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Python: [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces (#4990)

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* [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces

Also clean up follow-on docs, environment guidance, package metadata, and lab test stability.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix deleted semantic-kernel sample links

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address PR review feedback

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* improve foundry language

* Fix A2A Foundry sample regression

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-03-31 22:36:21 +02:00
committed by GitHub
Unverified
parent a5eacbbe65
commit 3a49b1d6dd
144 changed files with 669 additions and 18739 deletions
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python/samples/02-agents/chat_client/README.md
+12 -18
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@@ -15,22 +15,19 @@ This folder contains examples for direct chat client usage patterns.
`built_in_chat_clients.py` starts with:
```python
asyncio.run(main("openai_chat"))
asyncio.run(main("openai_responses"))
```
Change the argument to pick a client:
- `openai_chat`
- `openai_responses`
- `openai_assistants`
- `openai_chat_completion`
- `anthropic`
- `ollama`
- `bedrock`
- `azure_openai_chat`
- `azure_openai_responses`
- `azure_openai_responses_foundry`
- `azure_openai_assistants`
- `azure_ai_agent`
- `azure_openai_chat_completion`
- `foundry_chat`
Example:
@@ -42,22 +39,19 @@ uv run samples/02-agents/chat_client/built_in_chat_clients.py
Depending on the selected client, set the appropriate environment variables:
**For Azure clients:**
**For Azure OpenAI clients (`azure_openai_responses` and `azure_openai_chat_completion`):**
- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
- `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI chat deployment
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI responses deployment
- `AZURE_OPENAI_DEPLOYMENT_NAME`: The Azure OpenAI deployment used by the sample
- `AZURE_OPENAI_API_VERSION` (optional): Azure OpenAI API version override
- `AZURE_OPENAI_API_KEY` (optional): Azure OpenAI API key if you are not using `AzureCliCredential`
**For Azure OpenAI Foundry responses client (`azure_openai_responses_foundry`):**
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI responses deployment
**For Azure AI agent client (`azure_ai_agent`):**
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment (used by `azure_ai_agent`)
**For Foundry client (`foundry_chat`):**
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `FOUNDRY_MODEL`: The Foundry deployment used by the sample
**For OpenAI clients:**
- `OPENAI_API_KEY`: Your OpenAI API key
- `OPENAI_CHAT_MODEL`: The OpenAI model for `openai_chat` and `openai_assistants`
- `OPENAI_CHAT_MODEL`: The OpenAI model for `openai_chat_completion`
- `OPENAI_RESPONSES_MODEL`: The OpenAI model for `openai_responses`
**For Anthropic client (`anthropic`):**
python/samples/02-agents/chat_client/built_in_chat_clients.py
+17 -42
View File
@@ -6,13 +6,9 @@ from random import randint
from typing import Annotated, Any, Literal
from agent_framework import Message, SupportsChatGetResponse, tool
from agent_framework.azure import (
AzureOpenAIAssistantsClient,
)
from agent_framework.foundry import FoundryChatClient
from agent_framework.openai import OpenAIAssistantsClient
from agent_framework.openai import OpenAIChatClient, OpenAIChatCompletionClient
from azure.identity import AzureCliCredential
from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
from dotenv import load_dotenv
from pydantic import Field
@@ -26,31 +22,25 @@ This sample demonstrates how to run the same prompt flow against different built
chat clients using a single `get_client` factory.
Select one of these client names:
- openai_chat
- openai_responses
- openai_assistants
- openai_chat_completion
- anthropic
- ollama
- bedrock
- azure_openai_chat
- azure_openai_responses
- azure_openai_responses_foundry
- azure_openai_assistants
- azure_ai_agent
- azure_openai_chat_completion
- foundry_chat
"""
ClientName = Literal[
"openai_chat",
"openai_responses",
"openai_assistants",
"openai_chat_completion",
"anthropic",
"ollama",
"bedrock",
"azure_openai_chat",
"azure_openai_responses",
"azure_openai_responses_foundry",
"azure_openai_assistants",
"azure_ai_agent",
"azure_openai_chat_completion",
"foundry_chat",
]
@@ -71,55 +61,41 @@ def get_client(client_name: ClientName) -> SupportsChatGetResponse[Any]:
from agent_framework.amazon import BedrockChatClient
from agent_framework.anthropic import AnthropicClient
from agent_framework.ollama import OllamaChatClient
from agent_framework.openai import OpenAIResponsesClient
# 1. Create OpenAI clients.
if client_name == "openai_chat":
return FoundryChatClient()
if client_name == "openai_responses":
return OpenAIResponsesClient()
if client_name == "openai_assistants":
return OpenAIAssistantsClient()
return OpenAIChatClient()
if client_name == "openai_chat_completion":
return OpenAIChatCompletionClient()
if client_name == "anthropic":
return AnthropicClient()
if client_name == "ollama":
return OllamaChatClient()
if client_name == "bedrock":
return BedrockChatClient()
# 2. Create Azure OpenAI clients.
if client_name == "azure_openai_chat":
return FoundryChatClient(credential=AzureCliCredential())
if client_name == "azure_openai_responses":
return FoundryChatClient(credential=AzureCliCredential(), api_version="preview")
if client_name == "azure_openai_responses_foundry":
return OpenAIChatClient(credential=AzureCliCredential())
if client_name == "azure_openai_chat_completion":
return OpenAIChatCompletionClient(credential=AzureCliCredential())
if client_name == "foundry_chat":
return FoundryChatClient(
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
model=os.environ["FOUNDRY_MODEL"],
credential=AzureCliCredential(),
)
if client_name == "azure_openai_assistants":
return AzureOpenAIAssistantsClient(credential=AzureCliCredential())
# 3. Create Azure AI client.
if client_name == "azure_ai_agent":
return FoundryChatClient(credential=AsyncAzureCliCredential())
raise ValueError(f"Unsupported client name: {client_name}")
async def main(client_name: ClientName = "openai_chat") -> None:
async def main(client_name: ClientName = "openai_responses") -> None:
"""Run a basic prompt using a selected built-in client."""
client = get_client(client_name)
# 1. Configure prompt and streaming mode.
message = Message("user", text="What's the weather in Amsterdam and in Paris?")
stream = os.getenv("STREAM", "false").lower() == "true"
print(f"Client: {client_name}")
print(f"User: {message.text}")
# 2. Run with context-managed clients.
if isinstance(client, OpenAIAssistantsClient | AzureOpenAIAssistantsClient | FoundryChatClient):
if isinstance(client, FoundryChatClient):
async with client:
if stream:
response_stream = client.get_response([message], stream=True, options={"tools": get_weather})
@@ -134,7 +110,6 @@ async def main(client_name: ClientName = "openai_chat") -> None:
)
return
# 3. Run with non-context-managed clients.
if stream:
response_stream = client.get_response([message], stream=True, options={"tools": get_weather})
print("Assistant: ", end="")
@@ -147,7 +122,7 @@ async def main(client_name: ClientName = "openai_chat") -> None:
if __name__ == "__main__":
asyncio.run(main("openai_chat"))
asyncio.run(main("openai_responses"))
"""
python/samples/02-agents/context_providers/azure_ai_search/README.md
+12 -8
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@@ -49,14 +49,14 @@ Run `az login` if using Entra ID authentication.
**Common (both modes):**
- `AZURE_SEARCH_ENDPOINT`: Your Azure AI Search endpoint (e.g., `https://myservice.search.windows.net`)
- `AZURE_SEARCH_INDEX_NAME`: Name of your search index
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: Model deployment name (e.g., `gpt-4o`, defaults to `gpt-4o`)
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `FOUNDRY_MODEL`: Model deployment name (e.g., `gpt-4o`, defaults to `gpt-4o`)
- `AZURE_SEARCH_API_KEY`: _(Optional)_ Your search API key - if not provided, uses DefaultAzureCredential
**Agentic mode only:**
- `AZURE_SEARCH_KNOWLEDGE_BASE_NAME`: Name of your Knowledge Base in Azure AI Search
- `AZURE_OPENAI_RESOURCE_URL`: Your Azure OpenAI resource URL (e.g., `https://myresource.openai.azure.com`)
- **Important**: This is different from `AZURE_AI_PROJECT_ENDPOINT` - Knowledge Base needs the OpenAI endpoint for model calls
- **Important**: This is different from `FOUNDRY_PROJECT_ENDPOINT` - Knowledge Base needs the OpenAI endpoint for model calls
### Example .env file
@@ -64,8 +64,8 @@ Run `az login` if using Entra ID authentication.
```env
AZURE_SEARCH_ENDPOINT=https://myservice.search.windows.net
AZURE_SEARCH_INDEX_NAME=my-index
AZURE_AI_PROJECT_ENDPOINT=https://<resource-name>.services.ai.azure.com/api/projects/<project-name>
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
FOUNDRY_PROJECT_ENDPOINT=https://<resource-name>.services.ai.azure.com/api/projects/<project-name>
FOUNDRY_MODEL=gpt-4o
# Optional - omit to use Entra ID
AZURE_SEARCH_API_KEY=your-search-key
```
@@ -127,7 +127,8 @@ AZURE_OPENAI_RESOURCE_URL=https://myresource.openai.azure.com
```python
from agent_framework import Agent
from agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider
from agent_framework.azure import AzureAISearchContextProvider
from agent_framework.foundry import FoundryChatClient
from azure.identity.aio import DefaultAzureCredential
# Create search provider with semantic mode (default)
@@ -140,10 +141,13 @@ search_provider = AzureAISearchContextProvider(
)
# Create agent with search context
async with AzureAIAgentClient(credential=DefaultAzureCredential()) as client:
async with FoundryChatClient(
project_endpoint=project_endpoint,
model=model_deployment,
credential=DefaultAzureCredential(),
) as client:
async with Agent(
client=client,
model=model_deployment,
context_providers=[search_provider],
) as agent:
response = await agent.run("What information is in the knowledge base?")
python/samples/02-agents/context_providers/azure_ai_search/search_context_agentic.py
+2 -2
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@@ -34,7 +34,7 @@ Environment variables:
- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
- AZURE_SEARCH_API_KEY: (Optional) API key - if not provided, uses AzureCliCredential
- FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
- FOUNDRY_MODEL: Your model deployment name (e.g., "gpt-4o")
For using an existing Knowledge Base (recommended):
- AZURE_SEARCH_KNOWLEDGE_BASE_NAME: Your Knowledge Base name
@@ -59,7 +59,7 @@ async def main() -> None:
search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
search_key = os.environ.get("AZURE_SEARCH_API_KEY")
project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
model_deployment = os.environ.get("FOUNDRY_MODEL", "gpt-4o")
# Agentic mode requires exactly ONE of: knowledge_base_name OR index_name
# Option 1: Use existing Knowledge Base (recommended)
python/samples/02-agents/context_providers/azure_ai_search/search_context_semantic.py
+2 -2
View File
@@ -31,7 +31,7 @@ Prerequisites:
- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses AzureCliCredential for Entra ID
- AZURE_SEARCH_INDEX_NAME: Your search index name
- FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
- FOUNDRY_MODEL: Your model deployment name (e.g., "gpt-4o")
- AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: (Optional) Your Azure OpenAI embedding deployment for hybrid search
- AZURE_OPENAI_ENDPOINT: (Optional) Your Azure OpenAI resource URL, required if using Azure OpenAI embeddings
"""
@@ -54,7 +54,7 @@ async def main() -> None:
search_key = os.environ.get("AZURE_SEARCH_API_KEY")
index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
model_deployment = os.environ.get("FOUNDRY_MODEL", "gpt-4o")
openai_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
embedding_deployment = os.environ.get("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME")
python/samples/02-agents/context_providers/mem0/README.md
+2 -2
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@@ -33,8 +33,8 @@ Set the following environment variables:
- `OPENAI_API_KEY`: Your OpenAI API key (used by Mem0 OSS for embedding generation and automatic memory extraction)
**For Azure AI:**
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI project endpoint
- `FOUNDRY_MODEL`: The name of your model deployment
## Key Concepts
python/samples/02-agents/context_providers/redis/README.md
+6 -6
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@@ -51,8 +51,8 @@ See quickstart: `https://learn.microsoft.com/azure/redis/quickstart-create-manag
### Environment variables
- `AZURE_AI_PROJECT_ENDPOINT` (required): Azure AI Foundry project endpoint for `AzureOpenAIResponsesClient`
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME` (required): Azure OpenAI Responses deployment name
- `FOUNDRY_PROJECT_ENDPOINT` (required): Azure AI Foundry project endpoint for `FoundryChatClient`
- `FOUNDRY_MODEL` (required): Foundry model deployment name
- `OPENAI_API_KEY` (optional): Required only if you set `vectorizer_choice="openai"` to enable hybrid search.
### Provider configuration highlights
@@ -73,7 +73,7 @@ The provider supports both full‑text only and hybrid vector search:
2. Agent integration: teaches the agent a preference and verifies it is remembered across turns.
3. Agent + tool: calls a sample tool (flight search) and then asks the agent to recall details remembered from the tool output.
It uses `AzureOpenAIResponsesClient` (Foundry project endpoint setup) for chat and, in some steps, optional OpenAI embeddings for hybrid search.
It uses `FoundryChatClient` for chat and, in some steps, optional OpenAI embeddings for hybrid search.
## How to run
@@ -82,8 +82,8 @@ It uses `AzureOpenAIResponsesClient` (Foundry project endpoint setup) for chat a
2) Set Azure Foundry/OpenAI responses environment variables:
```bash
export AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="<deployment-name>"
export FOUNDRY_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
export FOUNDRY_MODEL="<deployment-name>"
```
3) (Optional) Set your OpenAI key if using embeddings:
@@ -119,6 +119,6 @@ You should see the agent responses and, when using embeddings, context retrieved
## Troubleshooting
- Ensure at least one of `application_id`, `agent_id`, `user_id`, or `thread_id` is set; the provider requires a scope.
- Verify `AZURE_AI_PROJECT_ENDPOINT` and `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME` are set for the chat client.
- Verify `FOUNDRY_PROJECT_ENDPOINT` and `FOUNDRY_MODEL` are set for the chat client.
- If using embeddings, verify `OPENAI_API_KEY` is set and reachable.
- Make sure Redis exposes RediSearch (Redis Stack image or managed service with search enabled).
python/samples/02-agents/declarative/mcp_tool_yaml.py
+3 -3
View File
@@ -10,11 +10,11 @@ Key Features Demonstrated:
1. Loading agent definitions from YAML using AgentFactory
2. Configuring MCP tools with different authentication methods:
- API key authentication (OpenAI.Responses provider)
- Azure AI Foundry connection references (AzureAI.ProjectProvider)
- Azure AI Foundry connection references (Foundry provider)
Authentication Options:
- OpenAI.Responses: Supports inline API key auth via headers
- AzureAI.ProjectProvider: Uses Foundry connections for secure credential storage
- Foundry: Uses project-backed chat with Foundry connections for secure credential storage
(no secrets passed in API calls - connection name references pre-configured auth)
Prerequisites:
@@ -79,7 +79,7 @@ instructions: |
model:
id: gpt-4o
provider: AzureAI.ProjectProvider
provider: Foundry
tools:
- kind: mcp
python/samples/02-agents/devui/README.md
+3 -3
View File
@@ -55,15 +55,15 @@ agent_name/
| Sample | Description | Features | Required Environment Variables |
| ------------------------------------------------ | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| [**weather_agent_azure/**](weather_agent_azure/) | Weather agent using Azure OpenAI with API key authentication | Azure OpenAI integration, function calling, mock weather tools | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`, `AZURE_OPENAI_ENDPOINT` |
| [**foundry_agent/**](foundry_agent/) | Weather agent using Azure AI Agent (Foundry) with Azure CLI authentication (run `az login` first) | Azure AI Agent integration, Azure CLI authentication, mock weather tools | `AZURE_AI_PROJECT_ENDPOINT`, `FOUNDRY_MODEL_DEPLOYMENT_NAME` |
| [**weather_agent_azure/**](weather_agent_azure/) | Weather agent using Azure OpenAI with API key authentication | Azure OpenAI integration, function calling, mock weather tools | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_DEPLOYMENT_NAME`, `AZURE_OPENAI_ENDPOINT` |
| [**foundry_agent/**](foundry_agent/) | Weather agent using Azure AI Agent (Foundry) with Azure CLI authentication (run `az login` first) | Azure AI Agent integration, Azure CLI authentication, mock weather tools | `FOUNDRY_PROJECT_ENDPOINT`, `FOUNDRY_MODEL` |
### Workflows
| Sample | Description | Features | Required Environment Variables |
| -------------------------------------------- | ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| [**declarative/**](declarative/) | Declarative YAML workflow with conditional branching | YAML-based workflow definition, conditional logic, no Python code required | None - uses mock data |
| [**workflow_agents/**](workflow_agents/) | Content review workflow with agents as executors | Agents as workflow nodes, conditional routing based on structured outputs, quality-based paths (Writer -> Reviewer -> Editor/Publisher) | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`, `AZURE_OPENAI_ENDPOINT` |
| [**workflow_agents/**](workflow_agents/) | Content review workflow with agents as executors | Agents as workflow nodes, conditional routing based on structured outputs, quality-based paths (Writer -> Reviewer -> Editor/Publisher) | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_DEPLOYMENT_NAME`, `AZURE_OPENAI_ENDPOINT` |
| [**spam_workflow/**](spam_workflow/) | 5-step email spam detection workflow with branching logic | Sequential execution, conditional branching (spam vs. legitimate), multiple executors, mock spam detection | None - uses mock data |
| [**fanout_workflow/**](fanout_workflow/) | Advanced data processing workflow with parallel execution | Fan-out/fan-in patterns, complex state management, multi-stage processing (validation -> transformation -> quality assurance) | None - uses mock data |
python/samples/02-agents/devui/azure_responses_agent/.env.example
+1 -1
View File
@@ -12,4 +12,4 @@ AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
AZURE_OPENAI_ENDPOINT=https://your-resource.cognitiveservices.azure.com/
# Required: Deployment name (must support Responses API)
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=gpt-4.1-mini
FOUNDRY_MODEL=gpt-4.1-mini
python/samples/02-agents/devui/foundry_agent/.env.example
+2 -2
View File
@@ -2,5 +2,5 @@
# Get your credentials from Azure AI Foundry portal
# Make sure to run 'az login' before starting devui
AZURE_AI_PROJECT_ENDPOINT=https://your-project.api.azureml.ms
FOUNDRY_MODEL_DEPLOYMENT_NAME=gpt-4o
FOUNDRY_PROJECT_ENDPOINT=https://your-project.api.azureml.ms
FOUNDRY_MODEL=gpt-4o
python/samples/02-agents/devui/foundry_agent/agent.py
+1 -1
View File
@@ -53,7 +53,7 @@ agent = Agent(
name="FoundryWeatherAgent",
client=FoundryChatClient(
project_endpoint=os.environ.get("FOUNDRY_PROJECT_ENDPOINT"),
model_model=os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME"),
model_model=os.environ.get("FOUNDRY_MODEL"),
credential=AzureCliCredential(),
),
instructions="""
python/samples/02-agents/devui/weather_agent_azure/.env.example
+1 -1
View File
@@ -2,5 +2,5 @@
# Get your credentials from Azure Portal
AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
python/samples/02-agents/devui/workflow_agents/.env.example
+1 -1
View File
@@ -2,6 +2,6 @@
# Get your credentials from Azure Portal
AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_API_VERSION=2024-10-21
python/samples/02-agents/evaluation/evaluate_multimodal.py
-1
View File
@@ -22,7 +22,6 @@ from agent_framework import (
evaluator,
)
# -- Custom evaluators that inspect multimodal content --
python/samples/02-agents/middleware/README.md
+1 -1
View File
@@ -21,7 +21,7 @@ This folder contains focused middleware samples for `Agent`, chat clients, tools
## Running the usage tracking sample
The new usage tracking sample uses `OpenAIResponsesClient`, so set the usual OpenAI responses environment variables first:
The new usage tracking sample uses `OpenAIChatClient`, so set the usual OpenAI responses environment variables first:
```bash
export OPENAI_API_KEY="your-openai-api-key"
python/samples/02-agents/middleware/override_result_with_middleware.py
+2 -2
View File
@@ -19,7 +19,7 @@ from agent_framework import (
ResponseStream,
tool,
)
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
from pydantic import Field
@@ -190,7 +190,7 @@ async def main() -> None:
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
agent = Agent(
client=OpenAIResponsesClient(
client=OpenAIChatClient(
middleware=[validate_weather_middleware, weather_override_middleware],
),
name="WeatherAgent",
python/samples/02-agents/middleware/usage_tracking_middleware.py
+2 -2
View File
@@ -19,7 +19,7 @@ from agent_framework import (
chat_middleware,
tool,
)
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
from pydantic import Field
@@ -53,7 +53,7 @@ def _reset_usage_counters() -> None:
def _create_agent() -> Agent:
"""Create the shared agent used by both demonstrations."""
return Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
instructions=(
"You are a weather assistant. Always call the weather tool before answering weather questions, "
"then summarize the tool result in one short paragraph."
python/samples/02-agents/multimodal_input/README.md
+6 -6
View File
@@ -32,8 +32,8 @@ Set the following environment variables before running the examples:
**For Azure OpenAI:**
- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
- `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI chat model deployment
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI responses model deployment
- `AZURE_OPENAI_DEPLOYMENT_NAME`: The name of your Azure OpenAI chat model deployment
- `AZURE_OPENAI_DEPLOYMENT_NAME`: The name of your Azure OpenAI responses model deployment
Optionally for Azure OpenAI:
- `AZURE_OPENAI_API_VERSION`: The API version to use (default is `2024-10-21`)
@@ -41,11 +41,11 @@ Optionally for Azure OpenAI:
**Note:** You can also provide configuration directly in code instead of using environment variables:
```python
# Example: Pass deployment_name directly
client = AzureOpenAIChatClient(
# Example: Pass the Foundry project endpoint directly
client = FoundryChatClient(
credential=AzureCliCredential(),
deployment_name="your-deployment-name",
endpoint="https://your-resource.openai.azure.com"
project_endpoint="https://your-project.services.ai.azure.com",
model="your-deployment-name",
)
```
python/samples/02-agents/observability/.env.example
+2 -2
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@@ -45,5 +45,5 @@ OPENAI_CHAT_MODEL="gpt-4o-2024-08-06"
# Azure AI Foundry specific variables
# ====================================
AZURE_AI_PROJECT_ENDPOINT="..."
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
FOUNDRY_PROJECT_ENDPOINT="..."
FOUNDRY_MODEL="gpt-4o-mini"
python/samples/02-agents/providers/anthropic/README.md
+2 -1
View File
@@ -33,7 +33,8 @@ This folder contains examples demonstrating how to use Anthropic's Claude models
### Foundry
- `ANTHROPIC_FOUNDRY_API_KEY`: Your Foundry Anthropic API key
- `ANTHROPIC_FOUNDRY_ENDPOINT`: The endpoint URL for your Foundry Anthropic resource
- `ANTHROPIC_FOUNDRY_RESOURCE`: Your Foundry resource name (for example `my-foundry-resource`)
- `ANTHROPIC_FOUNDRY_BASE_URL`: Optional full Foundry Anthropic base URL alternative to `ANTHROPIC_FOUNDRY_RESOURCE`
- `ANTHROPIC_CHAT_MODEL_ID`: The Claude model to use in Foundry (e.g., `claude-haiku-4-5`)
### Claude Agent
python/samples/02-agents/providers/anthropic/anthropic_foundry.py
+5 -2
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@@ -22,8 +22,11 @@ This example requires `anthropic>=0.74.0` and an endpoint in Foundry for Anthrop
To use the Foundry integration ensure you have the following environment variables set:
- ANTHROPIC_FOUNDRY_API_KEY
Alternatively you can pass in a azure_ad_token_provider function to the AsyncAnthropicFoundry constructor.
- ANTHROPIC_FOUNDRY_ENDPOINT
Should be something like https://<your-resource-name>.services.ai.azure.com/anthropic/
- ANTHROPIC_FOUNDRY_RESOURCE
Should be the resource name portion of your Foundry Anthropic URL, such as <your-resource-name>.
- ANTHROPIC_FOUNDRY_BASE_URL
Optional alternative to ANTHROPIC_FOUNDRY_RESOURCE. Should be something like
https://<your-resource-name>.services.ai.azure.com/anthropic/
- ANTHROPIC_CHAT_MODEL_ID
Should be something like claude-haiku-4-5
"""
python/samples/02-agents/providers/azure/openai_chat_completion_client_with_explicit_settings.py
+1 -1
View File
@@ -41,7 +41,7 @@ async def main() -> None:
# authentication option.
agent = Agent(
client=OpenAIChatCompletionClient(
model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
credential=AzureCliCredential(),
),
python/samples/02-agents/providers/custom/README.md
+4 -4
View File
@@ -27,7 +27,7 @@ Both approaches allow you to extend the framework for your specific use cases wh
## Understanding Raw Client Classes
The framework provides `Raw...Client` classes (e.g., `RawOpenAIChatClient`, `RawOpenAIChatCompletionClient`, `RawAzureAIClient`) that are intermediate implementations without middleware, telemetry, or function invocation support.
The framework provides `Raw...Client` classes (e.g., `RawOpenAIChatClient`, `RawOpenAIChatCompletionClient`, `RawFoundryChatClient`) that are intermediate implementations without middleware, telemetry, or function invocation support.
### Warning: Raw Clients Should Not Normally Be Used Directly
@@ -62,8 +62,8 @@ For most use cases, use the fully-featured public client classes which already h
- `OpenAIChatCompletionClient` - OpenAI Chat Completions API with all layers
- `OpenAIChatClient` - OpenAI Responses API with all layers
- `AzureOpenAIChatClient` - Azure OpenAI Chat with all layers
- `AzureOpenAIResponsesClient` - Azure OpenAI Responses with all layers
- `AzureAIClient` - Azure AI Project with all layers
- `OpenAIChatCompletionClient` - Azure OpenAI Chat Completions with all layers
- `OpenAIChatClient` - Azure OpenAI Responses with all layers
- `FoundryChatClient` - Azure AI Foundry project-backed chat with all layers
These clients handle the layer composition correctly and provide the full feature set out of the box.
python/samples/02-agents/skills/code_defined_skill/README.md
+2 -2
View File
@@ -27,8 +27,8 @@ code_defined_skill/
Set the required environment variables in a `.env` file (see `python/.env.example`):
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your model deployment (defaults to `gpt-4o-mini`)
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_DEPLOYMENT_NAME`: The name of your model deployment (defaults to `gpt-4o-mini`)
### Authentication
python/samples/02-agents/skills/file_based_skill/README.md
+2 -2
View File
@@ -47,8 +47,8 @@ file_based_skill/
Set the required environment variables in a `.env` file (see `python/.env.example`):
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your model deployment (defaults to `gpt-4o-mini`)
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_DEPLOYMENT_NAME`: The name of your model deployment (defaults to `gpt-4o-mini`)
### Authentication
python/samples/02-agents/skills/mixed_skills/README.md
+2 -2
View File
@@ -60,8 +60,8 @@ File scripts are executed as **local Python subprocesses** via the
Set environment variables (or create a `.env` file):
```
AZURE_AI_PROJECT_ENDPOINT=https://your-project.openai.azure.com/
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=gpt-4o-mini
FOUNDRY_PROJECT_ENDPOINT=https://your-project.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini
```
Authenticate with Azure CLI:
python/samples/02-agents/skills/script_approval/README.md
+2 -2
View File
@@ -28,8 +28,8 @@ When `require_script_approval=True` is set, the agent pauses before executing an
Set the required environment variables in a `.env` file (see `python/.env.example`):
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your model deployment (defaults to `gpt-4o-mini`)
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_DEPLOYMENT_NAME`: The name of your model deployment (defaults to `gpt-4o-mini`)
### Authentication
python/samples/02-agents/tools/function_invocation_configuration.py
+2 -2
View File
@@ -4,7 +4,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -28,7 +28,7 @@ def add(
async def main():
client = OpenAIResponsesClient()
client = OpenAIChatClient()
client.function_invocation_configuration["include_detailed_errors"] = True
client.function_invocation_configuration["max_iterations"] = 40
print(f"Function invocation configured as: \n{client.function_invocation_configuration}")
python/samples/02-agents/tools/function_tool_declaration_only.py
+2 -2
View File
@@ -3,7 +3,7 @@
import asyncio
from agent_framework import Agent, FunctionTool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -26,7 +26,7 @@ async def main():
)
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="DeclarationOnlyToolAgent",
instructions="You are a helpful agent that uses tools.",
tools=function_declaration,
python/samples/02-agents/tools/function_tool_from_dict_with_dependency_injection.py
+2 -2
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@@ -22,7 +22,7 @@ Usage:
import asyncio
from agent_framework import Agent, FunctionTool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -62,7 +62,7 @@ async def main() -> None:
tool = FunctionTool.from_dict(definition, dependencies={"function_tool": {"name:add_numbers": {"func": func}}})
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="FunctionToolAgent",
instructions="You are a helpful assistant.",
tools=tool,
python/samples/02-agents/tools/function_tool_with_explicit_schema.py
+2 -2
View File
@@ -18,7 +18,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
from pydantic import BaseModel, Field
@@ -70,7 +70,7 @@ def get_current_time(timezone: str = "UTC") -> str:
async def main():
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="AssistantAgent",
instructions="You are a helpful assistant. Use the available tools to answer questions.",
tools=[get_weather, get_current_time],
python/samples/02-agents/tools/function_tool_with_kwargs.py
+2 -2
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@@ -4,7 +4,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, FunctionInvocationContext, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
from pydantic import Field
@@ -44,7 +44,7 @@ def get_weather(
async def main() -> None:
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="WeatherAgent",
instructions="You are a helpful weather assistant.",
tools=[get_weather],
python/samples/02-agents/tools/function_tool_with_max_exceptions.py
+2 -2
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@@ -4,7 +4,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -36,7 +36,7 @@ def safe_divide(
async def main():
# tools = Tools()
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="ToolAgent",
instructions="Use the provided tools.",
tools=[safe_divide],
python/samples/02-agents/tools/function_tool_with_max_invocations.py
+2 -2
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@@ -4,7 +4,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -25,7 +25,7 @@ def unicorn_function(times: Annotated[int, "The number of unicorns to return."])
async def main():
# tools = Tools()
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="ToolAgent",
instructions="Use the provided tools.",
tools=[unicorn_function],
python/samples/02-agents/tools/function_tool_with_session_injection.py
+2 -2
View File
@@ -4,7 +4,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, AgentSession, FunctionInvocationContext, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
from pydantic import Field
@@ -37,7 +37,7 @@ async def get_weather(
async def main() -> None:
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="WeatherAgent",
instructions="You are a helpful weather assistant.",
tools=[get_weather],
python/samples/02-agents/tools/tool_in_class.py
+2 -2
View File
@@ -4,7 +4,7 @@ import asyncio
from typing import Annotated
from agent_framework import Agent, tool
from agent_framework.openai import OpenAIResponsesClient
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -50,7 +50,7 @@ async def main():
add_function = tool(description="Add two numbers.")(tools.add)
agent = Agent(
client=OpenAIResponsesClient(),
client=OpenAIChatClient(),
name="ToolAgent",
instructions="Use the provided tools.",
)