Memory System

import { Agent, Memory, createTool } from 'praisonai';

const memory = new Memory();

// Tool to save to memory
const rememberTool = createTool({
  name: 'remember',
  description: 'Save important information to memory for later recall',
  parameters: {
    type: 'object',
    properties: {
      information: { type: 'string', description: 'Information to remember' },
      category: { type: 'string', description: 'Category (preferences, facts, tasks)' }
    },
    required: ['information']
  },
  execute: async ({ information, category = 'general' }) => {
    await memory.add(information, 'memory', { category });
    return `Remembered: ${information}`;
  }
});

// Tool to search memory
const recallTool = createTool({
  name: 'recall',
  description: 'Search memory for relevant information',
  parameters: {
    type: 'object',
    properties: {
      query: { type: 'string', description: 'What to search for' }
    },
    required: ['query']
  },
  execute: async ({ query }) => {
    const results = await memory.search(query);
    if (results.length === 0) return 'No relevant memories found';
    return results.map(r => r.entry.content).join('\n');
  }
});

const agent = new Agent({
  name: 'Learning Agent',
  instructions: `You can remember and recall information.
Use 'remember' to save important facts the user tells you.
Use 'recall' to find relevant information when answering questions.`,
  tools: [rememberTool, recallTool]
});

await agent.chat('Remember that I am allergic to peanuts');
await agent.chat('What food restrictions do I have?'); // Agent recalls allergy

import { Agent, Memory, Agents } from 'praisonai';

const sharedMemory = new Memory();

// Agent 1: Learns from documents
const learnerAgent = new Agent({
  name: 'Learner',
  instructions: 'Extract and remember key facts from documents.',
  memory: sharedMemory
});

// Agent 2: Answers questions using shared memory
const answererAgent = new Agent({
  name: 'Answerer',
  instructions: 'Answer questions using information from memory.',
  memory: sharedMemory
});

// Learner processes documents
await learnerAgent.chat('Learn: The company was founded in 2020. CEO is Jane Smith. HQ in Austin.');

// Answerer can access learned information
await answererAgent.chat('Who is the CEO?'); // Recalls: Jane Smith
await answererAgent.chat('Where is the headquarters?'); // Recalls: Austin

import { Agent, Memory, createUpstashRedis } from 'praisonai';

const redis = createUpstashRedis({ url, token });

// Memory with persistence
class PersistentMemory extends Memory {
  private redis: any;
  private userId: string;
  
  constructor(redis: any, userId: string) {
    super();
    this.redis = redis;
    this.userId = userId;
  }
  
  async add(content: string, role: string, metadata?: any) {
    await super.add(content, role, metadata);
    // Persist to Redis
    const memories = this.toJSON();
    await this.redis.set(`memory:${this.userId}`, memories);
  }
  
  async load() {
    const data = await this.redis.get(`memory:${this.userId}`);
    if (data) this.fromJSON(data);
  }
}

const memory = new PersistentMemory(redis, 'user-123');
await memory.load(); // Load previous memories

const agent = new Agent({
  name: 'Long-Term Memory Agent',
  instructions: 'You remember everything about the user across all sessions.',
  memory
});

// Agent remembers from previous sessions
await agent.chat('What do you remember about me?');

import { Agent, Memory } from 'praisonai';

const memory = new Memory();

// Add various information
await memory.add('User prefers dark mode', 'system');
await memory.add('User is learning React', 'user');
await memory.add('User works at TechCorp', 'user');
await memory.add('User timezone is PST', 'system');

const agent = new Agent({
  name: 'Context-Aware Agent',
  instructions: 'Personalize responses based on user context.'
});

async function contextualChat(message: string) {
  // Search memory for relevant context
  const relevantMemories = await memory.search(message);
  
  // Build context string
  const context = memory.buildContext({
    entries: relevantMemories.slice(0, 5),
    format: 'bullet'
  });
  
  // Agent responds with context
  return await agent.chat(`
User Context:
${context}

User Message: ${message}
  `);
}

await contextualChat('Help me with a coding problem');
// Agent knows: user is learning React, works at TechCorp

import { Agent, Memory } from 'praisonai';

const memory = new Memory({
  embeddingProvider: {
    embed: async (text) => {
      // Use OpenAI embeddings
      const response = await openai.embeddings.create({
        model: 'text-embedding-3-small',
        input: text
      });
      return response.data[0].embedding;
    },
    embedBatch: async (texts) => {
      const response = await openai.embeddings.create({
        model: 'text-embedding-3-small',
        input: texts
      });
      return response.data.map(d => d.embedding);
    }
  }
});

const agent = new Agent({
  name: 'Semantic Memory Agent',
  instructions: 'You have semantic memory - you can find information by meaning.',
  memory
});

// Add diverse information
await memory.add('The quarterly revenue was $5.2 million', 'data');
await memory.add('Customer satisfaction score improved to 4.8/5', 'data');
await memory.add('We hired 15 new engineers this quarter', 'data');

// Semantic search finds relevant info
await agent.chat('How is the company doing financially?');
// Finds: quarterly revenue $5.2 million (by semantic similarity)

const memory = new Memory({
  maxEntries: 1000,        // Maximum memories to store
  maxTokens: 50000,        // Token limit for context
  embeddingProvider: {...} // Optional: for semantic search
});