mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
a2856d3b92
* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from typing import cast
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from agent_framework import (
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Executor,
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WorkflowBuilder,
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WorkflowContext,
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handler,
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)
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from typing_extensions import Never
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"""
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Sample: Sequential workflow with streaming.
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Two custom executors run in sequence. The first converts text to uppercase,
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the second reverses the text and completes the workflow. The streaming run loop prints events as they occur.
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Purpose:
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Show how to define explicit Executor classes with @handler methods, wire them in order with
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WorkflowBuilder, and consume streaming events. Demonstrate typed WorkflowContext[T_Out, T_W_Out] for outputs,
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ctx.send_message to pass intermediate values, and ctx.yield_output to provide workflow outputs.
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Prerequisites:
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- No external services required.
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"""
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class UpperCaseExecutor(Executor):
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"""Converts an input string to uppercase and forwards it.
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Concepts:
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- @handler methods define invokable steps.
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- WorkflowContext[str] indicates this step emits a string to the next node.
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"""
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@handler
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async def to_upper_case(self, text: str, ctx: WorkflowContext[str]) -> None:
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"""Transform the input to uppercase and send it downstream."""
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result = text.upper()
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# Pass the intermediate result to the next executor in the chain.
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await ctx.send_message(result)
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class ReverseTextExecutor(Executor):
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"""Reverses the incoming string and yields workflow output.
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Concepts:
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- Use ctx.yield_output to provide workflow outputs when the terminal result is ready.
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- The terminal node does not forward messages further.
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"""
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@handler
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async def reverse_text(self, text: str, ctx: WorkflowContext[Never, str]) -> None:
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"""Reverse the input string and yield the workflow output."""
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result = text[::-1]
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await ctx.yield_output(result)
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async def main() -> None:
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"""Build a two step sequential workflow and run it with streaming to observe events."""
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# Step 1: Build the workflow graph.
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# Order matters. We connect upper_case_executor -> reverse_text_executor and set the start.
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upper_case_executor = UpperCaseExecutor(id="upper_case_executor")
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reverse_text_executor = ReverseTextExecutor(id="reverse_text_executor")
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workflow = (
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WorkflowBuilder(start_executor=upper_case_executor)
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.add_edge(upper_case_executor, reverse_text_executor)
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.build()
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)
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# Step 2: Stream events for a single input.
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# The stream will include executor invoke and completion events, plus workflow outputs.
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outputs: list[str] = []
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async for event in workflow.run("hello world", stream=True):
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print(f"Event: {event}")
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if event.type == "output":
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outputs.append(cast(str, event.data))
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if outputs:
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print(f"Workflow outputs: {outputs}")
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if __name__ == "__main__":
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asyncio.run(main())
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