microsoft/Webwright
GitHub: microsoft/Webwright
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# Webwright
## 🎥 Demo
https://github.com/user-attachments/assets/4ed94cd5-11be-4daa-b2d7-1260a803baca
## 📊 Performance
State-of-the-art on two real-website benchmarks with a 100-step budget — see the [blog post](https://www.microsoft.com/en-us/research/articles/webwright-a-terminal-is-all-you-need-for-web-agents/) for full details.
- 🏆 **Online-Mind2Web (300 tasks):** **86.7%** with GPT-5.4 — highest among open-sourced harnesses in the AutoEval category. Claude Opus 4.7 reaches **84.7%**, and is stronger on the hard split (**80.5%** vs. 76.6% for GPT-5.4 at N=100).
- 🚀 **Odysseys (200 long-horizon tasks):** **60.1%** with GPT-5.4 (avg. 76.1 steps) — **+15.6 points** over the prior SOTA (Opus 4.6 at 44.5%, using vision based approach and persistent browser) and **+26.6 points** over base GPT-5.4 (33.5% using xy-coordinate prediction and persistent browser).
- 🧠 **Code-as-action beats coordinate prediction:** Webwright substantially outperforms a reproduced GPT-5.4 screenshot+xy-coordinate baseline across all difficulty splits.
- 🧰 **Small models + reusable tools:** generated scripts can be packaged as parameterized CLI tools — even **Qwen-3.5-9B** completes tasks well on Online-Mind2Web sites with 5+ tools available.
/ # task.json + report.json per task
├── tests/
└── outputs/ # run artifacts (trajectories, screenshots)
## 📰 Task Showcase (repeatable runs as a dashboard)
A tiny Flask app under [`assets/task_showcase/`](assets/task_showcase/README.md) consolidates
Webwright runs for **repeatable** odyssey tasks (deals, inventory, listings,
job boards, weather, etc.) into a single dashboard. Each task ships only two
files — `task.json` (metadata) and `report.json` (curated, structured output:
sources + result sections like tables, lists, summaries) — and the templates
render them generically, so adding a new task is just dropping a new folder
in `assets/task_showcase/tasks/`.
pip install flask
python assets/task_showcase/app.py # http://127.0.0.1:5005
To have Webwright produce a renderer-ready task folder at runtime, stack the
Task Showcase overlay:
python -m webwright.run.cli \
-c base.yaml -c model_openai.yaml -c task_showcase.yaml \
-t "" \
--task-id my_repeatable_task \
-o outputs/default
The run writes `task_showcase/tasks//task.json` and `report.json`
inside the output workspace. Render those generated files without copying them
back into the repo:
python assets/task_showcase/app.py \
--tasks-dir outputs/default//task_showcase/tasks
## 🚀 Quick Start
### Prerequisites
- Python 3.10+
- Chromium installed through Playwright
- An API key for your chosen backend (OpenAI, Anthropic, or OpenRouter)
### Install
pip install -e .
playwright install chromium
### Run
Export credentials for the configured backend (for example, `OPENAI_API_KEY`
with `model_openai.yaml` or `ANTHROPIC_API_KEY` with `model_claude.yaml`). The
`image_qa` and `self_reflection` tools use the same configured model by default,
so an Anthropic run does not require an OpenAI key. Then:
python -m webwright.run.cli \
-c base.yaml -c model_openai.yaml \
-t "Search for flights from SEA to JFK on 2026-08-15 to 2026-08-20" \
--start-url https://www.google.com/flights \
--task-id demo_openai \
-o outputs/default
### 🚩 Flags
| Flag | Description |
|------|-------------|
| `-c` | Config file(s) from `src/webwright/config/` (stackable). |
| `-t` | Task instruction. |
| `--start-url` | Initial page. |
| `--task-id` | Output subfolder name. |
| `-o` | Output directory. |
## 🔌 Use as a Plugin
Webwright ships plugin manifests for both [Claude Code](https://docs.claude.com/en/docs/claude-code/plugins) ([`.claude-plugin/plugin.json`](.claude-plugin/plugin.json)) and [OpenAI Codex](https://developers.openai.com/codex/plugins) ([`.codex-plugin/plugin.json`](.codex-plugin/plugin.json)), with the shared skill at [`skills/webwright/`](skills/webwright/) and slash commands at [`skills/webwright/commands/`](skills/webwright/commands/). The host agent drives the Webwright loop natively — no extra LLM API key or cost beyond your host subscription. Hosts that read PNG screenshots natively skip the `image_qa` / `self_reflection` tools.
Common runtime deps (install once after either path):
pip install -e .
playwright install chromium
/`, and visually self-verifies each critical point against the saved screenshots.
/`, and visually self-verifies each critical point against the saved screenshots.
To turn the plugin off without uninstalling, set its entry in `~/.codex/config.toml` to `enabled = false` and restart Codex.
`.
To uninstall: `openclaw plugins uninstall webwright`.
## 📃 Trajectory Comparison & Viewer
You can run the same tasks using the Webwright harness and its Codex / GitHub Copilot skill variant, and see how token usage and trajectories stack up between different harnesses. The trajectory viewer supports Codex, GitHub Copilot and Webwright harness traces.
### How to use
cd assets/compare_trajectory/
python3 -m http.server
Open the webpage in your browser and upload the Webwright `raw_responses.jsonl` and attach `trajectory.json` to view. Then on the other side you can upload your Codex or GitHub Copilot trace.
### Obtaining Codex traces:
ls ~/.codex/sessions/2026/MONTH/DAY/SESSION_ID.jsonl
### Obtaining GitHub Copilot traces:
/export file session
-> session.md is the uploadable trace
### Quick Comparison
#### "Find the cheapest used 8-cylinder bmw made between 2005-2015 and priced from 25,000 to 50,000 dollars with mileage less than 50,000 miles or less."
| Tokens | Webwright Harness (Local Browser Mode) | Codex Webwright Skill |
| --- | ---: | ---: |
| Input | 420,433 | 3,271,143 |
| Output | 3,593 | 20,040 |
| Reasoning | 0 | 4,410 |
| Cached | 217,216 | 3,081,3440 |
| Total | 424,026 | 3,291,183 |
Individual runs and results may vary.
## Citation
If you use Webwright in your research or build on it, please cite this repository:
@misc{webwright2026,
title = {Webwright: A terminal is all you need for web agents},
author = {Lu, Yadong and Xu, Lingrui and Huang, Chao and Awadallah, Ahmed},
year = {2026},
howpublished = {\url{https://github.com/microsoft/Webwright}},
note = {GitHub repository}
}
Turn Your Coding Models to Be State-of-the-art Browser Agents
💡 Motivation: Beyond Step-by-Step Web Interaction in a Stateful Browser
Most web agents today treat the browser session itself as the workspace: at each step the model receives the current page state and predicts a single next operation — a click, a type, a DOM selector, or a short tool call. Whatever the format, the agent is locked into predicting one web action at a time inside a predefined interaction loop. That harness was useful when LLMs were weaker. As models get stronger at writing and debugging code, the same harness becomes a bottleneck. Webwright takes a different stance: **separate the agent from the browser**, and treat the browser as something the agent can launch, inspect, and discard while developing a program. The persistent artifact is not the browser session — it's the **code and logs in the local workspace**. - 🧱 **Robust, reusable interaction with web environments** — instead of fragile pixel-level actions, a coding agent with a terminal queries elements, waits for conditions, and handles dynamic behaviors like lazy loading or re-rendering. The resulting scripts can be rerun, adapted, and shared across tasks rather than rediscovered from scratch. - ⚡ **Efficient composition of complex workflows** — multi-step interactions like selecting a date or filling a form become a compact program. Loops, functions, and abstractions let the agent generalize across similar tasks (e.g. different dates) without re-predicting the same low-level sequences. Fewer interaction rounds, faster execution, less error accumulation on long horizons. - 🧪 **Workspace-as-state, not browser-as-state** — the agent can write exploratory scripts, spawn fresh browser sessions, and decide for itself when to capture screenshots and inspect failures, much like a human engineer iterating on an RPA script. - 🪄 **Surprisingly effective despite being minimal** — this stripped-down setup turns out to handle complex and especially long-horizon web tasks well (see [Performance](#-performance)).🌟 Why Webwright
Most web agent frameworks bury the actual agent loop under layers of abstractions. Webwright takes the opposite stance: - 🪶 **Lightweight by design** — core agent loop in a single ~450-line file, Playwright environment in ~570 lines, CLI in ~150 lines. - 🧩 **Pluggable model backends** — OpenAI, Anthropic, and OpenRouter, each ~150–200 lines. - 🔍 **Zero hidden frameworks** — just `httpx`, `pydantic`, `playwright`, and `typer`. - 🔁 **Flat prompt → observe → execute script loop** — readable end-to-end, easy to debug, easy to fork. - 🧪 **Run-artifact first** — every run writes trajectories and screenshots to disk for inspection. If you want a minimal, easy-to-debug starting point for browser-using agents instead of another heavyweight platform, this is it.🆚 How Webwright Differs From Other Browser-Agent Repos
How they differ at the architectural level: | | **Stagehand (Browserbase)** | **agent-browser (Vercel)** | **browser-use** | **Webwright** | | ------------------- | ------------------------------------------------------------ | ------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------------------------- | | **Paradigm** | Hybrid: code + NL primitives (`act` / `extract` / `agent`) | CLI tool that *another* agent (Claude Code, Codex, etc.) calls | Autonomous LLM agent loop over DOM/AX snapshots | **Coding agent with a terminal**; browser is just an environment it spawns | | **Action space** | Playwright code, or NL → LLM-translated Playwright | Discrete subcommands (`open`, `click @e2`, `snapshot`, `eval`) | Indexed click/type actions selected by the LLM | **Free-form Python (writes Playwright scripts itself)** | | **What is "state"?**| The browser session | The browser session (held by daemon across CLI calls) | The browser session | **The local workspace — code, screenshots, logs.** Browser is disposable. | | **Loop shape** | Imperative; `agent()` does multi-step when needed | One CLI invocation per micro-step | observe → predict next action → execute → repeat | write code → execute → inspect screenshots → repair (code-as-action) |
