Rasa tutorial - Autoplay SDK

What this means in practice: a user is half-way through an upgrade flow. They open the chat bubble and ask “how do I finish this?” Without context, your bot has to ask which page they’re on. With Autoplay wired in, your bot already knows — and replies with the specific next click, not a walkthrough of the whole UI. This tutorial shows you exactly how to wire it together using Rasa 3.6 (open-source, self-hosted) and the Autoplay SDK. Everything stays on your infrastructure — your activity data, prompts, and LLM keys never leave it. Who this is for:

You already have (or are building) a Rasa-based support AI agent for your product.
You want it to give answers that adapt to where the user is in the UI, instead of canned responses.
You’re comfortable with Python, Docker, and a small FastAPI service.

Tech you’ll use: Rasa 3.6 (Docker), Python 3.10+ (host), PostHog (free tier), the Autoplay event connector (hosted), and any LLM with an async Python client (OpenAI, Anthropic, Gemini, local — your choice). SDK primitives you’ll touch: a plain httpx GET against the Autoplay live-activity REST endpoint (no client class to import — it’s just a URL, a Bearer token, and product_id/user_id), agent_state.v2.SessionState, ChatContextAssembly, plus (in Step 2) PredicateProactiveTrigger, ProactiveTriggerRegistry, and TourRegistry. The connector is pull-based, so there’s no connection to manage — the bridge fetches a user’s recent activity the moment it needs it, and the SDK still handles prompt assembly and FSM transitions.

✨ Final result

Without real-time context
With real-time context

User: how do I add a teammate?

Bot: Go to Settings → Team → Invite member, then enter their email
and pick a role.

(Generic reply — the bot doesn't know the user is already on the
Team page with the invite modal half-filled.)

User: how do I add a teammate?

Bot: You're nearly there — just pick a role from the dropdown and
hit Send invite. They'll get the email within a minute.

(Same question, but the bot infers from recent activity that the
user is already in the right modal and skips the steps they've
already done. No mention of "I see you clicked…".)

Video walkthrough — Open on Loom if the player does not load.

📋 Prerequisites

Before you start, you need:

A web app you can edit — anything that can load posthog-js. The examples use Next.js but it works with any frontend.
A free PostHog account — for click capture. You’ll need your Project API Key (starts with phc_, not phx_ — the personal one is rejected by posthog.init()).
An Autoplay product ID. If you already use PostHog, you can re-use your PostHog project id (the number after /project/ in the URL) as your product_id. The tutorial covers running onboard_product to mint the rest of the credentials.
Docker Desktop — Rasa runs in a container; this also sidesteps TensorFlow ABI issues on Apple Silicon.
Python 3.10+ on the host — for the small FastAPI bridge service.
An OpenAI API key (or any LLM provider with an async Python client) — the bridge calls it directly with a plain (str) -> str async function, so swap in Anthropic, Gemini, Mistral, or a local model if you prefer.

That’s all the setup. The rest of this tutorial walks through every code file step by step — copy-paste runnable.

Why a separate “bridge” service? Rasa runs in Docker and can’t import Python objects from your host. The bridge is a small FastAPI service that owns the Autoplay SDK pipeline and exposes a tiny HTTP surface (/reply/{user_id}) that Rasa’s action server calls when a user chats. This same pattern works for any cross-process support AI agent — Botpress on-prem, LangChain services, Twilio webhooks — not just Rasa.

Architecture

┌──────────────────┐   ┌──────────┐   ┌──────────┐
│  Your web app    │──▶│ PostHog  │──▶│ Autoplay │
│  + posthog-js    │   │ autoclick│   │ connector│
│  + chat widget   │   │ + HogQL  │   │ (pull API)│
└────────▲─────────┘   └──────────┘   └─────▲────┘
         │                                  │ GET .../live-activity
         │                                  │ (on demand, no open connection)
         │             ┌────────────────────┴────────────────┐
         │             │  bridge/  (autoplay-native, FastAPI)│
         │             │                                     │
         │             │  GET /reply/{user_id}?query=…       │
         │             │    → httpx GET live-activity        │
         │             │    → format actions as text         │
         │             │    → ChatContextAssembly /          │
         │             │      build_user_prompt_block         │
         │             │    → call your LLM                  │
         │             │    → return reply                   │
         │             │                                     │
         │             │  agent_state.v2.SessionState (FSM)  │
         │             └────────────────┬────────────────────┘
         │                              │ HTTP
         │                              │
┌────────┴──────────────────────────────┴─────────────────────┐
│  Rasa server (Docker) → Rasa action server (Docker)         │
│                              └─▶ HTTP GET to bridge /reply  │
└─────────────────────────────────────────────────────────────┘

Four processes: your web app, a small Python bridge, Rasa, Rasa’s action server. There is no persistent stream to manage — the bridge fetches a user’s recent activity synchronously, at request time, over a stateless REST call. Rasa stays a thin chat surface that calls the bridge over HTTP.

The tutorial

Step 1 — Connect real-time events — Pull a user’s live activity on demand into a Rasa-aware bridge using the Autoplay SDK’s REST read, expose it to Rasa over HTTP, and wire the chat widget. Reactive answers grounded in real activity. ~45 minutes.
Step 2 — Define proactive triggers — Make the bot proactive: notice when a user is on the slow path of a workflow and surface a toast with two CTAs — Show me (visual tour via Usertour) or Open chat (proactive bot message with an LLM-grounded auto-followup). Uses PredicateProactiveTrigger, agent_state.v2.SessionState, and TourRegistry. ~45 minutes.

Start with Step 1 — Step 2 builds directly on the bridge, Rasa, and widget you set up there.

​✨ Final result

​📋 Prerequisites

​Architecture

​The tutorial

✨ Final result

📋 Prerequisites

Architecture

The tutorial