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Python: restructure: Python samples into progressive 01-05 layout (#3862)
* 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file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since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
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Eduard van Valkenburg
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from typing import Any
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from agent_framework import Message
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.orchestrations import ConcurrentBuilder
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from azure.identity import AzureCliCredential
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"""
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Sample: Concurrent fan-out/fan-in (agent-only API) with default aggregator
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Build a high-level concurrent workflow using ConcurrentBuilder and three domain agents.
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The default dispatcher fans out the same user prompt to all agents in parallel.
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The default aggregator fans in their results and yields output containing
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a list[Message] representing the concatenated conversations from all agents.
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Demonstrates:
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- Minimal wiring with ConcurrentBuilder(participants=[...]).build()
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- Fan-out to multiple agents, fan-in aggregation of final ChatMessages
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- Workflow completion when idle with no pending work
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Prerequisites:
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- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
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- Familiarity with Workflow events (WorkflowEvent)
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"""
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async def main() -> None:
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# 1) Create three domain agents using AzureOpenAIChatClient
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client = AzureOpenAIChatClient(credential=AzureCliCredential())
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researcher = client.as_agent(
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instructions=(
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"You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
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" opportunities, and risks."
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),
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name="researcher",
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)
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marketer = client.as_agent(
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instructions=(
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"You're a creative marketing strategist. Craft compelling value propositions and target messaging"
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" aligned to the prompt."
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),
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name="marketer",
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)
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legal = client.as_agent(
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instructions=(
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"You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
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" based on the prompt."
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),
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name="legal",
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)
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# 2) Build a concurrent workflow
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# Participants are either Agents (type of SupportsAgentRun) or Executors
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workflow = ConcurrentBuilder(participants=[researcher, marketer, legal]).build()
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# 3) Run with a single prompt and pretty-print the final combined messages
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events = await workflow.run("We are launching a new budget-friendly electric bike for urban commuters.")
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outputs = events.get_outputs()
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if outputs:
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print("===== Final Aggregated Conversation (messages) =====")
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for output in outputs:
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messages: list[Message] | Any = output
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for i, msg in enumerate(messages, start=1):
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name = msg.author_name if msg.author_name else "user"
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print(f"{'-' * 60}\n\n{i:02d} [{name}]:\n{msg.text}")
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"""
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Sample Output:
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===== Final Aggregated Conversation (messages) =====
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------------------------------------------------------------
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01 [user]:
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We are launching a new budget-friendly electric bike for urban commuters.
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------------------------------------------------------------
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02 [researcher]:
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**Insights:**
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- **Target Demographic:** Urban commuters seeking affordable, eco-friendly transport;
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likely to include students, young professionals, and price-sensitive urban residents.
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- **Market Trends:** E-bike sales are growing globally, with increasing urbanization,
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higher fuel costs, and sustainability concerns driving adoption.
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- **Competitive Landscape:** Key competitors include brands like Rad Power Bikes, Aventon,
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Lectric, and domestic budget-focused manufacturers in North America, Europe, and Asia.
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- **Feature Expectations:** Customers expect reliability, ease-of-use, theft protection,
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lightweight design, sufficient battery range for daily city commutes (typically 25-40 miles),
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and low-maintenance components.
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**Opportunities:**
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- **First-time Buyers:** Capture newcomers to e-biking by emphasizing affordability, ease of
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operation, and cost savings vs. public transit/car ownership.
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...
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------------------------------------------------------------
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03 [marketer]:
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**Value Proposition:**
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"Empowering your city commute: Our new electric bike combines affordability, reliability, and
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sustainable design—helping you conquer urban journeys without breaking the bank."
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**Target Messaging:**
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*For Young Professionals:*
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...
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------------------------------------------------------------
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04 [legal]:
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**Constraints, Disclaimers, & Policy Concerns for Launching a Budget-Friendly Electric Bike for Urban Commuters:**
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**1. Regulatory Compliance**
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- Verify that the electric bike meets all applicable federal, state, and local regulations
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regarding e-bike classification, speed limits, power output, and safety features.
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- Ensure necessary certifications (e.g., UL certification for batteries, CE markings if sold internationally) are obtained.
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**2. Product Safety**
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- Include consumer safety warnings regarding use, battery handling, charging protocols, and age restrictions.
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...
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""" # noqa: E501
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if __name__ == "__main__":
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asyncio.run(main())
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