webwright

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).

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.