1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
|
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to use multiple agents using AgentTeam with traces.
The team consists of
• one leader agent - automatically created by AgentTeam from the
configuration in `utils/agent_team_config.yaml`
• two worker agents - `Coder` and `Reviewer`, defined in the code below
IMPORTANT - leader-agent model configuration
`utils/agent_team_config.yaml` contains the key TEAM_LEADER_MODEL.
Its value must be the name of a **deployed** model in your Azure AI
project (e.g. "gpt-4o-mini").
If this model deployment is not available, AgentTeam cannot instantiate
the leader agent and the sample will fail.
USAGE:
python sample_agents_agent_team.py
Before running the sample:
1. pip install azure-ai-projects azure-ai-agents azure-identity
2. Ensure `utils/agent_team_config.yaml` is present and TEAM_LEADER_MODEL points
to a valid model deployment.
3. Set these environment variables with your own values:
PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Azure AI Foundry portal.
MODEL_DEPLOYMENT_NAME - The model deployment name used for the worker agents.
"""
import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
from utils.agent_team import AgentTeam, _create_task
from utils.agent_trace_configurator import AgentTraceConfigurator
project_client = AIProjectClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
with project_client:
agents_client = project_client.agents
agents_client.enable_auto_function_calls({_create_task})
model_deployment_name = os.getenv("MODEL_DEPLOYMENT_NAME")
if model_deployment_name is not None:
AgentTraceConfigurator(agents_client=agents_client).setup_tracing()
with agents_client:
agent_team = AgentTeam("test_team", agents_client=agents_client)
agent_team.add_agent(
model=model_deployment_name,
name="Coder",
instructions="You are software engineer who writes great code. Your name is Coder.",
)
agent_team.add_agent(
model=model_deployment_name,
name="Reviewer",
instructions="You are software engineer who reviews code. Your name is Reviewer.",
)
agent_team.assemble_team()
print("A team of agents specialized in software engineering is available for requests.")
while True:
user_input = input("Input (type 'quit' or 'exit' to exit): ")
if user_input.lower() == "quit":
break
elif user_input.lower() == "exit":
break
agent_team.process_request(request=user_input)
agent_team.dismantle_team()
else:
print("Error: Please define the environment variable MODEL_DEPLOYMENT_NAME.")
|
