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# CrewaiTrading Crew
Welcome to the CrewaiTrading Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
## Installation
Ensure you have Python >=3.10 <3.14 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
First, if you haven't already, install uv:
```bash
pip install uv
```
Next, navigate to your project directory and install the dependencies:
(Optional) Lock the dependencies and install them by using the CLI command:
```bash
crewai install
```
### Customizing
**Add your `OPENAI_API_KEY` into the `.env` file**
- Modify `src/crewai_trading/config/agents.yaml` to define your agents
- Modify `src/crewai_trading/config/tasks.yaml` to define your tasks
- Modify `src/crewai_trading/crew.py` to add your own logic, tools and specific args
- Modify `src/crewai_trading/main.py` to add custom inputs for your agents and tasks
## Running the Project
To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
```bash
$ crewai run
```
This command initializes the crewai_trading Crew, assembling the agents and assigning them tasks as defined in your configuration.
This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
## Understanding Your Crew
The crewai_trading Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
## Support
For support, questions, or feedback regarding the CrewaiTrading Crew or crewAI.
- Visit our [documentation](https://docs.crewai.com)
- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
- [Chat with our docs](https://chatg.pt/DWjSBZn)
Let's create wonders together with the power and simplicity of crewAI.
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User name is John Doe.
User is an AI Engineer.
User is interested in AI Agents.
User is based in San Francisco, California.
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[project]
name = "crewai_trading"
version = "0.1.0"
description = "crewai_trading using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.11.0"
]
[project.scripts]
crewai_trading = "crewai_trading.main:run"
run_crew = "crewai_trading.main:run"
train = "crewai_trading.main:train"
replay = "crewai_trading.main:replay"
test = "crewai_trading.main:test"
run_with_trigger = "crewai_trading.main:run_with_trigger"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.crewai]
type = "crew"
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researcher:
role: >
{topic} Senior Data Researcher
goal: >
Uncover cutting-edge developments in {topic}
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. Known for your ability to find the most relevant
information and present it in a clear and concise manner.
reporting_analyst:
role: >
{topic} Reporting Analyst
goal: >
Create detailed reports based on {topic} data analysis and research findings
backstory: >
You're a meticulous analyst with a keen eye for detail. You're known for
your ability to turn complex data into clear and concise reports, making
it easy for others to understand and act on the information you provide.
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research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is {current_year}.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
agent: researcher
reporting_task:
description: >
Review the context you got and expand each topic into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
expected_output: >
A fully fledged report with the main topics, each with a full section of information.
Formatted as markdown without '```'
agent: reporting_analyst
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from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai.agents.agent_builder.base_agent import BaseAgent
# If you want to run a snippet of code before or after the crew starts,
# you can use the @before_kickoff and @after_kickoff decorators
# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
@CrewBase
class CrewaiTrading():
"""CrewaiTrading crew"""
agents: list[BaseAgent]
tasks: list[Task]
# Learn more about YAML configuration files here:
# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
# If you would like to add tools to your agents, you can learn more about it here:
# https://docs.crewai.com/concepts/agents#agent-tools
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'], # type: ignore[index]
verbose=True
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'], # type: ignore[index]
verbose=True
)
# To learn more about structured task outputs,
# task dependencies, and task callbacks, check out the documentation:
# https://docs.crewai.com/concepts/tasks#overview-of-a-task
@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config['research_task'], # type: ignore[index]
)
@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'], # type: ignore[index]
output_file='report.md'
)
@crew
def crew(self) -> Crew:
"""Creates the CrewaiTrading crew"""
# To learn how to add knowledge sources to your crew, check out the documentation:
# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
process=Process.sequential,
verbose=True,
# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
)
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#!/usr/bin/env python
import sys
import warnings
from datetime import datetime
from crewai_trading.crew import CrewaiTrading
warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
# This main file is intended to be a way for you to run your
# crew locally, so refrain from adding unnecessary logic into this file.
# Replace with inputs you want to test with, it will automatically
# interpolate any tasks and agents information
def run():
"""
Run the crew.
"""
inputs = {
'topic': 'AI LLMs',
'current_year': str(datetime.now().year)
}
try:
CrewaiTrading().crew().kickoff(inputs=inputs)
except Exception as e:
raise Exception(f"An error occurred while running the crew: {e}")
def train():
"""
Train the crew for a given number of iterations.
"""
inputs = {
"topic": "AI LLMs",
'current_year': str(datetime.now().year)
}
try:
CrewaiTrading().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
except Exception as e:
raise Exception(f"An error occurred while training the crew: {e}")
def replay():
"""
Replay the crew execution from a specific task.
"""
try:
CrewaiTrading().crew().replay(task_id=sys.argv[1])
except Exception as e:
raise Exception(f"An error occurred while replaying the crew: {e}")
def test():
"""
Test the crew execution and returns the results.
"""
inputs = {
"topic": "AI LLMs",
"current_year": str(datetime.now().year)
}
try:
CrewaiTrading().crew().test(n_iterations=int(sys.argv[1]), eval_llm=sys.argv[2], inputs=inputs)
except Exception as e:
raise Exception(f"An error occurred while testing the crew: {e}")
def run_with_trigger():
"""
Run the crew with trigger payload.
"""
import json
if len(sys.argv) < 2:
raise Exception("No trigger payload provided. Please provide JSON payload as argument.")
try:
trigger_payload = json.loads(sys.argv[1])
except json.JSONDecodeError:
raise Exception("Invalid JSON payload provided as argument")
inputs = {
"crewai_trigger_payload": trigger_payload,
"topic": "",
"current_year": ""
}
try:
result = CrewaiTrading().crew().kickoff(inputs=inputs)
return result
except Exception as e:
raise Exception(f"An error occurred while running the crew with trigger: {e}")
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from crewai.tools import BaseTool
from typing import Type
from pydantic import BaseModel, Field
class MyCustomToolInput(BaseModel):
"""Input schema for MyCustomTool."""
argument: str = Field(..., description="Description of the argument.")
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = (
"Clear description for what this tool is useful for, your agent will need this information to use it."
)
args_schema: Type[BaseModel] = MyCustomToolInput
def _run(self, argument: str) -> str:
# Implementation goes here
return "this is an example of a tool output, ignore it and move along."