from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import AgentWorkflow

# Create a multi-agent workflow
workflow = AgentWorkflow(
    name="customer-support",
    agents=[
        {"id": "triage-agent", "role": "classifier"},
        {"id": "technical-agent", "role": "specialist"},
        {"id": "billing-agent", "role": "specialist"}
    ],
    orchestration="sequential"
)

# Workflow automatically routes between agents
result = client.workflows.run(workflow, input="Need help with billing")

from azure.ai.projects.models import MemoryConfiguration

# Configure agent memory
memory_config = MemoryConfiguration(
    type="semantic",
    retention_policy="session",  # or "permanent"
    max_tokens=4000
)

agent = client.agents.create(
    model="gpt-4",
    instructions="You are a personal assistant with memory.",
    memory=memory_config
)

# Agent remembers context across conversations
thread = client.agents.create_thread()
client.agents.create_message(thread.id, "user", "My name is Alice")
# ... later in conversation ...
client.agents.create_message(thread.id, "user", "What's my name?")
# Agent responds: "Your name is Alice"

from azure.ai.projects.models import KnowledgeBase

# Create knowledge base
kb = client.knowledge.create(
    name="company-docs",
    sources=[
        {"type": "azure_blob", "container": "documents"},
        {"type": "sharepoint", "site": "company-portal"},
        {"type": "web", "urls": ["https://company.com/docs"]}
    ]
)

# Attach to agent
agent = client.agents.create(
    model="gpt-4",
    instructions="Answer questions using company documentation.",
    knowledge_base_id=kb.id,
    citation_mode="inline"  # Include source citations
)

curl -X POST https://your-endpoint.openai.azure.com/openai/threads/runs \
  -H "api-key: $FOUNDRY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "asst_abc123",
    "thread": {
      "messages": [
        {"role": "user", "content": "Hello, how can you help me?"}
      ]
    }
  }'

from azure.ai.projects.models import ModelRouter

router = ModelRouter(
    models=["gpt-4", "gpt-4-turbo", "gpt-3.5-turbo"],
    strategy="cost_optimized",  # or "performance_optimized"
    fallback=True
)

agent = client.agents.create(
    model_router=router,
    instructions="You are a helpful assistant."
)

from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

# Initialize
credential = DefaultAzureCredential()
client = AIProjectClient(
    credential=credential,
    subscription_id="your-subscription-id",
    resource_group_name="your-resource-group",
    project_name="your-project"
)

# Create agent
agent = client.agents.create(
    model="gpt-4",
    name="customer-support",
    instructions="""You are a helpful customer support agent.
    Be friendly, concise, and always cite sources when providing information.""",
    tools=[{"type": "code_interpreter"}, {"type": "file_search"}]
)

# Create conversation thread
thread = client.agents.create_thread()

# Send message
message = client.agents.create_message(
    thread_id=thread.id,
    role="user",
    content="How do I reset my password?"
)

# Run agent
run = client.agents.create_run(
    thread_id=thread.id,
    agent_id=agent.id
)

# Wait for completion
run = client.agents.wait_for_run(thread.id, run.id)

# Get response
messages = client.agents.list_messages(thread.id)
for msg in messages:
    if msg.role == "assistant":
        print(msg.content)