Merge branch 'main' of github.com:crewAIInc/crewAI into feat/sliding-context-window

This commit is contained in:
Lorenze Jay
2024-07-31 16:32:13 -07:00
38 changed files with 409934 additions and 7139 deletions

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@@ -254,7 +254,7 @@ pip install dist/*.tar.gz
CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
Data collected includes:
@@ -279,7 +279,7 @@ Data collected includes:
- Tools names available
- Understand out of the publically available tools, which ones are being used the most so we can improve them
Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
## License

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@@ -114,7 +114,7 @@ from langchain.agents import load_tools
langchain_tools = load_tools(["google-serper"], llm=llm)
agent1 = CustomAgent(
role="backstory agent",
role="agent role",
goal="who is {input}?",
backstory="agent backstory",
verbose=True,
@@ -127,7 +127,7 @@ task1 = Task(
)
agent2 = Agent(
role="bio agent",
role="agent role",
goal="summarize the short bio for {input} and if needed do more research",
backstory="agent backstory",
verbose=True,

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@@ -137,7 +137,7 @@ crew = Crew(
verbose=2
)
result = crew.kickoff()
crew_output = crew.kickoff()
# Accessing the crew output
print(f"Raw Output: {crew_output.raw}")

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@@ -18,4 +18,7 @@ pip install crewai
# Install the main crewAI package and the tools package
# that includes a series of helpful tools for your agents
pip install 'crewai[tools]'
# Alternatively, you can also use:
pip install crewai crewai-tools
```

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@@ -1,5 +1,5 @@
---
title: Starting a New CrewAI Project
title: Starting a New CrewAI Project - Using Template
description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
---
@@ -7,13 +7,62 @@ description: A comprehensive guide to starting a new CrewAI project, including t
Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
Beforre we start there are a couple of things to note:
1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run.
2. The preferred way of setting up CrewAI is using the `crewai create` command.This will create a new project folder and install a skeleton template for you to work on.
## Prerequisites
We assume you have already installed CrewAI. If not, please refer to the [installation guide](https://docs.crewai.com/how-to/Installing-CrewAI/) to install CrewAI and its dependencies.
Before getting started with CrewAI, make sure that you have installed it via pip:
```shell
$ pip install crewai crewai-tools
```
### Virtual Environemnts
It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
1. Use venv (Python's built-in virtual environment tool):
venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups.
To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html).
2. Use Conda (A Python virtual environment manager):
Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way.
To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
3. Use Poetry (A Python package manager and dependency management tool):
Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies.
Poetry is CrewAI's prefered tool for package / dependancy management in CrewAI.
### Code IDEs
Most users of CrewAI a Code Editor / Integrated Development Environment (IDE) for building there Crews. You can use any code IDE of your choice. Seee below for some popular options for Code Editors / Integrated Development Environments (IDE):
- [Visual Studio Code](https://code.visualstudio.com/) - Most popular
- [PyCharm](https://www.jetbrains.com/pycharm/)
- [Cursor AI](https://cursor.com)
Pick one that suits your style and needs.
## Creating a New Project
In this example we will be using Venv as our virtual environment manager.
To create a new project, run the following CLI command:
To setup a virtual environment, run the following CLI command:
```shell
$ python3 -m venv <venv-name>
```
Activate your virtual environment by running the following CLI command:
```shell
$ source <venv-name>/bin/activate
```
Now, to create a new CrewAI project, run the following CLI command:
```shell
$ crewai create <project_name>

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@@ -1,84 +0,0 @@
---
title: Assembling and Activating Your CrewAI Team
description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, code execution, integration with third-party agents, and improved task management.
---
## Introduction
Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience, including code execution capabilities, integration with third-party agents, and advanced task management.
## Step 0: Installation
Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13.
```shell
pip install crewai
pip install 'crewai[tools]'
```
## Step 1: Assemble Your Agents
Define your agents with distinct roles, backstories, and enhanced capabilities. The Agent class now supports a wide range of attributes for fine-tuned control over agent behavior and interactions, including code execution and integration with third-party agents.
```python
import os
from langchain.llms import OpenAI
from crewai import Agent
from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool
os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
os.environ["SERPER_API_KEY"] = "Your Serper Key"
os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key"
os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id"
search_tool = SerperDevTool()
browser_tool = BrowserbaseLoadTool()
exa_search_tool = EXASearchTool()
# Creating a senior researcher agent with advanced configurations
researcher = Agent(
role='Senior Researcher',
goal='Uncover groundbreaking technologies in {topic}',
backstory=("Driven by curiosity, you're at the forefront of innovation, "
"eager to explore and share knowledge that could change the world."),
memory=True,
verbose=True,
allow_delegation=False,
tools=[search_tool, browser_tool],
allow_code_execution=False, # New attribute for enabling code execution
max_iter=15, # Maximum number of iterations for task execution
max_rpm=100, # Maximum requests per minute
max_execution_time=3600, # Maximum execution time in seconds
system_template="Your custom system template here", # Custom system template
prompt_template="Your custom prompt template here", # Custom prompt template
response_template="Your custom response template here", # Custom response template
)
# Creating a writer agent with custom tools and specific configurations
writer = Agent(
role='Writer',
goal='Narrate compelling tech stories about {topic}',
backstory=("With a flair for simplifying complex topics, you craft engaging "
"narratives that captivate and educate, bringing new discoveries to light."),
verbose=True,
allow_delegation=False,
memory=True,
tools=[exa_search_tool],
function_calling_llm=OpenAI(model_name="gpt-3.5-turbo"), # Separate LLM for function calling
)
# Setting a specific manager agent
manager = Agent(
role='Manager',
goal='Ensure the smooth operation and coordination of the team',
verbose=True,
backstory=(
"As a seasoned project manager, you excel in organizing "
"tasks, managing timelines, and ensuring the team stays on track."
),
allow_code_execution=True, # Enable code execution for the manager
)
```
### New Agent Attributes and Features
1. `allow_code_execution`: Enable or disable code execution capabilities for the agent (default is False).
2. `max_execution_time`: Set a maximum execution time (in seconds) for the agent to complete a task.
3. `function_calling_llm`: Specify a separate language model for function calling.

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@@ -7,7 +7,7 @@ description: Learn how to force tool output as the result in of an Agent's task
In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
## Forcing Tool Output as Result
To force the tool output as the result of an agent's task, you can set the `force_tool_output` parameter to `True` when creating the task. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
To force the tool output as the result of an agent's task, you can set the `result_as_answer` parameter to `True` when creating the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
Here's an example of how to force the tool output as the result of an agent's task:

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@@ -5,6 +5,19 @@
Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
<div style="display:flex; margin:0 auto; justify-content: center;">
<div style="width:25%">
<h2>Getting Started</h2>
<ul>
<li><a href='./getting-started/Installing-CrewAI'>
Installing CrewAI
</a>
</li>
<li><a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
Start a New CrewAI Project: Template Method
</a>
</li>
</ul>
</div>
<div style="width:25%">
<h2>Core Concepts</h2>
<ul>
@@ -53,21 +66,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
<div style="width:30%">
<h2>How-To Guides</h2>
<ul>
<li>
<a href="./how-to/Start-a-New-CrewAI-Project">
Starting Your crewAI Project
</a>
</li>
<li>
<a href="./how-to/Installing-CrewAI">
Installing crewAI
</a>
</li>
<li>
<a href="./how-to/Creating-a-Crew-and-kick-it-off">
Getting Started
</a>
</li>
<li>
<a href="./how-to/Create-Custom-Tools">
Create Custom Tools

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@@ -5,7 +5,7 @@ description: Understanding the telemetry data collected by CrewAI and how it con
## Telemetry
CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users. We don't offer a way to disable it now, but we will in the future.
It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
@@ -22,7 +22,7 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
### Opt-In Further Telemetry Sharing
Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
### Updates and Revisions
We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.

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@@ -29,5 +29,70 @@ To effectively use the `SerperDevTool`, follow these steps:
2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
## Parameters
The `SerperDevTool` comes with several parameters that will be passed to the API :
- **search_url**: The URL endpoint for the search API. (Default is `https://google.serper.dev/search`)
- **country**: Optional. Specify the country for the search results.
- **location**: Optional. Specify the location for the search results.
- **locale**: Optional. Specify the locale for the search results.
- **n_results**: Number of search results to return. Default is `10`.
The values for `country`, `location`, `lovale` and `search_url` can be found on the [Serper Playground](https://serper.dev/playground).
## Example with Parameters
Here is an example demonstrating how to use the tool with additional parameters:
```python
from crewai_tools import SerperDevTool
tool = SerperDevTool(
search_url="https://google.serper.dev/scholar",
n_results=2,
)
print(tool.run(search_query="ChatGPT"))
# Using Tool: Search the internet
# Search results: Title: Role of chat gpt in public health
# Link: https://link.springer.com/article/10.1007/s10439-023-03172-7
# Snippet: … ChatGPT in public health. In this overview, we will examine the potential uses of ChatGPT in
# ---
# Title: Potential use of chat gpt in global warming
# Link: https://link.springer.com/article/10.1007/s10439-023-03171-8
# Snippet: … as ChatGPT, have the potential to play a critical role in advancing our understanding of climate
# ---
```
```python
from crewai_tools import SerperDevTool
tool = SerperDevTool(
country="fr",
locale="fr",
location="Paris, Paris, Ile-de-France, France",
n_results=2,
)
print(tool.run(search_query="Jeux Olympiques"))
# Using Tool: Search the internet
# Search results: Title: Jeux Olympiques de Paris 2024 - Actualités, calendriers, résultats
# Link: https://olympics.com/fr/paris-2024
# Snippet: Quels sont les sports présents aux Jeux Olympiques de Paris 2024 ? · Athlétisme · Aviron · Badminton · Basketball · Basketball 3x3 · Boxe · Breaking · Canoë ...
# ---
# Title: Billetterie Officielle de Paris 2024 - Jeux Olympiques et Paralympiques
# Link: https://tickets.paris2024.org/
# Snippet: Achetez vos billets exclusivement sur le site officiel de la billetterie de Paris 2024 pour participer au plus grand événement sportif au monde.
# ---
```
## Conclusion
By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The updated parameters allow for more customized and localized search results. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.

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@@ -119,6 +119,9 @@ theme:
nav:
- Home: '/'
- Getting Started:
- Installing CrewAI: 'getting-started/Installing-CrewAI.md'
- Starting a new CrewAI project: 'getting-started/Start-a-New-CrewAI-Project-Template-Method.md'
- Core Concepts:
- Agents: 'core-concepts/Agents.md'
- Tasks: 'core-concepts/Tasks.md'

146
poetry.lock generated
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@@ -1,14 +1,14 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "agentops"
version = "0.3.0"
version = "0.3.2"
description = "Python SDK for developing AI agent evals and observability"
optional = true
python-versions = ">=3.7"
files = [
{file = "agentops-0.3.0-py3-none-any.whl", hash = "sha256:22aeb3355e66b32a2b2a9f676048b81979b2488feddb088f9266034b3ed50539"},
{file = "agentops-0.3.0.tar.gz", hash = "sha256:6c0c08a57410fa5e826a7bafa1deeba9f7b3524709427d9e1abbd0964caaf76b"},
{file = "agentops-0.3.2-py3-none-any.whl", hash = "sha256:b35988e04378624204572bb3d7a454094f879ea573f05b57d4e75ab0bfbb82af"},
{file = "agentops-0.3.2.tar.gz", hash = "sha256:55559ac4a43634831dfa8937c2597c28e332809dc7c6bb3bc3c8b233442e224c"},
]
[package.dependencies]
@@ -294,38 +294,38 @@ files = [
[[package]]
name = "bcrypt"
version = "4.1.3"
version = "4.2.0"
description = "Modern password hashing for your software and your servers"
optional = false
python-versions = ">=3.7"
files = [
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python-versions = "<4.0,>=3.8"
files = [
{file = "together-1.2.2-py3-none-any.whl", hash = "sha256:7ce89f902dbaca67e46e693d90182514494f510f3bc16cb89d816a5031ab0433"},
{file = "together-1.2.2.tar.gz", hash = "sha256:fd026f4a604e1fb3ee2fa5803f31e5e36ad31b3d182ef47f611326de66907d13"},
{file = "together-1.2.3-py3-none-any.whl", hash = "sha256:bbafb4b8340e0f7e0ddb11ad447eb3467c591090910d0291cfbf74b47af045c1"},
{file = "together-1.2.3.tar.gz", hash = "sha256:4ea7626a9581d16fbf293e3eaf91557c43dea044627cf6dbe458bbf43408a6b2"},
]
[package.dependencies]

View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "crewai"
version = "0.41.1"
version = "0.46.0"
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
authors = ["Joao Moura <joao@crewai.com>"]
readme = "README.md"

View File

@@ -55,8 +55,6 @@ class Agent(BaseAgent):
tools: Tools at agents disposal
step_callback: Callback to be executed after each step of the agent execution.
callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
allow_code_execution: Enable code execution for the agent.
max_retry_limit: Maximum number of retries for an agent to execute a task when an error occurs.
"""
_times_executed: int = PrivateAttr(default=0)
@@ -262,6 +260,7 @@ class Agent(BaseAgent):
"tools_handler": self.tools_handler,
"function_calling_llm": self.function_calling_llm,
"callbacks": self.callbacks,
"max_tokens": self.max_tokens,
}
if self._rpm_controller:

View File

@@ -45,6 +45,7 @@ class BaseAgent(ABC, BaseModel):
i18n (I18N): Internationalization settings.
cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
max_tokens: Maximum number of tokens for the agent to generate in a response.
Methods:
@@ -118,6 +119,9 @@ class BaseAgent(ABC, BaseModel):
tools_handler: InstanceOf[ToolsHandler] = Field(
default=None, description="An instance of the ToolsHandler class."
)
max_tokens: Optional[int] = Field(
default=None, description="Maximum number of tokens for the agent's execution."
)
_original_role: str | None = None
_original_goal: str | None = None

View File

@@ -4,7 +4,6 @@ from typing import TYPE_CHECKING, Optional
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities import I18N
@@ -40,18 +39,17 @@ class CrewAgentExecutorMixin:
and "Action: Delegate work to coworker" not in output.log
):
try:
memory = ShortTermMemoryItem(
data=output.log,
agent=self.crew_agent.role,
metadata={
"observation": self.task.description,
},
)
if (
hasattr(self.crew, "_short_term_memory")
and self.crew._short_term_memory
):
self.crew._short_term_memory.save(memory)
self.crew._short_term_memory.save(
value=output.log,
metadata={
"observation": self.task.description,
},
agent=self.crew_agent.role,
)
except Exception as e:
print(f"Failed to add to short term memory: {e}")
pass

View File

@@ -6,9 +6,9 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
)
from .create_crew import create_crew
from .evaluate_crew import evaluate_crew
from .replay_from_task import replay_task_command
from .reset_memories_command import reset_memories_command
from .test_crew import test_crew
from .train_crew import train_crew
@@ -144,7 +144,7 @@ def reset_memories(long, short, entities, kickoff_outputs, all):
def test(n_iterations: int, model: str):
"""Test the crew and evaluate the results."""
click.echo(f"Testing the crew for {n_iterations} iterations with model {model}")
test_crew(n_iterations, model)
evaluate_crew(n_iterations, model)
if __name__ == "__main__":

View File

@@ -3,9 +3,9 @@ import subprocess
import click
def test_crew(n_iterations: int, model: str) -> None:
def evaluate_crew(n_iterations: int, model: str) -> None:
"""
Test the crew by running a command in the Poetry environment.
Test and Evaluate the crew by running a command in the Poetry environment.
Args:
n_iterations (int): The number of iterations to test the crew.

View File

@@ -9,10 +9,14 @@ from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandle
def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
"""
Replay the crew execution from a specific task.
Reset the crew memories.
Args:
task_id (str): The ID of the task to replay from.
long (bool): Whether to reset the long-term memory.
short (bool): Whether to reset the short-term memory.
entity (bool): Whether to reset the entity memory.
kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
all (bool): Whether to reset all memories.
"""
try:

View File

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = "^0.41.1" }
crewai = { extras = ["tools"], version = "^0.46.0" }
[tool.poetry.scripts]
{{folder_name}} = "{{folder_name}}.main:run"

View File

@@ -1,3 +1,4 @@
from typing import Any, Dict, Optional
from crewai.memory.memory import Memory
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.memory.storage.rag_storage import RAGStorage
@@ -18,8 +19,15 @@ class ShortTermMemory(Memory):
)
super().__init__(storage)
def save(self, item: ShortTermMemoryItem) -> None:
super().save(item.data, item.metadata, item.agent)
def save(
self,
value: Any,
metadata: Optional[Dict[str, Any]] = None,
agent: Optional[str] = None,
) -> None:
item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
super().save(value=item.data, metadata=item.metadata, agent=item.agent)
def search(self, query: str, score_threshold: float = 0.35):
return self.storage.search(query=query, score_threshold=score_threshold) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters

View File

@@ -3,7 +3,10 @@ from typing import Any, Dict, Optional
class ShortTermMemoryItem:
def __init__(
self, data: Any, agent: str, metadata: Optional[Dict[str, Any]] = None
self,
data: Any,
agent: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
):
self.data = data
self.agent = agent

View File

@@ -4,7 +4,7 @@ from typing import Any, Dict
class Storage:
"""Abstract base class defining the storage interface"""
def save(self, key: str, value: Any, metadata: Dict[str, Any]) -> None:
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
pass
def search(self, key: str) -> Dict[str, Any]: # type: ignore

View File

@@ -1,3 +1,4 @@
import datetime
import json
import os
import threading
@@ -107,6 +108,7 @@ class Task(BaseModel):
_original_description: str | None = None
_original_expected_output: str | None = None
_thread: threading.Thread | None = None
_execution_time: float | None = None
def __init__(__pydantic_self__, **data):
config = data.pop("config", {})
@@ -120,9 +122,15 @@ class Task(BaseModel):
"may_not_set_field", "This field is not to be set by the user.", {}
)
def _set_start_execution_time(self) -> float:
return datetime.datetime.now().timestamp()
def _set_end_execution_time(self, start_time: float) -> None:
self._execution_time = datetime.datetime.now().timestamp() - start_time
@field_validator("output_file")
@classmethod
def output_file_validattion(cls, value: str) -> str:
def output_file_validation(cls, value: str) -> str:
"""Validate the output file path by removing the / from the beginning of the path."""
if value.startswith("/"):
return value[1:]
@@ -216,6 +224,7 @@ class Task(BaseModel):
f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
)
start_time = self._set_start_execution_time()
self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
self.prompt_context = context
@@ -239,6 +248,7 @@ class Task(BaseModel):
)
self.output = task_output
self._set_end_execution_time(start_time)
if self.callback:
self.callback(self.output)
@@ -250,7 +260,9 @@ class Task(BaseModel):
content = (
json_output
if json_output
else pydantic_output.model_dump_json() if pydantic_output else result
else pydantic_output.model_dump_json()
if pydantic_output
else result
)
self._save_file(content)
@@ -355,13 +367,21 @@ class Task(BaseModel):
return OutputFormat.RAW
def _save_file(self, result: Any) -> None:
if self.output_file is None:
raise ValueError("output_file is not set.")
directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
if directory and not os.path.exists(directory):
os.makedirs(directory)
with open(self.output_file, "w", encoding="utf-8") as file: # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
file.write(result)
with open(self.output_file, "w", encoding="utf-8") as file:
if isinstance(result, dict):
import json
json.dump(result, file, ensure_ascii=False, indent=2)
else:
file.write(str(result))
return None
def __repr__(self):

View File

@@ -40,7 +40,7 @@ class Telemetry:
- Roles of agents in a crew
- Tools names available
Users can opt-in to sharing more complete data suing the `share_crew`
Users can opt-in to sharing more complete data using the `share_crew`
attribute in the Crew class.
"""

View File

@@ -86,7 +86,8 @@ class ToolUsage:
) -> str:
if isinstance(calling, ToolUsageErrorException):
error = calling.message
self._printer.print(content=f"\n\n{error}\n", color="red")
if self.agent.verbose:
self._printer.print(content=f"\n\n{error}\n", color="red")
self.task.increment_tools_errors()
return error
@@ -96,7 +97,8 @@ class ToolUsage:
except Exception as e:
error = getattr(e, "message", str(e))
self.task.increment_tools_errors()
self._printer.print(content=f"\n\n{error}\n", color="red")
if self.agent.verbose:
self._printer.print(content=f"\n\n{error}\n", color="red")
return error
return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
@@ -112,7 +114,8 @@ class ToolUsage:
result = self._i18n.errors("task_repeated_usage").format(
tool_names=self.tools_names
)
self._printer.print(content=f"\n\n{result}\n", color="purple")
if self.agent.verbose:
self._printer.print(content=f"\n\n{result}\n", color="purple")
self._telemetry.tool_repeated_usage(
llm=self.function_calling_llm,
tool_name=tool.name,
@@ -168,7 +171,10 @@ class ToolUsage:
f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
).message
self.task.increment_tools_errors()
self._printer.print(content=f"\n\n{error_message}\n", color="red")
if self.agent.verbose:
self._printer.print(
content=f"\n\n{error_message}\n", color="red"
)
return error # type: ignore # No return value expected
self.task.increment_tools_errors()
@@ -192,7 +198,8 @@ class ToolUsage:
calling=calling, output=result, should_cache=should_cache
)
self._printer.print(content=f"\n\n{result}\n", color="purple")
if self.agent.verbose:
self._printer.print(content=f"\n\n{result}\n", color="purple")
if agentops:
agentops.record(tool_event)
self._telemetry.tool_usage(
@@ -346,7 +353,8 @@ class ToolUsage:
if self._run_attempts > self._max_parsing_attempts:
self._telemetry.tool_usage_error(llm=self.function_calling_llm)
self.task.increment_tools_errors()
self._printer.print(content=f"\n\n{e}\n", color="red")
if self.agent.verbose:
self._printer.print(content=f"\n\n{e}\n", color="red")
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
)

View File

@@ -28,6 +28,7 @@ class CrewEvaluator:
"""
tasks_scores: defaultdict = defaultdict(list)
run_execution_times: defaultdict = defaultdict(list)
iteration: int = 0
def __init__(self, crew, openai_model_name: str):
@@ -40,9 +41,6 @@ class CrewEvaluator:
for task in self.crew.tasks:
task.callback = self.evaluate
def set_iteration(self, iteration: int) -> None:
self.iteration = iteration
def _evaluator_agent(self):
return Agent(
role="Task Execution Evaluator",
@@ -71,6 +69,9 @@ class CrewEvaluator:
output_pydantic=TaskEvaluationPydanticOutput,
)
def set_iteration(self, iteration: int) -> None:
self.iteration = iteration
def print_crew_evaluation_result(self) -> None:
"""
Prints the evaluation result of the crew in a table.
@@ -119,6 +120,16 @@ class CrewEvaluator:
]
table.add_row("Crew", *map(str, crew_scores), f"{crew_average:.1f}")
run_exec_times = [
int(sum(tasks_exec_times))
for _, tasks_exec_times in self.run_execution_times.items()
]
execution_time_avg = int(sum(run_exec_times) / len(run_exec_times))
table.add_row(
"Execution Time (s)",
*map(str, run_exec_times),
f"{execution_time_avg}",
)
# Display the table in the terminal
console = Console()
console.print(table)
@@ -145,5 +156,8 @@ class CrewEvaluator:
if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
self.run_execution_times[self.iteration].append(
current_task._execution_time
)
else:
raise ValueError("Evaluation result is not in the expected format")

View File

@@ -54,12 +54,12 @@ class TaskEvaluator:
def __init__(self, original_agent):
self.llm = original_agent.llm
def evaluate(self, task, ouput) -> TaskEvaluation:
def evaluate(self, task, output) -> TaskEvaluation:
evaluation_query = (
f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"
f"Task Description:\n{task.description}\n\n"
f"Expected Output:\n{task.expected_output}\n\n"
f"Actual Output:\n{ouput}\n\n"
f"Actual Output:\n{output}\n\n"
"Please provide:\n"
"- Bullet points suggestions to improve future similar tasks\n"
"- A score from 0 to 10 evaluating on completion, quality, and overall performance"

View File

@@ -1,17 +1,28 @@
import re
class YamlParser:
@staticmethod
def parse(file):
"""
Parses a YAML file, modifies specific patterns, and checks for unsupported 'context' usage.
Args:
file (file object): The YAML file to parse.
Returns:
str: The modified content of the YAML file.
Raises:
ValueError: If 'context:' is used incorrectly.
"""
content = file.read()
# Replace single { and } with doubled ones, while leaving already doubled ones intact and the other special characters {# and {%
modified_content = re.sub(r"(?<!\{){(?!\{)(?!\#)(?!\%)", "{{", content)
modified_content = re.sub(
r"(?<!\})(?<!\%)(?<!\#)\}(?!})", "}}", modified_content
)
modified_content = re.sub(r"(?<!\})(?<!\%)(?<!\#)\}(?!})", "}}", modified_content)
# Check for 'context:' not followed by '[' and raise an error
if re.search(r"context:(?!\s*\[)", modified_content):
raise ValueError(
"Context is currently only supported in code when creating a task. Please use the 'context' key in the task configuration."
"Context is currently only supported in code when creating a task. "
"Please use the 'context' key in the task configuration."
)
return modified_content

View File

@@ -397,7 +397,7 @@ def test_agent_moved_on_after_max_iterations():
)
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 yout final answer.",
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(
@@ -948,7 +948,7 @@ def test_agent_use_trained_data(crew_training_handler):
crew_training_handler().load.return_value = {
agent.role: {
"suggestions": [
"The result of the math operatio must be right.",
"The result of the math operation must be right.",
"Result must be better than 1.",
]
}
@@ -958,7 +958,7 @@ def test_agent_use_trained_data(crew_training_handler):
assert (
result == "What is 1 + 1?You MUST follow these feedbacks: \n "
"The result of the math operatio must be right.\n - Result must be better than 1."
"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()]

View File

@@ -135,29 +135,29 @@ def test_version_command_with_tools(runner):
)
@mock.patch("crewai.cli.cli.test_crew")
def test_test_default_iterations(test_crew, runner):
@mock.patch("crewai.cli.cli.evaluate_crew")
def test_test_default_iterations(evaluate_crew, runner):
result = runner.invoke(test)
test_crew.assert_called_once_with(3, "gpt-4o-mini")
evaluate_crew.assert_called_once_with(3, "gpt-4o-mini")
assert result.exit_code == 0
assert "Testing the crew for 3 iterations with model gpt-4o-mini" in result.output
@mock.patch("crewai.cli.cli.test_crew")
def test_test_custom_iterations(test_crew, runner):
@mock.patch("crewai.cli.cli.evaluate_crew")
def test_test_custom_iterations(evaluate_crew, runner):
result = runner.invoke(test, ["--n_iterations", "5", "--model", "gpt-4o"])
test_crew.assert_called_once_with(5, "gpt-4o")
evaluate_crew.assert_called_once_with(5, "gpt-4o")
assert result.exit_code == 0
assert "Testing the crew for 5 iterations with model gpt-4o" in result.output
@mock.patch("crewai.cli.cli.test_crew")
def test_test_invalid_string_iterations(test_crew, runner):
@mock.patch("crewai.cli.cli.evaluate_crew")
def test_test_invalid_string_iterations(evaluate_crew, runner):
result = runner.invoke(test, ["--n_iterations", "invalid"])
test_crew.assert_not_called()
evaluate_crew.assert_not_called()
assert result.exit_code == 2
assert (
"Usage: test [OPTIONS]\nTry 'test --help' for help.\n\nError: Invalid value for '-n' / '--n_iterations': 'invalid' is not a valid integer.\n"

View File

@@ -3,7 +3,7 @@ from unittest import mock
import pytest
from crewai.cli import test_crew
from crewai.cli import evaluate_crew
@pytest.mark.parametrize(
@@ -14,13 +14,13 @@ from crewai.cli import test_crew
(10, "gpt-4"),
],
)
@mock.patch("crewai.cli.test_crew.subprocess.run")
@mock.patch("crewai.cli.evaluate_crew.subprocess.run")
def test_crew_success(mock_subprocess_run, n_iterations, model):
"""Test the crew function for successful execution."""
mock_subprocess_run.return_value = subprocess.CompletedProcess(
args=f"poetry run test {n_iterations} {model}", returncode=0
)
result = test_crew.test_crew(n_iterations, model)
result = evaluate_crew.evaluate_crew(n_iterations, model)
mock_subprocess_run.assert_called_once_with(
["poetry", "run", "test", str(n_iterations), model],
@@ -31,26 +31,26 @@ def test_crew_success(mock_subprocess_run, n_iterations, model):
assert result is None
@mock.patch("crewai.cli.test_crew.click")
@mock.patch("crewai.cli.evaluate_crew.click")
def test_test_crew_zero_iterations(click):
test_crew.test_crew(0, "gpt-4o")
evaluate_crew.evaluate_crew(0, "gpt-4o")
click.echo.assert_called_once_with(
"An unexpected error occurred: The number of iterations must be a positive integer.",
err=True,
)
@mock.patch("crewai.cli.test_crew.click")
@mock.patch("crewai.cli.evaluate_crew.click")
def test_test_crew_negative_iterations(click):
test_crew.test_crew(-2, "gpt-4o")
evaluate_crew.evaluate_crew(-2, "gpt-4o")
click.echo.assert_called_once_with(
"An unexpected error occurred: The number of iterations must be a positive integer.",
err=True,
)
@mock.patch("crewai.cli.test_crew.click")
@mock.patch("crewai.cli.test_crew.subprocess.run")
@mock.patch("crewai.cli.evaluate_crew.click")
@mock.patch("crewai.cli.evaluate_crew.subprocess.run")
def test_test_crew_called_process_error(mock_subprocess_run, click):
n_iterations = 5
mock_subprocess_run.side_effect = subprocess.CalledProcessError(
@@ -59,7 +59,7 @@ def test_test_crew_called_process_error(mock_subprocess_run, click):
output="Error",
stderr="Some error occurred",
)
test_crew.test_crew(n_iterations, "gpt-4o")
evaluate_crew.evaluate_crew(n_iterations, "gpt-4o")
mock_subprocess_run.assert_called_once_with(
["poetry", "run", "test", "5", "gpt-4o"],
@@ -78,13 +78,13 @@ def test_test_crew_called_process_error(mock_subprocess_run, click):
)
@mock.patch("crewai.cli.test_crew.click")
@mock.patch("crewai.cli.test_crew.subprocess.run")
@mock.patch("crewai.cli.evaluate_crew.click")
@mock.patch("crewai.cli.evaluate_crew.subprocess.run")
def test_test_crew_unexpected_exception(mock_subprocess_run, click):
# Arrange
n_iterations = 5
mock_subprocess_run.side_effect = Exception("Unexpected error")
test_crew.test_crew(n_iterations, "gpt-4o")
evaluate_crew.evaluate_crew(n_iterations, "gpt-4o")
mock_subprocess_run.assert_called_once_with(
["poetry", "run", "test", "5", "gpt-4o"],

View File

@@ -8,7 +8,6 @@ from unittest.mock import MagicMock, patch
import pydantic_core
import pytest
from crewai.agent import Agent
from crewai.agents.cache import CacheHandler
from crewai.crew import Crew
@@ -69,7 +68,7 @@ def test_crew_config_conditional_requirement():
"agent": "Senior Researcher",
},
{
"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.",
"description": "Write a 1 amazing paragraph highlight for each idea 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.",
"expected_output": "A 4 paragraph article about AI.",
"agent": "Senior Writer",
},
@@ -657,7 +656,7 @@ def test_sequential_async_task_execution_completion():
sequential_result = sequential_crew.kickoff()
assert sequential_result.raw.startswith(
"**The Evolution of Artificial Intelligence: A Journey Through Milestones**"
"The history of artificial intelligence (AI) is marked by several pivotal events that have shaped its evolution and impact on various sectors."
)
@@ -1189,7 +1188,7 @@ def test_task_with_no_arguments():
)
task = Task(
description="Look at the available data nd give me a sense on the total number of sales.",
description="Look at the available data and give me a sense on the total number of sales.",
expected_output="The total number of sales as an integer",
agent=researcher,
)
@@ -1236,7 +1235,7 @@ def test_delegation_is_not_enabled_if_there_are_only_one_agent():
)
task = Task(
description="Look at the available data nd give me a sense on the total number of sales.",
description="Look at the available data and give me a sense on the total number of sales.",
expected_output="The total number of sales as an integer",
agent=researcher,
)
@@ -1312,14 +1311,14 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
)
result = crew.kickoff()
assert result.raw == '"Howdy!"'
assert result.raw == "Howdy!"
print(crew.usage_metrics)
assert crew.usage_metrics == {
"total_tokens": 311,
"prompt_tokens": 224,
"completion_tokens": 87,
"total_tokens": 219,
"prompt_tokens": 201,
"completion_tokens": 18,
"successful_requests": 1,
}
@@ -1599,16 +1598,16 @@ def test_tools_with_custom_caching():
writer1 = Agent(
role="Writer",
goal="You write lesssons of math for kids.",
backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
goal="You write lessons of math for kids.",
backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
tools=[multiplcation_tool],
allow_delegation=False,
)
writer2 = Agent(
role="Writer",
goal="You write lesssons of math for kids.",
backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
goal="You write lessons of math for kids.",
backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
tools=[multiplcation_tool],
allow_delegation=False,
)

View File

@@ -23,10 +23,7 @@ def short_term_memory():
expected_output="A list of relevant URLs based on the search query.",
agent=agent,
)
return ShortTermMemory(crew=Crew(
agents=[agent],
tasks=[task]
))
return ShortTermMemory(crew=Crew(agents=[agent], tasks=[task]))
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -38,7 +35,11 @@ def test_save_and_search(short_term_memory):
agent="test_agent",
metadata={"task": "test_task"},
)
short_term_memory.save(memory)
short_term_memory.save(
value=memory.data,
metadata=memory.metadata,
agent=memory.agent,
)
find = short_term_memory.search("test value", score_threshold=0.01)[0]
assert find["context"] == memory.data, "Data value mismatch."

View File

@@ -109,7 +109,7 @@ def test_task_callback():
task_completed.assert_called_once_with(task.output)
def test_task_callback_returns_task_ouput():
def test_task_callback_returns_task_output():
from crewai.tasks.output_format import OutputFormat
researcher = Agent(

View File

@@ -84,6 +84,10 @@ class TestCrewEvaluator:
1: [10, 9, 8],
2: [9, 8, 7],
}
crew_planner.run_execution_times = {
1: [24, 45, 66],
2: [55, 33, 67],
}
crew_planner.print_crew_evaluation_result()
@@ -98,6 +102,7 @@ class TestCrewEvaluator:
mock.call().add_row("Task 2", "9", "8", "8.5"),
mock.call().add_row("Task 3", "8", "7", "7.5"),
mock.call().add_row("Crew", "9.0", "8.0", "8.5"),
mock.call().add_row("Execution Time (s)", "135", "155", "145"),
]
)
console.assert_has_calls([mock.call(), mock.call().print(table())])