AutoGPT/agbenchmark

As a user

1. pip install auto-gpt-benchmarks
2. Add boilerplate code to run and kill agent
3. agbenchmark start
   • --category challenge_category to run tests in a specific category
   • --mock to only run mock tests if they exists for each test
   • --noreg to skip any tests that have passed in the past. When you run without this flag and a previous challenge that passed fails, it will now not be regression tests
4. We call boilerplate code for your agent
5. Show pass rate of tests, logs, and any other metrics

Contributing

Diagrams: https://whimsical.com/agbenchmark-5n4hXBq1ZGzBwRsK4TVY7x

To run the existing mocks

1. clone the repo auto-gpt-benchmarks
2. pip install poetry
3. poetry shell
4. poetry install
5. cp .env_example .env
6. agbenchmark start --mock Keep config the same and watch the logs :)

To run with mini-agi

1. Navigate to auto-gpt-benchmarks/agent/mini-agi
2. pip install -r requirements.txt
3. cp .env_example .env, set PROMPT_USER=false and add your OPENAI_API_KEY=. Sset MODEL="gpt-3.5-turbo" if you don't have access to gpt-4 yet. Also make sure you have Python 3.10^ installed
4. Make sure to follow the commands above, and remove mock flag agbenchmark start

• To add requirements poetry add requirement.

Feel free to create prs to merge with main at will (but also feel free to ask for review) - if you can't send msg in R&D chat for access.

If you push at any point and break things - it'll happen to everyone - fix it asap. Step 1 is to revert master to last working commit

Let people know what beautiful code you write does, document everything well

Share your progress :)

Pytest

an example of a test is below, use it as a template and change the class name, the .json name, what the test depends on and it's name, and the scoring logic

import pytest
from agbenchmark.tests.basic_abilities.BasicChallenge import BasicChallenge
import os


class TestWriteFile(BasicChallenge):
    """Testing if LLM can write to a file"""

    def test_method(self, config):
        # implement scoring logic by looking at workspace

All challenges will inherit from parent class which has the mark and any specific methods for their category

@pytest.mark.basic
class BasicChallenge(Challenge):
    pass

Add the below to create a file in the workspace prior to running a challenge. Only use when a file is needed to be created in the workspace prior to a test, such as with the read_file_test.

@pytest.fixture(
        scope="module", autouse=True
    )  # this is specific to setting up a file for the test, not all tests have this
    def setup_module(self, workspace):
        Challenge.write_to_file(
            workspace, self.data.ground.files[0], "this is how we're doing"
        )

The main Challenge class has all the parametrization and loading logic so that all tests can inherit from it. It lives within this file

Workspace

If --mock flag is used it is at agbenchmark/workspace. Otherwise for mini-agi it is at C:/Users/<name>/miniagi - it will be automitcally set on config

Dataset

Manually created, existing challenges within Auto-Gpt, https://osu-nlp-group.github.io/Mind2Web/

Repo

|-- auto-gpt-benchmarks/ **main project directory**
| |-- metrics.py **combining scores, metrics, final evaluation**
| |-- start_benchmark.py **entry point from cli**
| |-- conftest.py **config, workspace creation + teardown, regression tesst markers, parameterization**
| |-- Challenge.py **easy challenge creation class**
| |-- config.json **workspace folder**
| |-- challenges/ **challenges across different domains**
| | |-- adaptability/
| | |-- basic_abilities/
| | |-- code/
| | |-- memory/
| | |-- retrieval/
| | |-- web_navigation/
| | |-- writing/
| |-- tests/
| | |-- basic_abilities/ **every llm should pass these challenges**
| | |-- regression/ **challenges that already passed**

How to add new agents to agbenchmark ?

Example with smol developer.

1- Create a github branch with your agent following the same pattern as this example:

https://github.com/smol-ai/developer/pull/114/files

2- Create the submodule and the github workflow by following the same pattern as this example:

https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks/pull/48/files