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Agent Sessions

Sessions give your Agents memory across multiple interactions. Users can have ongoing conversations, and Agents remember the context from previous messages.

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Agent with Session Memory

import { Agent, Session } from 'praisonai';

const session = new Session({ id: 'user-123' });

const agent = new Agent({
  name: 'Support Agent',
  instructions: 'You are a helpful support agent. Use conversation history for context.',
  session  // Agent uses this session for memory
});

// First interaction
await agent.chat('My name is John and I need help with billing');
// Agent remembers: user's name is John, topic is billing

// Later interaction (same session)
await agent.chat('What was my issue again?');
// Agent responds: "You mentioned you need help with billing, John"

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Multi-Turn Conversations

import { Agent, Session, SessionManager } from 'praisonai';

const sessionManager = new SessionManager();

const agent = new Agent({
  name: 'Assistant',
  instructions: 'You are a helpful assistant.'
});

async function chat(userId: string, message: string) {
  // Get or create session for this user
  const session = sessionManager.getOrCreate(userId);
  
  // Add user message to session
  session.addMessage({ role: 'user', content: message });
  
  // Get full conversation history for context
  const history = session.getMessagesForLLM();
  
  // Agent responds with full context
  const response = await agent.chat(history);
  
  // Save agent response to session
  session.addMessage({ role: 'assistant', content: response });
  
  return response;
}

// User conversation
await chat('user-123', 'Hi, I want to learn TypeScript');
await chat('user-123', 'What should I start with?');  // Agent remembers the topic
await chat('user-123', 'Can you give me an example?'); // Agent knows context

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Agent with Persistent Sessions

Sessions that survive server restarts: 
import { Agent, Session, createNeonPostgres, createPostgresSessionStorage } from 'praisonai';

const db = createNeonPostgres({ connectionString: process.env.DATABASE_URL });
const sessionStorage = createPostgresSessionStorage(db);

const agent = new Agent({
  name: 'Persistent Agent',
  instructions: 'You remember all previous conversations.'
});

async function persistentChat(sessionId: string, message: string) {
  // Load session from database
  let sessionData = await sessionStorage.getSession(sessionId);
  const session = new Session({ id: sessionId });
  
  if (sessionData) {
    // Restore previous messages
    const messages = await sessionStorage.getMessages(sessionId);
    messages.forEach(m => session.addMessage(m));
  }
  
  // Add new message
  session.addMessage({ role: 'user', content: message });
  
  // Get Agent response
  const response = await agent.chat(session.getMessagesForLLM());
  session.addMessage({ role: 'assistant', content: response });
  
  // Persist to database
  await sessionStorage.createSession(sessionId, { updatedAt: new Date() });
  await sessionStorage.addMessage(sessionId, { role: 'user', content: message });
  await sessionStorage.addMessage(sessionId, { role: 'assistant', content: response });
  
  return response;
}

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Multi-Agent Shared Sessions

Multiple Agents sharing conversation context: 
import { Agent, Session, Agents } from 'praisonai';

const sharedSession = new Session({ id: 'support-case-456' });

// Agent 1: Gathers information
const intakeAgent = new Agent({
  name: 'Intake',
  instructions: 'Gather customer information and understand their issue.',
  session: sharedSession
});

// Agent 2: Provides solutions (sees intake conversation)
const solutionAgent = new Agent({
  name: 'Solution Expert',
  instructions: 'Review the conversation history and provide solutions.',
  session: sharedSession
});

// Agent 3: Follows up (sees entire history)
const followupAgent = new Agent({
  name: 'Follow-up',
  instructions: 'Check if the customer is satisfied based on the conversation.',
  session: sharedSession
});

// Intake gathers info
await intakeAgent.chat('I have a problem with my subscription');
await intakeAgent.chat('It keeps charging me twice');

// Solution agent sees the full context
const solution = await solutionAgent.chat('Please provide a solution for this customer');

// Follow-up agent sees everything
await followupAgent.chat('Check if the customer is satisfied');

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Session with Run Tracking

Track individual Agent executions: 
import { Agent, Session } from 'praisonai';

const session = new Session();

const agent = new Agent({
  name: 'Tracked Agent',
  instructions: 'You help with various tasks.'
});

async function trackedChat(message: string) {
  // Create a run for this interaction
  const run = session.createRun();
  run.start();
  
  try {
    session.addMessage({ role: 'user', content: message });
    
    const response = await agent.chat(session.getMessagesForLLM());
    
    session.addMessage({ role: 'assistant', content: response });
    
    run.complete({ tokensUsed: 150 });
    
    console.log(`Run ${run.id}: ${run.duration}ms`);
    return response;
  } catch (error) {
    run.fail(error);
    throw error;
  }
}

// Each chat creates a trackable run
await trackedChat('Hello');
await trackedChat('Help me with code');

// View all runs
console.log(`Total runs: ${session.runs.length}`);
console.log(`Total messages: ${session.messages.length}`);

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Session Context Window Management

Handle long conversations: 
import { Agent, Session } from 'praisonai';

const session = new Session({ 
  id: 'long-conversation',
  maxMessages: 50  // Keep last 50 messages
});

const agent = new Agent({
  name: 'Context-Aware Agent',
  instructions: 'You maintain context across long conversations.'
});

async function chatWithContextManagement(message: string) {
  session.addMessage({ role: 'user', content: message });
  
  // Get messages optimized for context window
  const messages = session.getMessagesForLLM({
    maxTokens: 4000,  // Fit within context window
    strategy: 'recent'  // Prioritize recent messages
  });
  
  const response = await agent.chat(messages);
  session.addMessage({ role: 'assistant', content: response });
  
  return response;
}

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Session Metadata

Store user and context information: 
import { Agent, Session } from 'praisonai';

const session = new Session({
  id: 'customer-session',
  metadata: {
    userId: 'user-789',
    customerTier: 'premium',
    language: 'en',
    timezone: 'America/New_York'
  }
});

const agent = new Agent({
  name: 'Personalized Agent',
  instructions: `You are a support agent. 
Check session metadata for customer context.
Premium customers get priority support.`
});

// Agent can access session metadata for personalization
const response = await agent.chat('I need help', { session });

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Core Concepts

ConceptDescription
SessionConversation container with message history
RunSingle Agent execution within a session
TraceDetailed span within a run (LLM call, tool call)
  • Memory - Semantic memory for Agents
  • Redis - Fast session storage
  • PostgreSQL - Persistent session storage