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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
@@ -4,8 +4,8 @@ import asyncio
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import os
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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 AzureOpenAIResponsesClient
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from agent_framework import Agent, Message
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import ConcurrentBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -27,22 +27,23 @@ Demonstrates:
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- Workflow completion when idle with no pending work
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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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 AzureOpenAIResponsesClient
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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# 1) Create three domain agents using FoundryChatClient
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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researcher = client.as_agent(
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researcher = Agent(
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client=client,
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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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@@ -50,7 +51,8 @@ async def main() -> None:
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name="researcher",
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)
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marketer = client.as_agent(
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marketer = Agent(
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client=client,
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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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@@ -58,7 +60,8 @@ async def main() -> None:
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name="marketer",
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)
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legal = client.as_agent(
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legal = Agent(
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client=client,
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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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@@ -13,7 +13,7 @@ from agent_framework import (
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WorkflowContext,
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handler,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import ConcurrentBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -30,15 +30,15 @@ and emit AgentExecutorResponse outputs, which allows reuse of the high-level
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ConcurrentBuilder API and the default aggregator.
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Demonstrates:
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- Executors that create their Agent in __init__ (via AzureOpenAIResponsesClient)
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- Executors that create their Agent in __init__ (via FoundryChatClient)
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- A @handler that converts AgentExecutorRequest -> AgentExecutorResponse
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- ConcurrentBuilder(participants=[...]) to build fan-out/fan-in
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- Default aggregator returning list[Message] (one user + one assistant per agent)
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- Workflow completion when all participants become idle
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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"""
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@@ -46,8 +46,9 @@ Prerequisites:
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class ResearcherExec(Executor):
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agent: Agent
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def __init__(self, client: AzureOpenAIResponsesClient, id: str = "researcher"):
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self.agent = client.as_agent(
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def __init__(self, client: FoundryChatClient, id: str = "researcher"):
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self.agent = Agent(
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client=client,
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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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@@ -66,8 +67,9 @@ class ResearcherExec(Executor):
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class MarketerExec(Executor):
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agent: Agent
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def __init__(self, client: AzureOpenAIResponsesClient, id: str = "marketer"):
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self.agent = client.as_agent(
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def __init__(self, client: FoundryChatClient, id: str = "marketer"):
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self.agent = Agent(
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client=client,
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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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@@ -86,8 +88,9 @@ class MarketerExec(Executor):
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class LegalExec(Executor):
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agent: Agent
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def __init__(self, client: AzureOpenAIResponsesClient, id: str = "legal"):
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self.agent = client.as_agent(
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def __init__(self, client: FoundryChatClient, id: str = "legal"):
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self.agent = Agent(
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client=client,
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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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@@ -104,9 +107,9 @@ class LegalExec(Executor):
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async def main() -> None:
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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@@ -4,8 +4,8 @@ import asyncio
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import os
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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 AzureOpenAIResponsesClient
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from agent_framework import Agent, Message
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import ConcurrentBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -18,7 +18,7 @@ Sample: Concurrent Orchestration with Custom Aggregator
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Build a concurrent workflow with ConcurrentBuilder that fans out one prompt to
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multiple domain agents and fans in their responses. Override the default
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aggregator with a custom async callback that uses AzureOpenAIResponsesClient.get_response()
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aggregator with a custom async callback that uses FoundryChatClient.get_response()
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to synthesize a concise, consolidated summary from the experts' outputs.
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The workflow completes when all participants become idle.
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@@ -29,34 +29,37 @@ Demonstrates:
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- Workflow output yielded with the synthesized summary string
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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"""
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async def main() -> None:
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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researcher = client.as_agent(
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researcher = Agent(
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client=client,
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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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marketer = Agent(
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client=client,
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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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legal = Agent(
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client=client,
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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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@@ -9,7 +9,7 @@ from agent_framework import (
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AgentResponseUpdate,
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Message,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import GroupChatBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -26,8 +26,8 @@ What it does:
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- Coordinates a researcher and writer agent to solve tasks collaboratively
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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"""
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@@ -43,9 +43,9 @@ Guidelines:
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async def main() -> None:
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# Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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@@ -10,7 +10,7 @@ from agent_framework import (
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AgentResponseUpdate,
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Message,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import GroupChatBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -39,8 +39,8 @@ Participants represent:
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- Doctor from Scandinavia (public health, equity, societal support)
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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"""
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@@ -48,10 +48,10 @@ Prerequisites:
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load_dotenv()
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def _get_chat_client() -> AzureOpenAIResponsesClient:
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return AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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def _get_chat_client() -> FoundryChatClient:
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return FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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@@ -9,7 +9,7 @@ from agent_framework import (
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AgentResponseUpdate,
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Message,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import GroupChatBuilder, GroupChatState
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -25,8 +25,8 @@ What it does:
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- Uses a pure Python function to control speaker selection based on conversation state
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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"""
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@@ -40,9 +40,9 @@ def round_robin_selector(state: GroupChatState) -> str:
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async def main() -> None:
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# Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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@@ -11,7 +11,7 @@ from agent_framework import (
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Message,
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resolve_agent_id,
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)
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import HandoffBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -29,8 +29,8 @@ Routing Pattern:
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User -> Coordinator -> Specialist (iterates N times) -> Handoff -> Final Output
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Prerequisites:
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- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- Azure OpenAI configured for FoundryChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.
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Key Concepts:
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@@ -43,10 +43,11 @@ load_dotenv()
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def create_agents(
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client: AzureOpenAIResponsesClient,
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client: FoundryChatClient,
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) -> tuple[Agent, Agent, Agent]:
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"""Create coordinator and specialists for autonomous iteration."""
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coordinator = client.as_agent(
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coordinator = Agent(
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client=client,
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instructions=(
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"You are a coordinator. You break down a user query into a research task and a summary task. "
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"Assign the two tasks to the appropriate specialists, one after the other."
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@@ -54,7 +55,8 @@ def create_agents(
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name="coordinator",
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)
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research_agent = client.as_agent(
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research_agent = Agent(
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client=client,
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instructions=(
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"You are a research specialist that explores topics thoroughly using web search. "
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"When given a research task, break it down into multiple aspects and explore each one. "
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@@ -66,7 +68,8 @@ def create_agents(
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name="research_agent",
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)
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summary_agent = client.as_agent(
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summary_agent = Agent(
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client=client,
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instructions=(
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"You summarize research findings. Provide a concise, well-organized summary. When done, return "
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"control to the coordinator."
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@@ -79,9 +82,9 @@ def create_agents(
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async def main() -> None:
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"""Run an autonomous handoff workflow with specialist iteration enabled."""
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
coordinator, research_agent, summary_agent = create_agents(client)
|
||||
|
||||
@@ -12,7 +12,7 @@ from agent_framework import (
|
||||
WorkflowRunState,
|
||||
tool,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -26,8 +26,8 @@ A handoff workflow defines a pattern that assembles agents in a mesh topology, a
|
||||
them to transfer control to each other based on the conversation context.
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for FoundryChatClient with required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.
|
||||
|
||||
Key Concepts:
|
||||
@@ -60,17 +60,18 @@ def process_return(order_number: Annotated[str, "Order number to process return
|
||||
return f"Return initiated successfully for order {order_number}. You will receive return instructions via email."
|
||||
|
||||
|
||||
def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Agent, Agent]:
|
||||
def create_agents(client: FoundryChatClient) -> tuple[Agent, Agent, Agent, Agent]:
|
||||
"""Create and configure the triage and specialist agents.
|
||||
|
||||
Args:
|
||||
client: The AzureOpenAIResponsesClient to use for creating agents.
|
||||
client: The FoundryChatClient to use for creating agents.
|
||||
|
||||
Returns:
|
||||
Tuple of (triage_agent, refund_agent, order_agent, return_agent)
|
||||
"""
|
||||
# Triage agent: Acts as the frontline dispatcher
|
||||
triage_agent = client.as_agent(
|
||||
triage_agent = Agent(
|
||||
client=client,
|
||||
instructions=(
|
||||
"You are frontline support triage. Route customer issues to the appropriate specialist agents "
|
||||
"based on the problem described."
|
||||
@@ -79,7 +80,8 @@ def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Age
|
||||
)
|
||||
|
||||
# Refund specialist: Handles refund requests
|
||||
refund_agent = client.as_agent(
|
||||
refund_agent = Agent(
|
||||
client=client,
|
||||
instructions="You process refund requests.",
|
||||
name="refund_agent",
|
||||
# In a real application, an agent can have multiple tools; here we keep it simple
|
||||
@@ -87,7 +89,8 @@ def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Age
|
||||
)
|
||||
|
||||
# Order/shipping specialist: Resolves delivery issues
|
||||
order_agent = client.as_agent(
|
||||
order_agent = Agent(
|
||||
client=client,
|
||||
instructions="You handle order and shipping inquiries.",
|
||||
name="order_agent",
|
||||
# In a real application, an agent can have multiple tools; here we keep it simple
|
||||
@@ -95,7 +98,8 @@ def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Age
|
||||
)
|
||||
|
||||
# Return specialist: Handles return requests
|
||||
return_agent = client.as_agent(
|
||||
return_agent = Agent(
|
||||
client=client,
|
||||
instructions="You manage product return requests.",
|
||||
name="return_agent",
|
||||
# In a real application, an agent can have multiple tools; here we keep it simple
|
||||
@@ -195,9 +199,9 @@ async def main() -> None:
|
||||
replace the scripted_responses with actual user input collection.
|
||||
"""
|
||||
# Initialize the Azure OpenAI Responses client
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@ Verifies GitHub issue #2718: files generated by code interpreter in
|
||||
HandoffBuilder workflows can be properly retrieved.
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- `az login` (Azure CLI authentication)
|
||||
- AZURE_AI_MODEL_DEPLOYMENT_NAME
|
||||
"""
|
||||
@@ -23,12 +23,13 @@ from collections.abc import AsyncIterable
|
||||
from typing import cast
|
||||
|
||||
from agent_framework import (
|
||||
Agent,
|
||||
AgentResponseUpdate,
|
||||
Message,
|
||||
WorkflowEvent,
|
||||
WorkflowRunState,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -90,13 +91,14 @@ async def main() -> None:
|
||||
"""Run a simple handoff workflow with code interpreter file generation."""
|
||||
print("=== Handoff Workflow with Code Interpreter File Generation ===\n")
|
||||
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
triage = client.as_agent(
|
||||
triage = Agent(
|
||||
client=client,
|
||||
name="triage_agent",
|
||||
instructions=(
|
||||
"You are a triage agent. Route code-related requests to the code_specialist. "
|
||||
@@ -107,7 +109,8 @@ async def main() -> None:
|
||||
|
||||
code_interpreter_tool = client.get_code_interpreter_tool()
|
||||
|
||||
code_specialist = client.as_agent(
|
||||
code_specialist = Agent(
|
||||
client=client,
|
||||
name="code_specialist",
|
||||
instructions=(
|
||||
"You are a Python code specialist. Use the code interpreter to execute Python code "
|
||||
|
||||
+13
-10
@@ -14,7 +14,7 @@ from agent_framework import (
|
||||
WorkflowEvent,
|
||||
tool,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -45,9 +45,9 @@ Pattern:
|
||||
workflow.run(stream=True, checkpoint_id=..., responses=responses).)
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure CLI authentication (az login).
|
||||
- Environment variables configured for AzureOpenAIResponsesClient.
|
||||
- Environment variables configured for FoundryChatClient.
|
||||
"""
|
||||
|
||||
CHECKPOINT_DIR = Path(__file__).parent / "tmp" / "handoff_checkpoints"
|
||||
@@ -60,10 +60,11 @@ def submit_refund(refund_description: str, amount: str, order_id: str) -> str:
|
||||
return f"refund recorded for order {order_id} (amount: {amount}) with details: {refund_description}"
|
||||
|
||||
|
||||
def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Agent]:
|
||||
def create_agents(client: FoundryChatClient) -> tuple[Agent, Agent, Agent]:
|
||||
"""Create a simple handoff scenario: triage, refund, and order specialists."""
|
||||
|
||||
triage = client.as_agent(
|
||||
triage = Agent(
|
||||
client=client,
|
||||
name="triage_agent",
|
||||
instructions=(
|
||||
"You are a customer service triage agent. Listen to customer issues and determine "
|
||||
@@ -72,7 +73,8 @@ def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Age
|
||||
),
|
||||
)
|
||||
|
||||
refund = client.as_agent(
|
||||
refund = Agent(
|
||||
client=client,
|
||||
name="refund_agent",
|
||||
instructions=(
|
||||
"You are a refund specialist. Help customers with refund requests. "
|
||||
@@ -83,7 +85,8 @@ def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Age
|
||||
tools=[submit_refund],
|
||||
)
|
||||
|
||||
order = client.as_agent(
|
||||
order = Agent(
|
||||
client=client,
|
||||
name="order_agent",
|
||||
instructions=(
|
||||
"You are an order tracking specialist. Help customers track their orders. "
|
||||
@@ -97,9 +100,9 @@ def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Age
|
||||
def create_workflow(checkpoint_storage: FileCheckpointStorage) -> Workflow:
|
||||
"""Build the handoff workflow with checkpointing enabled."""
|
||||
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
triage, refund, order = create_agents(client)
|
||||
|
||||
@@ -12,7 +12,7 @@ from agent_framework import (
|
||||
Message,
|
||||
WorkflowEvent,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import GroupChatRequestSentEvent, MagenticBuilder, MagenticProgressLedger
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -42,8 +42,8 @@ energy efficiency and CO2 emissions of several ML models, streams intermediate
|
||||
events, and prints the final answer. The workflow completes when idle.
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for FoundryChatClient with required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
||||
"""
|
||||
|
||||
@@ -52,9 +52,9 @@ load_dotenv()
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
|
||||
@@ -15,7 +15,7 @@ from agent_framework import (
|
||||
WorkflowEvent,
|
||||
WorkflowRunState,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import MagenticBuilder, MagenticPlanReviewRequest
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -39,8 +39,8 @@ Concepts highlighted here:
|
||||
`responses` mapping so we can inject the stored human reply during restoration.
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for FoundryChatClient with required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
||||
"""
|
||||
|
||||
@@ -64,9 +64,9 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
|
||||
name="ResearcherAgent",
|
||||
description="Collects background facts and references for the project.",
|
||||
instructions=("You are the research lead. Gather crisp bullet points the team should know."),
|
||||
client=AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client=FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
),
|
||||
)
|
||||
@@ -75,9 +75,9 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
|
||||
name="WriterAgent",
|
||||
description="Synthesizes the final brief for stakeholders.",
|
||||
instructions=("You convert the research notes into a structured brief with milestones and risks."),
|
||||
client=AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client=FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
),
|
||||
)
|
||||
@@ -87,9 +87,9 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
|
||||
name="MagenticManager",
|
||||
description="Orchestrator that coordinates the research and writing workflow",
|
||||
instructions="You coordinate a team to complete complex tasks efficiently.",
|
||||
client=AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client=FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
),
|
||||
)
|
||||
|
||||
@@ -12,7 +12,7 @@ from agent_framework import (
|
||||
Message,
|
||||
WorkflowEvent,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import MagenticBuilder, MagenticPlanReviewRequest, MagenticPlanReviewResponse
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -37,8 +37,8 @@ Plan review options:
|
||||
- revise(feedback): Provide textual feedback to modify the plan
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for FoundryChatClient with required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
||||
"""
|
||||
|
||||
@@ -100,9 +100,9 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
|
||||
@@ -4,8 +4,8 @@ import asyncio
|
||||
import os
|
||||
from typing import cast
|
||||
|
||||
from agent_framework import Message
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework import Agent, Message
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import SequentialBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -29,26 +29,28 @@ Note on internal adapters:
|
||||
You can safely ignore them when focusing on agent progress.
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for FoundryChatClient with required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
||||
"""
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
# 1) Create agents
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
|
||||
writer = client.as_agent(
|
||||
writer = Agent(
|
||||
client=client,
|
||||
instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
|
||||
name="writer",
|
||||
)
|
||||
|
||||
reviewer = client.as_agent(
|
||||
reviewer = Agent(
|
||||
client=client,
|
||||
instructions=("You are a thoughtful reviewer. Give brief feedback on the previous assistant message."),
|
||||
name="reviewer",
|
||||
)
|
||||
|
||||
@@ -5,13 +5,14 @@ import os
|
||||
from typing import Any
|
||||
|
||||
from agent_framework import (
|
||||
Agent,
|
||||
AgentExecutorResponse,
|
||||
Executor,
|
||||
Message,
|
||||
WorkflowContext,
|
||||
handler,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIResponsesClient
|
||||
from agent_framework.foundry import FoundryChatClient
|
||||
from agent_framework.orchestrations import SequentialBuilder
|
||||
from azure.identity import AzureCliCredential
|
||||
from dotenv import load_dotenv
|
||||
@@ -33,8 +34,8 @@ Custom executor contract:
|
||||
- Emit the updated conversation via ctx.send_message([...])
|
||||
|
||||
Prerequisites:
|
||||
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
|
||||
- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
||||
- Azure OpenAI configured for FoundryChatClient with required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
||||
"""
|
||||
|
||||
@@ -65,12 +66,13 @@ class Summarizer(Executor):
|
||||
|
||||
async def main() -> None:
|
||||
# 1) Create a content agent
|
||||
client = AzureOpenAIResponsesClient(
|
||||
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
client = FoundryChatClient(
|
||||
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
|
||||
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
credential=AzureCliCredential(),
|
||||
)
|
||||
content = client.as_agent(
|
||||
content = Agent(
|
||||
client=client,
|
||||
instructions="Produce a concise paragraph answering the user's request.",
|
||||
name="content",
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user