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XTrace Memory Manager is a hosted memory service for AI agents. You send conversation messages; we extract structured facts, artifacts, and episodes from them, embed each one, and store them in a per-org vector index. You retrieve them later with natural-language search — scoped to a single user, or shared across a group.

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Concepts in 30 seconds

TermWhat it is
FactA single semantic claim extracted from a conversation turn (e.g. “User is vegetarian”). Most ingested memory is facts.
ArtifactA structured object referenced by the conversation (e.g. a doc, code snippet, summary). Server-extracted when content warrants it.
EpisodeA session-scoped summary spanning a stretch of turns. Bounds the temporal scope of facts.
MemoryUmbrella term — every Fact / Artifact / Episode is a Memory, distinguished by its type field.
GroupA shared tagging target. Tag memories to a group at ingest, and every member can search them — memory shared across users, not just within one.
You don’t have to pre-classify anything when you ingest. The server decides what becomes a fact vs an artifact vs an episode.

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Five-minute path

Quickstart

Install the SDK, ingest a turn, search the result

Authentication

API keys, org headers, environment setup

Ingesting memories

Async vs sync ingest, polling for completion

Searching memories

Scope by user, group, agent, or app — plus recall

Groups

Share memory across users with group tagging
For the full endpoint and type reference, see the API Reference tab.
The TypeScript SDK (@xtraceai/memory) is the primary supported client today. A Python SDK is on the roadmap. All examples below use the TS SDK; the underlying HTTP API is documented in the API Reference tab if you need to wire something yourself.