This directory contains the baseline for The Agent Company. The baseline is conducted using OpenHands, an open-source platform for software development agents powered by AI.

If you need to use OpenHands for evaluation, you also need:

1. python 3.12 or above
2. poetry
3. install dependencies using poetry install under project root directory
4. docker buildx

Please create a config.toml file under evaluation directory. It should look like this:

[llm.group1]
model="<model_name>"
base_url="<base_url>"
api_key="<api_key>"

[llm.group2]
model="<model_name>"
base_url="<base_url>"
api_key="<api_key>"

you can add more groups as needed.

Under evaluation directory, run the following command:

bash run_eval.sh \
  --agent-llm-config group1 \
  --env-llm-config group2 \
  --outputs-path outputs \
  --server-hostname localhost \
  --version 1.0.0

where --outputs-path, --server-hostname, and --version are optional.

Here's a brief explanation of each argument:

• --agent-llm-config: the config name for the agent LLM. This should match the config name in config.toml. This is the LLM used by the agent (i.e. CodeActAgent).
• --env-llm-config: the config name for the environment LLM. This should match the config name in config.toml. This is used by the chat bots (NPCs) and LLM-based evaluators.
• --outputs-path: the path to save trajectories and evaluation results.
• --server-hostname: the hostname of the server that hosts all the web services. It could be localhost if you are running the evaluation and services on the same machine. If the services are hosted on a remote machine, you must use the hostname of the remote machine rather than IP address.
• --version: the version of the task images to use. Currently, the only supported version is 1.0.0.

The script is idempotent. If you run it again, it will resume from the last checkpoint. It would usually take a few days to finish evaluation.

Note: the script will automatically skip a task if it encounters an error. This usually happens when the OpenHands runtime dies due to some unexpected errors. This means even if the script finishes, it might not have evaluated all tasks. You can manually resume the evaluation by running the script again.

You can find the evaluation results in the outputs directory, including trajectories, evaluation scores, final agent states, and screenshots for all browsing steps.

You can run the following command to generate a summary of the evaluation results:

poetry run python summarise_results.py <outputs_path>

An example of the summary report can be found here.