How to Create Agent in Azure AI Foundry

 https://learn.microsoft.com/en-us/azure/ai-foundry/agents/overview?context=%2Fazure%2Fai-foundry%2Fcontext%2Fcontext 

pip install azure-ai-projects pip install azure-identity

import os from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from azure.ai.agents.models import CodeInterpreterTool # Create an Azure AI Client from an endpoint, copied from your Azure AI Foundry project. # You need to login to Azure subscription via Azure CLI and set the environment variables project_endpoint = os.environ["PROJECT_ENDPOINT"] # Ensure the PROJECT_ENDPOINT environment variable is set # Create an AIProjectClient instance project_client = AIProjectClient( endpoint=project_endpoint, credential=DefaultAzureCredential(), # Use Azure Default Credential for authentication api_version="latest", ) code_interpreter = CodeInterpreterTool() with project_client: # Create an agent with the Bing Grounding tool agent = project_client.agents.create_agent( model=os.environ["MODEL_DEPLOYMENT_NAME"], # Model deployment name name="my-agent", # Name of the agent instructions="You are a helpful agent", # Instructions for the agent tools=code_interpreter.definitions, # Attach the tool ) print(f"Created agent, ID: {agent.id}") # Create a thread for communication thread = project_client.agents.threads.create() print(f"Created thread, ID: {thread.id}") # Add a message to the thread message = project_client.agents.messages.create( thread_id=thread.id, role="user", # Role of the message sender content="What is the weather in Seattle today?", # Message content ) print(f"Created message, ID: {message['id']}") # Create and process an agent run run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id) print(f"Run finished with status: {run.status}") # Check if the run failed if run.status == "failed": print(f"Run failed: {run.last_error}") # Fetch and log all messages messages = project_client.agents.messages.list(thread_id=thread.id) for message in messages: print(f"Role: {message.role}, Content: {message.content}") # Delete the agent when done project_client.agents.delete_agent(agent.id) print("Deleted agent")