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[BREAKING] Python: Refactor SharedState to State with sync methods and superstep caching (#3667)

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* Refactor SharedState to State with sync methods and superstep caching

* Fixes

* Address PR feedback

* Remove dead links

* Fix lab test import
This commit is contained in:
Evan Mattson
2026-02-05 10:42:52 +09:00
committed by GitHub
Unverified
parent 4e25917644
commit 10afb86213
48 changed files with 1971 additions and 1724 deletions
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python/samples/getting_started/workflows/parallelism/map_reduce_and_visualization.py
+12 -12
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@@ -10,7 +10,7 @@ import aiofiles
from agent_framework import (
Executor, # Base class for custom workflow steps
WorkflowBuilder, # Fluent builder for executors and edges
WorkflowContext, # Per run context with shared state and messaging
WorkflowContext, # Per run context with workflow state and messaging
WorkflowOutputEvent, # Event emitted when workflow yields output
WorkflowViz, # Utility to visualize a workflow graph
handler, # Decorator to expose an Executor method as a step
@@ -26,7 +26,7 @@ It also demonstrates WorkflowViz for graph visualization.
Purpose:
Show how to:
- Partition input once and coordinate parallel mappers with shared state.
- Partition input once and coordinate parallel mappers with workflow state.
- Implement map, shuffle, and reduce executors that pass file paths instead of large payloads.
- Use fan out and fan in edges to express parallelism and joins.
- Persist intermediate results to disk to bound memory usage for large inputs.
@@ -49,8 +49,8 @@ TEMP_DIR = os.path.join(DIR, "tmp")
# Ensure the temporary directory exists
os.makedirs(TEMP_DIR, exist_ok=True)
# Define a key for the shared state to store the data to be processed
SHARED_STATE_DATA_KEY = "data_to_be_processed"
# Define a key for the workflow state to store the data to be processed
STATE_DATA_KEY = "data_to_be_processed"
class SplitCompleted:
@@ -69,17 +69,17 @@ class Split(Executor):
@handler
async def split(self, data: str, ctx: WorkflowContext[SplitCompleted]) -> None:
"""Tokenize input and assign contiguous index ranges to each mapper via shared state.
"""Tokenize input and assign contiguous index ranges to each mapper via workflow state.
Args:
data: The raw text to process.
ctx: Workflow context to persist shared state and send messages.
ctx: Workflow context to persist state and send messages.
"""
# Process data into a list of words and remove empty lines or words.
word_list = self._preprocess(data)
# Store tokenized words once so all mappers can read by index.
await ctx.set_shared_state(SHARED_STATE_DATA_KEY, word_list)
ctx.set_state(STATE_DATA_KEY, word_list)
# Divide indices into contiguous slices for each mapper.
map_executor_count = len(self._map_executor_ids)
@@ -90,8 +90,8 @@ class Split(Executor):
start_index = i * chunk_size
end_index = start_index + chunk_size if i < map_executor_count - 1 else len(word_list)
# The mapper reads its slice from shared state keyed by its own executor id.
await ctx.set_shared_state(self._map_executor_ids[i], (start_index, end_index))
# The mapper reads its slice from workflow state keyed by its own executor id.
ctx.set_state(self._map_executor_ids[i], (start_index, end_index))
await ctx.send_message(SplitCompleted(), self._map_executor_ids[i])
tasks = [asyncio.create_task(_process_chunk(i)) for i in range(map_executor_count)]
@@ -119,11 +119,11 @@ class Map(Executor):
Args:
_: SplitCompleted marker indicating maps can begin.
ctx: Workflow context for shared state access and messaging.
ctx: Workflow context for workflow state access and messaging.
"""
# Retrieve tokens and our assigned slice.
data_to_be_processed: list[str] = await ctx.get_shared_state(SHARED_STATE_DATA_KEY)
chunk_start, chunk_end = await ctx.get_shared_state(self.id)
data_to_be_processed: list[str] = ctx.get_state(STATE_DATA_KEY)
chunk_start, chunk_end = ctx.get_state(self.id)
results = [(item, 1) for item in data_to_be_processed[chunk_start:chunk_end]]