Fix import sorting and formatting

Author: DABH

langgraph_plugin/functional_api/continue_as_new/workflow.py

@@ -65,9 +65,9 @@ class PipelineFunctionalWorkflow:
 
     @workflow.run
     async def run(self, input_data: PipelineInput) -> dict[str, Any]:
-        result = await entrypoint(
-            "pipeline", cache=input_data.cache
-        ).ainvoke(input_data.data)
+        result = await entrypoint("pipeline", cache=input_data.cache).ainvoke(
+            input_data.data
+        )
 
         if input_data.phase < 3:
             workflow.continue_as_new(

langgraph_plugin/functional_api/control_flow/workflow.py

@@ -44,8 +44,7 @@ def summarize(results: list[str]) -> str:
     urgent_count = sum(1 for r in results if r.startswith("[PRIORITY]"))
     normal_count = sum(1 for r in results if r.startswith("[STANDARD]"))
     return (
-        f"Processed {len(results)} items "
-        f"({urgent_count} urgent, {normal_count} normal)"
+        f"Processed {len(results)} items ({urgent_count} urgent, {normal_count} normal)"
     )
 
 
@@ -58,9 +57,7 @@ async def control_flow_pipeline(items: list[str]) -> dict:
     # Creating task futures without awaiting launches them in parallel.
     validation_futures = [validate_item(item) for item in items]
     valid_flags = [await f for f in validation_futures]
-    valid_items = [
-        item for item, is_valid in zip(items, valid_flags) if is_valid
-    ]
+    valid_items = [item for item, is_valid in zip(items, valid_flags) if is_valid]
 
     # SEQUENTIAL + CONDITIONAL: Process each valid item
     results = []

langgraph_plugin/functional_api/human_in_the_loop/workflow.py

@@ -6,11 +6,11 @@
 from datetime import timedelta
 from typing import Any
 
+from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.func import entrypoint as lg_entrypoint
 from langgraph.func import task
 from langgraph.types import Command, interrupt
-from langchain_core.runnables import RunnableConfig
 from temporalio import workflow
 from temporalio.contrib.langgraph import entrypoint
 

langgraph_plugin/functional_api/react_agent/workflow.py

@@ -37,9 +37,7 @@ def agent_think(query: str, history: list[str]) -> dict:
         facts = "; ".join(tool_results)
         return {
             "action": "final",
-            "answer": (
-                f"Here's what I found about San Francisco: {facts}"
-            ),
+            "answer": (f"Here's what I found about San Francisco: {facts}"),
         }
 
 
@@ -48,9 +46,7 @@ def execute_tool(tool_name: str, tool_input: str) -> str:
     """Execute a tool by name. In production, dispatch to real implementations."""
     tool_registry = {
         "get_weather": lambda inp: f"[Tool] Weather in {inp}: 72°F and sunny.",
-        "get_population": lambda inp: (
-            f"[Tool] {inp} population: ~870,000 residents."
-        ),
+        "get_population": lambda inp: f"[Tool] {inp} population: ~870,000 residents.",
     }
     handler = tool_registry.get(tool_name)
     if handler:
@@ -69,9 +65,7 @@ async def react_agent_entrypoint(query: str) -> dict:
         if decision["action"] == "final":
             return {"answer": decision["answer"], "steps": len(history)}
 
-        result = await execute_tool(
-            decision["tool_name"], decision["tool_input"]
-        )
+        result = await execute_tool(decision["tool_name"], decision["tool_input"])
         history.append(result)
 
 

langgraph_plugin/graph_api/human_in_the_loop/workflow.py

@@ -6,10 +6,10 @@
 
 from datetime import timedelta
 
+from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import START, StateGraph
 from langgraph.types import Command, interrupt
-from langchain_core.runnables import RunnableConfig
 from temporalio import workflow
 from temporalio.contrib.langgraph import graph
 

langgraph_plugin/graph_api/react_agent/workflow.py

@@ -41,8 +41,7 @@ async def agent(state: AgentState) -> dict[str, Any]:
     if len(tool_results) == 0:
         return {
             "messages": [
-                "[Agent] I need weather data. "
-                "Calling get_weather for San Francisco."
+                "[Agent] I need weather data. Calling get_weather for San Francisco."
             ]
         }
     elif len(tool_results) == 1:
@@ -56,9 +55,7 @@ async def agent(state: AgentState) -> dict[str, Any]:
         facts = "; ".join(tool_results)
         return {
             "messages": ["[Agent] I have all the information I need."],
-            "final_answer": (
-                f"Here's what I found about San Francisco: {facts}"
-            ),
+            "final_answer": (f"Here's what I found about San Francisco: {facts}"),
         }
 
 
@@ -67,15 +64,9 @@ async def tools(state: AgentState) -> dict[str, Any]:
     last_msg = state["messages"][-1]
 
     if "get_weather" in last_msg:
-        return {
-            "messages": ["[Tool] Weather in San Francisco: 72°F and sunny."]
-        }
+        return {"messages": ["[Tool] Weather in San Francisco: 72°F and sunny."]}
     elif "get_population" in last_msg:
-        return {
-            "messages": [
-                "[Tool] San Francisco population: ~870,000 residents."
-            ]
-        }
+        return {"messages": ["[Tool] San Francisco population: ~870,000 residents."]}
     else:
         return {"messages": ["[Tool] Unknown tool requested."]}