Repository files navigation

Agent Workflow Memory

To run AWM on WebArena underwebarena/:

cd webarenapython pipeline.py --website"shopping"# choose one from ['shopping', 'shopping_admin', 'reddit', 'gitlab', 'map']

To run AWM on Mind2Web undermind2web/:

cd mind2webpython pipeline.py --setup"offline"# or "online"

Checkwebarena/ andmind2web/ folders for more detailed instructions about environment and data setups.

Agent Workflow Memory (AWM) proposes to induce, integrate, and utilize workflows via an agent memory.A workflow is usually a common sub-routine in solving tasks, with example-specific contexts being abstracted out.

AWM can operate in both offline and online settings:

• offline (left): when additional (e.g., training) examples are available, agents induce workflows from ground-truth annotated examples
• online (right): without any auxiliary data, agents induce workflows from past experiences on the fly.

We achieve the state-of-the-art result -- 35.6% success rate.

Check the code in./webarena/ directory.

We also get the best scores among text-based agents. Particularly, AWM offline effectively generalizes across a wide range of tasks, websites, and domains.

Check the code in./mind2web/ directory.

@inproceedings{awm2024wang,title ={Agent Workflow Memory},author ={Wang, Zhiruo anf Mao, Jiayuan, and Fried, Daniel and Neubig, Graham},journal={arXiv preprint arXiv:2409.07429},year ={2024},}