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OTA-Tech-AI web-agent-protocol
🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support
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License: MIT License
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The Web Agent Protocol (WAP) is a standardized framework designed to enable seamless interaction between users, web agents, and browsers by recording and replaying browser actions. It separates the concerns of action recording and execution, allowing for efficient automation and reusability. The Python SDK for WAP implements the full specification, making it easy to:
- Collect user‑interaction data with the OTA‑WAP Chrome extension.
- Convert the raw event stream into either exact‑replay or smart‑replay action lists.
- Convert recorded actions into MCP servers for reuse by any agent or user
- Replay those lists using the WAP-Replay protocol to ensure accurate browser operations.
Install the dependencies with the following command:
Create a conda env
conda create -n WAP python=3.11Activate the conda env
conda activate WAPInstall the dependencies
pip install -r requirements.txtSetup your repo source path:
set PYTHONPATH=C:/path/to/webagentprotocol # for Windows
export PYTHONPATH=/path/to/webagentprotocol # for Linux
Create .env file under the repo root directory with your own API keys:
OPENAI_API_KEY=sk-proj-...
DEEPSEEK_API_KEY=sk-...
Please refer to OTA‑WAP Chrome Extension to setup action capturer in your Chrome browser.
Run the following command to start the server to collect data from the extension:
python action_collect_server.pyOnce the server is up, you can start to record from the page using WAP Chrome extension.
The server listens on http://localhost:4934/action-data by default, please make sure the Host and Port in the extension settings match this server config. Each session will be saved to:
data/YYYYMMDD/taskid/summary_event_<timestamp>.jsonAn example of the formatted data which you will received in the WAP backend server is like:
{
"taskId": "MkCAhQsHgXn7YgaK",
"type": "click",
"actionTimestamp": 1746325231479,
"eventTarget": {
"type": "click",
"target": "<a ota-use-interactive-target=\"1\" data-ordinal=\"3\" href=\"https://www.allrecipes.com/recipe/68925/cheesy-baked-salmon/\" data-tax-levels=\"\" data-doc-id=\"6592066\" class=\"comp mntl-card-list-card--extendable mntl-universal-card mntl-document-card mntl-card card card--no-image\" id=\"mntl-card-list-card--extendable_3-0\">\n<div class=\"loc card__top\"><div class=\"card__media mntl-image card__media universal-image__container\">...",
"targetId": "mntl-card-list-card--extendable_3-0",
"targetClass": "comp mntl-card-list-card--extendable mntl-universal-card mntl-document-card mntl-card card card--no-image"
},
"allEvents": {},
"pageHTMLContent": "<header data-tracking-container=\"true\" data-collapsible=\"true\" class=\"comp header mntl-header mntl-header--magazine mntl-header--open-search-bar mntl-header--myr\" id=\"header_1-0\"><a data-tracking-container=\"true\" id=\"mntl-skip-to-content_1-0\" class=\"mntl-skip-to-content mntl-text-link\" rel=\"nocaes\" href=\"#main\"></a><div class=\"mntl-header__menu-top\">..."
}| Mode | Command |
|---|---|
| Exact replay – exactly reproduce every action | python wap_replay/generate_exact_replay_list.py --data_dir_path data/<date>/<task_id> --output_dir_path data_processed/exact_replay |
| Smart replay – condensed goal‑oriented steps | python wap_replay/generate_smart_replay_list.py --data_dir_path data/<date>/<task_id> --output_dir_path data_processed/smart_replay |
Replace <task_id> with the folder produced by the extension (e.g. em3h6UBDZykz0gnH).
Output structure:
data_processed/smart_replay/
├─ subgoals_<task_id>/ # intermediate prompts & replies
└─ wap_smart_replay_list_<task_id>.json # final smart replay list for the agent
data_processed/exact_replay/
└─ wap_smart_replay_list_<task_id>.json # final exact replay list for the agentpython run_replay.py --model-provider openai --wap_replay_list data_processed/exact_replay/wap_exact_replay_list_<task_id>.json --max-concurrent 1For smart-replay, replace the path with a smart‑replay JSON to test this mode.
python wap_replay\generate_mcp_server.py --task_id <task_id>converted MCP servers will be located under mcp_servers folder
You would need 2 terminals to replay with MCP. In the first termnial
python wap_service.pyIn the second termnial
python mcp_client.pyThen enter your prompt in the second terminal
example: find a top rated keyboard on amazon.ca using smart replayWe provide out-of-box desktop app for running replay lists. It is easy to install and you don't need any extra steps for setup and deployments. Visit WAP Replay Tool releases for more details.
ModuleNotFoundError – run commands from the project root or export PYTHONPATH=. (set PYTHONPATH=. for Windows).
“no task‑start file” – ensure the extension recorded a full session; the generators require exactly one task-start and one task-finish record.
Browser-Use: https://github.com/browser-use/browser-use
MCP: https://github.com/modelcontextprotocol/python-sdk
DOM Extension: https://github.com/kdzwinel/DOMListenerExtension
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- Developers working primarily with Python
- Teams exploring AI tooling, agents, or ML infrastructure
Potential use cases
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- Declared license: MIT License
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