First samples 1st batch

python/samples/01-get-started/README.md

@@ -9,13 +9,6 @@ concepts of **Agent Framework** one step at a time.
 pip install agent-framework --pre
 ```
 
-Set the required environment variables:
-
-```bash
-export AZURE_AI_PROJECT_ENDPOINT="https://your-project-endpoint"
-export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"   # optional, defaults to gpt-4o
-```
-
 ## Samples
 
 | # | File | What you'll learn |

python/samples/02-agents/compaction/compaction_provider.py

@@ -70,7 +70,7 @@ async def log_model_input(context: ChatContext, call_next: Any) -> None:
 
 
 async def main() -> None:
-    client = OpenAIChatClient(model_id="gpt-4o-mini")
+    client = OpenAIChatClient(model="gpt-4o-mini")
 
     # History provider loads/stores conversation messages in session.state.
     # skip_excluded=True means get_messages() will omit messages that were

python/samples/02-agents/context_providers/redis/redis_conversation.py

@@ -25,11 +25,11 @@ from agent_framework import Agent
 from agent_framework.foundry import FoundryChatClient
 from agent_framework.redis import RedisContextProvider
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 from redisvl.extensions.cache.embeddings import EmbeddingsCache
 from redisvl.utils.vectorize import OpenAITextVectorizer
 
-# Copyright (c) Microsoft. All rights reserved.
-
+load_dotenv()
 
 # Default Redis URL for local Redis Stack.
 # Override via the REDIS_URL environment variable for remote or authenticated instances.

python/samples/02-agents/providers/openai/README.md

@@ -45,8 +45,7 @@ This folder contains examples demonstrating different ways to create and use age
 Make sure to set the following environment variables before running the examples:
 
 - `OPENAI_API_KEY`: Your OpenAI API key
-- `OPENAI_CHAT_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
-- `OPENAI_RESPONSES_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
+- `OPENAI_MODEL`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
 - For image processing examples, use a vision-capable model like `gpt-4o` or `gpt-4o-mini`
 
 Optionally, you can set:

python/samples/02-agents/typed_options.py

@@ -3,7 +3,7 @@
 import asyncio
 from typing import Literal
 
-from agent_framework import Agent
+from agent_framework import Agent, Message
 from agent_framework.anthropic import AnthropicClient
 from agent_framework.foundry import FoundryChatClient
 from agent_framework.openai import OpenAIChatClient, OpenAIChatOptions
@@ -40,11 +40,11 @@ async def demo_anthropic_chat_client() -> None:
     print("\n=== Anthropic ChatClient with TypedDict Options ===\n")
 
     # Create Anthropic client
-    client = AnthropicClient(model="claude-sonnet-4-5-20250929")
+    client = AnthropicClient(model_id="claude-sonnet-4-5-20250929")
 
     # Standard options work great:
     response = await client.get_response(
-        "What is the capital of France?",
+        [Message("user", text="What is the capital of France?")],
         options={
             "temperature": 0.5,
             "max_tokens": 1000,
@@ -62,7 +62,7 @@ async def demo_anthropic_agent() -> None:
     """Demonstrate Agent with Anthropic client and typed options."""
     print("\n=== Agent with Anthropic and Typed Options ===\n")
 
-    client = AnthropicClient(model="claude-sonnet-4-5-20250929")
+    client = AnthropicClient(model_id="claude-sonnet-4-5-20250929")
 
     # Create a typed agent for Anthropic - IDE knows Anthropic-specific options!
     agent = Agent(
@@ -119,12 +119,12 @@ async def demo_openai_chat_client_reasoning_models() -> None:
     print("\n=== OpenAI ChatClient with TypedDict Options ===\n")
 
     # Create OpenAI client
-    client = OpenAIChatClient[OpenAIReasoningChatOptions](model_id="o3")
+    client = OpenAIChatClient[OpenAIReasoningChatOptions](model="o3")
 
     # With specific options, you get full IDE autocomplete!
     # Try typing `client.get_response("Hello", options={` and see the suggestions
     response = await client.get_response(
-        "What is 2 + 2?",
+        [Message("user", text="What is 2 + 2?")],
         options={
             "max_tokens": 100,
             "allow_multiple_tool_calls": True,

python/samples/README.md

@@ -44,8 +44,8 @@ Samples call `load_dotenv()` to automatically load environment variables from a
 
 **Option 2: Export environment variables directly**:
 ```bash
-export AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
-export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
+export FOUNDRY_PROJECT_ENDPOINT="your-foundry-project-endpoint"
+export FOUNDRY_MODEL="gpt-4o"
 ```
 
 **Option 3: Using `env_file_path` parameter** (for per-client configuration):
@@ -63,8 +63,8 @@ This allows different clients to use different configuration files if needed.
 
 For the getting-started samples, you'll need at minimum:
 ```bash
-AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
-AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
+FOUNDRY_PROJECT_ENDPOINT="your-foundry-project-endpoint"
+FOUNDRY_MODEL="gpt-4o"
 ```
 
 **Note for production**: In production environments, set environment variables through your deployment platform (e.g., Azure App Settings, Kubernetes ConfigMaps/Secrets) rather than using `.env` files. The `load_dotenv()` call in samples will have no effect when a `.env` file is not present, allowing environment variables to be loaded from the system.