Browser Use vs Steel

Caching

1. developerCache resolved actions or generated code so repeat runs replay deterministically at lower cost and latency than re-prompting the LLM

   weight 2 · round drawn
   Browser Use—none0/10

   No evidence of caching resolved actions or generated code for deterministic, lower-cost replay; the product is LLM-driven agent automation with sessions/runs but nothing about caching or replay without re-invoking the model. missing for 10: any mention of action/code caching, deterministic replay mechanism, or cost/latency savings from skipping re-prompting.

   ⚑ flag

   Steel—none0/10

   Steel's evidence covers session management, stealth, proxies, human-in-the-loop debugging, and agent traces, but nothing about caching resolved actions or generated code to enable deterministic, lower-cost replay without re-invoking the LLM. Agent traces (steel-docs-6/7) provide observability/export, not action-cache replay for cost savings.

   ⚑ flag

Dom

1. developerDrive the page through DOM-understanding action primitives (act/click/type on described elements) that survive selector and layout changes

   weight 3 · round to Browser Use
   Browser Use~partialcommunity6/10

   Browser Use's core agent is built around natural-language task instructions (e.g. "Find the top Hacker News story", "Fill in this job application") executed via an LLM-driven agent that perceives the DOM/page state and decides actions, which is the essence of DOM-understanding, layout-resilient automation — this is corroborated by GitHub task examples and community hands-on use (LinkedIn automation, resume filling). However, the evidence pack lacks explicit documentation of discrete act/click/type primitives with described-element targeting or any stated guarantee/mechanism for surviving selector/layout changes; it's inferred from the agent's general design rather than directly documented. missing for 10: explicit API/primitive-level documentation of click/type/act-on-described-element functions, and direct evidence/testing showing resilience to selector or layout changes rather than just general LLM-driven task completion.

   • [github] “Task: "Fill in this job application with my resume and information."”
   • [github] “Task: "Extract structured data about my followers and export it as a CSV."”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”
   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”

   proof ↗⚑ flag

   Steel—none0/10

   Steel's own docs describe CLI-level click/fill/type and CDP/Puppeteer control (steel-gh-1, steel-docs-8), which is standard selector-based automation, not a DOM-understanding 'act on described element' primitive that survives layout/selector changes. Semantic action-taking is delegated to third-party integrations (Claude/OpenAI/Gemini Computer Use, Browser-Use) rather than being a native Steel capability.

   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [claimed-docs] “Use Gemini Computer Use with Steel Browser.”
   • [claimed-docs] “Use Claude Computer Use with Steel Browser.”
   • [claimed-docs] “The OpenAI Computer Use integration runs OpenAI's vision-based agent loop on a Steel browser session.”
   • [claimed-docs] “Use Browser-Use to interact with a Steel browser.”

   proof ↗⚑ flag

Observe

1. developerPreview candidate actions on the current page (observe/plan) before committing the agent to act

   weight 1 · round drawn
   Browser Use—none0/10

   Evidence shows live-preview URLs for human intervention during CAPTCHA/2FA and event polling for observability, but nothing about an explicit preview/plan step where candidate actions are surfaced for developer review before the agent commits to acting.

   • [claimed-docs] “If the challenge remains, open the [live preview](/cloud/browser/live-preview) for human control.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event]”

   proof ↗⚑ flag

   Steel—none0/10

   Steel exposes session snapshot/extract/screenshot tools and a human-in-the-loop debug URL (steel-docs-5, steel-docs-39), but there is no evidence of an explicit preview/plan primitive that lets a developer see candidate actions on a page before committing the agent to execute them — it's an observability/handoff feature, not an action-preview mechanism.

   • [claimed-docs] “Steel's debug URL feature allows you to implement human-in-the-loop workflows where users can directly interact with and control browser ses…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session (open, navigate, snapshot, extract, return typed results)”
   • [github] “Browser Tools: Exposes APIs to quick convert pages to markdown, readability, screenshots, or PDFs.”

   proof ↗⚑ flag

Vision

1. developerSwitch to a vision or computer-use action mode that operates on screenshots for canvases and UIs the DOM path can't handle

   weight 2 · round to Steel
   Browser Use—none0/10

   Browser Use's docs describe DOM-based agent actions, live preview/human handoff for CAPTCHA/2FA, and CDP-based control, but there is no evidence of a dedicated vision or computer-use mode that acts directly on screenshots for canvas/UI elements the DOM can't reach.

   ⚑ flag

   Steel✓fullclaimed7/10

   Steel documents dedicated integrations for OpenAI, Claude, and Gemini Computer Use that run vision-based agent loops (operating on screenshots) atop a Steel browser session, directly enabling a vision/computer-use mode alongside its DOM-based Sessions API and CLI actions. missing for 10: independent/hands-on corroboration of vision mode working on canvas-heavy UIs, and detail on switching seamlessly between DOM and vision modes within one session.

   • [claimed-docs] “Use Gemini Computer Use with Steel Browser.”
   • [claimed-docs] “Use Claude Computer Use with Steel Browser.”
   • [claimed-docs] “The OpenAI Computer Use integration runs OpenAI's vision-based agent loop on a Steel browser session.”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”

   proof ↗⚑ flag

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round to Steel
   Browser Use✓fullprobed8/10

   Browser Use publishes a discoverable llms.txt at docs.browser-use.com/llms.txt confirmed live via probe (HTTP 200), which is exactly the agent-oriented docs entry point an AI-native user could point an agent at, and the broader docs site is structured/markdown-friendly for agent consumption. Missing for 10: no evidence of additional structured formats like llms-full.txt or explicit guidance encouraging agents to consume it, and no independent community confirmation of agents successfully using it.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.browser-use.com/llms.txt # Browser Use > Documentation for Browser Use Cloud Agent and Browser API…”

   proof ↗⚑ flag

   Steel✓fullprobed9/10

   Steel serves a live llms.txt file at docs.steel.dev/llms.txt (HTTP 200, confirmed via probe) summarizing its docs, which an agent can be pointed at directly, plus a full agent-oriented docs site with skills, MCP, and SDK integration guides. Missing for 10: no independent/community confirmation that agents actually consume the llms.txt file successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.steel.dev/llms.txt # Steel Documentation > Steel is the open-source browser API for AI agents — ma…”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”
   • [claimed-docs] “Expose a Steel browser to any MCP client”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to Steel
   Browser Use~partialclaimed6/10

   Browser Use ships both an open-source Python library and a cloud API (client.runs.create) that are inherently script/automatable, implying headless/CI use, and gh-3 explicitly pitches automating the web 'from your own code, and with any LLM.' However, there is no explicit documentation of headless mode flags, Docker images, or CI pipeline examples/integration guides. Missing for 10: explicit headless-mode configuration docs, CI/CD pipeline examples (e.g., GitHub Actions), and independent confirmation of running unattended in CI.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [claimed-docs] “For a local agent, use the [open-source library](/open-source/quickstart).”
   • [claimed-docs] “Launch a browser, connect to its CDP URL, then stop it”

   proof ↗⚑ flag

   Steel✓fullprobed8/10

   Steel is API-first and ships a CLI, Docker image (API+UI), and Sessions API that spins up isolated cloud browsers on demand, all designed to be driven programmatically with no GUI required — exactly the shape needed for CI/headless automation. It also supports standard automation clients (Puppeteer/Playwright/Selenium via CDP) which are routinely run headlessly in CI pipelines. Missing for 10: an explicit CI/CD example (e.g., GitHub Actions workflow) or independent report confirming real-world headless CI usage.

   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [github] “Pre-built Docker Image (combined API + UI)”
   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [probe] “official CLI documented at https://docs.steel.dev/overview/steel-cli”

   proof ↗⚑ flag

3. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Steel
   Browser Use!disputedcontradicted5/10

   Docs describe an official MCP server enabling Claude, Cursor, Windsurf or any MCP client to run Browser Use tasks (browser-use-docs-12, browser-use-probe-3), but a hands-on community report says the author had to switch tools because Browser Use 'doesn't support MCP integration' in Cursor (browser-use-comm-4), directly contradicting the documented claim. Missing for 10: independent corroboration that MCP connection actually works end-to-end, and resolution of the conflicting user report.

   • [claimed-docs] “Run browser automation tasks from your AI coding assistant. Connect to Claude, Cursor, Windsurf, or any MCP client.”
   • [probe] “official MCP server documented at https://docs.browser-use.com/cloud/guides/mcp-server”
   • [community] “I want to use browser-use in Cursor but I am using another option because it doesn't support MCP integration which is the common language th…”

   proof ↗⚑ flag

   Steel✓fullclaimed6/10

   Steel is a browser-API platform (not itself an agent), so the axis applies, and its docs explicitly describe exposing a Steel browser as an MCP server/tool set ('Expose a Steel browser to any MCP client' and 'exposes a cloud browser as in-process MCP tools' for the Claude Agent SDK integration), letting external agents connect via MCP. Missing for 10: deeper standalone MCP-server setup docs/config reference, and independent/hands-on confirmation beyond a single cookbook and integration mention.

   • [claimed-docs] “Expose a Steel browser to any MCP client”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the browser.”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the brow”

   proof ↗⚑ flag

4. ai-native userUse an official CLI

   weight 2 · round to Steel
   Browser Use—none0/10

   The evidence pack documents a Python SDK, cloud API, MCP server, and web dashboard, but never mentions an official standalone CLI tool for Browser Use. Since a browser-automation product could plausibly ship a CLI, absence of any such evidence means this axis is unmet.

   ⚑ flag

   Steel✓fullprobed7/10

   Steel ships an official CLI (documented at docs.steel.dev/overview/steel-cli) that lets users run full browser workflows from the terminal—start sessions, navigate, click/fill/type, extract content, and stop sessions—directly serving agentic/AI-native use cases. missing for 10: independent/hands-on community corroboration of the CLI itself, and detail on CLI scripting/composability beyond the basic workflow description.

   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [probe] “official CLI documented at https://docs.steel.dev/overview/steel-cli”
   • [claimed-docs] “Install Steel CLI and the steel-browser skill so your agent can control a browser.”

   proof ↗⚑ flag

5. ai-native userDrive the product through a documented public API

   weight 3 · round to Steel
   Browser Use~partialprobed7/10

   Browser Use documents a public Cloud API (client.runs.create, sessions, events polling, structured output, CDP connection) across multiple docs pages, indicating a real programmatic interface beyond the UI. However, probes for a formal OpenAPI/swagger spec returned 404s, so there's no machine-readable API contract, only prose docs and SDK examples. Missing for 10: a published OpenAPI/spec artifact, independent third-party confirmation of API usage beyond vendor docs.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.browser-use.com/llms.txt # Browser Use > Documentation for Browser Use Cloud Agent and Browser API…”

   proof ↗⚑ flag

   Steel✓fullprobed9/10

   Steel exposes a fully documented public REST API (Sessions, CAPTCHA, Credentials, Files, Agent Traces) confirmed by a live openapi.json spec, plus an official CLI and SDK/agent-framework integrations (CrewAI, OpenAI Agents SDK, AI SDK, Claude Agent SDK, AgentKit, Pydantic AI) that let AI-native users drive it programmatically. Community comments corroborate the API/docs quality and usability. Missing for 10: independent hands-on benchmarking of the API's reliability/limits beyond docs and one HN thread.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.steel.dev/openapi.json — contains "openapi" key”
   • [claimed-docs] “Explore API endpoints for building on Steel.”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “The Steel integration runs each agent's browser actions on a Steel cloud session, so AgentKit handles the orchestration and Steel handles th…”
   • [claimed-docs] “The Steel integration exposes a Steel browser as a CrewAI tool, so your crew can search, navigate, fill forms, extract data, and validate re…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”
   • [community] “beautiful docs + api ref! what are you using? (cool that you're doing open-source browserbase also, excited to check this out)”

   proof ↗⚑ flag

6. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   Browser Use—none0/10

   No evidence of scoped or least-privilege API key/credential issuance (e.g., role-based tokens, permission scopes) for agents; docs mention API keys implicitly via client usage but no mention of scoping, restricted permissions, or credential management features. Community/GitHub evidence also silent on this.

   ⚑ flag

   Steel—none0/10

   The evidence pack shows a general API key/auth model (referenced implicitly by API usage) and a 'Credentials' endpoint, but that endpoint is for storing website login credentials for browser sessions (steel-docs-15, steel-docs-24), not for issuing scoped/least-privilege API keys to control agent access to Steel's own platform. No docs mention role-based access control, permission scopes, or restricted API key issuance for the Steel API itself.

   • [claimed-docs] “Explore API endpoints for managing Steel Credentials.”
   • [claimed-docs] “Reusing Context & Auth | Steel Docs”

   proof ↗⚑ flag

7. ai-native userBuild against official SDKs

   weight 2 · round drawn
   Browser Use✓fullprobed7/10

   Browser Use ships an official open-source Python library (github, docs-19) and a cloud client SDK with documented usage patterns (client.runs.create, sessions, events polling) shown in docs-1/4/5/14, giving AI-native devs a concrete first-party SDK to build against. Missing for 10: evidence of SDKs beyond Python (e.g. JS/TS), and no OpenAPI spec was found (probe-2 all 404) or independent third-party corroboration of SDK usage.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “For a local agent, use the [open-source library](/open-source/quickstart).”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”

   proof ↗⚑ flag

   Steel✓fullprobed7/10

   Steel exposes a documented REST API (OpenAPI spec, steel-probe-2), an official CLI (steel-docs-8, steel-probe-3), and native connectivity via Puppeteer/Playwright/Selenium (steel-gh-1), plus first-party integrations with major agent frameworks (AI SDK, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Pydantic AI, AgentKit) that act as official SDK-level bindings for AI-native development. Missing for 10: explicit named client-library SDKs (e.g., dedicated Python/Node package docs) and independent/hands-on developer confirmation of SDK ergonomics beyond vendor docs.

   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.steel.dev/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://docs.steel.dev/overview/steel-cli”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session (open, navigate, snapshot, extract, return typed results)”
   • [claimed-docs] “The Steel integration exposes a Steel browser as a CrewAI tool, so your crew can search, navigate, fill forms, extract data, and validate re…”
   • [claimed-docs] “Build a Pydantic AI browser agent on Steel cloud browsers: provider-agnostic, typed Python agents with dependency injection and real web acc…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the brow”
   • [claimed-docs] “The Steel integration runs each agent's browser actions on a Steel cloud session, so AgentKit handles the orchestration and Steel handles th…”

   proof ↗⚑ flag

8. ai-native userSubscribe to events via webhooks

   weight 2 · round drawn
   Browser Use—none0/10

   The docs describe an event system based on polling `runs.events()` with a cursor, not webhook subscriptions; no evidence anywhere in the pack mentions webhooks, callback URLs, or push notifications.

   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”

   proof ↗⚑ flag

   Steel—none0/10

   No evidence pack mentions webhooks or event subscriptions anywhere in Steel's docs, CLI, API, or integrations; Steel's observability is presented via agent traces/session replays rather than push-based webhook events.

   ⚑ flag

Agentic features

1. ai-native userSet up automations that run autonomously in the background

   weight 2 · round drawn
   Browser Use~partialclaimed5/10

   Browser Use Cloud lets users kick off agent runs via API (client.runs.create) that execute asynchronously in a hosted browser, with session reuse and event polling to monitor progress without keeping a local process open, which supports a form of unattended background execution. However there is no documented scheduling, cron-like triggers, or webhook-based automation setup for recurring/background jobs, so it's unclear whether truly hands-off recurring automations are supported. Missing for 10: explicit scheduling/trigger mechanism, evidence of long-running unattended jobs beyond single API-invoked runs, and independent confirmation of background reliability.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”

   proof ↗⚑ flag

   Steel~partialclaimed5/10

   Steel provides infrastructure that agents can call programmatically (Sessions API, CLI, and integrations with agent frameworks like CrewAI, OpenAI Agents SDK, Claude Agent SDK) to run browser automations without human intervention, and sessions can be created on-demand for autonomous agent workflows. However, there is no evidence of a native scheduler, cron/trigger mechanism, or persistent background job runner within Steel itself — autonomy and 'background' execution depend entirely on the calling agent/framework, not a Steel-native automation scheduler. Missing for 10: native scheduling/triggers, evidence of long-running/persistent background jobs managed by Steel, and independent confirmation of autonomous unattended runs.

   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the browser.”
   • [claimed-docs] “The Steel integration runs each agent's browser actions on a Steel cloud session, so AgentKit handles the orchestration and Steel handles th…”
   • [claimed-docs] “The Steel integration exposes a Steel browser as a CrewAI tool, so your crew can search, navigate, fill forms, extract data, and validate re…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”

   proof ↗⚑ flag

2. ai-native userOperate the product with natural-language commands

   weight 2 · round to Browser Use
   Browser Use✓fullcommunity8/10

   Browser Use is fundamentally natural-language driven: tasks are issued as plain-English strings like "Find the top Hacker News story" or "Fill in this job application with my resume and information", with the agent interpreting and executing them autonomously, corroborated by GitHub examples and community hands-on use for LinkedIn automation. missing for 10: independent benchmark of instruction-following accuracy, and clearer docs on limits/failure modes of natural-language parsing.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [github] “Task: "Fill in this job application with my resume and information."”
   • [github] “Task: "Extract structured data about my followers and export it as a CSV."”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”

   proof ↗⚑ flag

   Steel—none0/10

   Steel is a browser-automation infrastructure API for agents/developers, not itself an interface that accepts natural-language commands from a user; the CLI (steel-docs-8) is a scriptable command-line tool with structured commands, not NL input, and no evidence shows a chat/NL interface for operating Steel directly. missing for 10: any documented NL command interface, chatbot/assistant front-end, or evidence that end users type plain-English instructions to control Steel.

   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “Install Steel CLI and the steel-browser skill so your agent can control a browser.”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round to Steel
   Browser Use—none0/10

   Docs show static code snippets (e.g., client.runs.create examples) but no evidence of an interactive, runnable API console/reference; explicit probes for OpenAPI/Swagger specs at standard paths all returned 404, indicating no interactive API explorer exists.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”
   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.browser-use.com/llms.txt # Browser Use > Documentation for Browser Use Cloud Agent and Browser API…”

   proof ↗⚑ flag

   Steel~partialprobed5/10

   Steel documents dedicated API-reference pages ('Explore API endpoints for...') and exposes a live OpenAPI spec (openapi.json), and a community commenter independently praised the 'beautiful docs + api ref', suggesting a solid reference exists. However, there's no explicit evidence of interactive 'try it now' / runnable-example functionality within that reference (e.g., embedded request builders or live code execution) — missing for 10: confirmation of in-browser runnable/try-it examples, independent hands-on demonstration of executing calls directly from the docs.

   • [claimed-docs] “Explore API endpoints for managing Steel Captchas.”
   • [claimed-docs] “Explore API endpoints for building on Steel.”
   • [claimed-docs] “Explore API endpoints for managing Steel Credentials.”
   • [claimed-docs] “Explore API Endpoints for managing Files on Steel.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.steel.dev/openapi.json — contains "openapi" key”
   • [community] “beautiful docs + api ref! what are you using? (cool that you're doing open-source browserbase also, excited to check this out)”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round to Steel
   Browser Use—none0/10

   A direct probe for OpenAPI/swagger spec files returned 404 on all candidate paths, and no documentation references a downloadable machine-readable API spec despite having a REST/cloud API.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”

   proof ↗⚑ flag

   Steel✓fullprobed9/10

   Steel exposes a live, machine-readable OpenAPI spec confirmed via probe (HTTP 200 with valid 'openapi' key), and docs reference extensive API endpoint documentation (steel-docs-13 through steel-docs-16) built on this spec. Missing for 10: no independent/community confirmation of the OpenAPI spec's completeness or third-party tooling built against it.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.steel.dev/openapi.json — contains "openapi" key”
   • [claimed-docs] “Explore API endpoints for building on Steel.”
   • [claimed-docs] “Explore API endpoints for managing Steel Captchas.”
   • [claimed-docs] “Explore API endpoints for managing Steel Credentials.”
   • [claimed-docs] “Explore API Endpoints for managing Files on Steel.”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Steel
   Browser Use—none0/10

   The evidence describes cloud browser sessions, live preview, and CDP connections, but nothing indicates a dedicated sandbox/staging mode that isolates test runs from production data or real accounts. Users are shown reusing real logged-in profiles (browser-use-comm-5) rather than isolated test environments, and no docs mention a sandbox distinct from production.

   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Log in once, save the profile, then reuse it to start future browsers already logged in.”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”

   proof ↗⚑ flag

   Steel✓fullclaimed7/10

   Steel's Sessions API explicitly provisions isolated browser instances, each with its own state, cookies, and storage, letting an agent test without affecting other sessions or shared/production data, and session lifecycle docs show sessions can be created/torn down independently (steel-docs-1, steel-docs-10, steel-docs-33). Self-hosted Docker image and CLI further support standing up isolated test environments (steel-gh-4, steel-docs-8). Missing for 10: independent/hands-on verification that isolation holds in practice, and no explicit framing of 'sandbox vs production' terminology in the docs.

   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Steel generates a session ID for you, but `create` also accepts one. Pass your own UUID when the ID has to exist before the browser does”
   • [claimed-docs] “Session Lifecycle | Steel Docs”
   • [github] “Pre-built Docker Image (combined API + UI)”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Browser Use—none0/10

   Evidence shows an API version label ("V4") in docs, but there is no documented deprecation policy, versioning changelog, or API stability guarantees anywhere in the pack; the OpenAPI spec probe also 404s, indicating no formal API contract is published.

   • [claimed-docs] “V4 returns `run.result` as a string. Ask for JSON only, then validate it client-side”
   • [claimed-docs] “Automatic CAPTCHA solving is enabled by default for API V4 Agent runs and standalone Cloud Browser sessions.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”

   proof ↗⚑ flag

   Steel—none0/10

   Evidence shows an OpenAPI spec and extensive API documentation, but nothing addresses API versioning scheme or a documented deprecation policy. missing for 10: versioning scheme documentation, deprecation policy, changelog/migration guides for breaking changes.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.steel.dev/openapi.json — contains "openapi" key”
   • [claimed-docs] “Explore API endpoints for building on Steel.”

   proof ↗⚑ flag

Compat

1. developerConnect my existing Playwright, Puppeteer, or CDP automation code to the product's browsers instead of rewriting it

   weight 2 · round to Steel
   Browser Use✓fullcommunity7/10

   Docs explicitly describe connecting existing Playwright/Puppeteer/CDP code to Browser Use's browsers via CDP URL, with a documented choice between Browser Use driving or the developer's own code connecting directly over CDP (docs-2, docs-18), and community reports confirm connecting to a real local Chrome profile via CDP for existing automation. missing for 10: independent hands-on validation specifically with Playwright/Puppeteer libraries (not just CDP raw), and more detail on session/auth persistence when using external code.

   • [claimed-docs] “Launch a browser, connect to its CDP URL, then stop it”
   • [claimed-docs] “Choose whether Browser Use drives the browser or your Playwright/Puppeteer code connects directly over CDP.”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”

   proof ↗⚑ flag

   Steel✓fullclaimed8/10

   GitHub docs explicitly state Steel exposes CDP endpoints that let developers connect using Puppeteer, Playwright, or Selenium, meaning existing automation code can target Steel's browsers with minimal rewrite, and the Sessions API/session lifecycle docs support this connection model. Missing for 10: independent hands-on developer reports confirming drop-in compatibility and more detail on any required code changes beyond endpoint swapping.

   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Steel generates a session ID for you, but `create` also accepts one. Pass your own UUID when the ID has to exist before the browser does”
   • [claimed-docs] “Session Lifecycle | Steel Docs”

   proof ↗⚑ flag

Credentials

1. automation-engineerStore credentials in a vault and have the agent complete logins including TOTP/2FA challenges without exposing secrets to the model

   weight 2 · round to Browser Use
   Browser Use~partialclaimed5/10

   Browser Use supports saved/reused login profiles (docs-10) and a documented 2FA workflow where the run pauses at the challenge and a human enters the code via live view (docs-11/16), but there is no vault/secrets-manager integration for storing credentials and injecting them without model exposure, and TOTP is handled via human-in-the-loop rather than automated secret injection. missing for 10: a credential vault/secrets-manager integration, evidence that passwords/TOTP secrets are injected without ever passing through the model context, fully automated TOTP handling without human intervention, independent confirmation of the 2FA flow working in practice.

   • [claimed-docs] “Log in once, save the profile, then reuse it to start future browsers already logged in.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event]”

   proof ↗⚑ flag

   Steel~partialclaimed3/10

   Steel documents a 'Credentials' API endpoint and separate 'Reusing Auth Context' feature for persisting authenticated sessions/cookies across runs, which addresses avoiding repeated logins, but there is no evidence describing a credential vault workflow, TOTP/2FA handling, or explicit guarantees that secrets are withheld from the model during login. missing for 10: dedicated vault/secrets-manager docs, TOTP/2FA challenge handling, explicit model-isolation guarantee for credentials, independent corroboration.

   • [claimed-docs] “Explore API endpoints for managing Steel Credentials.”
   • [claimed-docs] “This is particularly useful for maintaining authenticated states across multiple sessions, helping your AI agents access protected resources…”
   • [claimed-docs] “Reusing Context & Auth | Steel Docs”

   proof ↗⚑ flag

Profiles

1. developerPersist logged-in browser state in reusable profiles so agents skip the login wall on every subsequent run

   weight 3 · round drawn
   Browser Use✓fullcommunity8/10

   Docs explicitly describe saving a login profile once and reusing it to start future browsers already logged in, plus a 2FA guide for handling the initial login flow, and community evidence confirms local profile reuse works for logged-in automation (e.g., LinkedIn). missing for 10: independent/hands-on corroboration specifically of the cloud profile-reuse feature (only local profile reuse is community-validated) and no detail on profile storage/security guarantees.

   • [claimed-docs] “Log in once, save the profile, then reuse it to start future browsers already logged in.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event]”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”

   proof ↗⚑ flag

   Steel✓fullclaimed8/10

   Steel's docs explicitly describe reusing auth context/persistent profiles across sessions so agents can access protected resources without repeating login flows, backed by a dedicated docs page and general session state/cookie persistence via the Sessions API. Missing for 10: independent/hands-on developer confirmation that reused profiles reliably skip login walls in practice, and more detail on profile creation/management UX.

   • [claimed-docs] “This is particularly useful for maintaining authenticated states across multiple sessions, helping your AI agents access protected resources…”
   • [claimed-docs] “Reusing Context & Auth | Steel Docs”
   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Steel
   Browser Use~partialclaimed3/10

   The library is pitched for automating the web 'at scale' from custom code (gh-3) and cloud sessions can hold multiple runs, hinting at multi-task orchestration, but there is no explicit documentation of a batch/bulk API, parallel run submission, or looping over many items as a first-class feature. Missing for 10: dedicated bulk/batch endpoint or SDK pattern, concurrency limits/guidance, and hands-on evidence of running many items in one operation.

   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”

   proof ↗⚑ flag

   Steel~partialclaimed4/10

   Steel supports spinning up many isolated browser sessions concurrently (Sessions API, CLI, SDK integrations) which enables running many browser tasks in parallel, but there is no explicit documentation of a bulk/batch operations API (e.g., batch session creation, bulk item processing, or queue-based multi-item automation) — the evidence only shows single-session workflows orchestrated one at a time via various agent frameworks. missing for 10: explicit bulk/batch API or documented pattern for orchestrating many items at once, concurrency limits/guidance, and any hands-on evidence of large-scale parallel session usage.

   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Steel generates a session ID for you, but `create` also accepts one. Pass your own UUID when the ID has to exist before the browser does”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “This is particularly useful for maintaining authenticated states across multiple sessions, helping your AI agents access protected resources…”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round drawn
   Browser Use—none0/10

   Browser Use's evidence covers on-demand task runs, sessions, observability polling, CAPTCHA/stealth, and MCP integration, but nothing describes user-defined rules or triggers that fire actions automatically on external events (e.g., webhooks, schedules, conditional triggers). The product is presented as an agent you invoke to perform a task, not an event-driven automation/rules engine.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Run browser automation tasks from your AI coding assistant. Connect to Claude, Cursor, Windsurf, or any MCP client.”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”

   proof ↗⚑ flag

   Steel—none0/10

   Steel's evidence covers sessions, proxies, captcha solving, CLI, and agent traces, but nothing describes a rules engine or event-triggered automation (e.g., 'if X happens then do Y') that a user could configure declaratively.

   ⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   Browser Use—none0/10

   The evidence pack covers runs, sessions, observability, stealth/proxy/CAPTCHA handling, auth profiles, and MCP integration, but nowhere mentions cron-like scheduling, recurring triggers, or workflow automation over time. No docs or community evidence describe a scheduler or recurring-job feature.

   ⚑ flag

   Steel—none0/10

   Steel is a browser-automation infrastructure product (sessions, proxies, CAPTCHA solving, agent traces, integrations) with no evidence of any scheduling, cron, or recurring-workflow trigger mechanism; jobs must be invoked externally rather than scheduled by Steel itself.

   ⚑ flag

Local

1. developerRun the agent against a local browser on my own machine for development, without any cloud account

   weight 2 · round to Browser Use
   Browser Use✓fullcommunity8/10

   Browser Use ships an open-source Python library explicitly positioned for local, code-driven automation ('For a local agent, use the open-source library'; 'automate the web at scale, from your own code, and with any LLM'), and community reports confirm running it locally against a real local Chrome browser/profile without a cloud account. missing for 10: no explicit walkthrough showing zero network/account calls during local runs, and no independent benchmark of purely offline/local operation.

   • [claimed-docs] “For a local agent, use the [open-source library](/open-source/quickstart).”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”
   • [claimed-docs] “Launch a browser, connect to its CDP URL, then stop it”

   proof ↗⚑ flag

   Steel✓fullclaimed7/10

   Steel is explicitly open-source with a pre-built Docker image combining the API and UI, letting a developer self-host the browser locally, and it exposes CDP so Puppeteer/Playwright/Selenium can connect directly to a local instance without any cloud account (steel-gh-4, steel-gh-1, steel-gh-5). This directly satisfies running an agent against a local browser for development. Missing for 10: a dedicated 'local dev quickstart' doc walking through docker-compose setup and confirmation that no cloud sign-in/telemetry is required, plus independent hands-on confirmation of local-only usage.

   • [github] “Pre-built Docker Image (combined API + UI)”
   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [github] “Steel.dev is an open-source browser API that makes it easy to build AI apps and agents that interact with the web. Instead of building autom…”

   proof ↗⚑ flag

Frameworks

1. developerPlug the browser layer into agent frameworks (Claude Agent SDK, Vercel AI SDK, LangChain, CrewAI) through documented adapters

   weight 2 · round to Steel
   Browser Use—none0/10

   Evidence documents an MCP server for connecting to Claude, Cursor, or Windsurf, and a Python library for custom code, but there is no mention of documented adapters for Claude Agent SDK, Vercel AI SDK, LangChain, or CrewAI specifically.

   • [claimed-docs] “Run browser automation tasks from your AI coding assistant. Connect to Claude, Cursor, Windsurf, or any MCP client.”
   • [probe] “official MCP server documented at https://docs.browser-use.com/cloud/guides/mcp-server”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”

   proof ↗⚑ flag

   Steel✓fullclaimed8/10

   Steel documents dedicated integration guides for Claude Agent SDK, Vercel AI SDK, LangChain-adjacent (CrewAI), and other frameworks, each describing how Steel's cloud browser plugs in as a tool/MCP server for the agent loop (steel-docs-9/43, steel-docs-39, steel-docs-40, plus AgentKit, OpenAI Agents SDK, Pydantic AI). Missing for 10: explicit LangChain-named adapter page (only CrewAI/AgentKit/AI SDK/Claude named) and independent/hands-on confirmation beyond vendor docs.

   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the browser.”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session (open, navigate, snapshot, extract, return typed results)”
   • [claimed-docs] “The Steel integration exposes a Steel browser as a CrewAI tool, so your crew can search, navigate, fill forms, extract data, and validate re…”
   • [claimed-docs] “The Steel integration runs each agent's browser actions on a Steel cloud session, so AgentKit handles the orchestration and Steel handles th…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the brow”
   • [claimed-docs] “Expose a Steel browser to any MCP client”

   proof ↗⚑ flag

Models

1. developerBring my own LLM provider — the framework is model-agnostic rather than locked to one vendor's models

   weight 2 · round to Browser Use
   Browser Use✓fullcommunity8/10

   GitHub docs explicitly market the library as usable with any LLM ('Use the Python library ... with any LLM'), and independent community testing corroborates this by reporting successful use with Gemini models rather than being locked to one vendor. Missing for 10: a dedicated docs page enumerating specific supported providers/configuration examples and broader independent confirmation across multiple providers beyond Gemini.

   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [community] “From the first glance, browser-use is compatible with more models, and has (much) more github stars. Coincidentally I played with it over th…”

   proof ↗⚑ flag

   Steel✓fullclaimed7/10

   Steel documents integrations across many LLM/agent ecosystems — OpenAI Computer Use, Claude Computer Use, Gemini Computer Use, Browser-Use, CrewAI, Pydantic AI (explicitly 'provider-agnostic'), OpenAI Agents SDK, AI SDK, Claude Agent SDK — showing it is not locked to a single vendor's models and can be paired with whichever LLM the developer chooses. Missing for 10: an explicit first-party 'model-agnostic' positioning statement and independent/community corroboration of using Steel with a non-default LLM provider.

   • [claimed-docs] “Use Gemini Computer Use with Steel Browser.”
   • [claimed-docs] “Use Claude Computer Use with Steel Browser.”
   • [claimed-docs] “The OpenAI Computer Use integration runs OpenAI's vision-based agent loop on a Steel browser session.”
   • [claimed-docs] “Use Browser-Use to interact with a Steel browser.”
   • [claimed-docs] “Build a Pydantic AI browser agent on Steel cloud browsers: provider-agnostic, typed Python agents with dependency injection and real web acc…”
   • [claimed-docs] “The Steel integration runs each agent's browser actions on a Steel cloud session, so AgentKit handles the orchestration and Steel handles th…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session (open, navigate, snapshot, extract, return typed results)”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the brow”

   proof ↗⚑ flag

Tasks

1. ai agentSubmit a browser task over a hosted HTTP API and receive the result by polling or webhook, without managing any browser myself

   weight 2 · round to Browser Use
   Browser Use✓fullprobed8/10

   Docs show a hosted Cloud API (client.runs.create) that creates runs, supports polling via runs.events() with cursors, and returns structured results without the caller managing browser infrastructure (stealth, proxies, CAPTCHA solving handled server-side). Missing for 10: no explicit webhook callback mechanism is documented (only polling is shown), and no public OpenAPI spec was found to confirm full REST surface.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “V4 returns `run.result` as a string. Ask for JSON only, then validate it client-side”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”
   • [claimed-docs] “Every cloud browser session runs in a hardened Chromium fork with stealth enabled by default — no configuration needed.”
   • [claimed-docs] “Residential proxies are enabled by default across 195+ countries.”
   • [claimed-docs] “Every Browser Use Cloud browser enables automatic CAPTCHA solving.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”

   proof ↗⚑ flag

   Steel~partialclaimed4/10

   Steel's Sessions API lets an agent spin up a hosted, isolated browser via HTTP without managing browser infrastructure (steel-docs-1, steel-docs-33), and its agent-trace/timeline features (steel-docs-6, steel-docs-27, steel-docs-29) could support polling for run results. But there is no evidence of a discrete 'submit task, get result via polling/webhook' primitive — the model shown is that the agent (or a partner SDK like AgentKit/CrewAI) still drives actions step-by-step over CDP/Puppeteer/Playwright or the CLI (steel-gh-1, steel-docs-8), not a single task-submission endpoint. Missing for 10: an explicit task/job endpoint, webhook-based completion notification, and confirmation that the calling agent need not issue any browser actions itself.

   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Session Lifecycle | Steel Docs”
   • [claimed-docs] “It turns the run into a timeline of agent activity, so you can see what happened without scrubbing through the whole recording.”
   • [claimed-docs] “Agent Traces: Observability for Browser Agents | Steel Docs”
   • [claimed-docs] “Agent Traces API | Steel Docs”
   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”

   proof ↗⚑ flag

2. developerHand the product a natural-language goal and it completes a multi-step web task end to end — navigating, filling forms, and clicking through flows

   weight 3 · round to Browser Use
   Browser Use✓fullcommunity8/10

   Docs and GitHub examples show natural-language goals (e.g., "Find the top Hacker News story", "Fill in this job application") driving an agent that navigates, logs in, handles 2FA/CAPTCHA, and completes multi-step flows end-to-end via both cloud API and open-source library; community reports corroborate real-world use (e.g., LinkedIn automation). Missing for 10: independent third-party benchmark of complex multi-step task success rates and more robust evidence of reliability at scale beyond anecdotal community reports.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [github] “Task: "Fill in this job application with my resume and information."”
   • [github] “Task: "Extract structured data about my followers and export it as a CSV."”
   • [claimed-docs] “Log in once, save the profile, then reuse it to start future browsers already logged in.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Every Browser Use Cloud browser enables automatic CAPTCHA solving.”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”

   proof ↗⚑ flag

   Steel~partialclaimed6/10

   Steel provides the browser infrastructure (sessions, navigation, click/fill/extract via CLI or API) and integrates with third-party agent loops (Claude/OpenAI Computer Use, browser-use, CrewAI, OpenAI Agents SDK) that can take a natural-language goal and drive multi-step web flows end-to-end, but Steel itself explicitly delegates the 'agent loop'/reasoning to these other SDKs rather than natively interpreting NL goals itself (e.g. 'the SDK runs the agent loop and Steel handles the browser'). The CLI supports scripted end-to-end browser workflows but not NL-goal parsing on its own. missing for 10: a native Steel agent loop that itself parses an NL goal and autonomously plans/executes steps without pairing with an external agent SDK; independent hands-on evidence of a full NL-to-task run succeeding.

   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the browser.”
   • [claimed-docs] “The OpenAI Computer Use integration runs OpenAI's vision-based agent loop on a Steel browser session.”
   • [claimed-docs] “Use Browser-Use to interact with a Steel browser.”
   • [claimed-docs] “The Steel integration exposes a Steel browser as a CrewAI tool, so your crew can search, navigate, fill forms, extract data, and validate re…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”
   • [claimed-docs] “Use Gemini Computer Use with Steel Browser.”
   • [claimed-docs] “Use Claude Computer Use with Steel Browser.”

   proof ↗⚑ flag

Workflows

1. automation-engineerCompose repeatable multi-step workflows with loops, conditionals, and parameters instead of one-shot prompts

   weight 2 · round drawn
   Browser Use—none0/10

   Evidence shows Browser Use runs are essentially single natural-language task strings within a session/run model (create run, poll events, reuse session) with no documented constructs for loops, conditionals, or parameterized workflow templates; the Python library is described as scriptable but no workflow-composition API (branching, iteration, variables) is shown.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”

   proof ↗⚑ flag

   Steel—none0/10

   Steel's CLI and Sessions API support running sequential browser steps (navigate, click, extract, stop) via terminal or SDK, but nothing in the evidence describes composing workflows with loops, conditionals, or parameterized reusable flows — that logic is left to whichever orchestrating agent framework (CrewAI, AgentKit, etc.) sits on top of Steel. Missing for 10: any documentation of control-flow constructs (loops/branches), workflow parameters, or a workflow builder/editor within Steel itself.

   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [claimed-docs] “The Steel integration exposes a cloud browser as in-process MCP tools, so the SDK runs the agent loop and Steel handles the browser.”
   • [claimed-docs] “The Steel integration runs each agent's browser actions on a Steel cloud session, so AgentKit handles the orchestration and Steel handles th…”
   • [claimed-docs] “The Steel integration exposes a Steel browser as a CrewAI tool, so your crew can search, navigate, fill forms, extract data, and validate re…”

   proof ↗⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Steel
   Browser Use~partialprobed6/10

   Docs show the API (client.runs.create, sessions, events polling, structured output, live_view_url for 2FA/CAPTCHA handoff, stealth/proxy defaults) mirrors most cloud UI capabilities, and there's an official MCP server for coding-agent access, suggesting broad but not explicitly confirmed feature parity with the dashboard/UI. However there's no discoverable OpenAPI/formal API spec (404s on all candidate paths) and no explicit vendor statement that 100% of UI functionality is API-reachable; missing for 10: a canonical API reference/OpenAPI spec, and explicit parity documentation confirming every UI action (e.g., live preview manual takeover) is independently scriptable via API rather than requiring the UI.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “If the challenge remains, open the [live preview](/cloud/browser/live-preview) for human control.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Run browser automation tasks from your AI coding assistant. Connect to Claude, Cursor, Windsurf, or any MCP client.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.browser-use.com/openapi.json, https://docs.browser-use.com/swagger.json, https://docs.b…”
   • [probe] “official MCP server documented at https://docs.browser-use.com/cloud/guides/mcp-server”

   proof ↗⚑ flag

   Steel~partialprobed7/10

   Steel is API-first (Sessions API, CAPTCHA, proxies, agent traces, credentials, files all documented as API endpoints with an OpenAPI spec), and the UI features (debug URL, embed sessions, human-in-the-loop) are explicitly built to expose live sessions that are themselves API-created, suggesting strong parity. However, there's no explicit statement or independent confirmation that every UI-only feature (e.g., dashboard analytics, skill management, billing/team settings) has a documented API equivalent. missing for 10: explicit doc mapping UI dashboard features 1:1 to API endpoints, independent/hands-on confirmation of full parity, and detail on any UI-only settings lacking API access.

   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”
   • [claimed-docs] “Explore API endpoints for building on Steel.”
   • [claimed-docs] “Agent Traces: Observability for Browser Agents | Steel Docs”
   • [claimed-docs] “Session Lifecycle | Steel Docs”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.steel.dev/openapi.json — contains "openapi" key”
   • [claimed-docs] “Steel's debug URL feature allows you to implement human-in-the-loop workflows where users can directly interact with and control browser ses…”
   • [claimed-docs] “Embed Sessions](/overview/sessions-api/embed-sessions)”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round to Steel
   Browser Use—none0/10

   The evidence pack describes agent runs, sessions, CAPTCHA handling, and pricing, but nothing documents an account/data export feature (e.g., downloading all run history, sessions, or stored data in an open format) that would let a user leave the platform with their data intact; the open-source library allows self-hosting but that's a separate capability from exporting existing cloud account data.

   ⚑ flag

   Steel~partialclaimed4/10

   Steel documents exporting agent trace/session run data in open formats (markdown, JSON, ZIP with screenshots) and is itself open-source/self-hostable via Docker, giving users a path to leave the managed cloud. However there is no documentation of exporting other data types (credentials, files, session history/metadata, account data) in open formats, nor any explicit 'export everything and leave' workflow. missing for 10: full-account data export (credentials, files, session records) in open formats, documented data-portability/deletion workflow, independent confirmation of successful export/migration.

   • [claimed-docs] “Copy the run as markdown, download JSON, or grab a ZIP with markdown plus screenshots.”
   • [claimed-docs] “Agent Traces: Observability for Browser Agents | Steel Docs”
   • [github] “Pre-built Docker Image (combined API + UI)”
   • [github] “Steel.dev is an open-source browser API that makes it easy to build AI apps and agents that interact with the web. Instead of building autom…”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”

   proof ↗⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round to Steel
   Browser Use~partialprobed5/10

   The GitHub repo (browser-use/browser-use) and docs reference an 'open-source library' with a public quickstart, indicating the core Python library's source is publicly viewable, but no evidence explicitly states an open-source license (e.g., MIT/Apache) or shows license text. missing for 10: explicit license file/declaration, confirmation of license type, evidence of full source (vs. cloud API) being open.

   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [claimed-docs] “For a local agent, use the [open-source library](/open-source/quickstart).”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.browser-use.com/llms.txt # Browser Use > Documentation for Browser Use Cloud Agent and Browser API…”

   proof ↗⚑ flag

   Steel✓fullclaimed8/10

   Steel is explicitly described as an open-source browser API with a public GitHub repository (steel-dev/steel-browser) containing the source code, docs, and Docker image, confirming readable open-source code. Missing for 10: explicit statement of the license type (e.g., MIT/Apache) in the evidence pack and independent confirmation of license terms.

   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”
   • [github] “Pre-built Docker Image (combined API + UI)”
   • [github] “Steel.dev is an open-source browser API that makes it easy to build AI apps and agents that interact with the web. Instead of building autom…”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”
   • [claimed-docs] “Steel is an open source browser API that lets you control fleets of browsers in the cloud.”

   proof ↗⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round to Steel
   Browser Use✓fullprobed7/10

   Browser Use ships an open-source Python library (github.com/browser-use/browser-use) that runs locally and independently of the Cloud API, explicitly positioned as the option for self-hosted/local agents ("For a local agent, use the open-source library"), and community reports confirm running it locally connected to a real browser/profile. missing for 10: no first-party self-hosting guide covering infra/deployment (e.g. Docker, scaling), and no independent audit of parity between self-hosted and cloud feature sets (stealth, CAPTCHA solving, proxies are cloud-only per docs).

   • [claimed-docs] “For a local agent, use the [open-source library](/open-source/quickstart).”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”
   • [community] “If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for Linke…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.browser-use.com/llms.txt # Browser Use > Documentation for Browser Use Cloud Agent and Browser API…”

   proof ↗⚑ flag

   Steel✓fullcommunity8/10

   Steel is explicitly open-source (steel-dev/steel-browser repo) and ships a pre-built combined API+UI Docker image, letting an AI-native user self-host the core product; community comments corroborate its open-source nature. Missing for 10: detailed self-host deployment/config docs (scaling, storage, auth setup) beyond the single Docker image mention, and no independent hands-on report of a successful self-hosted deployment.

   • [github] “Pre-built Docker Image (combined API + UI)”
   • [github] “Steel.dev is an open-source browser API that makes it easy to build AI apps and agents that interact with the web. Instead of building autom…”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”
   • [claimed-docs] “Steel is an open source browser API that lets you control fleets of browsers in the cloud.”
   • [community] “beautiful docs + api ref! what are you using? (cool that you're doing open-source browserbase also, excited to check this out)”

   proof ↗⚑ flag

Pricing

1. developerSee transparent per-task or per-browser-hour pricing and documented rate/concurrency limits before committing

   weight 2 · round to Browser Use
   Browser Use~partialclaimed4/10

   Pricing page shows credits-based model ($5+ credits, no subscription, one-time $15 signup credit) but there is no documented per-task or per-browser-hour cost breakdown, and no documented rate/concurrency limits anywhere in the evidence. missing for 10: explicit per-task/per-browser-hour cost figures, documented rate limits, documented concurrency limits, any independent confirmation of pricing transparency.

   • [claimed-docs] “One-time $15 credit for eligible Google, GitHub or Microsoft signups ... No card required”
   • [claimed-docs] “Credits from $5. No subscription. No expiry.”

   proof ↗⚑ flag

   Steel!disputedcontradicted3/10

   There is a pricing page implied but no documented rate/concurrency limits in the evidence pack, and community evidence directly contradicts pricing transparency: a user reports the pricing page shows $59 while the docs show $99 for the developer tier, indicating inconsistent/undocumented pricing rather than clear transparent pricing. missing for 10: documented per-task or per-browser-hour rate card, documented concurrency/rate limits, resolution of the pricing discrepancy, independent confirmation of consistent current pricing.

   • [community] “Looking interesting, will definitely give it a go. Btw, there is inconsistency between pricing page and pricing on docs. Pricing page for de…”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Browser Use—none0/10

   No evidence pack material addresses data residency, region selection, or storage location controls for cloud runs/sessions; the open-source library option avoids the cloud entirely but that's not the same as choosable region/residency within the product. missing for 10: any mention of data center regions, residency options, or storage location controls.

   ⚑ flag

   Steel—none0/10

   No evidence in the pack mentions data residency, region selection, or geographic hosting options for Steel's cloud browser sessions; docs cover proxies, sessions, stealth, and integrations but never data storage location controls. Missing for 10: any mention of region selection, data residency options, or geographic data storage controls.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Browser Use—none0/10

   No evidence in the pack addresses data usage for AI model training, opt-out policies, or privacy/data-retention commitments for Browser Use Cloud or the open-source library.

   ⚑ flag

   Steel—none0/10

   No evidence pack item addresses AI training data usage or opt-out policies for Steel; the docs focus on browser automation, sessions, proxies, and integrations with no mention of a data-training privacy commitment.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Browser Use—none0/10

   No evidence of data retention controls, deletion APIs, or privacy/data-lifecycle policy documentation anywhere in the pack; only session/profile reuse and credit pricing are mentioned. missing for 10: retention policy documentation, data deletion API/UI, export/erasure controls, any privacy compliance statement.

   ⚑ flag

   Steel—none0/10

   While Steel manages sessions, credentials, and files (which could involve data retention concerns), no evidence describes user-facing controls for data retention periods, deletion policies, or GDPR-style data purging.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Browser Use—none0/10

   No evidence in the pack mentions telemetry, usage data collection, or any opt-out/privacy setting for Browser Use; the documentation and community items cover unrelated features like stealth browsing, CAPTCHA solving, and MCP integration.

   ⚑ flag

   Steel—none0/10

   No evidence pack item mentions telemetry, usage analytics, or an opt-out/opt-in setting for data collection; Steel is open-source and self-hostable which could make this a fair question, but no documentation addresses it.

   ⚑ flag

Live

1. automation-engineerWatch a session live and take human control mid-run when the agent gets stuck

   weight 2 · round to Steel
   Browser Use~partialclaimed6/10

   Docs describe a live_view_url/live preview that lets a human take control mid-run for cases like CAPTCHAs or 2FA, and events can be polled to monitor a run, which supports live-watch-and-intervene workflows. However, this is scoped to specific triggers (CAPTCHA/2FA) rather than a general 'agent gets stuck, operator takes over anytime' workflow, and there's no independent/hands-on evidence confirming smooth mid-run handoff in practice. missing for 10: general-purpose stuck-detection/handoff beyond CAPTCHA/2FA scenarios, independent hands-on confirmation of live takeover working reliably, clear UI/replay-debugging tooling details.

   • [claimed-docs] “If the challenge remains, open the [live preview](/cloud/browser/live-preview) for human control.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event]”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”

   proof ↗⚑ flag

   Steel✓fullclaimed7/10

   Steel's debug URL / human-in-the-loop feature explicitly enables watching a live session and taking direct control when an agent gets stuck, and embed-sessions support live viewing in an app context. Missing for 10: independent/hands-on verification of the live handoff experience and no detail on how control reverts to the agent after human intervention.

   • [claimed-docs] “Steel's debug URL feature allows you to implement human-in-the-loop workflows where users can directly interact with and control browser ses…”
   • [claimed-docs] “Implement Human-in-the-Loop Controls | Steel Docs”
   • [claimed-docs] “Embed Sessions](/overview/sessions-api/embed-sessions)”

   proof ↗⚑ flag

Replay

1. automation-engineerDebug a failed agent run from recorded replays — video, screenshots, step-by-step action timelines

   weight 2 · round to Steel
   Browser Use~partialclaimed4/10

   Docs describe an observability/events stream (runs.events()) for monitoring a run and a live_view_url for real-time human intervention, which could support building a step timeline, but there is no explicit mention of recorded video or screenshot capture for post-hoc replay debugging of failed runs. Missing for 10: documented video recording of sessions, screenshot capture per action, and a dedicated replay/timeline UI for past runs.

   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event], and have the user enter the code. The…”
   • [claimed-docs] “Ask the first run to stop at the 2FA screen, get its `live_view_url` from the [`browser.ready` event]”

   proof ↗⚑ flag

   Steel✓fullclaimed7/10

   Steel's Agent Traces feature explicitly turns a run into a step-by-step timeline of agent activity, and lets you export the run as markdown, JSON, or a ZIP with markdown plus screenshots, directly supporting replay debugging; session replays and debug URLs (live session view) are also documented. Missing for 10: independent/hands-on corroboration of the replay/timeline UI actually working, and explicit detail on 'video' replay format beyond the general 'session replays' tagline.

   • [claimed-docs] “It turns the run into a timeline of agent activity, so you can see what happened without scrubbing through the whole recording.”
   • [claimed-docs] “Copy the run as markdown, download JSON, or grab a ZIP with markdown plus screenshots.”
   • [claimed-docs] “Agent Traces: Observability for Browser Agents | Steel Docs”
   • [claimed-docs] “Timeline and exports | Steel Docs”
   • [claimed-docs] “Agent Traces API | Steel Docs”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”
   • [claimed-docs] “Steel's debug URL feature allows you to implement human-in-the-loop workflows where users can directly interact with and control browser ses…”

   proof ↗⚑ flag

Fleets

1. automation-engineerRun a fleet of concurrent browser sessions with documented concurrency limits and programmatic session management

   weight 2 · round to Steel
   Browser Use~partialclaimed4/10

   Docs show programmatic session/run creation (client.runs.create, session IDs holding multiple runs) and event polling for observability, implying some ability to manage sessions programmatically, but there is no documented concurrency limit, no fleet/parallel-session guidance, and no scaling architecture described. missing for 10: documented concurrency limits, guidance/examples for running multiple concurrent sessions at scale, rate-limit or quota specs, and independent evidence of parallel session management working in practice.

   • [claimed-docs] “run = client.runs.create("Find the top Hacker News story")”
   • [claimed-docs] “A **session** holds the agent’s conversation and can reuse its live browser. One session ID can contain multiple runs.”
   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”
   • [github] “Want to automate the web at scale, from your own code, and with any LLM? Use the Python library”

   proof ↗⚑ flag

   Steel~partialclaimed5/10

   Steel's Sessions API supports spinning up isolated on-demand browser sessions with programmatic lifecycle management (create, custom session IDs, session lifecycle docs) and a CLI/SDK for scripting sessions, which supports parallel automation use cases. However, there is no documented concurrency limit, fleet-level quota, or explicit guidance on running many sessions concurrently (rate limits, max parallel sessions per plan, autoscaling behavior) in the evidence provided. missing for 10: documented concurrency/session limits per plan, explicit fleet-scale orchestration guidance, benchmarks or independent confirmation of concurrent session handling at scale.

   • [claimed-docs] “the Sessions API lets your agents spin up isolated browser instances on demand. Each session maintains its own state, cookies, and storage”
   • [claimed-docs] “Steel generates a session ID for you, but `create` also accepts one. Pass your own UUID when the ID has to exist before the browser does”
   • [claimed-docs] “Session Lifecycle | Steel Docs”
   • [claimed-docs] “The Steel CLI lets you run full browser workflows from the terminal, end-to-end. You can start a browser session, navigate pages, click/fill…”
   • [github] “Uses Puppeteer and CDP for complete control over Chrome instances -- allowing you to connect using Puppeteer, Playwright, or Selenium.”

   proof ↗⚑ flag

Lifecycle

1. developerGet webhook notifications when tasks and sessions finish instead of polling for status

   weight 1 · round drawn
   Browser Use—none0/10

   Docs explicitly describe polling patterns for run status (runs.events() with cursor, polling ordered V4 events) but no webhook or callback-based notification mechanism is mentioned anywhere in the evidence pack.

   • [claimed-docs] “Poll `runs.events()` with the previous cursor to receive only new events”
   • [claimed-docs] “Poll ordered V4 events to monitor a run or build a custom UI.”

   proof ↗⚑ flag

   Steel—none0/10

   No evidence anywhere in the pack of a webhook or event-notification system for session/task completion; Steel's docs focus on session lifecycle, agent traces, and API endpoints but never mention webhooks or push notifications for status changes.

   ⚑ flag

Captcha

1. automation-engineerRely on a documented captcha stance — automatic solving, human fallback, or explicit non-support — instead of silent task failures

   weight 2 · round drawn
   Browser Use✓fullclaimed8/10

   Browser Use documents a clear captcha stance: automatic CAPTCHA solving is enabled by default for cloud Agent runs and standalone Cloud Browser sessions, with an explicit human-fallback path via the live preview if the challenge persists. This directly matches the story's requirement of a documented stance rather than silent failure. Missing for 10: independent/hands-on verification of captcha-solving success rates and explicit behavior/limits for the self-hosted open-source library (docs focus on Cloud).

   • [claimed-docs] “Every Browser Use Cloud browser enables automatic CAPTCHA solving.”
   • [claimed-docs] “If the challenge remains, open the [live preview](/cloud/browser/live-preview) for human control.”
   • [claimed-docs] “Automatic CAPTCHA solving is enabled by default for API V4 Agent runs and standalone Cloud Browser sessions.”

   proof ↗⚑ flag

   Steel✓fullclaimed8/10

   Steel documents an explicit CAPTCHA stance: automatic solving is built into the stealth mode (one flag covers detection/solving/verification) with a dedicated captcha-solving doc and API endpoints, plus human-in-the-loop debug URLs for manual takeover as fallback, avoiding silent failures. Missing for 10: independent/hands-on confirmation that captcha solving actually succeeds in practice and clearer documentation of failure/error signaling when solving fails.

   • [claimed-docs] “One flag covers detection, solving, and verification for the whole session”
   • [claimed-docs] “What Is a CAPTCHA Solver? Automatic CAPTCHA Solving API | Steel Docs”
   • [claimed-docs] “Explore API endpoints for managing Steel Captchas.”
   • [claimed-docs] “Steel's debug URL feature allows you to implement human-in-the-loop workflows where users can directly interact with and control browser ses…”
   • [claimed-docs] “Implement Human-in-the-Loop Controls | Steel Docs”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”

   proof ↗⚑ flag

Posture

1. automation-engineerPoint to the vendor's published acceptable-use and anti-abuse posture governing what its stealth and automation features may be used for

   weight 1 · round drawn
   Browser Use—none0/10

   The evidence pack documents stealth, proxy, and CAPTCHA-solving features in detail, but there is no published acceptable-use policy, terms of service, or anti-abuse statement governing what these stealth/automation features may or may not be used for; community discussion even raises security/abuse concerns without any vendor policy response cited.

   ⚑ flag

   Steel—none0/10

   The evidence pack contains extensive documentation of Steel's stealth, captcha-solving, and proxy features, but no published acceptable-use policy, anti-abuse guidelines, or terms governing what these features may legally/ethically be used for. No AUP, ToS excerpt, or abuse-prevention statement is cited anywhere in the pack.

   ⚑ flag

Stealth

1. automation-engineerEnable stealth fingerprinting and residential or geo-targeted proxies so legitimate automations aren't blocked as bots

   weight 2 · round to Steel
   Browser Use✓fullclaimed7/10

   Docs explicitly state cloud browsers run in a hardened Chromium fork with stealth enabled by default and residential proxies across 195+ countries, directly matching the story's stealth+proxy ask, with automatic CAPTCHA solving as a complementary layer. Missing for 10: explicit control/documentation for selecting a specific geo-target rather than automatic 195+ country rotation, and independent/hands-on evidence confirming bot-detection evasion actually works in practice.

   • [claimed-docs] “Every cloud browser session runs in a hardened Chromium fork with stealth enabled by default — no configuration needed.”
   • [claimed-docs] “Residential proxies are enabled by default across 195+ countries.”
   • [claimed-docs] “Every Browser Use Cloud browser enables automatic CAPTCHA solving.”
   • [claimed-docs] “Automatic CAPTCHA solving is enabled by default for API V4 Agent runs and standalone Cloud Browser sessions.”

   proof ↗⚑ flag

   Steel✓fullcommunity8/10

   Steel's docs explicitly describe stealth mode (bot-detection evasion + CAPTCHA solving in one flag) and both Managed Residential Proxies and Bring-Your-Own-Proxy options, directly matching the story; a community comment corroborates that custom proxies work. Missing for 10: explicit geo-targeting/country-selection documentation and independent hands-on verification that stealth actually reduces bot-blocking in production.

   • [claimed-docs] “One flag covers detection, solving, and verification for the whole session”
   • [claimed-docs] “Steel offers two powerful ways to use proxies: our built-in **Managed Residential Proxies** or connecting to your own proxy provider with ou…”
   • [claimed-docs] “Steel is the open-source browser API for AI agents — managed cloud browsers with stealth, residential proxies, CAPTCHA solving, persistent p…”
   • [community] “It appears you can set your own proxies to not use their cloud.”

   proof ↗⚑ flag

Extraction

1. developerExtract typed, schema-validated data (Zod/Pydantic-style) from pages the agent visits, not just raw text

   weight 3 · round drawn
   Browser Use~partialclaimed4/10

   Docs mention structured output but V4 returns `run.result` as a plain string with a recommendation to 'ask for JSON only, then validate it client-side' — there's no native Zod/Pydantic schema binding or first-party typed-schema extraction feature shown. This is a workaround rather than a built-in schema-validated extraction pipeline. missing for 10: no evidence of a documented schema/type-binding API (e.g., passing a Pydantic/Zod schema directly to the agent), no SDK-level validation helpers, no independent/hands-on confirmation that structured JSON output reliably conforms to a given schema.

   • [claimed-docs] “V4 returns `run.result` as a string. Ask for JSON only, then validate it client-side”
   • [github] “Task: "Extract structured data about my followers and export it as a CSV."”

   proof ↗⚑ flag

   Steel~partialclaimed4/10

   Steel doesn't natively expose a Zod/Pydantic-style schema-validated extraction API, but several ecosystem integrations reference typed extraction — the AI SDK integration explicitly does 'extract, return typed results' and the Pydantic AI integration builds 'typed Python agents' on Steel sessions, implying schema validation is handled by those frameworks atop Steel's browser access rather than by Steel itself. Missing for 10: first-party Steel API/SDK documentation showing a defined schema (Zod or Pydantic model) passed directly to a Steel extraction endpoint, and independent confirmation that this pattern works reliably.

   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session (open, navigate, snapshot, extract, return typed results)”
   • [claimed-docs] “Build a Pydantic AI browser agent on Steel cloud browsers: provider-agnostic, typed Python agents with dependency injection and real web acc…”
   • [claimed-docs] “The Steel integration runs each tool against a Steel cloud session, so you can stand up a typed browser agent that opens a session, navigate…”
   • [github] “Browser Tools: Exposes APIs to quick convert pages to markdown, readability, screenshots, or PDFs.”

   proof ↗⚑ flag

Files

1. developerMy agent can download files from and upload files to the sites it operates, with the artifacts retrievable afterwards

   weight 1 · round to Steel
   Browser Use—none0/10

   The evidence pack has no documentation of file upload/download handling or of artifacts being stored and retrievable after a run — GH task examples merely reference a resume being filled in and CSV export, but no confirmation these are handled as retrievable files via any API or session mechanism. Missing for 10: explicit file upload API/tooling, download/save-to-cloud-storage feature, and an artifact retrieval endpoint or docs section.

   • [github] “Task: "Fill in this job application with my resume and information."”
   • [github] “Task: "Extract structured data about my followers and export it as a CSV."”

   proof ↗⚑ flag

   Steel~partialclaimed5/10

   Steel documents a Files API endpoint for managing files on Steel sessions (steel-docs-16), plus agent traces that can be downloaded as JSON/ZIP/markdown artifacts (steel-docs-7), suggesting some upload/download and artifact retrieval capability. However, there's no detailed documentation of the file upload/download workflow itself (e.g., how an agent uploads a file to a site form or downloads a file from a page and retrieves it via API), no code examples, and no independent verification of this working end-to-end. missing for 10: detailed docs/examples of agent-initiated file upload to sites and file download from sites, confirmation of artifact retrieval workflow, independent/hands-on corroboration.

   • [claimed-docs] “Explore API Endpoints for managing Files on Steel.”
   • [claimed-docs] “Copy the run as markdown, download JSON, or grab a ZIP with markdown plus screenshots.”
   • [claimed-docs] “It turns the run into a timeline of agent activity, so you can see what happened without scrubbing through the whole recording.”

   proof ↗⚑ flag