67b04b30bf
* Cleaned up task execution to now have separate paths for async and sync execution. Updating all kickoff functions to return CrewOutput. WIP. Waiting for Joao feedback on async task execution with task_output * Consistently storing async and sync output for context * outline tests I need to create going forward * Major rehaul of TaskOutput and CrewOutput. Updated all tests to work with new change. Need to add in a few final tricky async tests and add a few more to verify output types on TaskOutput and CrewOutput. * Encountering issues with callback. Need to test on main. WIP * working on tests. WIP * WIP. Figuring out disconnect issue. * Cleaned up logs now that I've isolated the issue to the LLM * more wip. * WIP. It looks like usage metrics has always been broken for async * Update parent crew who is managing for_each loop * Merge in main to bugfix/kickoff-for-each-usage-metrics * Clean up code for review * Add new tests * Final cleanup. Ready for review. * Moving copy functionality from Agent to BaseAgent * Fix renaming issue * Fix linting errors * use BaseAgent instead of Agent where applicable * Fixing missing function. Working on tests. * WIP. Needing team to review change * Fixing issues brought about by merge * WIP: need to fix json encoder * WIP need to fix encoder * WIP * WIP: replay working with async. need to add tests * Implement major fixes from yesterdays group conversation. Now working on tests. * The majority of tasks are working now. Need to fix converter class * Fix final failing test * Fix linting and type-checker issues * Add more tests to fully test CrewOutput and TaskOutput changes * Add in validation for async cannot depend on other async tasks. * WIP: working replay feat fixing inputs, need tests * WIP: core logic of seq and heir for executing tasks added into one * Update validators and tests * better logic for seq and hier * replay working for both seq and hier just need tests * fixed context * added cli command + code cleanup TODO: need better refactoring * refactoring for cleaner code * added better tests * removed todo comments and fixed some tests * fix logging now all tests should pass * cleaner code * ensure replay is delcared when replaying specific tasks * ensure hierarchical works * better typing for stored_outputs and separated task_output_handler * added better tests * added replay feature to crew docs * easier cli command name * fixing changes * using sqllite instead of .json file for logging previous task_outputs * tools fix * added to docs and fixed tests * fixed .db * fixed docs and removed unneeded comments * separating ltm and replay db * fixed printing colors * added how to doc --------- Co-authored-by: Brandon Hancock <brandon@brandonhancock.io>
{{crew_name}} Crew
Welcome to the {{crew_name}} Crew project, powered by crewAI. 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 <=3.13 installed on your system. This project uses Poetry for dependency management and package handling, offering a seamless setup and execution experience.
First, if you haven't already, install Poetry:
pip install poetry
Next, navigate to your project directory and install the dependencies:
- First lock the dependencies and then install them:
poetry lock
poetry install
Customizing
Add your OPENAI_API_KEY into the .env file
- Modify
src/{{folder_name}}/config/agents.yamlto define your agents - Modify
src/{{folder_name}}/config/tasks.yamlto define your tasks - Modify
src/{{folder_name}}/crew.pyto add your own logic, tools and specific args - Modify
src/{{folder_name}}/main.pyto 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:
poetry run {{folder_name}}
This command initializes the {{name}} 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 {{name}} 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 {{crew_name}} Crew or crewAI.
- Visit our documentation
- Reach out to us through our GitHub repository
- Join our Discord
- Chat with our docs
Let's create wonders together with the power and simplicity of crewAI.