diff --git a/src/content/docs/workflows/python/bindings.mdx b/src/content/docs/workflows/python/bindings.mdx
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+---
+title: Binding to a Workflow
+pcx_content_type: concept
+sidebar:
+  order: 3
+  group:
+   hideIndex: true
+
+---
+import { WranglerConfig } from "~/components"
+
+The Python Workers platform leverages FFI to access bindings to Cloudflare resources. Refer to the [bindings](/workers/languages/python/ffi/#using-bindings-from-python-workers) documentation for more information.
+
+From the configuration perspective, there is no difference between configuring a Python workflow or a Javascript one.
+
+<WranglerConfig>
+
+```toml title="wrangler.toml"
+#:schema node_modules/wrangler/config-schema.json
+name = "workflows-starter"
+main = "src/index.ts"
+compatibility_date = "2024-10-22"
+
+[[workflows]]
+# name of your workflow
+name = "workflows-starter"
+# binding name env.MY_WORKFLOW
+binding = "MY_WORKFLOW"
+# this is class that extends the Workflow class in src/index.ts
+class_name = "MyWorkflow"
+```
+
+</WranglerConfig>
+
+### Creating an instance via binding
+
+Note that `env` is a Javascript object exposed to the Python script via [JsProxy](https://pyodide.org/en/stable/usage/api/python-api/ffi.html#pyodide.ffi.JsProxy). You can
+access the binding like you would on a Javascript worker. Refer to the [Workflow binding documentation](/workflows/build/workers-api/#workflow) to learn more about the methods available.
+
+Let's consider the previous binding called `MY_WORKFLOW`. Here's how you would create a new instance:
+
+```python
+async def on_fetch(request, env):
+    instance = await env.MY_WORKFLOW.create()
+    return Response.json({"status": "success"})
+```
+
+### Passing a payload to a workflow instance
+
+```python
+from pyodide.ffi import to_js
+
+async def on_fetch(request, env, ctx):
+    event = {"foo": "bar"}
+    # to_js here is required because the binding goes through ffi. Not something we can wrap or override on the runtime
+    await env.MY_WORKFLOW.create(to_js({"params": event}, dict_converter=Object.fromEntries))
+    return Response.json({"status": "success"})
+```
+:::note
+
+Values returned from steps need to be converted into Javascript objects using `to_js`. This is why we explicitly construct the payload using `Object.fromEntries`.
+
+:::
+
+
+And this is how you use the payload in your workflow:
+
+```python
+from pyodide.ffi import to_js
+
+class DemoWorkflowClass(WorkflowEntrypoint):
+    async def on_run(self, event, step):
+        @step.do('step-name')
+        async def first_step():
+            payload = event["payload"]
+            return payload
+```
+
+
diff --git a/src/content/docs/workflows/python/dag.mdx b/src/content/docs/workflows/python/dag.mdx
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+---
+title: DAG Workflows
+pcx_content_type: concept
+sidebar:
+  order: 4
+
+---
+
+The Python SDK supports DAG workflows in a declarative way, using the `step.do` decorator with the `depends` parameter to define dependencies (other steps that must complete before this step can run).
+
+```python
+from workers import WorkflowEntrypoint
+
+class MyWorkflow(WorkflowEntrypoint):
+    async def on_run(self, event, step):
+        @step.do("dependency a")
+        async def step_a():
+            # do some work
+            return 10
+
+        @step.do("dependency b")
+        async def step_b():
+            # do some work
+            return 20
+
+        @step.do("my final step", depends=[step_a, step_b], concurrent=True)
+        async def my_final_step(result_a=0, result_b=0):
+            # should return 30
+            return result_a + result_b
+        
+        await my_final_step()
+```
+
+Having `concurrent=True` allows the dependencies to be resolved concurrently. If one of the callables passed to `depends` has already completed, it will be skipped and its return value will be reused.
+
+This pattern is usefull for diamond shaped workflows, where a step depends on two or more other steps that can run concurrently.
\ No newline at end of file
diff --git a/src/content/docs/workflows/python/index.mdx b/src/content/docs/workflows/python/index.mdx
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+---
+title: Python SDK 
+pcx_content_type: navigation
+sidebar:
+  order: 5 
+  group:
+   hideIndex: true
+
+---
+
+import { DirectoryListing } from "~/components"
+
+<DirectoryListing />
diff --git a/src/content/docs/workflows/python/overview.mdx b/src/content/docs/workflows/python/overview.mdx
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+---
+title: Overview
+pcx_content_type: get-started
+sidebar:
+  order: 1
+  badge:
+    text: Beta
+
+---
+
+Workflow entrypoints can be declared using Python. To achieve this, you can export a `WorkflowEntrypoint` that runs on the Cloudflare Workers platform. 
+Refer to [Python Workers](/workers/languages/python) for more information about Python on the Workers runtime.
+
+:::caution[Python Workflows are in beta, as well as the underlying platform.]
+
+You must add the `python_workflows` compatibility flag to your `wrangler.toml` file, as well as `python_workers`.
+
+Join the #python-workflows channel in the [Cloudflare Developers Discord](https://discord.cloudflare.com/) and let us know what you'd like to see next.
+:::
+
+## Get Started
+Similarly to Typescript, the main entrypoint for a Python workflow is the [`WorkflowEntrypoint`](/workflows/build/workers-api/#workflowentrypoint) class. Your workflow logic should exist inside the [`run`](/workflows/build/workers-api/#run) handler. In a Python workflow, this handler is named `on_run`.
+
+```python
+from workers import WorkflowEntrypoint
+
+class MyWorkflow(WorkflowEntrypoint):
+    def on_run(self, event, step):
+        # steps here
+```
+
+To run a Python Workflow locally, you use [Wrangler](/workers/wrangler/), the CLI for Cloudflare Workers:
+
+```bash
+npx wrangler@latest dev
+```
+
+To deploy a Python Workflow to Cloudflare, run [`wrangler deploy`](/workers/wrangler/commands/#deploy):
+
+```bash
+npx wrangler@latest deploy
+```
diff --git a/src/content/docs/workflows/python/run.mdx b/src/content/docs/workflows/python/run.mdx
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+---
+title: Run method
+pcx_content_type: concept
+sidebar:
+  order: 2
+
+---
+
+The main difference between the [Typescript SDK](/workflows/build/workers-api/#run) and the Python SDK lives in the `run` method, and the parameters that it receives.
+
+## WorkflowStep
+
+* <code>step.do(name, depends=[], concurrent=False, config=None)</code> is a decorator that allows you to define a step in a workflow.
+  * `name` - the name of the step.
+  * `depends` - an optional list of steps that must complete before this step can run.
+  * `concurrent` - an optional boolean that indicates whether this step can run concurrently with other steps.
+  * `config` - an optional [`WorkflowStepConfig`](/workflows/build/workers-api/#workflowstepconfig) for configuring [step specific retry behaviour](/workflows/build/sleeping-and-retrying/). This is passed as a Python dictionary and then type translated into a `WorkflowStepConfig` object.
+
+```python
+from workers import WorkflowEntrypoint
+
+class MyWorkflow(WorkflowEntrypoint):
+    async def on_run(self, event, step):
+        @step.do("my first step")
+        async def my_first_step():
+            # do some work
+            return "Hello World!"
+        
+        await my_first_step()
+```
+
+When returning state from a step, you must make sure that the returned value is serializable. Since steps run through an FFI layer, the returned value gets type translated via [FFI.](https://pyodide.org/en/stable/usage/api/python-api/ffi.html#pyodide.ffi.to_js)
+Refer to [Pyodide's documentation](https://pyodide.org/en/stable/usage/type-conversions.html#type-translations-pyproxy-to-js) regarding type conversions for more information.
+
+* <code>step.sleep(name, duration)</code>
+
+  * `name` - the name of the step.
+  * `duration` - the duration to sleep until, in either seconds or as a `WorkflowDuration` compatible string.
+
+* <code>step.sleep_until(name, timestamp)</code>
+
+  * `name` - the name of the step.
+  * `timestamp` - a `datetime.date` object or seconds from the Unix epoch to sleep the Workflow instance until.
+
+* <code>step.wait_for_event(name, event_type, timeout="24 hours")</code>
+
+  * `name` - the name of the step.
+  * `event_type` - the type of event to wait for.
+  * `timeout` - the timeout for the `waitForEvent` call. The default timeout is 24 hours.
+
+## Error Handling
+
+Workflows semantics allow users to catch exceptions that get thrown to the top level. 
+
+:::note
+Catching specific exceptions within an `except` block may not work, as some Python errors will not be re-instantiated into the same type of error when they are passed through the RPC layer.
+:::
+
+### NonRetryableError
+
+Similarly to the [Typescript SDK](/workflows/build/workers-api/#nonretryableerror), the Python SDK provides a `NonRetryableError` class that can be used to signal that a step should not be retried.
+
+```python
+from workers.workflows import NonRetryableError
+
+throw NonRetryableError(message)
+```
+
+## Configuring a workflow instance
+
+You can bind a step to a specific retry policy by passing a `WorkflowStepConfig` object to the `config` parameter of the `step.do` decorator. 
+In Python , you need to make sure that your `dict` respects the [`WorkflowStepConfig`](/workflows/build/workers-api/#workflowstepconfig) type.
+
+```python
+class DemoWorkflowClass(WorkflowEntrypoint):
+    async def on_run(self, event, step):
+        @step.do('step-name', config={"retries": {"limit": 1, "delay": "10 seconds"}})
+        async def first_step():
+            # do some work
+            pass
+```