L0 - Agent Integration Examples

Agent Integration Examples

Example 1: Add Memory from Chat Messages (Memory Acquisition)

Store every user message as a memory for future context retrieval:

TypeScript

// npm install @memorymodel/client
import { MemoryClient } from '@memorymodel/client';
import { generateText } from 'ai'; // Vercel AI SDK example

const memory = new MemoryClient({
  apiKey: process.env.MEMORY_API_KEY!,
  defaultEndUserId: "user_123"
});

async function handleUserMessage(message: string) {
  // 1. Store the message as memory
  await memory.add(message, {
    userContext: "User chat message"
  });

  // 2. Generate AI response (your LLM logic)
  const response = await generateText({
    model: openai('gpt-4o'),
    prompt: message
  });

  return response.text;
}

Python

# pip install memory-model
import os
from memorymodel import MemoryClient

memory = MemoryClient(
    api_key=os.environ["MEMORY_API_KEY"],
    default_end_user_id="user_123"
)

def handle_user_message(message: str) -> str:
    memory.add(message, user_context="User chat message")

    response_text = your_llm.generate(prompt=message)
    return response_text

Example 2: Context-Aware Agent (Search Memory)

Search relevant memories before generating a response:

TypeScript

// npm install @memorymodel/client
import { MemoryClient } from '@memorymodel/client';
import { generateText } from 'ai'; // Vercel AI SDK example

const memory = new MemoryClient({
  apiKey: process.env.MEMORY_API_KEY!,
  defaultEndUserId: "user_123"
});

async function handleUserQuery(query: string) {
  // 1. Search for relevant memories
  const memories = await memory.search(query, {
    limit: 5
  });

  // 2. Build context from memories
  const context = memories
    .map(m => `- ${m.content}`)
    .join('\n');

  // 3. Generate response with context
  const response = await generateText({
    model: openai('gpt-4o'),
    system: `You are a helpful assistant. Use this context:\n${context}`,
    prompt: query
  });

  return response.text;
}

Python

# pip install memory-model
import os
from memorymodel import MemoryClient

memory = MemoryClient(
    api_key=os.environ["MEMORY_API_KEY"],
    default_end_user_id="user_123"
)

def handle_user_query(query: str) -> str:
    memories = memory.search(query, limit=5)

    context = "\n".join([f"- {m.payload['content']}" for m in memories])

    response_text = your_llm.generate(
        system=f"You are a helpful assistant. Use this context:\n{context}",
        prompt=query
    )

    return response_text

Example 3: Full Agent Loop (Add Memory + Search Memory)

Complete pattern for a memory-powered chatbot:

TypeScript

// npm install @memorymodel/client
import { MemoryClient } from '@memorymodel/client';

const memory = new MemoryClient({
  apiKey: process.env.MEMORY_API_KEY!,
  defaultEndUserId: "user_123"
});

async function agentLoop(userMessage: string) {
  // A. Add Memory (the new message)
  await memory.add(userMessage);

  // B. Search Memory (relevant past context)
  const context = await memory.search(userMessage, {
    limit: 3
  });

  // C. Generate response with memory context
  const systemPrompt = context.length > 0
    ? `Context from past conversations:\n${context.map(m => m.content).join('\n')}`
    : "No prior context available.";

  const aiResponse = await yourLLM.generate({
    system: systemPrompt,
    user: userMessage
  });

  // D. Optionally Add Memory (the AI response) too
  await memory.add(aiResponse, {
    userContext: "AI response"
  });

  return aiResponse;
}

Python

# pip install memory-model
import os
from memorymodel import MemoryClient

memory = MemoryClient(
    api_key=os.environ["MEMORY_API_KEY"],
    default_end_user_id="user_123"
)

def agent_loop(user_message: str) -> str:
    # A. Add Memory (the new message)
    memory.add(user_message)

    # B. Search Memory (relevant past context)
    context = memory.search(user_message, limit=3)

    system_prompt = (
        "Context from past conversations:\n"
        + "\n".join([m.payload["content"] for m in context])
        if len(context) > 0
        else "No prior context available."
    )

    ai_response = your_llm.generate(system=system_prompt, user=user_message)

    # D. Optionally Add Memory (the AI response) too
    memory.add(ai_response, user_context="AI response")

    return ai_response