temporalio
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diff --git a/‎workflow_streams/README.md‎
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diff --git a/‎workflow_streams/__init__.py‎ b/‎workflow_streams/__init__.py‎
diff --git a/‎workflow_streams/activities/__init__.py‎ b/‎workflow_streams/activities/__init__.py‎
diff --git a/‎workflow_streams/activities/llm_activity.py‎
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diff --git a/‎workflow_streams/activities/payment_activity.py‎
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diff --git a/‎workflow_streams/llm_shared.py‎
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@@ -79,6 +79,7 @@ Some examples require extra dependencies. See each sample's directory for specif
 * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`.
 * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion.
 * [prometheus](prometheus) - Configure Prometheus metrics on clients/workers.
+* [workflow_streams](workflow_streams) - Workflow-hosted durable event stream via `temporalio.contrib.workflow_streams`. **Experimental**
 * [pydantic_converter](pydantic_converter) - Data converter for using Pydantic models.
 * [schedules](schedules) - Demonstrates a Workflow Execution that occurs according to a schedule.
 * [sentry](sentry) - Report errors to Sentry.
 
@@ -48,6 +48,7 @@ openai-agents = [
 pydantic-converter = ["pydantic>=2.10.6,<3"]
 sentry = ["sentry-sdk>=2.13.0"]
 trio-async = ["trio>=0.28.0,<0.29", "trio-asyncio>=0.15.0,<0.16"]
+llm-stream = ["openai>=1.0"]
 cloud-export-to-parquet = [
     "pandas>=2.2.2,<3 ; python_version >= '3.10' and python_version < '4.0'",
     "numpy>=1.26.0,<2 ; python_version >= '3.10' and python_version < '3.13'",
@@ -94,6 +95,7 @@ packages = [
     "updatable_timer",
     "worker_specific_task_queues",
     "worker_versioning",
+    "workflow_streams",
 ]
 
 [tool.hatch.build.targets.wheel.sources]
 
@@ -0,0 +1,120 @@
+# Workflow Streams
+
+> **Experimental.** These samples use
+> `temporalio.contrib.workflow_streams`, which ships in
+> `temporalio>=1.27.0`. The module is considered experimental and its
+> API may change in future versions.
+
+`temporalio.contrib.workflow_streams` lets a workflow host a durable,
+offset-addressed event channel. The workflow holds an append-only log;
+external clients (activities, starters, web backends) publish to topics via
+signals and subscribe via long-poll updates. This packages the
+boilerplate — batching, offset tracking, topic filtering,
+continue-as-new hand-off — into a reusable stream.
+
+This directory has five scenarios. The first four share one worker;
+the fifth has its own worker because it needs the `openai` package
+and an `OPENAI_API_KEY`.
+
+**Scenario 1 — basic publish/subscribe with heterogeneous topics:**
+
+* `workflows/order_workflow.py` — a workflow that hosts a
+  `WorkflowStream` and publishes status events as it processes an order.
+* `activities/payment_activity.py` — an activity that publishes
+  intermediate progress to the stream via
+  `WorkflowStreamClient.from_within_activity()`.
+* `run_publisher.py` — starts the workflow, subscribes to both topics,
+  decodes each by `item.topic`, and prints events as they arrive.
+
+**Scenario 2 — reconnecting subscriber:**
+
+* `workflows/pipeline_workflow.py` — a multi-stage pipeline that
+  publishes stage transitions over ~10 seconds, leaving room for a
+  consumer to disconnect and reconnect mid-run.
+* `run_reconnecting_subscriber.py` — connects, reads a couple of
+  events, "disconnects," then reopens a fresh client and resumes via
+  `subscribe(from_offset=...)`. This is the central Workflow Streams
+  use case: a consumer can disappear (page refresh, server restart,
+  laptop closed) and resume later without missing events or seeing
+  duplicates.
+
+**Scenario 3 — external (non-Activity) publisher:**
+
+* `workflows/hub_workflow.py` — a passive workflow that does no work
+  of its own; it exists only to host a `WorkflowStream` and shut down
+  when signaled.
+* `run_external_publisher.py` — starts the hub, then publishes events
+  into it from a plain Python coroutine using
+  `WorkflowStreamClient.create(client, workflow_id)`. A subscriber
+  task runs alongside; when the publisher is done it emits a sentinel
+  event and signals `HubWorkflow.close`. The shape that fits a
+  backend service or scheduled job pushing events into a workflow it
+  didn't itself start.
+
+**Scenario 4 — bounded log via `truncate()`:**
+
+* `workflows/ticker_workflow.py` — a long-running workflow that
+  publishes events at a fixed cadence and calls
+  `self.stream.truncate(...)` periodically to bound log growth,
+  keeping only the most recent N entries.
+* `run_truncating_ticker.py` — runs a fast subscriber and a slow
+  subscriber side by side. The fast one keeps up and sees every
+  offset in order; the slow one falls behind a truncation and
+  silently jumps forward to the new base offset. The output makes
+  the trade visible: bounded log size in exchange for intermediate
+  events being invisible to slow consumers.
+
+**Scenario 5 — LLM streaming:**
+
+* `workflows/llm_workflow.py` — hosts a `WorkflowStream` and runs
+  `stream_completion` as a single activity. The workflow itself
+  does no streaming; the activity owns the non-deterministic OpenAI
+  call.
+* `activities/llm_activity.py` — calls
+  `openai.AsyncOpenAI().chat.completions.create(stream=True)`,
+  publishes each token chunk on the `delta` topic, the final
+  accumulated text on `complete`, and a `RetryEvent` on `retry`
+  when running on attempt > 1.
+* `run_llm.py` — subscribes to all three topics, renders deltas to
+  the terminal as they arrive, and on a `retry` event uses ANSI
+  escapes to rewind the printed output before the retried attempt
+  re-publishes.
+
+Scenario 5 runs on its own worker (`run_llm_worker.py`, on
+`workflow-stream-llm-task-queue`) because it needs the `openai`
+dependency and an `OPENAI_API_KEY`, and because killing this worker
+mid-stream is the easiest way to demonstrate retry handling without
+disrupting the other four scenarios.
+
+## Run it
+
+For scenarios 1–4, start the shared worker:
+
+```bash
+uv run workflow_streams/run_worker.py
+```
+
+For scenario 5, install the extra, export the key, and start the
+LLM worker:
+
+```bash
+uv sync --group llm-stream
+export OPENAI_API_KEY=...
+uv run workflow_streams/run_llm_worker.py
+```
+
+Then in another terminal, pick a scenario:
+
+```bash
+uv run workflow_streams/run_publisher.py              # scenario 1
+uv run workflow_streams/run_reconnecting_subscriber.py  # scenario 2
+uv run workflow_streams/run_external_publisher.py     # scenario 3
+uv run workflow_streams/run_truncating_ticker.py      # scenario 4
+uv run workflow_streams/run_llm.py                    # scenario 5
+```
+
+To exercise scenario 5's retry path, kill `run_llm_worker.py`
+(`Ctrl-C`) while output is streaming and start it again. The
+activity's next attempt sends a `RetryEvent` first; the consumer
+clears its on-screen output via ANSI escapes and re-renders from
+scratch.
@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+from datetime import timedelta
+
+from openai import AsyncOpenAI
+from temporalio import activity
+from temporalio.contrib.workflow_streams import WorkflowStreamClient
+
+from workflow_streams.llm_shared import (
+    TOPIC_COMPLETE,
+    TOPIC_DELTA,
+    TOPIC_RETRY,
+    LLMInput,
+    RetryEvent,
+    TextComplete,
+    TextDelta,
+)
+
+
+@activity.defn
+async def stream_completion(input: LLMInput) -> str:
+    """Stream an LLM completion to the parent workflow's stream.
+
+    Activity-as-publisher: each delta from the OpenAI streaming API is
+    pushed to the workflow's stream as a ``TextDelta`` event on the
+    ``delta`` topic. The accumulated full text returns as the
+    activity's result and is also published on the ``complete`` topic
+    as a terminator. On retry attempts (``activity.info().attempt > 1``)
+    a ``RetryEvent`` lands on the ``retry`` topic before the new
+    attempt's deltas, so consumers can reset their accumulated state
+    instead of concatenating the failed attempt's partial output with
+    the retried attempt's full output.
+
+    No ``force_flush=True``: the 200ms ``batch_interval`` is fast
+    enough for an interactive feel, and the WorkflowStreamClient's
+    ``__aexit__`` cancels a sleeping flusher cleanly.
+    """
+    stream_client = WorkflowStreamClient.from_within_activity(
+        batch_interval=timedelta(milliseconds=200),
+    )
+    # Disable provider-side retries; let Temporal own retry policy at
+    # the activity layer.
+    openai_client = AsyncOpenAI(max_retries=0)
+
+    async with stream_client:
+        deltas = stream_client.topic(TOPIC_DELTA, type=TextDelta)
+        complete = stream_client.topic(TOPIC_COMPLETE, type=TextComplete)
+        retry = stream_client.topic(TOPIC_RETRY, type=RetryEvent)
+
+        attempt = activity.info().attempt
+        if attempt > 1:
+            retry.publish(RetryEvent(attempt=attempt))
+
+        full: list[str] = []
+        oai_stream = await openai_client.chat.completions.create(
+            model=input.model,
+            messages=[{"role": "user", "content": input.prompt}],
+            stream=True,
+        )
+        async for chunk in oai_stream:
+            if not chunk.choices:
+                continue
+            text = chunk.choices[0].delta.content
+            if not text:
+                continue
+            deltas.publish(TextDelta(text=text))
+            full.append(text)
+
+        full_text = "".join(full)
+        complete.publish(TextComplete(full_text=full_text))
+    return full_text
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+import asyncio
+from datetime import timedelta
+
+from temporalio import activity
+from temporalio.contrib.workflow_streams import WorkflowStreamClient
+
+from workflow_streams.shared import TOPIC_PROGRESS, ProgressEvent
+
+
+@activity.defn
+async def charge_card(order_id: str) -> str:
+    """Pretend to charge a card, publishing progress to the parent workflow.
+
+    `WorkflowStreamClient.from_within_activity()` reads the parent
+    workflow id and the Temporal client from the activity context, so
+    this activity can push events back without any wiring.
+    """
+    client = WorkflowStreamClient.from_within_activity(
+        batch_interval=timedelta(milliseconds=200)
+    )
+    async with client:
+        progress = client.topic(TOPIC_PROGRESS, type=ProgressEvent)
+        progress.publish(ProgressEvent(message="charging card..."))
+        await asyncio.sleep(1.0)
+        progress.publish(
+            ProgressEvent(message="card charged"),
+        )
+    return f"charge-{order_id}"
@@ -0,0 +1,44 @@
+"""Types and constants for the LLM-streaming scenario.
+
+Kept separate from ``shared.py`` because the other scenarios don't
+use these — and this scenario runs on its own worker and task queue
+so the ``openai`` dependency stays out of everyone else's path.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from temporalio.contrib.workflow_streams import WorkflowStreamState
+
+# Scenario 5 runs on its own worker so the openai dependency only
+# matters for that scenario.
+LLM_TASK_QUEUE = "workflow-stream-llm-task-queue"
+
+# Topics published by the activity.
+TOPIC_DELTA = "delta"
+TOPIC_COMPLETE = "complete"
+TOPIC_RETRY = "retry"
+
+
+@dataclass
+class LLMInput:
+    prompt: str
+    model: str = "gpt-5-mini"
+    # Carries stream state across continue-as-new. None on a fresh start.
+    stream_state: WorkflowStreamState | None = None
+
+
+@dataclass
+class TextDelta:
+    text: str
+
+
+@dataclass
+class TextComplete:
+    full_text: str
+
+
+@dataclass
+class RetryEvent:
+    attempt: int