Browser Service

AI-assisted element locator extraction for web pages. Give it a page URL and a plain-English description of an element; it drives a real browser, finds the element, and returns validated locators.

Scope — read this first

This package was extracted from Natural-Language-to-Robot-Framework (NLRF) and exists to serve it. It is published to PyPI so that project can pin a version, not because it is a general-purpose automation library.

Concretely:

It ships no runnable entrypoint — no __main__, no console script. You mount it into a Flask app yourself.
Its public API is deliberately small: a version string, a config object, four config classes, and four Flask helpers. Everything else is internal and changes without notice.
Configuration falls back to NLRF's settings when that package happens to be importable (src.backend.core.config). Every such import is wrapped in try/except ImportError, so the service runs standalone — it just defaults to MODEL_PROVIDER=vertex instead of reading NLRF's choice.

If you want a browser-automation library, use browser-use directly — this is a thin, opinionated service around it.

Install

pip install browser-service

Requires Python 3.11 or newer (requires-python = ">=3.11,<4.0").

Running it

There is no built-in server. Register the routes onto a Flask app you own and supply a task processor:

from flask import Flask
from browser_service.api import register_routes

app = Flask(__name__)
register_routes(app, task_processor)   # task_processor: your queue/executor
app.run(host="0.0.0.0", port=4999)

NLRF does exactly this in tools/browser_use_service.py and talks to the result over HTTP. It never imports this package's internals.

HTTP endpoints

Method	Path	Purpose
`GET`	`/`	Service banner
`GET`	`/health`	Health check
`GET`	`/probe`	Legacy health check
`POST`	`/workflow`	Submit a workflow task — the primary endpoint
`POST`	`/batch`	Deprecated alias for `/workflow`
`GET`	`/query/<task_id>`	Task status by id
`GET`	`/tasks`	List tasks with summaries

Configuration

Config is nested, not flat. Import the shared instance:

from browser_service.config import config

config.llm.model_provider      # "vertex" (default) | "gemini"  ("local" is rejected)
config.headless                # bool
config.batch                   # BatchConfig
config.locator                 # LocatorConfig
config.agent_vision_mode
config.enable_custom_actions
config.max_concurrent_tasks
config.robot_library

There is no single resolution rule — it differs per setting. Four read the environment first and fall back to NLRF's settings when src.backend.core.config is importable:

Variable	Resolution	Notes
`MODEL_PROVIDER`	env → NLRF → `vertex`	`gemini` or `vertex`. `local` is rejected — this service requires a Google vision model, and `validate()` reports an error for it
`GEMINI_API_KEY`	env → NLRF → unset	required when `MODEL_PROVIDER=gemini`
`VERTEXAI_PROJECT`	env → NLRF → unset	required when `MODEL_PROVIDER=vertex`
`VERTEXAI_LOCATION`	env → NLRF → unset	required when `MODEL_PROVIDER=vertex`

One resolves the other way round — NLRF wins over the environment:

Variable	Resolution	Notes
`GOOGLE_MODEL`	NLRF → env → `gemini-2.5-flash`	when NLRF is importable and sets `ONLINE_MODEL`, `GOOGLE_MODEL` is ignored. Any provider prefix is stripped

The rest read the environment and fall straight through to a built-in default. They never consult NLRF:

Variable	Default	Notes
`VERTEXAI_CREDENTIALS`	unset	service-account JSON path; not an NLRF settings field
`ROBOT_LIBRARY`	`browser`	any other value raises at construction
`BROWSER_HEADLESS`	`true`	run without a visible browser
`AGENT_VISION_MODE`	`auto`	`auto` or `on`; any other value raises at construction
`ENABLE_CUSTOM_ACTIONS`	`true`	enable the custom action set
`ENABLE_ACTION_FIT`	`true`	E2 action-fit, read-target and extended stability rules
`MAX_CONCURRENT_TASKS`	`10`	each task spawns a headless Chrome (~250MB)
`MAX_AGENT_STEPS`	`15`	agent step ceiling per workflow
`MAX_RETRIES_PER_ELEMENT`	`2`
`ELEMENT_TIMEOUT`	`120`	seconds
`CONTENT_BASED_RETRIES`	`7`
`COORDINATE_BASED_RETRIES`	`7`
`ELEMENT_TYPE_RETRIES`	`5`
`COORDINATE_OFFSET_ATTEMPTS`	`7`
`CUSTOM_ACTION_TIMEOUT`	`5`	seconds

These tables are asserted against config.py by tests/test_docs_match_code.py — adding a variable without documenting it fails CI.

Public API

from browser_service import (
    __version__,
    config,                 # shared BrowserServiceConfig instance
    BrowserServiceConfig,
    BatchConfig,
    LocatorConfig,
    LLMConfig,
)

from browser_service.api import (
    register_routes,
    validate_workflow_request,
    format_task_response,
    format_error_response,
)

That is the whole supported surface — it is exactly the two __all__ lists, asserted by tests/test_docs_match_code.py. Anything else you can import is internal.

Element actions

The action dispatcher in browser_service/agent/registration.py handles:

Action	Aliases	Effect
`input`	`type`	Type text into a field
`click`	`submit`	Click an element
`select`	—	Choose a dropdown option
`check`	`uncheck`	Toggle a checkbox

Example

Submit a workflow, then poll for the result. This is the only supported way to drive the service — process_workflow_task and the rest of browser_service.tasks are internal, and NLRF itself never imports them.

curl -X POST http://localhost:4999/workflow   -H 'Content-Type: application/json'   -d '{
    "url": "https://example.com/login",
    "user_query": "Log in with a username",
    "elements": [
      {"id": "elem_1", "description": "Username field", "action": "input"},
      {"id": "elem_2", "description": "Login button",   "action": "click"}
    ]
  }'
# → 202 {"task_id": "...", "status": "processing", "message": "..."}

curl http://localhost:4999/query/<task_id>

elements is required and must be a non-empty list; url, user_query, session_config and enable_custom_actions are optional. Submission returns 429 when MAX_CONCURRENT_TASKS tasks are already active.

Contributing

Issues and pull requests: GitHub.

Releases are automated — merging to main triggers .github/workflows/publish.yml, which derives the next version from PyPI and publishes. Do not hand-edit the version in pyproject.toml; CI overwrites it at build time.

License

MIT — see LICENSE.

Metadata

Release files for browser-service 1.0.39

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Source distribution for browser-service 1.0.39
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Uploaded via	`twine/6.1.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/monkscode/browser-service
Publishing commit: github.com/monkscode/browser-service/tree/50f961837084c1dcb1d5f891da1157294668428f

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Publishing configuration: github.com/monkscode/browser-service/blob/50f961837084c1dcb1d5f891da1157294668428f/.github/workflows/publish.yml
Publishing logs: github.com/monkscode/browser-service/actions/runs/35992839707/attempts/1

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