browsergym-core
BrowserGym: a gym environment for web task automation in the Chromium browser
Decision gist · record as of 2026-08-14
Yes, if you are building or researching web automation agents. The low install friction, active maintenance, permissive license, and gymnasium integration make it a solid foundation. Not recommended for production web scraping or automation—it is explicitly a research tool. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >3.9 and Playwright browser installation (run `playwright install chromium` after pip install).
- Low friction installation with a pure Python wheel.
- The package is actively maintained with recent commits and has 1315 repository stars, indicating active development and community use.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions—just retain attribution and license notices.
last release 2026-01-20 (206 days) · last repo commit 2026-07-17 · 1,315 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,548,879 downloads/mo, #3,776 on PyPI
Alternatives
Verify before relying
pip install browsergym-core
import gymnasium as gym
import browsergym.core
env = gym.make("browsergym/openended", task_kwargs={"start_url": "https://www.google.com/"})
obs, info = env.reset()
action = ... # your agent logic
obs, reward, terminated, truncated, info = env.step(action)
env.close()- Whether browsergym-core alone is sufficient for typical use cases or if additional benchmark packages are typically required.
- Performance characteristics and resource requirements when running agents on complex web tasks.
- Compatibility and integration details with specific LLM backends beyond the demo agent examples.
What it is and what it does
BrowserGym is a gymnasium-based framework for building and evaluating web automation agents. It wraps Chromium via Playwright and provides a standardized interface for agents to interact with websites—observing page state, taking actions, and receiving rewards. The core package registers open-ended web tasks as gym environments, allowing you to implement custom agents that navigate, click, type, and reason about web content using HTML parsing (beautifulsoup4, lxml) and visual features (pillow, numpy).
The package is designed for research into web agent capabilities rather than production automation. It integrates with gymnasium's standard environment loop, making it straightforward to plug in your own agent logic—whether rule-based, learning-based, or LLM-driven. The framework is extensible: you can define new tasks by inheriting from AbstractBrowserTask. While the core package provides the foundation, additional benchmark packages (miniwob, webarena, workarena, etc.) add pre-built task suites; the core alone supports open-ended tasks and custom benchmarks.
Use it for
- Research and development of web automation agents using reinforcement learning or LLM-based reasoning.
- Benchmarking agent performance on standardized web tasks like form filling, navigation, and information retrieval.
- Building custom web task evaluation suites by extending the AbstractBrowserTask class.
- Testing agent behavior on interactive, open-ended web scenarios with real Chromium rendering.
- Prototyping multi-step web workflows that require visual understanding and DOM interaction.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or researching web automation agents.
The low install friction, active maintenance, permissive license, and gymnasium integration make it a solid foundation. Not recommended for production web scraping or automation—it is explicitly a research tool. No known security vulnerabilities.
Install
browsergym-core on PyPI
Before you install
Low friction installation with a pure Python wheel. The package is actively maintained with recent commits and has 1315 repository stars, indicating active development and community use.
Requires Python >3.9 and Playwright browser installation (run `playwright install chromium` after pip install).
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions—just retain attribution and license notices.
Quickstart
pip install browsergym-core
import gymnasium as gym
import browsergym.core
env = gym.make("browsergym/openended", task_kwargs={"start_url": "https://www.google.com/"})
obs, info = env.reset()
action = ... # your agent logic
obs, reward, terminated, truncated, info = env.step(action)
env.close()Verify before relying
- Whether browsergym-core alone is sufficient for typical use cases or if additional benchmark packages are typically required.
- Performance characteristics and resource requirements when running agents on complex web tasks.
- Compatibility and integration details with specific LLM backends beyond the demo agent examples.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesbeautifulsoup4gymnasiumlxmlmcpnumpypillowplaywrightpyparsing |
| Maintenance | Actively maintained 206 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,548,879 / month, #3,776 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: browsergym_core-0.14.3-py3-none-any.whl
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