# Webwright **Repository Path**: xlinliu/Webwright ## Basic Information - **Project Name**: Webwright - **Description**: A simple SWE style browser agent framework that achieves SOTA results on long horizon web tasks. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-07-02 - **Last Updated**: 2026-07-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Webwright

Webwright logo

Turn Your Coding Models to Be State-of-the-art Browser Agents

Python Playwright Backends Footprint

- πŸ“ **Blog:** [Webwright: A Terminal Is All You Need For Web Agents](https://www.microsoft.com/en-us/research/articles/webwright-a-terminal-is-all-you-need-for-web-agents/) - 🌐 **Project Page:** [microsoft.github.io/Webwright](https://microsoft.github.io/Webwright/) Webwright gives LLM a terminal where it can launch multiple browser sessions to inspect the page and complete a web task. It captures and inspects page screenshots/states only when needed. It enforces each web task to be completed end-to-end within a re-runnable Python script, i.e. your web agent browsing history is a single code file. No multi-agent system, no graph engine, no plugin layer, no hidden orchestration β€” just a terminal, a browser, and a model. Already got your favorite agents, and wonder how to make Claude Code, Codex, Hermes, OpenClaw more capable in browser tasks? Consider adding [Webwright plugin/skills](#-use-as-a-claude-code-skill)! --- ## πŸ“° News - **2026-05-11** β€” Support Task2UI mode: Webwright completes the task and renders task results into an HTML-based web app you can easily view and reuse. - **2026-05-06** β€” Codex and Claude Code plugin manifests added; install via `/plugin install webwright@webwright`. OpenClaw and Hermes Agent integrations shipped; the same `skills/webwright/` folder now loads across Claude Code, Codex, OpenClaw, and Hermes. - **2026-05-04** β€” Initial public release: ~1.5k LoC, OpenAI / Anthropic / OpenRouter backends, Playwright environment. ---
πŸ’‘ 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 β€” 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 β€” it's the **code and logs in the local workspace**. - 🧱 **Robust, reusable interaction with web environments** β€” 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** β€” 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. - πŸ§ͺ **Workspace-as-state, not browser-as-state** β€” 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. - πŸͺ„ **Surprisingly effective despite being minimal** β€” this stripped-down setup turns out to handle complex and especially long-horizon web tasks well (see [Performance](#-performance)).
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🌟 Why Webwright Most web agent frameworks bury the actual agent loop under layers of abstractions. Webwright takes the opposite stance: - πŸͺΆ **Lightweight by design** β€” core agent loop in a single ~450-line file, Playwright environment in ~570 lines, CLI in ~150 lines. - 🧩 **Pluggable model backends** β€” OpenAI, Anthropic, and OpenRouter, each ~150–200 lines. - πŸ” **Zero hidden frameworks** β€” just `httpx`, `pydantic`, `playwright`, and `typer`. - πŸ” **Flat prompt β†’ observe β†’ execute script loop** β€” readable end-to-end, easy to debug, easy to fork. - πŸ§ͺ **Run-artifact first** β€” 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.
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πŸ†š 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 β†’ 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 β€” code, screenshots, logs.** Browser is disposable. | | **Loop shape** | Imperative; `agent()` does multi-step when needed | One CLI invocation per micro-step | observe β†’ predict next action β†’ execute β†’ repeat | write code β†’ execute β†’ inspect screenshots β†’ repair (code-as-action) |
--- ## πŸŽ₯ Demo https://github.com/user-attachments/assets/4ed94cd5-11be-4daa-b2d7-1260a803baca --- ## πŸ“Š Performance State-of-the-art on two real-website benchmarks with a 100-step budget β€” 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. - πŸ† **Online-Mind2Web (300 tasks):** **86.7%** with GPT-5.4 β€” 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). - πŸš€ **Odysseys (200 long-horizon tasks):** **60.1%** with GPT-5.4 (avg. 76.1 steps) β€” **+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 β€” even **Qwen-3.5-9B** completes tasks well on Online-Mind2Web sites with 5+ tools available.

Odysseys long-horizon eval @ 100 steps Online-Mind2Web AutoEval @ 100 steps

--- ## πŸ—ΊοΈ Project Map ``` webwright/ β”œβ”€β”€ pyproject.toml # package: webwright β”œβ”€β”€ src/webwright/ β”‚ β”œβ”€β”€ run/cli.py # CLI entrypoint (`webwright`) β”‚ β”œβ”€β”€ agents/default.py # core agent loop β”‚ β”œβ”€β”€ environments/ # Playwright browser workspace β”‚ β”œβ”€β”€ tools/ # image_qa, self_reflection β”‚ β”œβ”€β”€ models/ # openai_model, anthropic_model, base β”‚ β”œβ”€β”€ config/ # base.yaml, model_openai.yaml, model_claude.yaml β”‚ └── utils/ β”œβ”€β”€ assets/ β”‚ └── task_showcase/ # tiny Flask dashboard for repeatable runs β”‚ β”œβ”€β”€ app.py β”‚ β”œβ”€β”€ templates/ # dashboard.html, task.html β”‚ └── tasks// # task.json + report.json per task β”œβ”€β”€ tests/ └── outputs/ # run artifacts (trajectories, screenshots) ``` --- ## πŸ“° 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 β€” `task.json` (metadata) and `report.json` (curated, structured output: sources + result sections like tables, lists, summaries) β€” and the templates render them generically, so adding a new task is just dropping a new folder in `assets/task_showcase/tasks/`. ```bash 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: ```bash 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 ``` > **Note:** `report.json` is only generated when `-c task_showcase.yaml` is > included. A plain `base.yaml` run produces `trajectory.json` and debug > artifacts but no `report.json`. 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: ```bash python assets/task_showcase/app.py \ --tasks-dir outputs/default//task_showcase/tasks ``` --- ## πŸš€ Quick Start ### Prerequisites - Python 3.10+ - Chromium installed through Playwright - An API key for your chosen backend (OpenAI, Anthropic, or OpenRouter) ### Install ```bash 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: ```bash 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. | --- ## πŸ”Œ 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 β€” 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): ```bash pip install -e . playwright install chromium ```
Claude Code ### Install Install through the bundled marketplace inside Claude Code: ```text # 1. Add this repo as a Claude Code plugin marketplace /plugin marketplace add microsoft/Webwright # 2. Install the plugin from that marketplace /plugin install webwright@webwright ``` Prefer a local checkout? Point the marketplace command at the cloned repo instead: ```text /plugin marketplace add /absolute/path/to/Webwright /plugin install webwright@webwright ``` ### Use **Start a new Claude Code session** after installing β€” plugins are loaded at session start and won't appear until you restart. You can either ask Claude Code in plain English (the skill auto-activates from its description), or use one of the slash commands: ``` /webwright:run search Google Flights for flights from SEA to JFK on 2026-08-15 to 2026-08-20 /webwright:craft search a ticket on Google Flights from LAX to SFO depart June 7 return June 14 ``` - `/webwright:run` (or any plain prompt) produces a **one-shot** `final_script.py` for the literal task values. - `/webwright:craft` produces a **reusable CLI tool**: `final_script.py` becomes one parameterized function with a Google-style `Args:` docstring and an `argparse` wrapper whose flags default to the concrete task values, so you can rerun it later with different arguments β€” e.g. `python final_script.py --origin JFK --destination LAX --depart-date 2026-07-01`. In both modes Claude Code scaffolds a workspace with `plan.md`, runs instrumented Playwright scripts under `final_runs/run_/`, and visually self-verifies each critical point against the saved screenshots.
OpenAI Codex ### Install Codex reads Claude-style marketplaces, so the same repo works as a Codex plugin marketplace. From the Codex CLI: ```bash # 1. Add this repo as a Codex plugin marketplace codex plugin marketplace add microsoft/Webwright # 2. Open the plugin browser and install Webwright codex /plugins ``` Prefer a local checkout? ```bash codex plugin marketplace add /absolute/path/to/Webwright ``` Then restart Codex so the new marketplace and plugin are picked up. ### Use In a new Codex thread, either ask in plain English (the skill auto-activates from its description) or invoke the bundled skill explicitly with `@webwright`: ``` @webwright search Google Flights for flights from SEA to JFK on 2026-08-15 to 2026-08-20 ``` Codex scaffolds a workspace with `plan.md`, runs instrumented Playwright scripts under `final_runs/run_/`, 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.
🦞 OpenClaw ### Install Install directly from a local checkout (path, archive, npm spec, git repo, or `clawhub:` spec all work): ```bash openclaw plugins install /absolute/path/to/Webwright openclaw gateway restart # reload so the plugin and skill are picked up ``` Verify: ```bash openclaw plugins list | grep webwright openclaw skills list | grep webwright # should show "βœ“ ready" ``` ### Use The `webwright` skill is now available to any OpenClaw agent surface (CLI, Telegram, etc.) β€” invoke it by asking the agent in natural language, or via the slash commands shipped under [`skills/webwright/commands/`](skills/webwright/commands/), e.g. `/webwright run `. To uninstall: `openclaw plugins uninstall webwright`.
Hermes Agent ### Install [Hermes Agent](https://github.com/NousResearch/hermes-agent) is a [skills-compatible client](https://agentskills.io), so the same `skills/webwright/` folder loads as a Hermes skill. Symlink it into your Hermes user-skills directory: ```bash mkdir -p ~/.hermes/skills ln -sfn /absolute/path/to/Webwright/skills/webwright ~/.hermes/skills/webwright ``` No Hermes-specific manifest is needed; only `SKILL.md` is loaded. ### Use Start Hermes (`hermes`) and ask it to drive a web task in natural language β€” the skill auto-activates from its description. You can also invoke it explicitly with `/webwright`. Note: the named subcommands shipped under [`skills/webwright/commands/`](skills/webwright/commands/) (`/webwright:run`, `/webwright:craft`) are a Claude Code / Codex convention and are inert in Hermes; the skill itself still works end-to-end.
## πŸ“ƒ 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. ![Trajectory comparison](assets/trajectory-compare.png) ### How to use ```bash 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. --- ## Credits - [SWE-agent/mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent/tree/main) β€” design inspiration for the minimal agent loop. - [Playwright](https://playwright.dev/) β€” browser automation. ## Citation If you use Webwright in your research or build on it, please cite this repository: ```bibtex @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} } ```