Build a chatbot memory API workflow without stitching together five separate layers.

What developers actually need

Most chatbot projects do not fail because the model is weak. They fail because the app forgets user context, repeats onboarding questions, and mixes memory logic directly into prompt-building code. A usable chatbot stack separates concerns cleanly.

• Your app stores the user profile, conversation history, or relevant facts.
• Your memory layer decides which context is worth retrieving for the next request.
• BrainAPI handles the AI request itself through one consistent API.

Where BrainAPI fits

If you already keep context in Postgres, Redis, a vector database, or a graph store, BrainAPI gives you one stable place to send the final AI request. That means you can change providers, pricing mode, or multi-modal workflows without rewriting the memory handoff every time.

Example chatbot memory flow

const memory = await loadUserContext(userId);

const input = [
  "You are the support assistant for BrainAPI.",
  "Relevant user context:",
  memory.summary,
  "Latest message:",
  userMessage
].join("\\n\\n");

const response = await fetch("https://api.brainapi.site/api/v1/ai", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "X-API-Key": process.env.BRAINAPI_KEY
  },
  body: JSON.stringify({
    type: "text",
    input,
    mode: "fast",
    max_output_tokens: 220
  })
});

This keeps memory retrieval inside your app while BrainAPI stays responsible for execution, fallback, and consistent response formatting.

Best use cases

• Customer support chatbots that should remember product plan, last ticket, or onboarding state.
• Personal assistants that need preferences without stuffing the full transcript into every request.
• SaaS copilots that need account context plus a consistent AI gateway across providers.