* autoglm-os initialize * clean code * chore: use proxy for download setup * feat(autoglm-os): add parameter to toggle images * fix: use temporary directory for files pulled from the vm to prevent potential collision when running multiple instances of the same task in parallel * update * add client_password * update multienv * fix * fix prompt * fix prompt * fix prompt * fix sys prompt * feat: use proxy in file evaluator * fix client_password * fix note_prompt * fix autoglm agent cmd type * fix * revert: fix: use temporary directory for files pulled from the vm to prevent potential collision when running multiple instances of the same task in parallel reverts commit bab5473eea1de0e61b0e1d68b23ce324a5b0ee57 * feat(autoglm): setup tools * fix(autoglm): remove second time of get a11y tree * add osworld server restart * Revert "add osworld server restart" This reverts commit 7bd9d84122e246ce2a26de0e49c25494244c2b3d. * fix _launch_setup * fix autoglm agent tools & xml tree * fix desktop_env * fix bug for tool name capitalization * fix: always use proxy for setup download * add fail after exceeding max turns * fix(autoglm): avoid adding image to message when screenshot is empty * fix maximize_window * fix maximize_window * fix maximize_window * fix import browsertools module bug * fix task proxy config bug * restore setup * refactor desktop env * restore image in provider * restore file.py * refactor desktop_env * quick fix * refactor desktop_env.step * fix our env reset * add max truns constraint * clean run script * clean lib_run_single.py --------- Co-authored-by: hanyullai <hanyullai@outlook.com> Co-authored-by: JingBh <jingbohao@yeah.net>
Evaluation examples
Here we put the data examples to benchmark the ability of agents when interacting with GUI.
The examples are stored in ./examples where each data item formatted as:
{
"id": "uid", # unique id
"snapshot": "snapshot_id", # the snapshot id of the environment, with some data already there and apps already opened, or just desktop
"instruction": "natural_language_instruction", # the natural language instruction of the task, what we want the agent to do
"source": "website_url", # where we know this example, some forum, or some website, or some paper
"config": {xxx}, # the scripts to setup the donwload and open files actions, as the initial state of a task
# (coming in next project) "trajectory": "trajectory_directory", # the trajectory directory, which contains the action sequence file, the screenshots and the recording video
"related_apps": ["app1", "app2", ...], # the related apps, which are opened during the task
"evaluator": "evaluation_dir", # the directory of the evaluator, which contains the evaluation script for this example
…
}
The ./trajectories file contains the annotated trajectories for each data item in ./examples for finishing the task.
For now, it is under construction, and only tested on Windows 10. Please:
- Modify the path accordingly to run the evaluation;
- Remind us if some parts are overfit to our environment.