autoplay-sdk 0.7.8

Real-time event streaming client for Autoplay connectors

autoplay-sdk

Real-time event streaming client for Autoplay connectors.

Receive live UI actions and session summaries from the Autoplay event connector via SSE (Server-Sent Events) — ideal as a real-time data source for RAG pipelines, analytics systems, or custom automation.

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Installation

pip install autoplay-sdk

Requirements: Python 3.10+

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Components (state models)

agent_states      — session FSM (v1, 5 states, deprecated)
agent_state_v2    — session FSM (v2, 3 states, recommended for new code)

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Two RAG surfaces (don’t confuse them)

Most chatbot integrations use both surfaces in sequence:

1. ContextStore.enrich(...) to build retrieval text for embeddings/vector search.
2. assemble_rag_chat_context(...) to build structured chat prompts for the reply model.

Module	Purpose
autoplay_sdk.rag — RagPipeline / AsyncRagPipeline	Ingestion: embed events + upsert into a vector store from the live stream.
autoplay_sdk.rag_query — assemble_rag_chat_context, formatters, prompts, rag_query.watermark	Query-time chat: build the user message block and system prompt text for an LLM from user query, real-time activity, conversation history, and optional KB — swap memory/KB via ChatMemoryProvider / KnowledgeBaseRetriever. Use InboundWatermarkStore + cutoff_for_delta_activity to drive activity since last user message on follow-up turns.

Use ContextStore.enrich() for retrieval-query text and rag_query for final chat prompt assembly.

For which loggers to tune and safe structured extra fields when debugging rag_query in production, see docs/sdk/logging.mdx.

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Query-time RAG enrichment (primary use case)

The most common use case is enriching a user's query with their live session context — recent UI actions and an LLM-generated rolling summary — before sending the query to your vector database.

ContextStore (or AsyncContextStore) accumulates that context in memory and exposes a single enrich() call:

enriched = context_store.enrich(session_id, user_message)
results  = vector_db.query(embed(enriched))

Setup

from autoplay_sdk import AsyncConnectorClient
from autoplay_sdk.summarizer import AsyncSessionSummarizer
from autoplay_sdk.context_store import AsyncContextStore

# Summarise after every 10 actions (optional but recommended)
summarizer = AsyncSessionSummarizer(llm=my_async_llm, threshold=10)

# Keep only the last 5 minutes of actions; cap at 20 per query
context_store = AsyncContextStore(
    summarizer=summarizer,
    lookback_seconds=300,
    max_actions=20,
)

# IMPORTANT: if events are ingested with product_id (default in connector payloads),
# pass the same product_id to get()/enrich()/reset() calls.

# Wire the client — one line each
client = AsyncConnectorClient(url=STREAM_URL, token=API_TOKEN)
client.on_actions(context_store.add)
task = client.run_in_background()

In your chatbot handler

async def chat(session_id: str, product_id: str, user_message: str) -> str:
    enriched = context_store.enrich(session_id, user_message, product_id=product_id)
    results  = await vector_db.query(await embed(enriched))
    return await llm(results, user_message)

Output format (context is injected before the query):

[Session context]
Summary: User navigated to the Dashboard and exported a CSV.

Recent activity:
1. Opened billing settings — /settings/billing
2. Clicked Upgrade plan — /settings/billing

[Query]
How do I add a team member?

If no context exists for a session, enrich() returns the raw query unchanged.

Configuration

All options can be set as defaults at construction time and overridden individually on any enrich() / get() call:

Option	Default	Description
include_summary	True	Include the rolling LLM summary
include_actions	True	Include pending (not-yet-summarised) actions
lookback_seconds	None	Only include actions within the last N seconds (forwarded_at timestamp)
max_actions	None	Cap on the most recent N actions (applied after lookback_seconds)

# Per-call overrides — change behaviour for a single request
context_store.enrich(session_id, query, include_actions=False)   # summaries only
context_store.enrich(session_id, query, include_summary=False)   # actions only
context_store.enrich(session_id, query, lookback_seconds=60)     # last 60 s of actions
context_store.enrich(session_id, query, max_actions=5)           # last 5 actions only

Sync version

from autoplay_sdk import ConnectorClient
from autoplay_sdk.summarizer import SessionSummarizer
from autoplay_sdk.context_store import ContextStore

summarizer    = SessionSummarizer(llm=my_llm, threshold=10)
context_store = ContextStore(summarizer=summarizer, lookback_seconds=300)

client = ConnectorClient(url=STREAM_URL, token=API_TOKEN)
client.on_actions(context_store.add)
client.run_in_background()

def chat(session_id: str, product_id: str, query: str) -> str:
    enriched = context_store.enrich(session_id, query, product_id=product_id)
    results  = vector_db.query(embed(enriched))
    return llm(results, query)

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Quickstart

from autoplay_sdk import ConnectorClient, ActionsPayload

STREAM_URL = "https://your-connector.onrender.com/stream/YOUR_PRODUCT_ID"
API_TOKEN  = "uk_live_..."
# Or set AUTOPLAY_APP_UNKEY_TOKEN and leave token="".

def on_actions(payload: ActionsPayload):
    print(payload.to_text())

ConnectorClient(url=STREAM_URL, token=API_TOKEN) \
    .on_actions(on_actions) \
    .run()

run() blocks and reconnects automatically on any network failure. Press Ctrl-C to stop.

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Typed payloads

All callbacks receive typed dataclass instances — not raw dicts. Your IDE will autocomplete fields and you get a .to_text() method on every payload that returns an embedding-ready string.

from autoplay_sdk import ActionsPayload, SummaryPayload

def on_actions(payload: ActionsPayload):
    print(payload.session_id)       # str | None
    print(payload.email)            # str | None
    print(payload.count)            # int
    for action in payload.actions:
        print(action.title)         # str
        print(action.description)   # str
        print(action.canonical_url) # str

    # embedding-ready text — one call, no formatting logic needed
    text = payload.to_text()
    # "Session ps_abc123 — 3 actions\n1. Viewed Dashboard — https://...\n..."

def on_summary(payload: SummaryPayload):
    print(payload.session_id)
    print(payload.replaces)         # number of actions this summary replaces
    text = payload.to_text()        # the prose summary string directly

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Background usage (non-blocking)

client = ConnectorClient(url=STREAM_URL, token=API_TOKEN)
client.on_actions(on_actions)
client.run_in_background()

# your application continues here
import time
time.sleep(60)

client.stop()

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Context manager

with ConnectorClient(url=STREAM_URL, token=API_TOKEN) as client:
    client.on_actions(on_actions).run_in_background()
    do_other_work()
# stop() is called automatically on exit

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Async RAG pipeline (recommended)

Use AsyncConnectorClient when your RAG pipeline is built on asyncio — LangChain, LlamaIndex, FastAPI, or any framework where embedding/vector calls are already async.

import asyncio
import openai
from autoplay_sdk import AsyncConnectorClient, ActionsPayload, SummaryPayload

openai_client = openai.AsyncOpenAI()

async def on_actions(payload: ActionsPayload):
    """Embed the batch and upsert into your vector store."""
    text = payload.to_text()  # embedding-ready string, no formatting needed
    response = await openai_client.embeddings.create(
        input=text, model="text-embedding-3-small"
    )
    embedding = response.data[0].embedding

    # upsert into Pinecone, Weaviate, Chroma, pgvector, etc.
    await your_vector_store.upsert(
        id=payload.session_id or "unknown",
        vector=embedding,
        metadata={
            "session_id": payload.session_id,
            "email":      payload.email,
            "count":      payload.count,
        },
    )

async def on_summary(payload: SummaryPayload):
    """Replace the raw action history with a compact prose summary."""
    text = payload.to_text()  # the prose summary string directly
    print(f"[summary] session={payload.session_id}: {text}")
    # store in your RAG context window / vector store as well

async def main():
    async with AsyncConnectorClient(url=STREAM_URL, token=API_TOKEN) as client:
        client.on_actions(on_actions).on_summary(on_summary)
        await client.run()

asyncio.run(main())

Non-blocking inside an existing event loop

client = AsyncConnectorClient(url=STREAM_URL, token=API_TOKEN)
client.on_actions(on_actions)
task = client.run_in_background()  # asyncio.Task — returns immediately

await do_other_async_work()

client.stop()
await task

Sync RAG pipeline

If your pipeline is synchronous (no async/await), use ConnectorClient. Slow callbacks are safe — the SSE reader and the callback executor run on separate threads.

import openai
from autoplay_sdk import ConnectorClient, ActionsPayload

openai_client = openai.OpenAI()

def on_actions(payload: ActionsPayload):
    text = payload.to_text()
    embedding = openai_client.embeddings.create(
        input=text, model="text-embedding-3-small"
    ).data[0].embedding
    your_vector_store.upsert(id=payload.session_id or "unknown", vector=embedding)

ConnectorClient(url=STREAM_URL, token=API_TOKEN) \
    .on_actions(on_actions) \
    .run()

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Drop handling (high-volume)

If your callback is slower than the incoming event rate, events are queued internally. When the queue is full, events are dropped. Register a handler to be notified:

def on_drop(payload, total_dropped):
    print(f"WARNING: dropped event (type={payload['type']}, total={total_dropped})")
    # alert your on-call rotation, increment a metric, etc.

ConnectorClient(url=STREAM_URL, token=API_TOKEN, max_queue_size=1000) \
    .on_actions(on_actions) \
    .on_drop(on_drop) \
    .run()

# Check the running drop count at any time:
print(client.dropped_count)   # events dropped so far
print(client.queue_size)      # events waiting in the queue right now

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Payload schemas

Known stream event types:

• actions → parsed as ActionsPayload and delivered to on_actions
• summary → parsed as SummaryPayload and delivered to on_summary
• heartbeat (SSE event frame) → ignored as keep-alive
• usertour_trigger may appear on connector streams but is currently ignored by SDK clients

actions event

{
  "type":         "actions",
  "product_id":   "my_product",
  "session_id":   "ps_abc123",
  "user_id":      "usr_456",
  "email":        "[e-mail]",
  "actions": [
    {
      "title":         "Dashboard",
      "description":   "Viewed the Dashboard page",
      "canonical_url": "https://app.example.com/dashboard"
    },
    {
      "title":         "Export CSV button",
      "description":   "Clicked Export CSV button",
      "canonical_url": "https://app.example.com/dashboard"
    }
  ],
  "count":        2,
  "forwarded_at": 1705314600.123
}

Field	Type	Description
type	string	Always "actions"
product_id	string	Connector product identifier
session_id	string|null	Unique user session identifier
user_id	string|null	External user identifier (may be null)
email	string|null	User email if available (may be null)
actions	array	Ordered list of UI actions in this batch
actions[].title	string	Human-readable page or element title
actions[].description	string	Natural-language description of what the user did
actions[].canonical_url	string	Normalised URL of the page (dynamic segments collapsed)
count	integer	Number of actions in this batch
forwarded_at	float	Unix timestamp when the connector forwarded this batch

summary event

{
  "type":         "summary",
  "product_id":   "my_product",
  "session_id":   "ps_abc123",
  "summary":      "The user navigated to the Dashboard, exported a CSV report, then opened account settings to update their billing plan.",
  "replaces":     12,
  "forwarded_at": 1705314900.456
}

Field	Type	Description
type	string	Always "summary"
product_id	string	Connector product identifier
session_id	string|null	Unique user session identifier
summary	string	Prose summary of the session up to this point
replaces	integer	Number of individual actions this summary replaces
forwarded_at	float	Unix timestamp when the connector forwarded this summary

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API reference

ContextStore / AsyncContextStore

The primary tool for query-time RAG enrichment. See the Query-time RAG enrichment section above for a full walkthrough.

Constructor — ContextStore(summarizer=None, *, include_summary=True, include_actions=True, lookback_seconds=None, max_actions=None)

Method	Description
.add(payload)	Store an ActionsPayload; wire to client.on_actions()
.on_summary(session_id, text)	Store a summary; auto-wired when summarizer is provided
.get(session_id, product_id=..., **overrides)	Return the formatted context string (product_id strongly recommended)
.enrich(session_id, query, product_id=..., **overrides)	get() + append the user query; primary entry point
.reset(session_id, product_id=...)	Clear all context for a session/product bucket
.active_sessions	Property — list of sessions with stored context

AsyncContextStore has the same interface; add and on_summary are coroutines while get and enrich remain synchronous (safe to call from any coroutine).

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Typed models

Class	Description
SlimAction	One UI action: title, description, canonical_url + .to_text()
ActionsPayload	A batch of actions for a session + .to_text() for embedding
SummaryPayload	LLM prose summary for a session + .to_text() for embedding

ConnectorClient(url, token="", max_queue_size=500)

Parameter	Type	Default	Description
url	str	—	Full URL to GET /stream/{product_id} on the connector
token	str	""	Unkey API key (uk_live_...)
max_queue_size	int	500	Max events buffered before drops start occurring

Methods

Method	Returns	Description
.on_actions(fn)	ConnectorClient	Register callback; fn receives ActionsPayload
.on_summary(fn)	ConnectorClient	Register callback; fn receives SummaryPayload
.on_drop(fn)	ConnectorClient	Register callback when events are dropped (queue full)
.run()	None (blocks)	Connect and process events; reconnects automatically
.run_in_background()	threading.Thread	Start the client on a daemon thread; returns immediately
.stop()	None	Signal the client to stop cleanly

Properties

Property	Type	Description
dropped_count	int	Running total of events dropped due to a full queue
queue_size	int	Number of events currently waiting in the queue

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AsyncConnectorClient(url, token="")

Async-native client for asyncio environments. Callbacks are async def coroutines.

Parameter	Type	Default	Description
url	str	—	Full URL to GET /stream/{product_id} on the connector
token	str	""	Unkey API key (uk_live_...)

Methods

Method	Returns	Description
.on_actions(fn)	AsyncConnectorClient	Register async callback; fn receives ActionsPayload
.on_summary(fn)	AsyncConnectorClient	Register async callback; fn receives SummaryPayload
.run()	Coroutine	Connect and process events (await this)
.run_in_background()	asyncio.Task	Schedule run() as a background task; returns immediately
.stop()	None	Signal the client to stop cleanly

Supports async with for automatic stop() on exit.

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Reconnection behaviour

The client reconnects automatically on any network failure or non-fatal HTTP error. It uses exponential backoff starting at 1 s and capping at 30 s.

Fatal HTTP errors (401 Unauthorized, 403 Forbidden, 404 Not Found) are not retried and will raise immediately — check your url and token.

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Logging and observability

The SDK uses the standard library logging module. It does not call logging.basicConfig() or attach handlers — configure logging in your app (see Logging for the full hierarchy, extra fields, secrets guidance, and third-party subclass notes).

import logging

logging.basicConfig(level=logging.INFO)  # your app baseline
logging.getLogger("autoplay_sdk").setLevel(logging.DEBUG)  # verbose SDK traces

Child loggers follow the module layout, e.g. autoplay_sdk.chatbot, autoplay_sdk.async_client. The autoplay_sdk.integrations package emits no log lines (constants and URL helpers only).

For counters (drops, latency, queue depth), implement SdkMetricsHook (autoplay_sdk.metrics) and pass metrics= into the clients, summarizer, or RedisEventBuffer as documented in the Changelog.

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Compatibility

• autoplay-sdk currently depends on unkey.py>=3.0.1, which requires pydantic v2.
• Frameworks pinned to pydantic v1 (for example, Rasa 3.x stacks) cannot coexist in the same environment.
• Recommended workaround: run the SDK in a separate process and bridge to your chatbot runtime over HTTP or Redis.

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Self-hosted chatbot helpers

Use these when your chatbot backend is user-keyed and runs in a different process:

• UserSessionIndex (autoplay_sdk.user_index) — choose this when your reply endpoint receives user_id and you need multi-session context joins.
• compose_chat_pipeline(...) (autoplay_sdk.chat_pipeline) — choose this when you want safe callback wiring for summarizer + context store + agent writer in one on-actions hook.
• build_copilot_app(...) (autoplay_sdk.serve) — choose this when you want an out-of-the-box HTTP bridge (/healthz, /context/{user_id}, /reply/{user_id}, /admin/reset/{user_id}).

Install optional serve extras:

pip install "autoplay-sdk[serve]"

Reference guide: Self-hosted chatbot bridge.

Self-hosted troubleshooting

1. /context or /reply returns 404 (no activity)

   • User has no recent indexed actions, or lookback eviction removed old sessions.
   • Verify stream ingest is active and user_id is present on incoming actions.

2. User appears active but chatbot says no recent activity

   • Identity mismatch across event source (distinct_id/user_id), widget metadata, and chatbot sender id.
   • Ensure the same user key flows end-to-end.

3. Context missing for known sessions in multi-product setups

   • Product-scoped retrieval requires matching product_id.
   • Preserve product_id through indexing/retrieval and pass it into context lookups.

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Operator onboarding (autoplay_sdk.admin)

Trusted operators only — call onboard_product with product_id and contact_email (plus optional URL / flags). POST /products is open registration (no admin header / no registration env vars on the client). connector_url defaults to DEFAULT_CONNECTOR_URL (https://event-connector-luda.onrender.com).

Render, Unkey, and related secrets exist only on the connector host — not in application code.

v1 registers event_stream products only. The connector dual-writes Redis/Render and creates Unkey keys on POST /products. scripts/onboard_customer.py still uses run_product_onboarding for CLI-style flows.

from autoplay_sdk.admin import onboard_product

async def register_product_job() -> None:
    result = await onboard_product("acme-corp", contact_email="[e-mail]")
    # result.webhook_url, result.stream_url, result.webhook_secret,
    # result.unkey_key, result.unkey_key_id, …

Optional kwargs: connector_url, webhook_secret, force_reregister=True, print_operator_summary=True, key_name, connector_http_timeout_seconds. Lower-level run_product_onboarding (OnboardingRunParams) requires contact_email alongside product_id. HTTP headers apply for rare merged-config flows (no admin key for standard registration).

If the default connector_url is wrong or unreachable, registration raises ConnectorRegistrationHttpError with context instead of a bare httpx.ConnectError.

• Re-registering the same product_id: the connector returns 409 unless force_reregister=True. Catch ProductAlreadyRegisteredError if you handle 409 in code.

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Publishing (Test PyPI and PyPI)

You do not need to merge to main first. CI builds from the commit the tag points to, on whatever branch that commit lives on.

In this repo, .github/workflows/publish-sdk.yml publishes when you push a version tag:

Tag pattern	Target
sdk-test-v0.6.8	Test PyPI
sdk-v0.6.8	PyPI

Before tagging, set the package version in pyproject.toml to the same release you intend to upload. Test PyPI rejects re-uploading an existing version.

Trusted publishing (OIDC): on Test PyPI / PyPI → Publishing, the workflow filename must be publish-sdk.yml (same as in .github/workflows/). A common mistake is entering publish-dsk.yaml, which produces invalid-publisher.

Event connector in this monorepo

The connector always installs autoplay-sdk from src/customer_sdk (Dockerfile, CI, local). pip install autoplay-sdk from PyPI is for external applications only.

Lockfiles: This repo keeps two uv.lock files for reproducible installs: later-rho-event-connector/uv.lock (connector workspace) and later-rho-event-connector/src/customer_sdk/uv.lock (the SDK package). After changing dependencies in either pyproject.toml, run uv lock from that same directory so the matching lockfile updates. PRs that touch both packages may legitimately change both lockfiles in one commit—call that out in the PR description when it happens.

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License

MIT