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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.

Available now.

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What you get

Every action a user takes in your product — page views, clicks, form inputs — is captured and delivered to your agent as a structured, LLM-ready payload. This is not raw browser telemetry. Before events reach your agent, Autoplay’s pipeline processes them:
  1. Extraction — raw DOM events are extracted and normalised into typed actions
  2. Labelling — each action is given a human-readable description with inferred intent
  3. Grouping — actions are collected into a session with an inferred goal
  4. Summarisation (optional) — the connector can run an LLM pass to produce a compact prose summary of the full session
The result is an ActionsPayload that your agent can read and reason over directly.
session_id scoping is the compulsory base in this recipe. Live events are always ingested and stored by session.
user_id can be optional in some companies depending on what their session replay provider captures. Your pipeline should still work with session-scoped activity even when user identity is absent.

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What your copilot can do at this stage

Your agent can see exactly what the user is doing right now — which page they’re on, what they just clicked, and what their inferred intent is. It can respond contextually to any action without waiting for the user to describe the situation in chat.

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Limitation

Every session still looks like a first session. Your agent has no memory of what this user has done before, which workflows they’ve already completed, or where they typically get stuck — and without a workflow ontology it cannot compare live behaviour to an ideal journey.

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Code

from autoplay_sdk import AsyncConnectorClient, ActionsPayload

async def on_actions(payload: ActionsPayload) -> None:
    # payload.to_text() is ready to inject directly into your LLM context
    current_context = payload.to_text()

    suggestion = await your_llm(
        system="You are a product copilot. Based on what the user is doing, "
               "suggest one helpful next step. Be brief.",
        user=f"## What the user is doing\n{current_context}",
    )

    if suggestion:
        await push_to_ui(payload.session_id, suggestion)


client = AsyncConnectorClient(url=CONNECTOR_URL, token=API_KEY)
client.on_actions(on_actions)
See Async client and Webhook receiver for setup details.

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Optional identity enhancement: UserSessionIndex

Live events arrive keyed by session_id first. The compulsory requirement is to keep session-scoped activity stable and then map it to the correct conversation_id once chat begins. If your deployment captures reliable user_id, wire UserSessionIndex to map one user to recent session_id refs so cross-session retrieval can be user-keyed too. If user_id is not captured, keep operating on the session-scoped base path; you can add user scoping later without changing the core session mapping model. Next: Step 2 — Add actions to LLM context