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 + structured filters.Documentation Index
Fetch the complete documentation index at: https://docs.mem.xtrace.ai/llms.txt
Use this file to discover all available pages before exploring further.
Concepts in 30 seconds
| Term | What it is |
|---|---|
| Fact | A single semantic claim extracted from a conversation turn (e.g. “User is vegetarian”). Most ingested memory is facts. |
| Artifact | A structured object referenced by the conversation (e.g. a doc, code snippet, summary). Server-extracted when content warrants it. |
| Episode | A session-scoped summary spanning a stretch of turns. Bounds the temporal scope of facts. |
| Memory | Umbrella term — every Fact / Artifact / Episode is a Memory, distinguished by its type field. |
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
Natural-language queries with structured filters
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.