Migrate to uv

.github/workflows/ci.yml

@@ -21,6 +21,7 @@ jobs:
             runsOn: macos-14
     runs-on: ${{ matrix.runsOn || matrix.os }}
     steps:
+      - uses: astral-sh/setup-uv@v5
       - name: Print build information
         run: "echo head_ref: ${{ github.head_ref }}, ref: ${{ github.ref }}, os: ${{ matrix.os }}, python: ${{ matrix.python }}"
       - uses: actions/checkout@v4
@@ -29,10 +30,8 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python }}
-      # Using fixed Poetry version until
-      # https://github.com/python-poetry/poetry/pull/7694 is fixed
-      - run: python -m pip install --upgrade wheel "poetry==1.4.0" poethepoet
-      - run: poetry install --with pydantic_converter --with dsl --with encryption --with trio_async
+      - run: uv tool install poethepoet
+      - run: uv sync --group=dsl --group=encryption --group=trio-async
       - run: poe lint
       - run: mkdir junit-xml
       - run: poe test -s --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}.xml
@@ -41,17 +40,17 @@ jobs:
       - name: Uninstall pydantic
         shell: bash
         run: |
-          echo y | poetry run pip uninstall pydantic
-          echo y | poetry run pip uninstall pydantic-core
-          poetry run pip install pydantic==1.10
+          echo y | uv run pip uninstall pydantic
+          echo y | uv run pip uninstall pydantic-core
+          uv run pip install pydantic==1.10
           poe test -s --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}--pydantic-v1.xml tests/pydantic_converter_v1/workflow_test.py
 
       # On latest, run gevent test
       - name: Gevent test
         if: ${{ matrix.python == '3.12' }}
         run: |
-          poetry install --with gevent
-          poetry run python gevent_async/test/run_combined.py
+          uv sync --group gevent
+          uv run gevent_async/test/run_combined.py
 
       - name: Upload junit-xml artifacts
         uses: actions/upload-artifact@v4

README.md

@@ -1,23 +1,26 @@
 # Temporal Python SDK Samples
 
-This is the set of Python samples for the [Python SDK](https://github.com/temporalio/sdk-python).
+This is a collection of samples showing how to use the [Python SDK](https://github.com/temporalio/sdk-python).
 
 ## Usage
 
 Prerequisites:
 
-* Python >= 3.9
-* [Poetry](https://python-poetry.org)
+* [uv](https://docs.astral.sh/uv/)
 * [Temporal CLI installed](https://docs.temporal.io/cli#install)
 * [Local Temporal server running](https://docs.temporal.io/cli/server#start-dev)
 
+The SDK requires Python >= 3.9. You can install Python using uv. For example,
+
+    uv python install 3.13
+
 With this repository cloned, run the following at the root of the directory:
 
-    poetry install
+    uv sync
 
-That loads all required dependencies. Then to run a sample, usually you just run it in Python. For example:
+That loads all required dependencies. Then to run a sample, usually you just run it under uv. For example:
 
-    poetry run python hello/hello_activity.py
+    uv run hello/hello_activity.py
 
 Some examples require extra dependencies. See each sample's directory for specific instructions.
 
@@ -81,7 +84,7 @@ Some examples require extra dependencies. See each sample's directory for specif
 
 Running the tests requires `poe` to be installed.
 
-    python -m pip install poethepoet
+    uv tool install poethepoet
 
 Once you have `poe` installed you can run:
 

activity_worker/README.md

@@ -8,6 +8,6 @@ First run the Go workflow worker by running this in the `go_workflow` directory
 
 Then in another terminal, run the sample from this directory:
 
-    poetry run python activity_worker.py
+    uv run activity_worker.py
 
 The Python code will invoke the Go workflow which will execute the Python activity and return.

bedrock/README.md

@@ -20,6 +20,6 @@ These examples use Amazon's Python SDK (Boto3). To configure Boto3 to use your A
 
 For these sample, the optional `bedrock` dependency group must be included. To include, run:
 
-    poetry install --with bedrock
+    uv sync --group bedrock
 
 There are 3 Bedrock samples, see the README.md in each sub-directory for instructions on running each.

bedrock/basic/README.md

@@ -4,7 +4,7 @@ A basic Bedrock workflow. Starts a workflow with a prompt, generates a response
 
 To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
 
-1. Run the worker: `poetry run python run_worker.py`
+1. Run the worker: `uv run run_worker.py`
 2. In another terminal run the client with a prompt:
 
-    e.g. `poetry run python send_message.py 'What animals are marsupials?'`
+    e.g. `uv run send_message.py 'What animals are marsupials?'`

bedrock/entity/README.md

@@ -4,16 +4,16 @@ Multi-Turn Chat using an Entity Workflow. The workflow runs forever unless expli
 
 To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
 
-1. Run the worker: `poetry run python run_worker.py`
+1. Run the worker: `uv run run_worker.py`
 2. In another terminal run the client with a prompt.
 
-    Example: `poetry run python send_message.py 'What animals are marsupials?'`
+    Example: `uv run send_message.py 'What animals are marsupials?'`
 
 3. View the worker's output for the response.
 4. Give followup prompts by signaling the workflow.
 
-    Example: `poetry run python send_message.py 'Do they lay eggs?'`
+    Example: `uv run send_message.py 'Do they lay eggs?'`
 5. Get the conversation history summary by querying the workflow.
 
-    Example: `poetry run python get_history.py`
-6. To end the chat session, run `poetry run python end_chat.py`
+    Example: `uv run get_history.py`
+6. To end the chat session, run `uv run end_chat.py`

bedrock/signals_and_queries/README.md

@@ -4,16 +4,16 @@ Adding signals & queries to the [basic Bedrock sample](../1_basic). Starts a wor
 
 To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
 
-1. Run the worker: `poetry run python run_worker.py`
+1. Run the worker: `uv run run_worker.py`
 2. In another terminal run the client with a prompt.
 
-    Example: `poetry run python send_message.py 'What animals are marsupials?'`
+    Example: `uv run send_message.py 'What animals are marsupials?'`
 
 3. View the worker's output for the response.
 4. Give followup prompts by signaling the workflow.
 
-    Example: `poetry run python send_message.py 'Do they lay eggs?'`
+    Example: `uv run send_message.py 'Do they lay eggs?'`
 5. Get the conversation history by querying the workflow.
 
-    Example: `poetry run python get_history.py`
+    Example: `uv run get_history.py`
 6. The workflow will timeout after inactivity.

cloud_export_to_parquet/README.md

@@ -4,20 +4,20 @@ This is an example workflow to convert exported file from proto to parquet file.
 
 Please make sure your python is 3.9 above. For this sample, run:
 
-    poetry install --with cloud_export_to_parquet
+    uv sync --group=cloud-export-to-parquet
 
 Before you start, please modify workflow input in `create_schedule.py` with your s3 bucket and namespace. Also make sure you've the right AWS permission set up in your environment to allow this workflow read and write to your s3 bucket. 
 
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker:
 
 ```bash
-poetry run python run_worker.py
+uv run run_worker.py
 ```
 
 This will start the worker. Then, in another terminal, run the following to execute the schedule:
 
 ```bash
-poetry run python create_schedule.py
+uv run create_schedule.py
 ```
 
 The workflow should convert exported file in your input s3 bucket to parquet in your specified location.

context_propagation/README.md

@@ -6,11 +6,11 @@ this example, [contextvars](https://docs.python.org/3/library/contextvars.html)
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
 worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 The starter terminal should complete with the hello result and the worker terminal should show the logs with the
 propagated user ID contextual information flowing through the workflows/activities.

custom_converter/README.md

@@ -5,11 +5,11 @@ This sample shows how to make a custom payload converter for a type not natively
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
 worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 The workflow should complete with the hello result. If the custom converter was not set for the custom input and output
 classes, we would get an error on the client side and on the worker side.

custom_decorator/README.md

@@ -6,11 +6,11 @@ decorator that automatically configures an activity to heartbeat twice as freque
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
 worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 The workflow will be started, and then after 5 seconds will be sent a signal to cancel its forever-running activity.
 The activity has a heartbeat timeout set to 2s, so since it has the `@auto_heartbeater` decorator set, it will heartbeat

dsl/README.md

@@ -6,24 +6,24 @@ samples [in TypeScript](https://github.com/temporalio/samples-typescript/tree/ma
 
 For this sample, the optional `dsl` dependency group must be included. To include, run:
 
-    poetry install --with dsl
+    uv sync --group dsl
 
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
 worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute a workflow of steps defined in
 [workflow1.yaml](workflow1.yaml):
 
-    poetry run python starter.py workflow1.yaml
+    uv run starter.py workflow1.yaml
 
 This will run the workflow and show the final variables that the workflow returns. Looking in the worker terminal, each
 step executed will be visible.
 
 Similarly we can do the same for the more advanced [workflow2.yaml](workflow2.yaml) file:
 
-    poetry run python starter.py workflow2.yaml
+    uv run starter.py workflow2.yaml
 
 This sample gives a guide of how one can write a workflow to interpret arbitrary steps from a user-provided DSL. Many
 DSL models are more advanced and are more specific to conform to business logic needs.

encryption/README.md

@@ -7,16 +7,16 @@ samples [in TypeScript](https://github.com/temporalio/samples-typescript/tree/ma
 
 For this sample, the optional `encryption` dependency group must be included. To include, run:
 
-    poetry install --with encryption
+    uv sync --group encryption
 
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
 worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 The workflow should complete with the hello result. To view the workflow, use [temporal](https://docs.temporal.io/cli):
 
@@ -31,7 +31,7 @@ Note how the result looks like (with wrapping removed):
 This is because the data is encrypted and not visible. To make data visible to external Temporal tools like `temporal` and
 the UI, start a codec server in another terminal:
 
-    poetry run python codec_server.py
+    uv run codec_server.py
 
 Now with that running, run `temporal` again with the codec endpoint:
 

gevent_async/README.md

@@ -12,17 +12,17 @@ workflows.
 
 For this sample, the optional `gevent` dependency group must be included. To include, run:
 
-    poetry install --with gevent
+    uv sync --group gevent
 
 To run the sample, first see [README.md](../README.md) for prerequisites such as having a localhost Temporal server
 running. Then, run the following from this directory to start the worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. The worker has a workflow and two activities, one `asyncio` based and one gevent based. Now
 in another terminal, run the following from this directory to execute the workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 The workflow should run and complete with the hello result. Note on the worker terminal there will be logs of the
 workflow and activity executions.

hello/README.md

@@ -5,7 +5,7 @@ These samples show basic workflow and activity features.
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to run the
 `hello_activity.py` sample:
 
-    poetry run python hello_activity.py
+    uv run hello_activity.py
 
 The result will be:
 

langchain/README.md

@@ -4,7 +4,7 @@ This sample shows you how you can use Temporal to orchestrate workflows for [Lan
 
 For this sample, the optional `langchain` dependency group must be included. To include, run:
 
-    poetry install --with langchain
+    uv sync --group langchain
 
 Export your [OpenAI API key](https://platform.openai.com/api-keys) as an environment variable. Replace `YOUR_API_KEY` with your actual OpenAI API key.
 
@@ -13,11 +13,11 @@ Export your [OpenAI API key](https://platform.openai.com/api-keys) as an environ
 To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
 worker:
 
-    poetry run python worker.py
+    uv run worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute a workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 Then, in another terminal, run the following command to translate a phrase:
 

langchain/activities.py

@@ -26,4 +26,6 @@ async def translate_phrase(params: TranslateParams) -> dict:
     )
     chain = chat_prompt | ChatOpenAI()
     # Use the asynchronous invoke method
-    return await chain.ainvoke({"phrase": params.phrase, "language": params.language})
+    return dict(
+        await chain.ainvoke({"phrase": params.phrase, "language": params.language})
+    )

message_passing/introduction/README.md

@@ -10,9 +10,9 @@ Then create two terminals and `cd` to this directory.
 
 Run the worker in one terminal:
 
-    poetry run python worker.py
+    uv run worker.py
 
 And execute the workflow in the other terminal:
 
-    poetry run python starter.py
+    uv run starter.py
 

message_passing/safe_message_handlers/README.md

@@ -12,10 +12,10 @@ To run, first see [README.md](../../README.md) for prerequisites.
 
 Then, run the following from this directory to run the worker:
 \
-    poetry run python worker.py
+    uv run worker.py
 
 Then, in another terminal, run the following to execute the workflow:
 
-    poetry run python starter.py
+    uv run starter.py
 
 This will start a worker to run your workflow and activities, then start a ClusterManagerWorkflow and put it through its paces.

message_passing/update_with_start/lazy_initialization/README.md

@@ -8,11 +8,11 @@ To run, first see the main [README.md](../../../README.md) for prerequisites.
 
 Then run the following from this directory:
 
-    poetry run python worker.py
+    uv run worker.py
 
 Then, in another terminal:
 
-    poetry run python starter.py
+    uv run starter.py
 
 This will start a worker to run your workflow and activities, then simulate a backend application receiving
 requests to add items to a shopping cart, before finalizing the order.