Documentation Index
Fetch the complete documentation index at: https://developers.autoplay.ai/llms.txt
Use this file to discover all available pages before exploring further.
Coming soon. A session analysis API is planned to list and fetch per-session documents by user email, time range, and related filters.
Why user memory exists
The SDK already gives live context (what the user is doing now). User memory adds history: what this person has already completed, where they stalled, and what they have never tried. Without it, every user looks like a first-time visitor; with it, the copilot can focus on gaps instead of repeating mastered flows.What does this user already know — and what is still new to them?
Three signals for a proactive copilot
See Proactive copilot.| Signal | Answers |
|---|---|
| Real-time events | What is the user doing right now? |
| Golden paths | What should they be doing on the journey? |
| User memory | What have they already done, and where do they struggle? |
How it works
Raw browser events are noisy for an LLM. Autoplay extracts, labels, groups, and compresses them into a short list of meaningful actions per session. That list is fed through the Terra pipeline; each session yields onesession_analysis document. Many sessions roll up into the cross-session memory profile.
In practice
| User history | Copilot leans toward |
|---|---|
| Workflow mastered | Skip explaining it; suggest something else |
| In progress | Resume at the missed step, not step one |
| Untouched high-value flow | Introduce when context fits |
| Repeated struggle | Change approach instead of repeating the same hint |
Session analysis document (one session)
Session-level output is one JSON per session (Terra). Cross-session user memory merges many of these over time — not the same as a single session export.Planned session API and response shape
We will ship an API to query session analyses (e.g. filter by user email, time range, organization, product). Each matching session is returned as its ownsession_analysis document. The example below is the exact shape of one such response — also checked in as session-analysis.example.json so you can diff or copy without scrolling the docs.
Top-level sections
| Section | What it holds |
|---|---|
schema | Document version / compatibility |
meta | Session id, org, product, duration, pipeline (terra), model, timestamps |
urls | Routing shape and page timeline (raw_url / canonical_url, dwell times) |
actions | Numbered labelled steps (types, titles, descriptions, URLs, timestamps) |
insights | LLM summary, bullets, flow, intent, friction, highlights, optional suggested_email |
tags | Often tags.values[] with { key, value, type } entries |
tasks | Golden-path fit: expected_path, attempts, matched_actions / missed_steps, similarity |
prompts | Prompt names and versions used to generate sections |











