a0cae78ba3
LLMs are probabilistic systems. Reproducibility of completions is not guaranteed. It only makes sense to account for this, by running challenges multiple times to obtain a success ratio rather than a boolean success/failure result. Changes: - Add `-N`, `--attempts` option to CLI and `attempts_per_challenge` parameter to `main.py:run_benchmark`. - Add dynamic `i_attempt` fixture through `pytest_generate_tests` hook in conftest.py to achieve multiple runs per challenge. - Modify `pytest_runtest_makereport` hook in conftest.py to handle multiple reporting calls per challenge. - Refactor report_types.py, reports.py, process_report.ty to allow multiple results per challenge. - Calculate `success_percentage` from results of the current run, rather than all known results ever. - Add docstrings to a number of models in report_types.py. - Allow `None` as a success value, e.g. for runs that did not render any results before being cut off. - Make SingletonReportManager thread-safe.
As a user
pip install auto-gpt-benchmarks- Add boilerplate code to run and kill agent
agbenchmark--category challenge_categoryto run tests in a specific category--mockto only run mock tests if they exists for each test--noregto 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
- We call boilerplate code for your agent
- Show pass rate of tests, logs, and any other metrics
Contributing
Diagrams: https://whimsical.com/agbenchmark-5n4hXBq1ZGzBwRsK4TVY7x
To run the existing mocks
- clone the repo
auto-gpt-benchmarks pip install poetrypoetry shellpoetry installcp .env_example .envgit submodule update --init --remote --recursiveuvicorn server:app --reloadagbenchmark --mockKeep config the same and watch the logs :)
To run with mini-agi
- Navigate to
auto-gpt-benchmarks/agent/mini-agi pip install -r requirements.txtcp .env_example .env, setPROMPT_USER=falseand add yourOPENAI_API_KEY=. SsetMODEL="gpt-3.5-turbo"if you don't have access togpt-4yet. Also make sure you have Python 3.10^ installed- set
AGENT_NAME=mini-agiin.envfile and where you want yourREPORT_LOCATIONto be - Make sure to follow the commands above, and remove mock flag
agbenchmark
- 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 :)
Dataset
Manually created, existing challenges within Auto-Gpt, https://osu-nlp-group.github.io/Mind2Web/
How do I 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
How do I run agent in different environments?
To just use as the benchmark for your agent. pip install the package and run agbenchmark
For internal Auto-GPT ci runs, specify the AGENT_NAME you want you use and set the HOME_ENV.
Ex. AGENT_NAME=mini-agi
To develop agent alongside benchmark, you can specify the AGENT_NAME you want you use and add as a submodule to the repo