Conversation Management

Memoer makes it easy to manage conversations with LLMs by providing a robust conversation API.

Adding Messages to a Conversation

You can easily add messages to a conversation:

import { memoer, MemoryConfig } from "memoer";

// Initialize memory
const memory = memoer.createMemory({
  id: "conversation-1",
  systemMessage: {
    role: "system",
    content: "You are a helpful assistant."
  }
});

// Add user message to conversation
memory.conversation.add({
  role: "user",
  content: "Hello, how are you today?"
});

// Add assistant response
memory.conversation.add({
  role: "assistant",
  content: "I'm doing well, thank you! How can I help you today?"
});

Getting Conversation Context

Retrieve the optimized conversation context for sending to an LLM:

// Get the current context (filtered by your chosen memory strategy)
const context = await memory.conversation.getContext();

// Use the context with your LLM
const response = await yourLLMProvider.generate({
  messages: context
});

Retrieving Full Conversation History

You can also retrieve the complete conversation history:

// Get complete conversation history regardless of memory strategy
const fullHistory = await memory.conversation.getFullContext();

Integration with Vercel AI SDK

Memoer integrates seamlessly with Vercel's AI SDK:

import { StreamingTextResponse, Message } from "ai";
import { memoer, MemoryConfig } from "memoer";

// Initialize memory
const memory = memoer.createMemory({
  id: "conversation-1",
  systemMessage: {
    role: "system",
    content: "You are a helpful assistant."
  }
});

export async function POST(req) {
  const { messages } = await req.json();

  // Add the latest user message
  const latestMessage = messages[messages.length - 1];
  memory.conversation.add(latestMessage);

  // Get the conversation context from memory
  const context = await memory.conversation.getContext();

  // Send to LLM using Vercel AI SDK
  const response = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: context,
    stream: true
  });

  // Add the assistant's response to memory
  const assistantMessage = {
    role: "assistant",
    content: response.choices[0].message.content
  };
  memory.conversation.add(assistantMessage);

  // Return the streaming response
  return new StreamingTextResponse(response);
}

This integration makes it simple to build robust, context-aware chatbots while efficiently managing token usage.