mirror of
https://github.com/crewAIInc/crewAI.git
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2256462b7e
* feat: Start migrating to UV * feat: add uv to flows * feat: update docs on Poetry -> uv * feat: update docs and uv.locl * feat: update tests and github CI * feat: run ruff format * feat: update typechecking * feat: fix type checking * feat: update python version * feat: type checking gic * feat: adapt uv command to run the tool repo * Adapt tool build command to uv * feat: update logic to let only projects with crew to be deployed * feat: add uv to tools * fix; tests * fix: remove breakpoint * fix :test * feat: add crewai update to migrate from poetry to uv * fix: tests * feat: add validation for ˆ character on pyproject * feat: add run_crew to pyproject if doesnt exist * feat: add validation for poetry migration * fix: warning --------- Co-authored-by: Vinicius Brasil <vini@hey.com>
1571 lines
47 KiB
Python
1571 lines
47 KiB
Python
"""Test Agent creation and execution basic functionality."""
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import os
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from unittest import mock
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from unittest.mock import patch
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import pytest
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from crewai_tools import tool
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from crewai import Agent, Crew, Task
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from crewai.agents.cache import CacheHandler
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from crewai.agents.crew_agent_executor import CrewAgentExecutor
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from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
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from crewai.llm import LLM
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from crewai.tools.tool_calling import InstructorToolCalling
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from crewai.tools.tool_usage import ToolUsage
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from crewai.tools.tool_usage_events import ToolUsageFinished
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from crewai.utilities import RPMController
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from crewai.utilities.events import Emitter
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def test_agent_llm_creation_with_env_vars():
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# Store original environment variables
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original_api_key = os.environ.get("OPENAI_API_KEY")
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original_api_base = os.environ.get("OPENAI_API_BASE")
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original_model_name = os.environ.get("OPENAI_MODEL_NAME")
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# Set up environment variables
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os.environ["OPENAI_API_KEY"] = "test_api_key"
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os.environ["OPENAI_API_BASE"] = "https://test-api-base.com"
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os.environ["OPENAI_MODEL_NAME"] = "gpt-4-turbo"
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# Create an agent without specifying LLM
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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# Check if LLM is created correctly
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assert isinstance(agent.llm, LLM)
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assert agent.llm.model == "gpt-4-turbo"
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assert agent.llm.api_key == "test_api_key"
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assert agent.llm.base_url == "https://test-api-base.com"
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# Clean up environment variables
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del os.environ["OPENAI_API_KEY"]
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del os.environ["OPENAI_API_BASE"]
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del os.environ["OPENAI_MODEL_NAME"]
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# Create an agent without specifying LLM
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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# Check if LLM is created correctly
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assert isinstance(agent.llm, LLM)
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assert agent.llm.model != "gpt-4-turbo"
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assert agent.llm.api_key != "test_api_key"
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assert agent.llm.base_url != "https://test-api-base.com"
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# Restore original environment variables
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if original_api_key:
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os.environ["OPENAI_API_KEY"] = original_api_key
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if original_api_base:
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os.environ["OPENAI_API_BASE"] = original_api_base
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if original_model_name:
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os.environ["OPENAI_MODEL_NAME"] = original_model_name
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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(role="test role", goal="test goal", backstory="test backstory")
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assert agent.llm.model == "gpt-4o-mini"
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assert agent.allow_delegation is False
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def test_custom_llm():
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agent = Agent(
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role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
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)
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assert agent.llm.model == "gpt-4"
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def test_custom_llm_with_langchain():
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from langchain_openai import ChatOpenAI
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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=ChatOpenAI(temperature=0, model="gpt-4"),
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)
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assert agent.llm.model == "gpt-4"
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def test_custom_llm_temperature_preservation():
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from langchain_openai import ChatOpenAI
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langchain_llm = ChatOpenAI(temperature=0.7, model="gpt-4")
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agent = Agent(
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role="temperature test role",
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goal="temperature test goal",
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backstory="temperature test backstory",
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llm=langchain_llm,
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)
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assert isinstance(agent.llm, LLM)
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assert agent.llm.model == "gpt-4"
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assert agent.llm.temperature == 0.7
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_execute_task():
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from langchain_openai import ChatOpenAI
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from crewai import Task
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agent = Agent(
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role="Math Tutor",
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goal="Solve math problems accurately",
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backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
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llm=ChatOpenAI(temperature=0.7, model="gpt-4o-mini"),
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)
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task = Task(
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description="Calculate the area of a circle with radius 5 cm.",
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expected_output="The calculated area of the circle in square centimeters.",
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agent=agent,
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)
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result = agent.execute_task(task)
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assert result is not None
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assert (
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result
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== "The calculated area of the circle is approximately 78.5 square centimeters."
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)
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assert "square centimeters" in result.lower()
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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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task = Task(
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description="How much is 1 + 1?",
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agent=agent,
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expected_output="the result of the math operation.",
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)
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output = agent.execute_task(task)
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assert output == "1 + 1 is 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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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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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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task = Task(
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description="What is 3 times 4?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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with patch.object(Emitter, "emit") as emit:
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output = agent.execute_task(task)
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assert output == "The result of the multiplication is 12."
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assert emit.call_count == 1
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args, _ = emit.call_args
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assert isinstance(args[1], ToolUsageFinished)
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assert not args[1].from_cache
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assert args[1].tool_name == "multiplier"
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assert args[1].tool_args == {"first_number": 3, "second_number": 4}
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_logging_tool_usage():
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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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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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verbose=True,
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)
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assert agent.llm.model == "gpt-4o-mini"
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assert agent.tools_handler.last_used_tool == {}
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task = Task(
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description="What is 3 times 4?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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# force cleaning cache
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agent.tools_handler.cache = CacheHandler()
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output = agent.execute_task(task)
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tool_usage = InstructorToolCalling(
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tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
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)
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assert output == "The result of the multiplication is 12."
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assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
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assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_cache_hitting():
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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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cache_handler = CacheHandler()
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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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cache_handler=cache_handler,
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verbose=True,
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)
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task1 = Task(
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description="What is 2 times 6?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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task2 = Task(
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description="What is 3 times 3?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task1)
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output = agent.execute_task(task2)
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assert cache_handler._cache == {
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"multiplier-{'first_number': 2, 'second_number': 6}": 12,
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"multiplier-{'first_number': 3, 'second_number': 3}": 9,
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}
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task = Task(
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description="What is 2 times 6 times 3? Return only the number",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task)
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assert output == "36"
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assert cache_handler._cache == {
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"multiplier-{'first_number': 2, 'second_number': 6}": 12,
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"multiplier-{'first_number': 3, 'second_number': 3}": 9,
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"multiplier-{'first_number': 12, 'second_number': 3}": 36,
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}
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with patch.object(CacheHandler, "read") as read, patch.object(
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Emitter, "emit"
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) as emit:
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read.return_value = "0"
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task = Task(
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description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
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agent=agent,
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expected_output="The number that is the result of the multiplication tool.",
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)
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output = agent.execute_task(task)
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assert output == "0"
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read.assert_called_with(
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tool="multiplier", input={"first_number": 2, "second_number": 6}
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)
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assert emit.call_count == 1
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args, _ = emit.call_args
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assert isinstance(args[1], ToolUsageFinished)
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assert args[1].from_cache
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_disabling_cache_for_agent():
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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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cache_handler = CacheHandler()
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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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cache_handler=cache_handler,
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cache=False,
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verbose=True,
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)
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task1 = Task(
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description="What is 2 times 6?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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task2 = Task(
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description="What is 3 times 3?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task1)
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output = agent.execute_task(task2)
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assert cache_handler._cache != {
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"multiplier-{'first_number': 2, 'second_number': 6}": 12,
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"multiplier-{'first_number': 3, 'second_number': 3}": 9,
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}
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task = Task(
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description="What is 2 times 6 times 3? Return only the number",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task)
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assert output == "36"
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assert cache_handler._cache != {
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"multiplier-{'first_number': 2, 'second_number': 6}": 12,
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"multiplier-{'first_number': 3, 'second_number': 3}": 9,
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"multiplier-{'first_number': 12, 'second_number': 3}": 36,
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}
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with patch.object(CacheHandler, "read") as read:
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read.return_value = "0"
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task = Task(
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description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool.",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task)
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assert output == "12"
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read.assert_not_called()
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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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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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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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task = Task(
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description="What is 3 times 4",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task=task, tools=[multiplier])
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assert output == "The result of the multiplication is 12."
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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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="o1-preview",
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max_iter=3,
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use_system_prompt=False,
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allow_delegation=False,
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)
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task = Task(
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description="What is 3 times 4?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task=task, tools=[multiplier])
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assert output == "12"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_powered_by_new_o_model_family_that_uses_tool():
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@tool
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def comapny_customer_data() -> float:
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"""Useful for getting customer related data."""
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return "The company has 42 customers"
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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="o1-preview",
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max_iter=3,
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use_system_prompt=False,
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allow_delegation=False,
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)
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task = Task(
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description="How many customers does the company have?",
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agent=agent,
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expected_output="The number of customers",
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)
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output = agent.execute_task(task=task, tools=[comapny_customer_data])
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assert output == "42"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_custom_max_iterations():
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@tool
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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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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max_iter=1,
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allow_delegation=False,
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)
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with patch.object(
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LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation:"]).call
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) as private_mock:
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task = Task(
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description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
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expected_output="The final answer",
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)
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agent.execute_task(
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task=task,
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tools=[get_final_answer],
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)
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assert private_mock.call_count == 2
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_repeated_tool_usage(capsys):
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@tool
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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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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max_iter=4,
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llm="gpt-4",
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allow_delegation=False,
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verbose=True,
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)
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task = Task(
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description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
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expected_output="The final answer, don't give it until I tell you so",
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)
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# force cleaning cache
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agent.tools_handler.cache = CacheHandler()
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agent.execute_task(
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task=task,
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tools=[get_final_answer],
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)
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captured = capsys.readouterr()
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assert (
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"I tried reusing the same input, I must stop using this action input. I'll try something else instead."
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in captured.out
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)
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
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@tool
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def get_final_answer(anything: str) -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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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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|
max_iter=4,
|
|
llm="gpt-4",
|
|
allow_delegation=False,
|
|
verbose=True,
|
|
cache=False,
|
|
)
|
|
|
|
task = Task(
|
|
description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
|
|
expected_output="The final answer, don't give it until I tell you so",
|
|
)
|
|
|
|
agent.execute_task(
|
|
task=task,
|
|
tools=[get_final_answer],
|
|
)
|
|
|
|
captured = capsys.readouterr()
|
|
assert (
|
|
"I tried reusing the same input, I must stop using this action input. I'll try something else instead."
|
|
in captured.out
|
|
)
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_moved_on_after_max_iterations():
|
|
@tool
|
|
def get_final_answer() -> float:
|
|
"""Get the final answer but don't give it yet, just re-use this
|
|
tool non-stop."""
|
|
return 42
|
|
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_iter=3,
|
|
allow_delegation=False,
|
|
)
|
|
|
|
task = Task(
|
|
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool over and over until you're told you can give your final answer.",
|
|
expected_output="The final answer",
|
|
)
|
|
output = agent.execute_task(
|
|
task=task,
|
|
tools=[get_final_answer],
|
|
)
|
|
assert output == "The final answer is 42."
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_respect_the_max_rpm_set(capsys):
|
|
@tool
|
|
def get_final_answer() -> float:
|
|
"""Get the final answer but don't give it yet, just re-use this
|
|
tool non-stop."""
|
|
return 42
|
|
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_iter=5,
|
|
max_rpm=1,
|
|
verbose=True,
|
|
allow_delegation=False,
|
|
)
|
|
|
|
with patch.object(RPMController, "_wait_for_next_minute") as moveon:
|
|
moveon.return_value = True
|
|
task = Task(
|
|
description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
|
|
expected_output="The final answer",
|
|
)
|
|
output = agent.execute_task(
|
|
task=task,
|
|
tools=[get_final_answer],
|
|
)
|
|
assert output == "The final answer is 42."
|
|
captured = capsys.readouterr()
|
|
assert "Max RPM reached, waiting for next minute to start." in captured.out
|
|
moveon.assert_called()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
|
|
from unittest.mock import patch
|
|
|
|
from crewai_tools import tool
|
|
|
|
@tool
|
|
def get_final_answer() -> float:
|
|
"""Get the final answer but don't give it yet, just re-use this
|
|
tool non-stop."""
|
|
return 42
|
|
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_iter=4,
|
|
max_rpm=10,
|
|
verbose=True,
|
|
)
|
|
|
|
task = Task(
|
|
description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
|
|
expected_output="The final answer",
|
|
tools=[get_final_answer],
|
|
agent=agent,
|
|
)
|
|
|
|
crew = Crew(agents=[agent], tasks=[task], max_rpm=1, verbose=True)
|
|
|
|
with patch.object(RPMController, "_wait_for_next_minute") as moveon:
|
|
moveon.return_value = True
|
|
crew.kickoff()
|
|
captured = capsys.readouterr()
|
|
assert "Max RPM reached, waiting for next minute to start." not in captured.out
|
|
moveon.assert_not_called()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_without_max_rpm_respet_crew_rpm(capsys):
|
|
from unittest.mock import patch
|
|
|
|
from crewai_tools import tool
|
|
|
|
@tool
|
|
def get_final_answer() -> float:
|
|
"""Get the final answer but don't give it yet, just re-use this
|
|
tool non-stop."""
|
|
return 42
|
|
|
|
agent1 = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_rpm=10,
|
|
max_iter=2,
|
|
verbose=True,
|
|
allow_delegation=False,
|
|
)
|
|
|
|
agent2 = Agent(
|
|
role="test role2",
|
|
goal="test goal2",
|
|
backstory="test backstory2",
|
|
max_iter=1,
|
|
verbose=True,
|
|
allow_delegation=False,
|
|
)
|
|
|
|
tasks = [
|
|
Task(
|
|
description="Just say hi.", agent=agent1, expected_output="Your greeting."
|
|
),
|
|
Task(
|
|
description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
|
|
expected_output="The final answer",
|
|
tools=[get_final_answer],
|
|
agent=agent2,
|
|
),
|
|
]
|
|
|
|
crew = Crew(agents=[agent1, agent2], tasks=tasks, max_rpm=1, verbose=True)
|
|
|
|
with patch.object(RPMController, "_wait_for_next_minute") as moveon:
|
|
moveon.return_value = True
|
|
crew.kickoff()
|
|
captured = capsys.readouterr()
|
|
assert "get_final_answer" in captured.out
|
|
assert "Max RPM reached, waiting for next minute to start." in captured.out
|
|
moveon.assert_called_once()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_error_on_parsing_tool(capsys):
|
|
from unittest.mock import patch
|
|
|
|
from crewai_tools import tool
|
|
|
|
@tool
|
|
def get_final_answer() -> float:
|
|
"""Get the final answer but don't give it yet, just re-use this
|
|
tool non-stop."""
|
|
return 42
|
|
|
|
agent1 = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_iter=1,
|
|
verbose=True,
|
|
)
|
|
tasks = [
|
|
Task(
|
|
description="Use the get_final_answer tool.",
|
|
expected_output="The final answer",
|
|
agent=agent1,
|
|
tools=[get_final_answer],
|
|
)
|
|
]
|
|
|
|
crew = Crew(
|
|
agents=[agent1],
|
|
tasks=tasks,
|
|
verbose=True,
|
|
function_calling_llm="gpt-4o",
|
|
)
|
|
with patch.object(ToolUsage, "_original_tool_calling") as force_exception_1:
|
|
force_exception_1.side_effect = Exception("Error on parsing tool.")
|
|
with patch.object(ToolUsage, "_render") as force_exception_2:
|
|
force_exception_2.side_effect = Exception("Error on parsing tool.")
|
|
crew.kickoff()
|
|
captured = capsys.readouterr()
|
|
assert "Error on parsing tool." in captured.out
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_remembers_output_format_after_using_tools_too_many_times():
|
|
from unittest.mock import patch
|
|
|
|
from crewai_tools import tool
|
|
|
|
@tool
|
|
def get_final_answer() -> float:
|
|
"""Get the final answer but don't give it yet, just re-use this
|
|
tool non-stop."""
|
|
return 42
|
|
|
|
agent1 = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_iter=6,
|
|
verbose=True,
|
|
)
|
|
tasks = [
|
|
Task(
|
|
description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
|
|
expected_output="The final answer",
|
|
agent=agent1,
|
|
tools=[get_final_answer],
|
|
)
|
|
]
|
|
|
|
crew = Crew(agents=[agent1], tasks=tasks, verbose=True)
|
|
|
|
with patch.object(ToolUsage, "_remember_format") as remember_format:
|
|
crew.kickoff()
|
|
remember_format.assert_called()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_use_specific_tasks_output_as_context(capsys):
|
|
agent1 = Agent(role="test role", goal="test goal", backstory="test backstory")
|
|
agent2 = Agent(role="test role2", goal="test goal2", backstory="test backstory2")
|
|
|
|
say_hi_task = Task(
|
|
description="Just say hi.", agent=agent1, expected_output="Your greeting."
|
|
)
|
|
say_bye_task = Task(
|
|
description="Just say bye.", agent=agent1, expected_output="Your farewell."
|
|
)
|
|
answer_task = Task(
|
|
description="Answer accordingly to the context you got.",
|
|
expected_output="Your answer.",
|
|
context=[say_hi_task],
|
|
agent=agent2,
|
|
)
|
|
|
|
tasks = [say_hi_task, say_bye_task, answer_task]
|
|
|
|
crew = Crew(agents=[agent1, agent2], tasks=tasks)
|
|
result = crew.kickoff()
|
|
|
|
assert "bye" not in result.raw.lower()
|
|
assert "hi" in result.raw.lower() or "hello" in result.raw.lower()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_step_callback():
|
|
class StepCallback:
|
|
def callback(self, step):
|
|
pass
|
|
|
|
with patch.object(StepCallback, "callback") as callback:
|
|
|
|
@tool
|
|
def learn_about_AI() -> str:
|
|
"""Useful for when you need to learn about AI to write an paragraph about it."""
|
|
return "AI is a very broad field."
|
|
|
|
agent1 = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
tools=[learn_about_AI],
|
|
step_callback=StepCallback().callback,
|
|
)
|
|
|
|
essay = Task(
|
|
description="Write and then review an small paragraph on AI until it's AMAZING",
|
|
expected_output="The final paragraph.",
|
|
agent=agent1,
|
|
)
|
|
tasks = [essay]
|
|
crew = Crew(agents=[agent1], tasks=tasks)
|
|
|
|
callback.return_value = "ok"
|
|
crew.kickoff()
|
|
callback.assert_called()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_function_calling_llm():
|
|
llm = "gpt-4o"
|
|
|
|
@tool
|
|
def learn_about_AI() -> str:
|
|
"""Useful for when you need to learn about AI to write an paragraph about it."""
|
|
return "AI is a very broad field."
|
|
|
|
agent1 = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
tools=[learn_about_AI],
|
|
llm="gpt-4o",
|
|
max_iter=2,
|
|
function_calling_llm=llm,
|
|
)
|
|
|
|
essay = Task(
|
|
description="Write and then review an small paragraph on AI until it's AMAZING",
|
|
expected_output="The final paragraph.",
|
|
agent=agent1,
|
|
)
|
|
tasks = [essay]
|
|
crew = Crew(agents=[agent1], tasks=tasks)
|
|
from unittest.mock import patch
|
|
|
|
import instructor
|
|
|
|
from crewai.tools.tool_usage import ToolUsage
|
|
|
|
with patch.object(
|
|
instructor, "from_litellm", wraps=instructor.from_litellm
|
|
) as mock_from_litellm, patch.object(
|
|
ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception")
|
|
) as mock_original_tool_calling:
|
|
crew.kickoff()
|
|
mock_from_litellm.assert_called()
|
|
mock_original_tool_calling.assert_called()
|
|
|
|
|
|
def test_agent_count_formatting_error():
|
|
from unittest.mock import patch
|
|
|
|
agent1 = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
verbose=True,
|
|
)
|
|
|
|
parser = CrewAgentParser(agent=agent1)
|
|
|
|
with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
|
|
test_text = "This text does not match expected formats."
|
|
with pytest.raises(OutputParserException):
|
|
parser.parse(test_text)
|
|
mock_count_errors.assert_called_once()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
|
|
from crewai_tools import BaseTool
|
|
|
|
class MyCustomTool(BaseTool):
|
|
name: str = "Get Greetings"
|
|
description: str = "Get a random greeting back"
|
|
|
|
def _run(self) -> str:
|
|
return "Howdy!"
|
|
|
|
agent1 = Agent(
|
|
role="Data Scientist",
|
|
goal="Product amazing resports on AI",
|
|
backstory="You work with data and AI",
|
|
tools=[MyCustomTool(result_as_answer=True)],
|
|
)
|
|
|
|
essay = Task(
|
|
description="Write and then review an small paragraph on AI until it's AMAZING. But first use the `Get Greetings` tool to get a greeting.",
|
|
expected_output="The final paragraph with the full review on AI and no greeting.",
|
|
agent=agent1,
|
|
)
|
|
tasks = [essay]
|
|
crew = Crew(agents=[agent1], tasks=tasks)
|
|
|
|
result = crew.kickoff()
|
|
assert result.raw == "Howdy!"
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_tool_usage_information_is_appended_to_agent():
|
|
from crewai_tools import BaseTool
|
|
|
|
class MyCustomTool(BaseTool):
|
|
name: str = "Decide Greetings"
|
|
description: str = "Decide what is the appropriate greeting to use"
|
|
|
|
def _run(self) -> str:
|
|
return "Howdy!"
|
|
|
|
agent1 = Agent(
|
|
role="Friendly Neighbor",
|
|
goal="Make everyone feel welcome",
|
|
backstory="You are the friendly neighbor",
|
|
tools=[MyCustomTool(result_as_answer=True)],
|
|
)
|
|
|
|
greeting = Task(
|
|
description="Say an appropriate greeting.",
|
|
expected_output="The greeting.",
|
|
agent=agent1,
|
|
)
|
|
tasks = [greeting]
|
|
crew = Crew(agents=[agent1], tasks=tasks)
|
|
|
|
crew.kickoff()
|
|
assert agent1.tools_results == [
|
|
{
|
|
"result": "Howdy!",
|
|
"tool_name": "Decide Greetings",
|
|
"tool_args": {},
|
|
"result_as_answer": True,
|
|
}
|
|
]
|
|
|
|
|
|
def test_agent_definition_based_on_dict():
|
|
config = {
|
|
"role": "test role",
|
|
"goal": "test goal",
|
|
"backstory": "test backstory",
|
|
"verbose": True,
|
|
}
|
|
|
|
agent = Agent(**config)
|
|
|
|
assert agent.role == "test role"
|
|
assert agent.goal == "test goal"
|
|
assert agent.backstory == "test backstory"
|
|
assert agent.verbose is True
|
|
assert agent.tools == []
|
|
|
|
|
|
# test for human input
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_human_input():
|
|
from unittest.mock import patch
|
|
|
|
config = {
|
|
"role": "test role",
|
|
"goal": "test goal",
|
|
"backstory": "test backstory",
|
|
}
|
|
|
|
agent = Agent(**config)
|
|
|
|
task = Task(
|
|
agent=agent,
|
|
description="Say the word: Hi",
|
|
expected_output="The word: Hi",
|
|
human_input=True,
|
|
)
|
|
|
|
with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input:
|
|
mock_human_input.return_value = "Don't say hi, say Hello instead!"
|
|
output = agent.execute_task(task)
|
|
mock_human_input.assert_called_once()
|
|
assert output == "Hello"
|
|
|
|
|
|
def test_interpolate_inputs():
|
|
agent = Agent(
|
|
role="{topic} specialist",
|
|
goal="Figure {goal} out",
|
|
backstory="I am the master of {role}",
|
|
)
|
|
|
|
agent.interpolate_inputs({"topic": "AI", "goal": "life", "role": "all things"})
|
|
assert agent.role == "AI specialist"
|
|
assert agent.goal == "Figure life out"
|
|
assert agent.backstory == "I am the master of all things"
|
|
|
|
agent.interpolate_inputs({"topic": "Sales", "goal": "stuff", "role": "nothing"})
|
|
assert agent.role == "Sales specialist"
|
|
assert agent.goal == "Figure stuff out"
|
|
assert agent.backstory == "I am the master of nothing"
|
|
|
|
|
|
def test_not_using_system_prompt():
|
|
agent = Agent(
|
|
role="{topic} specialist",
|
|
goal="Figure {goal} out",
|
|
backstory="I am the master of {role}",
|
|
use_system_prompt=False,
|
|
)
|
|
|
|
agent.create_agent_executor()
|
|
assert not agent.agent_executor.prompt.get("user")
|
|
assert not agent.agent_executor.prompt.get("system")
|
|
|
|
|
|
def test_using_system_prompt():
|
|
agent = Agent(
|
|
role="{topic} specialist",
|
|
goal="Figure {goal} out",
|
|
backstory="I am the master of {role}",
|
|
)
|
|
|
|
agent.create_agent_executor()
|
|
assert agent.agent_executor.prompt.get("user")
|
|
assert agent.agent_executor.prompt.get("system")
|
|
|
|
|
|
def test_system_and_prompt_template():
|
|
agent = Agent(
|
|
role="{topic} specialist",
|
|
goal="Figure {goal} out",
|
|
backstory="I am the master of {role}",
|
|
system_template="""<|start_header_id|>system<|end_header_id|>
|
|
|
|
{{ .System }}<|eot_id|>""",
|
|
prompt_template="""<|start_header_id|>user<|end_header_id|>
|
|
|
|
{{ .Prompt }}<|eot_id|>""",
|
|
response_template="""<|start_header_id|>assistant<|end_header_id|>
|
|
|
|
{{ .Response }}<|eot_id|>""",
|
|
)
|
|
|
|
expected_prompt = """<|start_header_id|>system<|end_header_id|>
|
|
|
|
You are {role}. {backstory}
|
|
Your personal goal is: {goal}
|
|
To give my best complete final answer to the task use the exact following format:
|
|
|
|
Thought: I now can give a great answer
|
|
Final Answer: my best complete final answer to the task.
|
|
Your final answer must be the great and the most complete as possible, it must be outcome described.
|
|
|
|
I MUST use these formats, my job depends on it!<|eot_id|>
|
|
<|start_header_id|>user<|end_header_id|>
|
|
|
|
|
|
Current Task: {input}
|
|
|
|
Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!
|
|
|
|
Thought:<|eot_id|>
|
|
<|start_header_id|>assistant<|end_header_id|>
|
|
|
|
"""
|
|
|
|
with patch.object(CrewAgentExecutor, "_format_prompt") as mock_format_prompt:
|
|
mock_format_prompt.return_value = expected_prompt
|
|
|
|
# Trigger the _format_prompt method
|
|
agent.agent_executor._format_prompt("dummy_prompt", {})
|
|
|
|
# Assert that _format_prompt was called
|
|
mock_format_prompt.assert_called_once()
|
|
|
|
# Assert that the returned prompt matches the expected prompt
|
|
assert mock_format_prompt.return_value == expected_prompt
|
|
|
|
|
|
@patch("crewai.agent.CrewTrainingHandler")
|
|
def test_agent_training_handler(crew_training_handler):
|
|
task_prompt = "What is 1 + 1?"
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
verbose=True,
|
|
)
|
|
crew_training_handler().load.return_value = {
|
|
f"{str(agent.id)}": {"0": {"human_feedback": "good"}}
|
|
}
|
|
|
|
result = agent._training_handler(task_prompt=task_prompt)
|
|
|
|
assert result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n good"
|
|
|
|
crew_training_handler.assert_has_calls(
|
|
[mock.call(), mock.call("training_data.pkl"), mock.call().load()]
|
|
)
|
|
|
|
|
|
@patch("crewai.agent.CrewTrainingHandler")
|
|
def test_agent_use_trained_data(crew_training_handler):
|
|
task_prompt = "What is 1 + 1?"
|
|
agent = Agent(
|
|
role="researcher",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
verbose=True,
|
|
)
|
|
crew_training_handler().load.return_value = {
|
|
agent.role: {
|
|
"suggestions": [
|
|
"The result of the math operation must be right.",
|
|
"Result must be better than 1.",
|
|
]
|
|
}
|
|
}
|
|
|
|
result = agent._use_trained_data(task_prompt=task_prompt)
|
|
|
|
assert (
|
|
result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n"
|
|
" - The result of the math operation must be right.\n - Result must be better than 1."
|
|
)
|
|
crew_training_handler.assert_has_calls(
|
|
[mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
|
|
)
|
|
|
|
|
|
def test_agent_max_retry_limit():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
max_retry_limit=1,
|
|
)
|
|
|
|
task = Task(
|
|
agent=agent,
|
|
description="Say the word: Hi",
|
|
expected_output="The word: Hi",
|
|
human_input=True,
|
|
)
|
|
|
|
error_message = "Error happening while sending prompt to model."
|
|
with patch.object(
|
|
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
|
|
) as invoke_mock:
|
|
invoke_mock.side_effect = Exception(error_message)
|
|
|
|
assert agent._times_executed == 0
|
|
assert agent.max_retry_limit == 1
|
|
|
|
with pytest.raises(Exception) as e:
|
|
agent.execute_task(
|
|
task=task,
|
|
)
|
|
assert e.value.args[0] == error_message
|
|
assert agent._times_executed == 2
|
|
|
|
invoke_mock.assert_has_calls(
|
|
[
|
|
mock.call(
|
|
{
|
|
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
|
|
"tool_names": "",
|
|
"tools": "",
|
|
"ask_for_human_input": True,
|
|
}
|
|
),
|
|
mock.call(
|
|
{
|
|
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
|
|
"tool_names": "",
|
|
"tools": "",
|
|
"ask_for_human_input": True,
|
|
}
|
|
),
|
|
]
|
|
)
|
|
|
|
|
|
def test_agent_with_llm():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo", temperature=0.7),
|
|
)
|
|
|
|
assert isinstance(agent.llm, LLM)
|
|
assert agent.llm.model == "gpt-3.5-turbo"
|
|
assert agent.llm.temperature == 0.7
|
|
|
|
|
|
def test_agent_with_custom_stop_words():
|
|
stop_words = ["STOP", "END"]
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo", stop=stop_words),
|
|
)
|
|
|
|
assert isinstance(agent.llm, LLM)
|
|
assert set(agent.llm.stop) == set(stop_words + ["\nObservation:"])
|
|
assert all(word in agent.llm.stop for word in stop_words)
|
|
assert "\nObservation:" in agent.llm.stop
|
|
|
|
|
|
def test_agent_with_callbacks():
|
|
def dummy_callback(response):
|
|
pass
|
|
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback]),
|
|
)
|
|
|
|
assert isinstance(agent.llm, LLM)
|
|
assert len(agent.llm.callbacks) == 1
|
|
assert agent.llm.callbacks[0] == dummy_callback
|
|
|
|
|
|
def test_agent_with_additional_kwargs():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(
|
|
model="gpt-3.5-turbo",
|
|
temperature=0.8,
|
|
top_p=0.9,
|
|
presence_penalty=0.1,
|
|
frequency_penalty=0.1,
|
|
),
|
|
)
|
|
|
|
assert isinstance(agent.llm, LLM)
|
|
assert agent.llm.model == "gpt-3.5-turbo"
|
|
assert agent.llm.temperature == 0.8
|
|
assert agent.llm.top_p == 0.9
|
|
assert agent.llm.presence_penalty == 0.1
|
|
assert agent.llm.frequency_penalty == 0.1
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_llm_call():
|
|
llm = LLM(model="gpt-3.5-turbo")
|
|
messages = [{"role": "user", "content": "Say 'Hello, World!'"}]
|
|
|
|
response = llm.call(messages)
|
|
assert "Hello, World!" in response
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_llm_call_with_error():
|
|
llm = LLM(model="non-existent-model")
|
|
messages = [{"role": "user", "content": "This should fail"}]
|
|
|
|
with pytest.raises(Exception):
|
|
llm.call(messages)
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_handle_context_length_exceeds_limit():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
)
|
|
original_action = AgentAction(
|
|
tool="test_tool",
|
|
tool_input="test_input",
|
|
text="test_log",
|
|
thought="test_thought",
|
|
)
|
|
|
|
with patch.object(
|
|
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
|
|
) as private_mock:
|
|
task = Task(
|
|
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
|
|
expected_output="The final answer",
|
|
)
|
|
agent.execute_task(
|
|
task=task,
|
|
)
|
|
private_mock.assert_called_once()
|
|
with patch.object(
|
|
CrewAgentExecutor, "_handle_context_length"
|
|
) as mock_handle_context:
|
|
mock_handle_context.side_effect = ValueError(
|
|
"Context length limit exceeded"
|
|
)
|
|
|
|
long_input = "This is a very long input. " * 10000
|
|
|
|
# Attempt to handle context length, expecting the mocked error
|
|
with pytest.raises(ValueError) as excinfo:
|
|
agent.agent_executor._handle_context_length(
|
|
[(original_action, long_input)]
|
|
)
|
|
|
|
assert "Context length limit exceeded" in str(excinfo.value)
|
|
mock_handle_context.assert_called_once()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_handle_context_length_exceeds_limit_cli_no():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
sliding_context_window=False,
|
|
)
|
|
task = Task(description="test task", agent=agent, expected_output="test output")
|
|
|
|
with patch.object(
|
|
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
|
|
) as private_mock:
|
|
task = Task(
|
|
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
|
|
expected_output="The final answer",
|
|
)
|
|
agent.execute_task(
|
|
task=task,
|
|
)
|
|
private_mock.assert_called_once()
|
|
pytest.raises(SystemExit)
|
|
with patch.object(
|
|
CrewAgentExecutor, "_handle_context_length"
|
|
) as mock_handle_context:
|
|
mock_handle_context.assert_not_called()
|
|
|
|
|
|
def test_agent_with_all_llm_attributes():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(
|
|
model="gpt-3.5-turbo",
|
|
timeout=10,
|
|
temperature=0.7,
|
|
top_p=0.9,
|
|
n=1,
|
|
stop=["STOP", "END"],
|
|
max_tokens=100,
|
|
presence_penalty=0.1,
|
|
frequency_penalty=0.1,
|
|
logit_bias={50256: -100}, # Example: bias against the EOT token
|
|
response_format={"type": "json_object"},
|
|
seed=42,
|
|
logprobs=True,
|
|
top_logprobs=5,
|
|
base_url="https://api.openai.com/v1",
|
|
api_version="2023-05-15",
|
|
api_key="sk-your-api-key-here",
|
|
),
|
|
)
|
|
|
|
assert isinstance(agent.llm, LLM)
|
|
assert agent.llm.model == "gpt-3.5-turbo"
|
|
assert agent.llm.timeout == 10
|
|
assert agent.llm.temperature == 0.7
|
|
assert agent.llm.top_p == 0.9
|
|
assert agent.llm.n == 1
|
|
assert set(agent.llm.stop) == set(["STOP", "END", "\nObservation:"])
|
|
assert all(word in agent.llm.stop for word in ["STOP", "END", "\nObservation:"])
|
|
assert agent.llm.max_tokens == 100
|
|
assert agent.llm.presence_penalty == 0.1
|
|
assert agent.llm.frequency_penalty == 0.1
|
|
assert agent.llm.logit_bias == {50256: -100}
|
|
assert agent.llm.response_format == {"type": "json_object"}
|
|
assert agent.llm.seed == 42
|
|
assert agent.llm.logprobs
|
|
assert agent.llm.top_logprobs == 5
|
|
assert agent.llm.base_url == "https://api.openai.com/v1"
|
|
assert agent.llm.api_version == "2023-05-15"
|
|
assert agent.llm.api_key == "sk-your-api-key-here"
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_llm_call_with_all_attributes():
|
|
llm = LLM(
|
|
model="gpt-3.5-turbo",
|
|
temperature=0.7,
|
|
max_tokens=50,
|
|
stop=["STOP"],
|
|
presence_penalty=0.1,
|
|
frequency_penalty=0.1,
|
|
)
|
|
messages = [{"role": "user", "content": "Say 'Hello, World!' and then say STOP"}]
|
|
|
|
response = llm.call(messages)
|
|
assert "Hello, World!" in response
|
|
assert "STOP" not in response
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_with_ollama_gemma():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(
|
|
model="ollama/gemma2:latest",
|
|
base_url="http://localhost:8080",
|
|
),
|
|
)
|
|
|
|
assert isinstance(agent.llm, LLM)
|
|
assert agent.llm.model == "ollama/gemma2:latest"
|
|
assert agent.llm.base_url == "http://localhost:8080"
|
|
|
|
task = "Respond in 20 words. Who are you?"
|
|
response = agent.llm.call([{"role": "user", "content": task}])
|
|
|
|
assert response
|
|
assert len(response.split()) <= 25 # Allow a little flexibility in word count
|
|
assert "Gemma" in response or "AI" in response or "language model" in response
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_llm_call_with_ollama_gemma():
|
|
llm = LLM(
|
|
model="ollama/gemma2:latest",
|
|
base_url="http://localhost:8080",
|
|
temperature=0.7,
|
|
max_tokens=30,
|
|
)
|
|
messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
|
|
|
|
response = llm.call(messages)
|
|
|
|
assert response
|
|
assert len(response.split()) <= 25 # Allow a little flexibility in word count
|
|
assert "Gemma" in response or "AI" in response or "language model" in response
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_execute_task_basic():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo"),
|
|
)
|
|
|
|
task = Task(
|
|
description="Calculate 2 + 2",
|
|
expected_output="The result of the calculation",
|
|
agent=agent,
|
|
)
|
|
|
|
result = agent.execute_task(task)
|
|
assert "4" in result
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_execute_task_with_context():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo"),
|
|
)
|
|
|
|
task = Task(
|
|
description="Summarize the given context in one sentence",
|
|
expected_output="A one-sentence summary",
|
|
agent=agent,
|
|
)
|
|
|
|
context = "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet."
|
|
|
|
result = agent.execute_task(task, context=context)
|
|
assert len(result.split(".")) == 3
|
|
assert "fox" in result.lower() and "dog" in result.lower()
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_execute_task_with_tool():
|
|
@tool
|
|
def dummy_tool(query: str) -> str:
|
|
"""Useful for when you need to get a dummy result for a query."""
|
|
return f"Dummy result for: {query}"
|
|
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo"),
|
|
tools=[dummy_tool],
|
|
)
|
|
|
|
task = Task(
|
|
description="Use the dummy tool to get a result for 'test query'",
|
|
expected_output="The result from the dummy tool",
|
|
agent=agent,
|
|
)
|
|
|
|
result = agent.execute_task(task)
|
|
assert "Dummy result for: test query" in result
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_execute_task_with_custom_llm():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="gpt-3.5-turbo", temperature=0.7, max_tokens=50),
|
|
)
|
|
|
|
task = Task(
|
|
description="Write a haiku about AI",
|
|
expected_output="A haiku (3 lines, 5-7-5 syllable pattern) about AI",
|
|
agent=agent,
|
|
)
|
|
|
|
result = agent.execute_task(task)
|
|
assert result.startswith(
|
|
"Artificial minds,\nCoding thoughts in circuits bright,\nAI's silent might."
|
|
)
|
|
|
|
|
|
@pytest.mark.vcr(filter_headers=["authorization"])
|
|
def test_agent_execute_task_with_ollama():
|
|
agent = Agent(
|
|
role="test role",
|
|
goal="test goal",
|
|
backstory="test backstory",
|
|
llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
|
|
)
|
|
|
|
task = Task(
|
|
description="Explain what AI is in one sentence",
|
|
expected_output="A one-sentence explanation of AI",
|
|
agent=agent,
|
|
)
|
|
|
|
result = agent.execute_task(task)
|
|
assert len(result.split(".")) == 2
|
|
assert "AI" in result or "artificial intelligence" in result.lower()
|