Package Exports

@xtraceai/memory
@xtraceai/memory/ai-sdk

Readme

Long-term memory for AI agents.
Send conversation messages, get back structured facts you can search.

Documentation | X | LinkedIn

_{Encrypted vector search → xtrace-sdk}

What is xtrace memory?

@xtraceai/memory is the TypeScript SDK for the xtrace memory API — a hosted memory service for AI agents. Send raw conversation messages and the service extracts structured facts, artifacts, and episodes in the background. Search them later with vector + filter queries to give your agent durable, long-term memory.

Ingest — drop in conversation messages; extraction runs async (or sync for short turns). Tag memories to shared groups for cross-user recall.
Search — semantic vector search scoped by user_id / group_ids / agent_id / app_id (AND-everything). recall() merges a user's own memories with a shared group's in one call.
Manage — list, get, and (hard) delete memories. Register tagging targets with the groups API.
Vercel AI SDK — a drop-in @xtraceai/memory/ai-sdk subpath for auto-context and tool-based recall.

Works in Node 18+ (native fetch) and in the browser.

Quick Start

[!TIP] 🚀 Create a free account at app.xtrace.ai to get your API key and org ID. The free tier is rate-limited but fully functional.

Install

npm install @xtraceai/memory

Requires Node 18+ (uses native fetch). Works in the browser too.

Get credentials

API key — xtk_…
Org id — your organization identifier

Both are required on every request. See the full docs for storage best practices.

TypeScript SDK

import { MemoryClient } from "@xtraceai/memory";

const client = new MemoryClient({
  apiKey: process.env.XTRACE_API_KEY!, // xtk_...
  orgId: process.env.XTRACE_ORG_ID!,   // org_...
});

// Ingest — async by default. `conv_id` is currently required.
const job = await client.memories.ingest({
  messages: [{ role: "user", content: "I keep a daily log of every dog I see." }],
  user_id: "alice",
  conv_id: "conv_2026_05_15",
});

// Wait for extraction to finish
const done = await client.memories.jobs.pollUntilDone(job.id);
console.log(done.result?.memories_created);

// Or ingest synchronously (server waits up to 30s; falls back to async otherwise)
const sync = await client.memories.ingest(
  {
    messages: [{ role: "user", content: "I love Thai food." }],
    user_id: "alice",
    conv_id: "conv_2026_05_15",
  },
  { wait: true },
);
if (sync.status === "succeeded") {
  console.log(sync.result?.memories_created);
}

// Search — scope by what you pass (user_id / group_ids / agent_id / app_id all AND-narrow)
const results = await client.memories.search({
  query: "what does the user like to eat?",
  user_id: "alice",
});

// Personal + shared (group) recall in one call — unions the pools you pass
const { prompt } = await client.memories.recall({
  query: "what should we plan for dinner?",
  pools: [{ user_id: "alice" }, { group_ids: ["grp_tokyo2026"] }],
});

// List with auto-pagination
for await (const memory of client.memories.list({ user_id: "alice" })) {
  console.log(memory.text);
}

// Get one
const memory = await client.memories.get(results.data[0]!.id);

// Delete (hard — the point is removed; get/list/search no longer return it, a second delete 404s)
await client.memories.delete(memory.id);

Vercel AI SDK integration

A separate subpath, @xtraceai/memory/ai-sdk, ships two ways to use the SDK with the Vercel AI SDK. Peer dependencies (ai, zod) are optional — they're only required if you import from this subpath.

Memory-aware model wrapper (auto-context + auto-ingest)

Wraps any LanguageModel so it searches your memory before each call and ingests the turn after. Set it and forget it:

import { streamText } from "ai";
import { openai } from "@ai-sdk/openai";
import { createXtraceMemory } from "@xtraceai/memory/ai-sdk";

const xtrace = createXtraceMemory({
  apiKey: process.env.XTRACE_API_KEY!,
  orgId:  process.env.XTRACE_ORG_ID!,
  user_id: "alice",
  conv_id: "conv_42",
});

const result = streamText({
  model: xtrace(openai("gpt-4o-mini")),  // memory-aware wrapper
  messages,
});

Memory as tools (LLM decides when to recall / save)

For agent loops where you want the model in control of memory access:

import { streamText } from "ai";
import { openai } from "@ai-sdk/openai";
import { MemoryClient } from "@xtraceai/memory";
import { memoryTools } from "@xtraceai/memory/ai-sdk";

const client = new MemoryClient({ apiKey, orgId });

const result = streamText({
  model: openai("gpt-4o-mini"),
  tools: memoryTools(client, { user_id: "alice", conv_id: "conv_42" }),
  messages,
});

The model gets two tools: search_memory(query, limit?) and save_memory(fact). Use { includeSave: false } for read-only.

Webhooks

Register one endpoint per org and XTrace POSTs a signed event when an ingest job finishes — memory.learning.completed (fires even when nothing was extracted, so you can unblock the user) or memory.learning.failed — instead of you polling client.memories.jobs.

// Register (or replace) the org's webhook. The full `secret` is returned only
// here — on first create or `{ rotateSecret: true }`. Store it.
const wh = await client.webhooks.set({ url: "https://your-app.com/hooks/xtrace" });
console.log(wh.secret); // whsec_… — save this

await client.webhooks.get();    // current config (secret masked)
await client.webhooks.delete(); // remove it (idempotent)

On your endpoint, verify the X-Webhook-Signature over the raw body before trusting the event. verifyWebhookSignature / parseWebhookEvent use Web Crypto, so they run in Node 18+, browsers, and edge runtimes:

import express from "express";
import { parseWebhookEvent, type WebhookEventPayload } from "@xtraceai/memory";

const app = express();

// Capture the RAW body — re-serializing a parsed object breaks the signature.
app.post("/hooks/xtrace", express.raw({ type: "application/json" }), async (req, res) => {
  let event: WebhookEventPayload;
  try {
    event = await parseWebhookEvent({
      payload: req.body, // Buffer / raw bytes
      signature: req.header("X-Webhook-Signature") ?? "",
      secret: process.env.XTRACE_WEBHOOK_SECRET!,
    });
  } catch {
    return res.status(401).end(); // signature didn't verify
  }

  if (event.event === "memory.learning.completed") {
    // event.conv_id / event.user_id correlate back to your ingest call
    console.log("ready:", event.conv_id, event.memories.map((m) => m.id));
  }
  res.status(200).end(); // ack with any 2xx
});

Delivery is best-effort with a few retries — an event may be dropped or duplicated, so make your handler idempotent (dedupe on job_id), and use client.memories.jobs.pollUntilDone(jobId) as the fallback for anything you can't miss. See the Webhooks guide for the full contract.

Error handling

All errors extend MemoryError. Match on error.code for stable machine-readable handling:

import { MemoryNotFound, RateLimited } from "@xtraceai/memory";

try {
  await client.memories.get("fact_does_not_exist");
} catch (err) {
  if (err instanceof MemoryNotFound) {
    // ...
  } else if (err instanceof RateLimited) {
    // err.retryAfter is the seconds to wait
  }
}

Documentation

Full documentation at docs.xtrace.ai.

License

MIT — see LICENSE.