorithm to problem structure: value-based (discrete actions), policy gradients (continuous control), or hybrid approaches.

In practice:

• Q-Learning for discrete decisions (navigation, resource allocation) with sample-efficient off-policy learning
• Actor-Critic for continuous control (robotics, simulations) with variance reduction via value baseline
• SARSA for safety-critical applications requiring on-policy conservative exploration

Principle 3: Experience Replay and Batch Training Accelerate Convergence

Store experiences in vector database for efficient sampling, prioritization, and multi-agent training across distributed nodes.

In practice:

• Prioritized experience replay focuses on high TD-error transitions (unexpected outcomes) for faster learning
• Batch training (32-128 samples) reduces variance and enables GPU acceleration (10-100x speedup)
• Vector similarity enables retrieval of relevant past experiences for transfer learning and few-shot adaptation

Common Anti-Patterns

Anti-Pattern	Problem	Solution
Online Training Without Replay Buffer	Each experience used once then discarded, requiring 10-100x more environment interactions	Store experiences in AgentDB with embeddings; sample random batches (32-64) for training; reuse high-value transitions
Wrong Algorithm for Problem Type	Q-Learning on continuous actions requires discretization (action space explosion), Actor-Critic on small discrete spaces wastes capacity	Match algorithm to action space: Q-Learning/SARSA for discrete (<100 actions), Actor-Critic/PPO for continuous, Decision Transformer for offline
Ignoring Confidence and Usage Tracking	All experiences weighted equally despite varying quality and relevance	Store confidence (reward-based or TD-error), increment usage_count/success_count; prioritize high-confidence experiences; prune low-quality patterns

Conclusion

AgentDB Learning Plugins transforms static vector databases into self-improving AI systems by integrating 9 reinforcement learning algorithms with persistent memory for experience accumulation and retrieval. By storing experiences as embeddings in AgentDB, agents learn from past successes and failures, retrieve similar patterns for transfer learning, and continuously improve through offline RL without risking catastrophic exploration.

Use this skill when building autonomous agents requiring continuous improvement (chatbots, recommendation systems, game AI), implementing safe learning from historical data (medical diagnosis, financial trading), or enabling multi-agent knowledge sharing through federated learning. The key insight is persistence: unlike traditional RL where experiences are discarded after training, AgentDB stores them permanently for retrieval, reuse, and transfer across tasks. Start with Decision Transformer for safe offline learning from logged data, add experience replay for sample efficiency, and enable distributed training when scaling to multiple agents or environments.