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Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818)
* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
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GitHub
Unverified
GitHub
parent
4b533608b6
commit
5e056b672e
@@ -7,8 +7,8 @@ registered agents, demonstrating how to interact with agents from external proce
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Prerequisites:
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- The worker must be running with the agent registered
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Sign in with Azure CLI for AzureCliCredential authentication
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- Durable Task Scheduler must be running
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"""
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@@ -17,7 +17,7 @@ import logging
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import os
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from agent_framework.azure import DurableAIAgentClient
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from azure.identity import DefaultAzureCredential
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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@@ -48,7 +48,7 @@ def get_client(
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
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dts_client = DurableTaskSchedulerClient(
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host_address=endpoint_url,
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@@ -1,11 +1,13 @@
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# Agent Framework packages
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# To use the deployed version, uncomment the line below and comment out the local installation lines
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# To use the deployed version, uncomment the lines below and comment out the local installation lines
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# agent-framework-foundry
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# agent-framework-durabletask
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# Local installation (for development and testing)
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# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
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# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
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-e ../../../../packages/core # Core framework - base dependency for all packages
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-e ../../../../packages/foundry # Foundry support - dependency for hosted chat/agent samples
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-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
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# Azure authentication
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@@ -1,16 +1,13 @@
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# Copyright (c) Microsoft. All rights reserved.
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"""Single Agent Sample - Durable Task Integration (Combined Worker + Client)
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This sample demonstrates running both the worker and client in a single process.
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The worker is started first to register the agent, then client operations are
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performed against the running worker.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Sign in with Azure CLI for AzureCliCredential authentication
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- Durable Task Scheduler must be running (e.g., using Docker)
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To run this sample:
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python sample.py
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"""
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@@ -30,27 +27,21 @@ logger = logging.getLogger(__name__)
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def main():
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"""Main entry point - runs both worker and client in single process."""
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logger.debug("Starting Durable Task Agent Sample (Combined Worker + Client)...")
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silent_handler = logging.NullHandler()
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# Create and start the worker using helper function and context manager
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with get_worker(log_handler=silent_handler) as dts_worker:
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# Register agents using helper function
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setup_worker(dts_worker)
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# Start the worker
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dts_worker.start()
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logger.debug("Worker started and listening for requests...")
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# Create the client using helper function
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agent_client = get_client(log_handler=silent_handler)
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try:
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# Run client interactions using helper function
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run_client(agent_client)
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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logger.debug("Sample completed. Worker shutting down...")
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@@ -6,8 +6,8 @@ This worker registers agents as durable entities and continuously listens for re
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The worker should run as a background service, processing incoming agent requests.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Sign in with Azure CLI for AzureCliCredential authentication
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- Start a Durable Task Scheduler (e.g., using Docker)
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"""
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@@ -16,8 +16,10 @@ import logging
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import os
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from agent_framework import Agent
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from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentWorker
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from azure.identity import AzureCliCredential, DefaultAzureCredential
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from agent_framework.azure import DurableAIAgentWorker
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
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@@ -35,7 +37,12 @@ def create_joker_agent() -> Agent:
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Returns:
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Agent: The configured Joker agent
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"""
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return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
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return Agent(
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client=FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],
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credential=AsyncAzureCliCredential(),
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),
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name="Joker",
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instructions="You are good at telling jokes.",
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)
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@@ -60,7 +67,7 @@ def get_worker(
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
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return DurableTaskSchedulerWorker(
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host_address=endpoint_url,
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@@ -8,8 +8,8 @@ each with their own specialized capabilities and tools.
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Prerequisites:
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- The worker must be running with both agents registered
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Sign in with Azure CLI for AzureCliCredential authentication
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- Durable Task Scheduler must be running
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"""
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@@ -18,7 +18,7 @@ import logging
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import os
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from agent_framework.azure import DurableAIAgentClient
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from azure.identity import DefaultAzureCredential
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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@@ -49,7 +49,7 @@ def get_client(
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
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dts_client = DurableTaskSchedulerClient(
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host_address=endpoint_url,
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@@ -1,11 +1,13 @@
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# Agent Framework packages
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# To use the deployed version, uncomment the line below and comment out the local installation lines
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# To use the deployed version, uncomment the lines below and comment out the local installation lines
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# agent-framework-foundry
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# agent-framework-durabletask
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# Local installation (for development and testing)
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# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
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# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
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-e ../../../../packages/core # Core framework - base dependency for all packages
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-e ../../../../packages/foundry # Foundry support - dependency for hosted chat/agent samples
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-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
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# Azure authentication
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@@ -1,16 +1,13 @@
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# Copyright (c) Microsoft. All rights reserved.
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"""Multi-Agent Sample - Durable Task Integration (Combined Worker + Client)
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This sample demonstrates running both the worker and client in a single process
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for multiple agents with different tools. The worker registers two agents
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(WeatherAgent and MathAgent), each with their own specialized capabilities.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Sign in with Azure CLI for AzureCliCredential authentication
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- Durable Task Scheduler must be running (e.g., using Docker)
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To run this sample:
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python sample.py
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"""
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@@ -30,26 +27,21 @@ logger = logging.getLogger(__name__)
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def main():
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"""Main entry point - runs both worker and client in single process."""
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logger.debug("Starting Durable Task Multi-Agent Sample (Combined Worker + Client)...")
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silent_handler = logging.NullHandler()
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# Create and start the worker using helper function and context manager
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with get_worker(log_handler=silent_handler) as dts_worker:
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# Register agents using helper function
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setup_worker(dts_worker)
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# Start the worker
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dts_worker.start()
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logger.debug("Worker started and listening for requests...")
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# Create the client using helper function
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agent_client = get_client(log_handler=silent_handler)
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try:
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# Run client interactions using helper function
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run_client(agent_client)
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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logger.debug("Sample completed. Worker shutting down...")
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@@ -7,8 +7,8 @@ with their own specialized tools. This demonstrates how to host multiple agents
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with different capabilities in a single worker process.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Sign in with Azure CLI for AzureCliCredential authentication
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- Start a Durable Task Scheduler (e.g., using Docker)
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"""
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@@ -17,9 +17,11 @@ import logging
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import os
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from typing import Any
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from agent_framework import tool
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from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentWorker
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from azure.identity import AzureCliCredential, DefaultAzureCredential
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from agent_framework import Agent, tool
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from agent_framework.azure import DurableAIAgentWorker
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
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@@ -71,7 +73,13 @@ def create_weather_agent():
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Returns:
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Agent: The configured Weather agent with weather tool
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"""
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return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
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_client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],
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credential=AsyncAzureCliCredential(),
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)
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return Agent(
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client=_client,
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name=WEATHER_AGENT_NAME,
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instructions="You are a helpful weather assistant. Provide current weather information.",
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tools=[get_weather],
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@@ -84,7 +92,13 @@ def create_math_agent():
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Returns:
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Agent: The configured Math agent with calculation tools
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"""
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return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
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_client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],
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credential=AsyncAzureCliCredential(),
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)
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return Agent(
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client=_client,
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name=MATH_AGENT_NAME,
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instructions="You are a helpful math assistant. Help users with calculations like tip calculations.",
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tools=[calculate_tip],
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@@ -110,7 +124,7 @@ def get_worker(
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
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return DurableTaskSchedulerWorker(
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host_address=endpoint_url,
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@@ -9,7 +9,7 @@ This client demonstrates:
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Prerequisites:
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- The worker must be running with the TravelPlanner agent registered
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
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- Redis must be running
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- Durable Task Scheduler must be running
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"""
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@@ -21,7 +21,7 @@ from datetime import timedelta
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import redis.asyncio as aioredis
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from agent_framework.azure import DurableAIAgentClient
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from azure.identity import DefaultAzureCredential
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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from redis_stream_response_handler import RedisStreamResponseHandler
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@@ -76,7 +76,7 @@ def get_client(
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
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dts_client = DurableTaskSchedulerClient(
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host_address=endpoint_url,
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@@ -1,15 +1,14 @@
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# Agent Framework packages
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# To use the deployed version, uncomment the line below and comment out the local installation lines
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# To use the deployed version, uncomment the lines below and comment out the local installation lines
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# agent-framework-foundry
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# agent-framework-durabletask
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# Local installation (for development and testing)
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# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
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# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
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-e ../../../../packages/core # Core framework - base dependency for all packages
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-e ../../../../packages/foundry # Foundry support - dependency for hosted chat/agent samples
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-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
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# Azure authentication
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azure-identity
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# Redis client
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redis
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@@ -1,19 +1,15 @@
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# Copyright (c) Microsoft. All rights reserved.
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"""Single Agent Streaming Sample - Durable Task Integration (Combined Worker + Client)
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This sample demonstrates running both the worker and client in a single process
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with reliable Redis-based streaming for agent responses.
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The worker is started first to register the TravelPlanner agent with Redis streaming
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callback, then client operations are performed against the running worker.
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Prerequisites:
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
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- Durable Task Scheduler must be running (e.g., using Docker)
|
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- Redis must be running (e.g., docker run -d --name redis -p 6379:6379 redis:latest)
|
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|
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To run this sample:
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python sample.py
|
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"""
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@@ -33,27 +29,21 @@ logger = logging.getLogger(__name__)
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def main():
|
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"""Main entry point - runs both worker and client in single process."""
|
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logger.debug("Starting Durable Task Agent Sample with Redis Streaming...")
|
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|
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silent_handler = logging.NullHandler()
|
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|
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# Create and start the worker using helper function and context manager
|
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with get_worker(log_handler=silent_handler) as dts_worker:
|
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# Register agents and callbacks using helper function
|
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setup_worker(dts_worker)
|
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|
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# Start the worker
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dts_worker.start()
|
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logger.debug("Worker started and listening for requests...")
|
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|
||||
# Create the client using helper function
|
||||
agent_client = get_client(log_handler=silent_handler)
|
||||
|
||||
try:
|
||||
# Run client interactions using helper function
|
||||
run_client(agent_client)
|
||||
except Exception as e:
|
||||
logger.exception(f"Error during agent interaction: {e}")
|
||||
|
||||
logger.debug("Sample completed. Worker shutting down...")
|
||||
|
||||
|
||||
|
||||
@@ -6,8 +6,8 @@ This worker registers the TravelPlanner agent with the Durable Task Scheduler
|
||||
and uses RedisStreamCallback to persist streaming responses to Redis for reliable delivery.
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Start a Durable Task Scheduler (e.g., using Docker)
|
||||
- Start Redis (e.g., docker run -d --name redis -p 6379:6379 redis:latest)
|
||||
"""
|
||||
@@ -22,10 +22,11 @@ from agent_framework import Agent, AgentResponseUpdate
|
||||
from agent_framework.azure import (
|
||||
AgentCallbackContext,
|
||||
AgentResponseCallbackProtocol,
|
||||
AzureOpenAIChatClient,
|
||||
DurableAIAgentWorker,
|
||||
)
|
||||
from azure.identity import AzureCliCredential, DefaultAzureCredential
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from azure.identity import AzureCliCredential
|
||||
from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
|
||||
from redis_stream_response_handler import RedisStreamResponseHandler
|
||||
@@ -153,7 +154,13 @@ def create_travel_agent() -> "Agent":
|
||||
Returns:
|
||||
Agent: The configured TravelPlanner agent with travel planning tools.
|
||||
"""
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
_client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["FOUNDRY_MODEL"],
|
||||
credential=AsyncAzureCliCredential(),
|
||||
)
|
||||
return Agent(
|
||||
client=_client,
|
||||
name="TravelPlanner",
|
||||
instructions="""You are an expert travel planner who creates detailed, personalized travel itineraries.
|
||||
When asked to plan a trip, you should:
|
||||
@@ -191,7 +198,7 @@ def get_worker(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerWorker(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+4
-4
@@ -8,8 +8,8 @@ how conversation context is maintained across multiple agent invocations.
|
||||
|
||||
Prerequisites:
|
||||
- The worker must be running with the writer agent and orchestration registered
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running
|
||||
"""
|
||||
|
||||
@@ -18,7 +18,7 @@ import json
|
||||
import logging
|
||||
import os
|
||||
|
||||
from azure.identity import DefaultAzureCredential
|
||||
from azure.identity import AzureCliCredential
|
||||
from durabletask.azuremanaged.client import DurableTaskSchedulerClient
|
||||
|
||||
# Configure logging
|
||||
@@ -45,7 +45,7 @@ def get_client(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerClient(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+3
-1
@@ -1,11 +1,13 @@
|
||||
# Agent Framework packages
|
||||
# To use the deployed version, uncomment the line below and comment out the local installation lines
|
||||
# To use the deployed version, uncomment the lines below and comment out the local installation lines
|
||||
# agent-framework-foundry
|
||||
# agent-framework-durabletask
|
||||
|
||||
# Local installation (for development and testing)
|
||||
# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
|
||||
# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
|
||||
-e ../../../../packages/core # Core framework - base dependency for all packages
|
||||
-e ../../../../packages/foundry # Foundry support - dependency for hosted chat/agent samples
|
||||
-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
|
||||
|
||||
# Azure authentication
|
||||
|
||||
+3
-13
@@ -1,22 +1,18 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
"""Single Agent Orchestration Chaining Sample - Durable Task Integration
|
||||
|
||||
This sample demonstrates chaining two invocations of the same agent inside a Durable Task
|
||||
orchestration while preserving the conversation state between runs. The orchestration
|
||||
runs the writer agent sequentially on a shared thread to refine text iteratively.
|
||||
|
||||
Components used:
|
||||
- AzureOpenAIChatClient to construct the writer agent
|
||||
- FoundryChatClient to construct the writer agent
|
||||
- DurableTaskSchedulerWorker and DurableAIAgentWorker for agent hosting
|
||||
- DurableTaskSchedulerClient and orchestration for sequential agent invocations
|
||||
- Thread management to maintain conversation context across invocations
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running (e.g., using Docker emulator)
|
||||
|
||||
To run this sample:
|
||||
python sample.py
|
||||
"""
|
||||
@@ -36,22 +32,17 @@ logger = logging.getLogger(__name__)
|
||||
def main():
|
||||
"""Main entry point - runs both worker and client in single process."""
|
||||
logger.debug("Starting Single Agent Orchestration Chaining Sample...")
|
||||
|
||||
silent_handler = logging.NullHandler()
|
||||
# Create and start the worker using helper function and context manager
|
||||
with get_worker(log_handler=silent_handler) as dts_worker:
|
||||
# Register agents and orchestrations using helper function
|
||||
setup_worker(dts_worker)
|
||||
|
||||
# Start the worker
|
||||
dts_worker.start()
|
||||
logger.debug("Worker started and listening for requests...")
|
||||
|
||||
# Create the client using helper function
|
||||
client = get_client(log_handler=silent_handler)
|
||||
|
||||
logger.debug("CLIENT: Starting orchestration...")
|
||||
|
||||
# Run the client in the same process
|
||||
try:
|
||||
run_client(client)
|
||||
@@ -61,7 +52,6 @@ def main():
|
||||
logger.exception(f"Error during orchestration: {e}")
|
||||
finally:
|
||||
logger.debug("Worker stopping...")
|
||||
|
||||
logger.debug("")
|
||||
logger.debug("Sample completed")
|
||||
|
||||
|
||||
+14
-6
@@ -7,8 +7,8 @@ chaining behavior by running the agent twice sequentially on the same thread,
|
||||
preserving conversation context between invocations.
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Start a Durable Task Scheduler (e.g., using Docker)
|
||||
"""
|
||||
|
||||
@@ -18,8 +18,10 @@ import os
|
||||
from collections.abc import Generator
|
||||
|
||||
from agent_framework import Agent, AgentResponse
|
||||
from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from azure.identity import AzureCliCredential, DefaultAzureCredential
|
||||
from agent_framework.azure import DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from azure.identity import AzureCliCredential
|
||||
from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
|
||||
from durabletask.task import OrchestrationContext, Task
|
||||
@@ -49,7 +51,13 @@ def create_writer_agent() -> "Agent":
|
||||
"when given an improved sentence you polish it further."
|
||||
)
|
||||
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
_client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["FOUNDRY_MODEL"],
|
||||
credential=AsyncAzureCliCredential(),
|
||||
)
|
||||
return Agent(
|
||||
client=_client,
|
||||
name=WRITER_AGENT_NAME,
|
||||
instructions=instructions,
|
||||
)
|
||||
@@ -139,7 +147,7 @@ def get_worker(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerWorker(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+4
-4
@@ -8,8 +8,8 @@ displays the aggregated results.
|
||||
|
||||
Prerequisites:
|
||||
- The worker must be running with both agents and orchestration registered
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running
|
||||
"""
|
||||
|
||||
@@ -18,7 +18,7 @@ import json
|
||||
import logging
|
||||
import os
|
||||
|
||||
from azure.identity import DefaultAzureCredential
|
||||
from azure.identity import AzureCliCredential
|
||||
from durabletask.azuremanaged.client import DurableTaskSchedulerClient
|
||||
|
||||
# Configure logging
|
||||
@@ -45,7 +45,7 @@ def get_client(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerClient(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+3
-1
@@ -1,11 +1,13 @@
|
||||
# Agent Framework packages
|
||||
# To use the deployed version, uncomment the line below and comment out the local installation lines
|
||||
# To use the deployed version, uncomment the lines below and comment out the local installation lines
|
||||
# agent-framework-foundry
|
||||
# agent-framework-durabletask
|
||||
|
||||
# Local installation (for development and testing)
|
||||
# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
|
||||
# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
|
||||
-e ../../../../packages/core # Core framework - base dependency for all packages
|
||||
-e ../../../../packages/foundry # Foundry support - dependency for hosted chat/agent samples
|
||||
-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
|
||||
|
||||
# Azure authentication
|
||||
|
||||
+2
-12
@@ -1,19 +1,15 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
"""Multi-Agent Orchestration Sample - Durable Task Integration (Combined Worker + Client)
|
||||
|
||||
This sample demonstrates running both the worker and client in a single process for
|
||||
concurrent multi-agent orchestration. The worker registers two domain-specific agents
|
||||
(physicist and chemist) and an orchestration function that runs them in parallel.
|
||||
|
||||
The orchestration uses OrchestrationAgentExecutor to execute agents concurrently
|
||||
and aggregate their responses.
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running (e.g., using Docker)
|
||||
|
||||
To run this sample:
|
||||
python sample.py
|
||||
"""
|
||||
@@ -33,30 +29,24 @@ logger = logging.getLogger(__name__)
|
||||
def main():
|
||||
"""Main entry point - runs both worker and client in single process."""
|
||||
logger.debug("Starting Durable Task Multi-Agent Orchestration Sample (Combined Worker + Client)...")
|
||||
|
||||
silent_handler = logging.NullHandler()
|
||||
# Create and start the worker using helper function and context manager
|
||||
with get_worker(log_handler=silent_handler) as dts_worker:
|
||||
# Register agents and orchestrations using helper function
|
||||
setup_worker(dts_worker)
|
||||
|
||||
# Start the worker
|
||||
dts_worker.start()
|
||||
logger.debug("Worker started and listening for requests...")
|
||||
|
||||
# Create the client using helper function
|
||||
client = get_client(log_handler=silent_handler)
|
||||
|
||||
# Define the prompt
|
||||
prompt = "What is temperature?"
|
||||
logger.debug("CLIENT: Starting orchestration...")
|
||||
|
||||
try:
|
||||
# Run the client to start the orchestration
|
||||
run_client(client, prompt)
|
||||
except Exception as e:
|
||||
logger.exception(f"Error during sample execution: {e}")
|
||||
|
||||
logger.debug("Sample completed. Worker shutting down...")
|
||||
|
||||
|
||||
|
||||
+21
-7
@@ -7,8 +7,8 @@ function that runs them concurrently. The orchestration uses OrchestrationAgentE
|
||||
to execute agents in parallel and aggregate their responses.
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Start a Durable Task Scheduler (e.g., using Docker)
|
||||
"""
|
||||
|
||||
@@ -19,8 +19,10 @@ from collections.abc import Generator
|
||||
from typing import Any
|
||||
|
||||
from agent_framework import Agent, AgentResponse
|
||||
from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from azure.identity import AzureCliCredential, DefaultAzureCredential
|
||||
from agent_framework.azure import DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from azure.identity import AzureCliCredential
|
||||
from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
|
||||
from durabletask.task import OrchestrationContext, Task, when_all
|
||||
@@ -43,7 +45,13 @@ def create_physicist_agent() -> "Agent":
|
||||
Returns:
|
||||
Agent: The configured Physicist agent
|
||||
"""
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
_client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["FOUNDRY_MODEL"],
|
||||
credential=AsyncAzureCliCredential(),
|
||||
)
|
||||
return Agent(
|
||||
client=_client,
|
||||
name=PHYSICIST_AGENT_NAME,
|
||||
instructions="You are an expert in physics. You answer questions from a physics perspective.",
|
||||
)
|
||||
@@ -55,7 +63,13 @@ def create_chemist_agent() -> "Agent":
|
||||
Returns:
|
||||
Agent: The configured Chemist agent
|
||||
"""
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
_client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["FOUNDRY_MODEL"],
|
||||
credential=AsyncAzureCliCredential(),
|
||||
)
|
||||
return Agent(
|
||||
client=_client,
|
||||
name=CHEMIST_AGENT_NAME,
|
||||
instructions="You are an expert in chemistry. You answer questions from a chemistry perspective.",
|
||||
)
|
||||
@@ -138,7 +152,7 @@ def get_worker(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerWorker(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+5
@@ -14,6 +14,11 @@ This sample demonstrates conditional orchestration logic with two agents that an
|
||||
|
||||
See the [README.md](../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.
|
||||
|
||||
This sample uses Azure OpenAI credentials:
|
||||
|
||||
- `AZURE_OPENAI_ENDPOINT`
|
||||
- `AZURE_OPENAI_DEPLOYMENT_NAME`
|
||||
|
||||
## Running the Sample
|
||||
|
||||
With the environment setup, you can run the sample using the combined approach or separate worker and client processes:
|
||||
|
||||
+4
-4
@@ -7,8 +7,8 @@ that uses conditional logic to either handle spam emails or draft professional r
|
||||
|
||||
Prerequisites:
|
||||
- The worker must be running with both agents, orchestration, and activities registered
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running
|
||||
"""
|
||||
|
||||
@@ -16,7 +16,7 @@ import asyncio
|
||||
import logging
|
||||
import os
|
||||
|
||||
from azure.identity import DefaultAzureCredential
|
||||
from azure.identity import AzureCliCredential
|
||||
from durabletask.azuremanaged.client import DurableTaskSchedulerClient
|
||||
|
||||
# Configure logging
|
||||
@@ -43,7 +43,7 @@ def get_client(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerClient(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+3
-1
@@ -1,11 +1,13 @@
|
||||
# Agent Framework packages
|
||||
# To use the deployed version, uncomment the line below and comment out the local installation lines
|
||||
# To use the deployed version, uncomment the lines below and comment out the local installation lines
|
||||
# agent-framework-openai
|
||||
# agent-framework-durabletask
|
||||
|
||||
# Local installation (for development and testing)
|
||||
# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
|
||||
# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
|
||||
-e ../../../../packages/core # Core framework - base dependency for all packages
|
||||
-e ../../../../packages/openai # OpenAI support - dependency for Azure OpenAI chat samples
|
||||
-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
|
||||
|
||||
# Azure authentication
|
||||
|
||||
+2
-2
@@ -10,8 +10,8 @@ The orchestration branches based on spam detection results, calling different
|
||||
activity functions to handle spam or send legitimate email responses.
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running (e.g., using Docker)
|
||||
|
||||
To run this sample:
|
||||
|
||||
+22
-7
@@ -7,8 +7,8 @@ orchestration function that routes execution based on spam detection results. Ac
|
||||
handle side effects (spam handling and email sending).
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_DEPLOYMENT_NAME
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Start a Durable Task Scheduler (e.g., using Docker)
|
||||
"""
|
||||
|
||||
@@ -19,8 +19,11 @@ from collections.abc import Generator
|
||||
from typing import Any, cast
|
||||
|
||||
from agent_framework import Agent, AgentResponse
|
||||
from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from azure.identity import AzureCliCredential, DefaultAzureCredential
|
||||
from agent_framework.azure import DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from agent_framework.openai import OpenAIChatCompletionClient
|
||||
from azure.identity import AzureCliCredential
|
||||
from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
|
||||
from azure.identity.aio import get_bearer_token_provider as get_async_bearer_token_provider
|
||||
from dotenv import load_dotenv
|
||||
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
|
||||
from durabletask.task import ActivityContext, OrchestrationContext, Task
|
||||
@@ -64,7 +67,13 @@ def create_spam_agent() -> "Agent":
|
||||
Returns:
|
||||
Agent: The configured Spam Detection agent
|
||||
"""
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
return Agent(
|
||||
client=OpenAIChatCompletionClient(
|
||||
model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
|
||||
api_key=get_async_bearer_token_provider(
|
||||
AsyncAzureCliCredential(), "https://cognitiveservices.azure.com/.default"
|
||||
),
|
||||
),
|
||||
name=SPAM_AGENT_NAME,
|
||||
instructions="You are a spam detection assistant that identifies spam emails.",
|
||||
)
|
||||
@@ -76,7 +85,13 @@ def create_email_agent() -> "Agent":
|
||||
Returns:
|
||||
Agent: The configured Email Assistant agent
|
||||
"""
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
return Agent(
|
||||
client=OpenAIChatCompletionClient(
|
||||
model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
|
||||
api_key=get_async_bearer_token_provider(
|
||||
AsyncAzureCliCredential(), "https://cognitiveservices.azure.com/.default"
|
||||
),
|
||||
),
|
||||
name=EMAIL_AGENT_NAME,
|
||||
instructions="You are an email assistant that helps users draft responses to emails with professionalism.",
|
||||
)
|
||||
@@ -220,7 +235,7 @@ def get_worker(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerWorker(
|
||||
host_address=endpoint_url,
|
||||
|
||||
@@ -7,8 +7,8 @@ by starting an orchestration, sending approval/rejection events, and monitoring
|
||||
|
||||
Prerequisites:
|
||||
- The worker must be running with the agent, orchestration, and activities registered
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running
|
||||
"""
|
||||
|
||||
@@ -18,7 +18,7 @@ import logging
|
||||
import os
|
||||
import time
|
||||
|
||||
from azure.identity import DefaultAzureCredential
|
||||
from azure.identity import AzureCliCredential
|
||||
from durabletask.azuremanaged.client import DurableTaskSchedulerClient
|
||||
from durabletask.client import OrchestrationState
|
||||
|
||||
@@ -49,7 +49,7 @@ def get_client(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerClient(
|
||||
host_address=endpoint_url,
|
||||
|
||||
+3
-1
@@ -1,11 +1,13 @@
|
||||
# Agent Framework packages
|
||||
# To use the deployed version, uncomment the line below and comment out the local installation lines
|
||||
# To use the deployed version, uncomment the lines below and comment out the local installation lines
|
||||
# agent-framework-foundry
|
||||
# agent-framework-durabletask
|
||||
|
||||
# Local installation (for development and testing)
|
||||
# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
|
||||
# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
|
||||
-e ../../../../packages/core # Core framework - base dependency for all packages
|
||||
-e ../../../../packages/foundry # Foundry support - dependency for hosted chat/agent samples
|
||||
-e ../../../../packages/durabletask # Durable Task support - the main package for this sample
|
||||
|
||||
# Azure authentication
|
||||
|
||||
@@ -1,19 +1,16 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
"""Human-in-the-Loop Orchestration Sample - Durable Task Integration
|
||||
|
||||
This sample demonstrates the HITL pattern with a WriterAgent that generates content
|
||||
and waits for human approval. The orchestration handles:
|
||||
- External event waiting (approval/rejection)
|
||||
- Timeout handling
|
||||
- Iterative refinement based on feedback
|
||||
- Activity functions for notifications and publishing
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Durable Task Scheduler must be running (e.g., using Docker)
|
||||
|
||||
To run this sample:
|
||||
python sample.py
|
||||
"""
|
||||
@@ -32,28 +29,21 @@ logger = logging.getLogger()
|
||||
def main():
|
||||
"""Main entry point - runs both worker and client in single process."""
|
||||
logger.debug("Starting Durable Task HITL Content Generation Sample (Combined Worker + Client)...")
|
||||
|
||||
silent_handler = logging.NullHandler()
|
||||
# Create and start the worker using helper function and context manager
|
||||
with get_worker(log_handler=silent_handler) as dts_worker:
|
||||
# Register agent, orchestration, and activities using helper function
|
||||
setup_worker(dts_worker)
|
||||
|
||||
# Start the worker
|
||||
dts_worker.start()
|
||||
logger.debug("Worker started and listening for requests...")
|
||||
|
||||
# Create the client using helper function
|
||||
client = get_client(log_handler=silent_handler)
|
||||
|
||||
try:
|
||||
logger.debug("CLIENT: Starting orchestration tests...")
|
||||
|
||||
run_interactive_client(client)
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"Error during sample execution: {e}")
|
||||
|
||||
logger.debug("Sample completed. Worker shutting down...")
|
||||
|
||||
|
||||
|
||||
@@ -7,8 +7,8 @@ a human-in-the-loop review workflow. The orchestration pauses for external event
|
||||
(human approval/rejection) with timeout handling, and iterates based on feedback.
|
||||
|
||||
Prerequisites:
|
||||
- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
|
||||
(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
|
||||
- Set FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL
|
||||
- Sign in with Azure CLI for AzureCliCredential authentication
|
||||
- Start a Durable Task Scheduler (e.g., using Docker)
|
||||
"""
|
||||
|
||||
@@ -20,8 +20,10 @@ from datetime import timedelta
|
||||
from typing import Any, cast
|
||||
|
||||
from agent_framework import Agent, AgentResponse
|
||||
from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from azure.identity import AzureCliCredential, DefaultAzureCredential
|
||||
from agent_framework.azure import DurableAIAgentOrchestrationContext, DurableAIAgentWorker
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from azure.identity import AzureCliCredential
|
||||
from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
|
||||
from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any # type: ignore
|
||||
@@ -74,7 +76,13 @@ def create_writer_agent() -> "Agent":
|
||||
"Limit response to 300 words or less."
|
||||
)
|
||||
|
||||
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
|
||||
_client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["FOUNDRY_MODEL"],
|
||||
credential=AsyncAzureCliCredential(),
|
||||
)
|
||||
return Agent(
|
||||
client=_client,
|
||||
name=WRITER_AGENT_NAME,
|
||||
instructions=instructions,
|
||||
)
|
||||
@@ -298,7 +306,7 @@ def get_worker(
|
||||
logger.debug(f"Using taskhub: {taskhub_name}")
|
||||
logger.debug(f"Using endpoint: {endpoint_url}")
|
||||
|
||||
credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
|
||||
credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
|
||||
|
||||
return DurableTaskSchedulerWorker(
|
||||
host_address=endpoint_url,
|
||||
|
||||
@@ -55,22 +55,6 @@ az role assignment create `
|
||||
|
||||
More information on how to configure RBAC permissions for Azure OpenAI can be found in the [Azure OpenAI documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=cli).
|
||||
|
||||
### Setting an API key for the Azure OpenAI service
|
||||
|
||||
As an alternative to configuring Azure RBAC permissions, you can set an API key for the Azure OpenAI service by setting the `AZURE_OPENAI_API_KEY` environment variable.
|
||||
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
export AZURE_OPENAI_API_KEY="your-api-key"
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
$env:AZURE_OPENAI_API_KEY="your-api-key"
|
||||
```
|
||||
|
||||
### Start Durable Task Scheduler
|
||||
|
||||
Most samples use the Durable Task Scheduler (DTS) to support hosted agents and durable orchestrations. DTS also allows you to view the status of orchestrations and their inputs and outputs from a web UI.
|
||||
@@ -90,15 +74,15 @@ Each sample reads configuration from environment variables. You'll need to set t
|
||||
Bash (Linux/macOS/WSL):
|
||||
|
||||
```bash
|
||||
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
|
||||
export AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="your-deployment-name"
|
||||
export FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project"
|
||||
export FOUNDRY_MODEL="your-deployment-name"
|
||||
```
|
||||
|
||||
PowerShell:
|
||||
|
||||
```powershell
|
||||
$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
|
||||
$env:AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="your-deployment-name"
|
||||
$env:FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project"
|
||||
$env:FOUNDRY_MODEL="your-deployment-name"
|
||||
```
|
||||
|
||||
### Installing Dependencies
|
||||
|
||||
Reference in New Issue
Block a user