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from dotenv import load_dotenv
from swarm_models import OpenAIChat
from swarms.structs.agent import Agent
from swarms.structs.groupchat import GroupChat, expertise_based
def setup_test_agents():
model = OpenAIChat(
openai_api_key=os.getenv("OPENAI_API_KEY"),
model_name="gpt-4",
temperature=0.1,
)
return [
Agent(
agent_name="Agent1",
system_prompt="You only respond with 'A'",
llm=model,
),
Agent(
agent_name="Agent2",
system_prompt="You only respond with 'B'",
llm=model,
),
Agent(
agent_name="Agent3",
system_prompt="You only respond with 'C'",
llm=model,
),
]
def test_round_robin_speaking():
chat = GroupChat(agents=setup_test_agents())
history = chat.run("Say your letter")
# Verify agents speak in order
responses = [
r.message for t in history.turns for r in t.responses
]
assert responses == ["A", "B", "C"] * (len(history.turns))
def test_concurrent_processing():
chat = GroupChat(agents=setup_test_agents())
tasks = ["Task1", "Task2", "Task3"]
histories = chat.concurrent_run(tasks)
assert len(histories) == len(tasks)
for history in histories:
assert history.total_messages > 0
def test_expertise_based_speaking():
agents = setup_test_agents()
chat = GroupChat(agents=agents, speaker_fn=expertise_based)
# Test each agent's expertise trigger
for agent in agents:
history = chat.run(f"Trigger {agent.system_prompt}")
first_response = history.turns[0].responses[0]
assert first_response.agent_name == agent.agent_name
def test_max_turns_limit():
max_turns = 3
chat = GroupChat(agents=setup_test_agents(), max_turns=max_turns)
history = chat.run("Test message")
assert len(history.turns) == max_turns
def test_error_handling():
broken_agent = Agent(
agent_name="BrokenAgent",
system_prompt="You raise errors",
llm=None,
)
chat = GroupChat(agents=[broken_agent])
history = chat.run("Trigger error")
assert "Error" in history.turns[0].responses[0].message
def test_conversation_context():
agents = setup_test_agents()
complex_prompt = "Previous message refers to A. Now trigger B. Finally discuss C."
chat = GroupChat(agents=agents, speaker_fn=expertise_based)
history = chat.run(complex_prompt)
responses = [
r.agent_name for t in history.turns for r in t.responses
]
assert all(agent.agent_name in responses for agent in agents)
def test_large_agent_group():
large_group = setup_test_agents() * 5 # 15 agents
chat = GroupChat(agents=large_group)
history = chat.run("Test scaling")
assert history.total_messages > len(large_group)
def test_long_conversations():
chat = GroupChat(agents=setup_test_agents(), max_turns=50)
history = chat.run("Long conversation test")
assert len(history.turns) == 50
assert history.total_messages > 100
def test_stress_batched_runs():
chat = GroupChat(agents=setup_test_agents())
tasks = ["Task"] * 100
histories = chat.batched_run(tasks)
assert len(histories) == len(tasks)
total_messages = sum(h.total_messages for h in histories)
assert total_messages > len(tasks) * 3
if __name__ == "__main__":
load_dotenv()
functions = [
test_round_robin_speaking,
test_concurrent_processing,
test_expertise_based_speaking,
test_max_turns_limit,
test_error_handling,
test_conversation_context,
test_large_agent_group,
test_long_conversations,
test_stress_batched_runs,
]
for func in functions:
try:
print(f"Running {func.__name__}...")
func()
print("✓ Passed")
except Exception as e:
print(f"✗ Failed: {str(e)}")
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