Add README for graph_api/streaming sample

Every other graph_api/ sample has a standalone README with run
instructions and a "what this demonstrates" blurb; streaming was the
only one without.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: brianstrauch

langgraph_plugin/graph_api/streaming/README.md

@@ -0,0 +1,31 @@
+# Streaming (Graph API)
+
+Streams a LangGraph run to an external client while the workflow is still running, using Temporal's durable, offset-addressed [`WorkflowStream`](https://docs.temporal.io/). The graph writes a short story about a topic and emits both fine-grained tokens and node-completion progress on separate topics.
+
+## What This Sample Demonstrates
+
+- **Node token streaming** — the `write_story` node calls LangGraph's `get_stream_writer()` to emit tokens. The plugin's `streaming_topic="tokens"` routes those writes onto the `"tokens"` topic.
+- **Workflow-side `astream` publish** — the workflow drives the graph with `app.astream(...)` and publishes each node-completion chunk onto a `"progress"` topic it owns.
+- A single client subscribing to all topics and demultiplexing on `item.topic`.
+- Waiting for the client to acknowledge (via signal) before completing, since the stream disappears when the workflow ends.
+- **Idempotent consumption** — each token chunk carries a monotonic sequence id so the client can dedupe, because streaming is at-least-once per activity attempt (a retried node re-runs and re-publishes its writes).
+
+## Running the Sample
+
+Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`).
+
+```bash
+# Terminal 1
+uv run langgraph_plugin/graph_api/streaming/run_worker.py
+
+# Terminal 2
+uv run langgraph_plugin/graph_api/streaming/run_workflow.py
+```
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `workflow.py` | Graph node functions, graph definition, and `StreamingWorkflow` that publishes to the stream |
+| `run_worker.py` | Registers graph with `LangGraphPlugin` (`streaming_topic="tokens"`), starts worker |
+| `run_workflow.py` | Starts the workflow, subscribes to the stream, prints tokens and progress, then acks |