Documentation

Everything you need to build powerful, self-learning AI agents with Pulsar framework.

Getting Started

Pulsar makes it easy to build AI agents that learn and evolve. Get up and running in minutes with our comprehensive installation guide.

Installation

Install Pulsar using pip or npm:

pip install pulsar-framework # or npm install @pulsar/framework

Quick Start

Create your first learning agent in just a few lines of code:

from pulsar import Agent, LearningModule # Initialize a new agent agent = Agent(name="recommendation-engine") # Add learning capabilities agent.add_module(LearningModule.collaborative_filtering()) agent.add_module(LearningModule.user_behavior_analysis()) # Start learning from data agent.train(data_source="user_interactions.json") # Deploy and begin continuous learning agent.deploy(environment="production", auto_scale=True)

Core Concepts

Understanding the fundamental concepts behind Pulsar's adaptive framework.

Learning Agents

Autonomous entities that can adapt their behavior based on experience and environmental feedback.

Learning Modules

Reusable components that provide specific learning capabilities like pattern recognition, decision making, or data analysis.

Adaptive Framework

The underlying system that manages continuous learning, performance monitoring, and automatic optimization.

Evolution Engine

Advanced algorithms that enable agents to evolve their strategies and improve performance over time.

Learning Modules

Pre-built components that add specific learning capabilities to your agents.

Available Modules

Data Analysis Modules

  • Pattern Recognition - Identify trends and patterns in large datasets
  • Anomaly Detection - Detect unusual behaviors or data points
  • Statistical Learning - Learn from statistical distributions and correlations

Decision Making Modules

  • Reinforcement Learning - Learn optimal actions through trial and reward
  • Multi-Criteria Decision Analysis - Balance multiple objectives and constraints
  • Adaptive Strategy Selection - Choose the best strategy for current conditions

Natural Language Processing

  • Sentiment Analysis - Understand emotional context in text
  • Intent Recognition - Identify user intentions and goals
  • Conversational Learning - Improve responses through dialogue

Code Examples

Real-world examples to help you get started quickly.

Recommendation Engine

from pulsar import Agent, LearningModule # Create recommendation agent recommender = Agent("product-recommender") recommender.add_module(LearningModule.collaborative_filtering()) recommender.add_module(LearningModule.content_based_filtering()) # Train on historical data recommender.train("user_purchase_history.json") # Make recommendations recommendations = recommender.recommend(user_id=12345, limit=10)

Fraud Detection System

from pulsar import Agent, LearningModule # Create fraud detection agent fraud_detector = Agent("fraud-detection") fraud_detector.add_module(LearningModule.anomaly_detection()) fraud_detector.add_module(LearningModule.behavioral_analysis()) # Continuous learning from transactions fraud_detector.stream_learn("transaction_stream") # Evaluate transaction risk_score = fraud_detector.evaluate_transaction(transaction_data)

Customer Service Bot

from pulsar import Agent, LearningModule # Create customer service agent service_bot = Agent("customer-service") service_bot.add_module(LearningModule.intent_recognition()) service_bot.add_module(LearningModule.sentiment_analysis()) service_bot.add_module(LearningModule.response_optimization()) # Learn from customer interactions service_bot.train("customer_conversations.json") # Handle customer query response = service_bot.handle_query("I need help with my order")

Best Practices

Guidelines for building effective and reliable learning agents.

Agent Design

  • Start with simple learning modules and add complexity gradually
  • Define clear success metrics and monitoring strategies
  • Implement proper data validation and preprocessing
  • Use version control for agent configurations

Performance Optimization

  • Monitor learning curves and adjust parameters accordingly
  • Implement early stopping to prevent overfitting
  • Use distributed learning for large-scale deployments
  • Regular performance audits and model updates

Production Deployment

  • Implement gradual rollouts with A/B testing
  • Set up comprehensive monitoring and alerting
  • Plan for model rollback scenarios
  • Ensure data privacy and security compliance

Agent Lifecycle

Understanding the complete journey from agent creation to continuous evolution.

1. Initialization

Create agent instance, configure learning modules, and set up data pipelines.

2. Training

Learn from historical data, validate performance, and optimize parameters.

3. Deployment

Deploy to production environment with monitoring and scaling capabilities.

4. Evolution

Continuous learning, adaptation, and improvement based on real-world feedback.

API Reference

Complete reference for all Pulsar classes and methods.

Agent Class

class Agent: def __init__(self, name: str, config: dict = None) def add_module(self, module: LearningModule) def train(self, data_source: str, validation_split: float = 0.2) def deploy(self, environment: str, auto_scale: bool = False) def predict(self, input_data: dict) def evaluate(self, test_data: str)

LearningModule Class

class LearningModule: @staticmethod def collaborative_filtering(**kwargs) @staticmethod def anomaly_detection(**kwargs) @staticmethod def intent_recognition(**kwargs) @staticmethod def sentiment_analysis(**kwargs) @staticmethod def reinforcement_learning(**kwargs)

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