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Greyson Lalonde
@@ -1,139 +1,125 @@
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"""Test Agent creation and execution basic functionality."""
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import pytest
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from langchain.chat_models import ChatOpenAI as OpenAI
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from ..crewai import Agent
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def test_agent_creation():
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory"
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)
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assert agent.role == "test role"
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assert agent.goal == "test goal"
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assert agent.backstory == "test backstory"
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assert agent.tools == []
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def test_agent_creation():
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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assert agent.role == "test role"
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assert agent.goal == "test goal"
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assert agent.backstory == "test backstory"
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assert agent.tools == []
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def test_agent_default_values():
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory"
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)
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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assert isinstance(agent.llm, OpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0.7
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assert agent.llm.verbose == False
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assert agent.allow_delegation == True
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assert isinstance(agent.llm, OpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0.7
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assert agent.llm.verbose == False
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assert agent.allow_delegation == True
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def test_custom_llm():
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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llm=OpenAI(
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temperature=0,
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model="gpt-4"
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)
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)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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llm=OpenAI(temperature=0, model="gpt-4"),
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)
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assert isinstance(agent.llm, OpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0
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assert isinstance(agent.llm, OpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_without_memory():
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no_memory_agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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memory=False,
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llm=OpenAI(
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temperature=0,
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model="gpt-4"
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)
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)
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no_memory_agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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memory=False,
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llm=OpenAI(temperature=0, model="gpt-4"),
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)
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memory_agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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memory=True,
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llm=OpenAI(
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temperature=0,
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model="gpt-4"
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)
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)
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memory_agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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memory=True,
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llm=OpenAI(temperature=0, model="gpt-4"),
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)
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result = no_memory_agent.execute_task("How much is 1 + 1?")
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result = no_memory_agent.execute_task("How much is 1 + 1?")
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assert result == "1 + 1 equals 2."
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assert no_memory_agent.agent_executor.memory is None
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assert memory_agent.agent_executor.memory is not None
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assert result == "1 + 1 equals 2."
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assert no_memory_agent.agent_executor.memory is None
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assert memory_agent.agent_executor.memory is not None
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execution():
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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allow_delegation=False
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)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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allow_delegation=False,
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)
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output = agent.execute_task("How much is 1 + 1?")
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assert output == "2"
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output = agent.execute_task("How much is 1 + 1?")
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assert output == "2"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execution_with_tools():
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from langchain.tools import tool
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from langchain.tools import tool
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@tool
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def multiplier(numbers) -> float:
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"""Useful for when you need to multiply two numbers together.
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The input to this tool should be a comma separated list of numbers of
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length two, representing the two numbers you want to multiply together.
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For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
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a, b = numbers.split(',')
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return int(a) * int(b)
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@tool
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def multiplier(numbers) -> float:
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"""Useful for when you need to multiply two numbers together.
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The input to this tool should be a comma separated list of numbers of
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length two, representing the two numbers you want to multiply together.
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For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
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a, b = numbers.split(",")
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return int(a) * int(b)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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tools=[multiplier],
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allow_delegation=False
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)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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tools=[multiplier],
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allow_delegation=False,
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)
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output = agent.execute_task("What is 3 times 4")
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assert output == "12"
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output = agent.execute_task("What is 3 times 4")
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assert output == "12"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execution_with_specific_tools():
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from langchain.tools import tool
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from langchain.tools import tool
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@tool
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def multiplier(numbers) -> float:
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"""Useful for when you need to multiply two numbers together.
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The input to this tool should be a comma separated list of numbers of
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length two, representing the two numbers you want to multiply together.
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For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
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a, b = numbers.split(',')
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return int(a) * int(b)
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@tool
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def multiplier(numbers) -> float:
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"""Useful for when you need to multiply two numbers together.
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The input to this tool should be a comma separated list of numbers of
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length two, representing the two numbers you want to multiply together.
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For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
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a, b = numbers.split(",")
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return int(a) * int(b)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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allow_delegation=False
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)
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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allow_delegation=False,
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)
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output = agent.execute_task(
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task="What is 3 times 4",
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tools=[multiplier]
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)
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assert output == "3 times 4 is 12."
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output = agent.execute_task(task="What is 3 times 4", tools=[multiplier])
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assert output == "3 times 4 is 12."
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@@ -1,3 +1,4 @@
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# conftest.py
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from dotenv import load_dotenv
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load_result = load_dotenv(override=True)
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load_result = load_dotenv(override=True)
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@@ -1,114 +1,131 @@
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"""Test Agent creation and execution basic functionality."""
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import json
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import pytest
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from ..crewai import Agent, Crew, Task, Process
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from ..crewai import Agent, Crew, Process, Task
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ceo = Agent(
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role="CEO",
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goal="Make sure the writers in your company produce amazing content.",
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backstory="You're an long time CEO of a content creation agency with a Senior Writer on the team. You're now working on a new project and want to make sure the content produced is amazing.",
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allow_delegation=True
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role="CEO",
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goal="Make sure the writers in your company produce amazing content.",
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backstory="You're an long time CEO of a content creation agency with a Senior Writer on the team. You're now working on a new project and want to make sure the content produced is amazing.",
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allow_delegation=True,
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)
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researcher = Agent(
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role="Researcher",
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goal="Make the best research and analysis on content about AI and AI agents",
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backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
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allow_delegation=False
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role="Researcher",
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goal="Make the best research and analysis on content about AI and AI agents",
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backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
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allow_delegation=False,
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)
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writer = Agent(
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role="Senior Writer",
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goal="Write the best content about AI and AI agents.",
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backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
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allow_delegation=False
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role="Senior Writer",
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goal="Write the best content about AI and AI agents.",
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backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
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allow_delegation=False,
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)
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def test_crew_config_conditional_requirement():
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with pytest.raises(ValueError):
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Crew(process=Process.sequential)
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with pytest.raises(ValueError):
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Crew(process=Process.sequential)
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config = json.dumps({
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"agents": [
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{
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"role": "Senior Researcher",
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"goal": "Make the best research and analysis on content about AI and AI agents",
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"backstory": "You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer."
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},
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{
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"role": "Senior Writer",
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"goal": "Write the best content about AI and AI agents.",
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"backstory": "You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer."
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}
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],
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"tasks": [
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{
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"description": "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
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"agent": "Senior Researcher"
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},
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{
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"description": "Write a 1 amazing paragraph highlight for each idead that showcases how good an article about this topic could be, check references if necessary or search for more content but make sure it's unique, interesting and well written. Return the list of ideas with their paragraph and your notes.",
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"agent": "Senior Writer"
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}
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]
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})
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parsed_config = json.loads(config)
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config = json.dumps(
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{
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"agents": [
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{
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"role": "Senior Researcher",
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"goal": "Make the best research and analysis on content about AI and AI agents",
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"backstory": "You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
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},
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{
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"role": "Senior Writer",
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"goal": "Write the best content about AI and AI agents.",
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"backstory": "You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
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},
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],
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"tasks": [
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{
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"description": "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
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"agent": "Senior Researcher",
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},
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{
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"description": "Write a 1 amazing paragraph highlight for each idead that showcases how good an article about this topic could be, check references if necessary or search for more content but make sure it's unique, interesting and well written. Return the list of ideas with their paragraph and your notes.",
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"agent": "Senior Writer",
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},
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],
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}
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)
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parsed_config = json.loads(config)
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try:
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crew = Crew(process=Process.sequential, config=config)
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except ValueError:
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pytest.fail("Unexpected ValidationError raised")
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try:
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crew = Crew(process=Process.sequential, config=config)
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except ValueError:
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pytest.fail("Unexpected ValidationError raised")
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assert [agent.role for agent in crew.agents] == [
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agent["role"] for agent in parsed_config["agents"]
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]
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assert [task.description for task in crew.tasks] == [
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task["description"] for task in parsed_config["tasks"]
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]
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assert [agent.role for agent in crew.agents] == [agent['role'] for agent in parsed_config['agents']]
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assert [task.description for task in crew.tasks] == [task['description'] for task in parsed_config['tasks']]
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def test_crew_config_with_wrong_keys():
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no_tasks_config = json.dumps({
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"agents": [
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{
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"role": "Senior Researcher",
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"goal": "Make the best research and analysis on content about AI and AI agents",
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"backstory": "You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer."
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}
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]
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})
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no_tasks_config = json.dumps(
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{
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"agents": [
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{
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"role": "Senior Researcher",
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"goal": "Make the best research and analysis on content about AI and AI agents",
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"backstory": "You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
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}
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]
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}
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)
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no_agents_config = json.dumps(
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{
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"tasks": [
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{
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"description": "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
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"agent": "Senior Researcher",
|
||||
}
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]
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}
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)
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with pytest.raises(ValueError):
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Crew(process=Process.sequential, config='{"wrong_key": "wrong_value"}')
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with pytest.raises(ValueError):
|
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Crew(process=Process.sequential, config=no_tasks_config)
|
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with pytest.raises(ValueError):
|
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Crew(process=Process.sequential, config=no_agents_config)
|
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|
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no_agents_config = json.dumps({
|
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"tasks": [
|
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{
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"description": "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
||||
"agent": "Senior Researcher"
|
||||
}
|
||||
]
|
||||
})
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||||
with pytest.raises(ValueError):
|
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Crew(process=Process.sequential, config='{"wrong_key": "wrong_value"}')
|
||||
with pytest.raises(ValueError):
|
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Crew(process=Process.sequential, config=no_tasks_config)
|
||||
with pytest.raises(ValueError):
|
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Crew(process=Process.sequential, config=no_agents_config)
|
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|
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_crew_creation():
|
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tasks = [
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Task(
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description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
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agent=researcher
|
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),
|
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Task(
|
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description="Write a 1 amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
|
||||
agent=writer
|
||||
)
|
||||
]
|
||||
tasks = [
|
||||
Task(
|
||||
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
||||
agent=researcher,
|
||||
),
|
||||
Task(
|
||||
description="Write a 1 amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
|
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agent=writer,
|
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),
|
||||
]
|
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|
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crew = Crew(
|
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agents=[researcher, writer],
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process=Process.sequential,
|
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tasks=tasks,
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
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process=Process.sequential,
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tasks=tasks,
|
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)
|
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|
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assert crew.kickoff() == """1. **The Evolution of AI: From Old Concepts to New Frontiers** - Journey with us as we traverse the fascinating timeline of artificial intelligence - from its philosophical and mathematical infancy to the sophisticated, problem-solving tool it has become today. This riveting account will not only educate but also inspire, as we delve deep into the milestones that brought us here and shine a beacon on the potential that lies ahead.
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assert (
|
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crew.kickoff()
|
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== """1. **The Evolution of AI: From Old Concepts to New Frontiers** - Journey with us as we traverse the fascinating timeline of artificial intelligence - from its philosophical and mathematical infancy to the sophisticated, problem-solving tool it has become today. This riveting account will not only educate but also inspire, as we delve deep into the milestones that brought us here and shine a beacon on the potential that lies ahead.
|
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|
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2. **AI Agents in Healthcare: The Future of Medicine** - Imagine a world where illnesses are diagnosed before symptoms appear, where patient outcomes are not mere guesses but accurate predictions. This is the world AI is crafting in healthcare - a revolution that's saving lives and changing the face of medicine as we know it. This article will spotlight this transformative journey, underlining the profound impact AI is having on our health and well-being.
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|
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@@ -117,60 +134,60 @@ def test_crew_creation():
|
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4. **Demystifying AI Algorithms: A Deep Dive into Machine Learning** - Ever wondered what goes on behind the scenes of AI? This enlightening article will break down the complex world of machine learning algorithms into digestible insights, unraveling the mystery of AI's 'black box'. It's a rare opportunity for the non-technical audience to appreciate the inner workings of AI, fostering a deeper understanding of this revolutionary technology.
|
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|
||||
5. **AI Startups: The Game Changers of the Tech Industry** - In the world of tech, AI startups are the bold pioneers charting new territories. This article will spotlight these game changers, showcasing how their innovative products and services are driving the AI revolution. It's a unique opportunity to catch a glimpse of the entrepreneurial side of AI, offering inspiration for the tech enthusiasts and dreamers alike."""
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_crew_with_delegating_agents():
|
||||
tasks = [
|
||||
Task(
|
||||
description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
|
||||
agent=ceo
|
||||
)
|
||||
]
|
||||
tasks = [
|
||||
Task(
|
||||
description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
|
||||
agent=ceo,
|
||||
)
|
||||
]
|
||||
|
||||
crew = Crew(
|
||||
agents=[ceo, writer],
|
||||
process=Process.sequential,
|
||||
tasks=tasks,
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[ceo, writer],
|
||||
process=Process.sequential,
|
||||
tasks=tasks,
|
||||
)
|
||||
|
||||
assert (
|
||||
crew.kickoff()
|
||||
== "The Senior Writer has created a compelling and engaging 1 paragraph draft about AI agents. The paragraph provides a brief yet comprehensive overview of AI agents, their uses, and implications in the current world. It emphasizes their potential and the role they can play in the future. The tone is informative but captivating, meeting the objectives of the task."
|
||||
)
|
||||
|
||||
assert crew.kickoff() == 'The Senior Writer has created a compelling and engaging 1 paragraph draft about AI agents. The paragraph provides a brief yet comprehensive overview of AI agents, their uses, and implications in the current world. It emphasizes their potential and the role they can play in the future. The tone is informative but captivating, meeting the objectives of the task.'
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_crew_verbose_output(capsys):
|
||||
tasks = [
|
||||
Task(
|
||||
description="Research AI advancements.",
|
||||
agent=researcher
|
||||
),
|
||||
Task(
|
||||
description="Write about AI in healthcare.",
|
||||
agent=writer
|
||||
)
|
||||
]
|
||||
tasks = [
|
||||
Task(description="Research AI advancements.", agent=researcher),
|
||||
Task(description="Write about AI in healthcare.", agent=writer),
|
||||
]
|
||||
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=tasks,
|
||||
process=Process.sequential,
|
||||
verbose=True
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=tasks,
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
crew.kickoff()
|
||||
captured = capsys.readouterr()
|
||||
expected_strings = [
|
||||
"Working Agent: Researcher",
|
||||
"Starting Task: Research AI advancements. ...",
|
||||
"Task output:",
|
||||
"Working Agent: Senior Writer",
|
||||
"Starting Task: Write about AI in healthcare. ...",
|
||||
"Task output:"
|
||||
]
|
||||
crew.kickoff()
|
||||
captured = capsys.readouterr()
|
||||
expected_strings = [
|
||||
"Working Agent: Researcher",
|
||||
"Starting Task: Research AI advancements. ...",
|
||||
"Task output:",
|
||||
"Working Agent: Senior Writer",
|
||||
"Starting Task: Write about AI in healthcare. ...",
|
||||
"Task output:",
|
||||
]
|
||||
|
||||
for expected_string in expected_strings:
|
||||
assert expected_string in captured.out
|
||||
for expected_string in expected_strings:
|
||||
assert expected_string in captured.out
|
||||
|
||||
# Now test with verbose set to False
|
||||
crew.verbose = False
|
||||
crew.kickoff()
|
||||
captured = capsys.readouterr()
|
||||
assert captured.out == ""
|
||||
# Now test with verbose set to False
|
||||
crew.verbose = False
|
||||
crew.kickoff()
|
||||
captured = capsys.readouterr()
|
||||
assert captured.out == ""
|
||||
|
||||
@@ -1,57 +1,56 @@
|
||||
"""Test Agent creation and execution basic functionality."""
|
||||
|
||||
import pytest
|
||||
|
||||
from ..crewai import Agent, Task
|
||||
|
||||
|
||||
def test_task_tool_reflect_agent_tools():
|
||||
from langchain.tools import tool
|
||||
from langchain.tools import tool
|
||||
|
||||
@tool
|
||||
def fake_tool() -> None:
|
||||
"Fake tool"
|
||||
pass
|
||||
|
||||
researcher = Agent(
|
||||
role="Researcher",
|
||||
goal="Make the best research and analysis on content about AI and AI agents",
|
||||
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
|
||||
tools=[fake_tool],
|
||||
allow_delegation=False
|
||||
)
|
||||
@tool
|
||||
def fake_tool() -> None:
|
||||
"Fake tool"
|
||||
|
||||
researcher = Agent(
|
||||
role="Researcher",
|
||||
goal="Make the best research and analysis on content about AI and AI agents",
|
||||
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
|
||||
tools=[fake_tool],
|
||||
allow_delegation=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
assert task.tools == [fake_tool]
|
||||
|
||||
task = Task(
|
||||
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
||||
agent=researcher
|
||||
)
|
||||
|
||||
assert task.tools == [fake_tool]
|
||||
|
||||
def test_task_tool_takes_precedence_ove_agent_tools():
|
||||
from langchain.tools import tool
|
||||
from langchain.tools import tool
|
||||
|
||||
@tool
|
||||
def fake_tool() -> None:
|
||||
"Fake tool"
|
||||
pass
|
||||
@tool
|
||||
def fake_tool() -> None:
|
||||
"Fake tool"
|
||||
|
||||
@tool
|
||||
def fake_task_tool() -> None:
|
||||
"Fake tool"
|
||||
pass
|
||||
|
||||
researcher = Agent(
|
||||
role="Researcher",
|
||||
goal="Make the best research and analysis on content about AI and AI agents",
|
||||
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
|
||||
tools=[fake_tool],
|
||||
allow_delegation=False
|
||||
)
|
||||
@tool
|
||||
def fake_task_tool() -> None:
|
||||
"Fake tool"
|
||||
|
||||
task = Task(
|
||||
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
||||
agent=researcher,
|
||||
tools=[fake_task_tool],
|
||||
allow_delegation=False
|
||||
)
|
||||
|
||||
assert task.tools == [fake_task_tool]
|
||||
researcher = Agent(
|
||||
role="Researcher",
|
||||
goal="Make the best research and analysis on content about AI and AI agents",
|
||||
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
|
||||
tools=[fake_tool],
|
||||
allow_delegation=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
|
||||
agent=researcher,
|
||||
tools=[fake_task_tool],
|
||||
allow_delegation=False,
|
||||
)
|
||||
|
||||
assert task.tools == [fake_task_tool]
|
||||
|
||||
Reference in New Issue
Block a user