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Eduard van Valkenburg a2856d3b92 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- One superseded minimal_sample.py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

2026-02-12 17:36:36 +00:00

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Single Agent

This sample demonstrates how to create a worker-client setup that hosts a single AI agent and provides interactive conversation via the Durable Task Scheduler.

Key Concepts Demonstrated

Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
Registering durable agents with the worker and interacting with them via a client.
Conversation management (via threads) for isolated interactions.
Worker-client architecture for distributed agent execution.

Environment Setup

See the README.md file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.

Running the Sample

With the environment setup, you can run the sample using the combined approach or separate worker and client processes:

Option 1: Combined (Recommended for Testing)

cd samples/04-hosting/durabletask/01_single_agent
python sample.py

Option 2: Separate Processes

Start the worker in one terminal:

python worker.py

In a new terminal, run the client:

python client.py

The client will interact with the Joker agent:

Starting Durable Task Agent Client...
Using taskhub: default
Using endpoint: http://localhost:8080

Getting reference to Joker agent...
Created conversation thread: a1b2c3d4-e5f6-7890-abcd-ef1234567890

User: Tell me a short joke about cloud computing.

Joker: Why did the cloud break up with the server?
Because it found someone more "uplifting"!

User: Now tell me one about Python programming.

Joker: Why do Python programmers prefer dark mode?
Because light attracts bugs!

Viewing Agent State

You can view the state of the agent in the Durable Task Scheduler dashboard:

Open your browser and navigate to http://localhost:8082
In the dashboard, you can view:
- The state of the Joker agent entity (dafx-Joker)
- Conversation history and current state
- How the durable agents extension manages conversation context

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