add api reference for Pregel (#3437)

libs/langgraph/langgraph/pregel/__init__.py

@@ -197,6 +197,79 @@ class Channel:
 
 
 class Pregel(PregelProtocol):
+    """Pregel manages the runtime behavior for LangGraph applications.
+
+    ## Channels
+
+    Channels are used to communicate between chains. Each channel has a value type,
+    an update type, and an update function – which takes a sequence of updates and
+    modifies the stored value. Channels can be used to send data from one chain to
+    another, or to send data from a chain to itself in a future step. LangGraph
+    provides a number of built-in channels:
+
+    ### Basic channels: LastValue and Topic
+
+    - `LastValue`: The default channel, stores the last value sent to the channel,
+       useful for input and output values, or for sending data from one step to the next
+    - `Topic`: A configurable PubSub Topic, useful for sending multiple values
+       between chains, or for accumulating output. Can be configured to deduplicate
+       values, and/or to accumulate values over the course of multiple steps.
+
+    ### Advanced channels: Context and BinaryOperatorAggregate
+
+    - `Context`: exposes the value of a context manager, managing its lifecycle.
+      Useful for accessing external resources that require setup and/or teardown. eg.
+      `client = Context(httpx.Client)`
+    - `BinaryOperatorAggregate`: stores a persistent value, updated by applying
+       a binary operator to the current value and each update
+       sent to the channel, useful for computing aggregates over multiple steps. eg.
+      `total = BinaryOperatorAggregate(int, operator.add)`
+
+    ## Chains
+
+    Chains are LCEL Runnables which subscribe to one or more channels, and write to
+    one or more channels. Any valid LCEL expression can be used as a chain. Chains
+    can be combined into a Pregel application, which coordinates the execution of the
+    chains across multiple steps.
+
+    ## Pregel
+
+    Pregel combines multiple chains (or actors) into a single application. It
+    coordinates the execution of the chains across multiple steps, following the
+    Pregel/Bulk Synchronous Parallel model. Each step consists of three phases:
+
+    - **Plan**: Determine which chains to execute in this step, ie. the chains that
+      subscribe to channels updated in the previous step (or, in the first step,
+      chains that subscribe to input channels)
+    - **Execution**: Execute those chains in parallel, until all complete, or one fails,
+      or a timeout is reached. Any channel updates are invisible to other
+      chains until the next step.
+     - **Update**: Update the channels with the values written by the
+      chains in this step.
+
+    Repeat until no chains are planned for execution, or a maximum number of steps
+    is reached.
+
+    Example:
+        ```python
+        from langgraph import Channel, Pregel
+
+        grow_value = (
+            Channel.subscribe_to("value")
+            | (lambda x: x + x)
+            | Channel.write_to(value=lambda x: x if len(x) < 10 else None)
+        )
+
+        app = Pregel(
+            chains={"grow_value": grow_value},
+            input="value",
+            output="value",
+        )
+
+        assert app.invoke("a") == "aaaaaaaa"
+        ```
+    """
+
     nodes: dict[str, PregelNode]
 
     channels: dict[str, Union[BaseChannel, ManagedValueSpec]]