agentdb-learning-plugins

# 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)

# List installed plugins
npx agentdb@latest list-plugins
 
# Get plugin information
npx agentdb@latest plugin-info my-agent
 
# Shows: algorithm, configuration, training status

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');

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

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

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

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

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

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

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

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

// 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(),
    });
  }
}

// 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
// }

// 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);

// 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,
});

// 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
});

// 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,
});

// 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
});

// 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

// 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;

# 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

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

// 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
});

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