Agent Model

Choosing the browser-use agent brain

The retry_with_browser_use_agent tool and cloud sessions run an autonomous agent loop that needs its own LLM to think with (it makes its own API calls — it does not borrow this Claude session). This skill decides which LLM, per task. The Python MCP server is deliberately dumb: it reads config and accepts an override. The judgment lives here.

Selection precedence (how the server resolves the brain)

1. Session override — set at runtime by the browser_set_agent_model tool (this turn only).
2. Configured — the "browser-use"."agentModel" block merged from settings files: ~/.claude/settings.json → <project>/.claude/settings.json → <project>/.claude/settings.local.json (local wins).
3. Legacy — if BROWSER_USE_API_KEY is set and nothing above, Browser Use's bu-latest.
4. Default — anthropic / LATEST_SONNET_MODEL (uses ANTHROPIC_API_KEY, already available).

Providers

Current Anthropic vision models: LATEST_SONNET_MODEL (default; best multi-step + vision), LATEST_OPUS_MODEL (hardest reasoning), LATEST_HAIKU_MODEL (cheapest/fastest). Confirm live ids against the claude-api skill before quoting one — do not rely on memory.

Picking a model for the task

• Simple scrape / read one page → cheap+fast is fine: LATEST_HAIKU_MODEL, or a cheap openai_compatible model.
• Multi-step form fill, navigation, recovering from popups → LATEST_SONNET_MODEL (default).
• Login / auth flows, ambiguous pages, hard reasoning → LATEST_SONNET_MODEL or LATEST_OPUS_MODEL.
• CAPTCHA / stealth / anti-bot → a cloud session (browser_start_cloud_session) with browser_use / bu-latest, which is tuned for it.
• If the user names a model or provider (Kimi, GLM, "the cheap one", "smartest"), honor it.

Suggest the right model even when it differs from their configured default — that's the point of this skill. Read the task, recommend, then apply one of the two ways below.

Two ways to apply a choice

Ephemeral (this session, reverts next launch) — call the tool:

browser_set_agent_model(provider="anthropic", model="LATEST_SONNET_MODEL")
browser_set_agent_model(provider="openai_compatible", model="moonshot-v1-128k",
                        base_url="https://api.moonshot.cn/v1", api_key_env="MOONSHOT_API_KEY")

No file edit; takes effect on the next agent call.

Persistent (per project or per user) — write to settings (relaunch the MCP server after). Use the update-config skill to edit .claude/settings.json (committed) or .claude/settings.local.json (gitignored):

{ "browser-use": { "agentModel": {
  "provider": "openai_compatible",
  "model": "glm-4.6",
  "baseUrl": "https://open.bigmodel.cn/api/paas/v4",
  "apiKeyEnv": "ZHIPU_API_KEY",
  "temperature": 0.2
} } }

Secrets — never store keys in settings

apiKeyEnv holds the name of an environment variable, never the key itself (settings.json is often committed). For a custom openai_compatible provider, that env var must also reach the MCP server: add it to the plugin's .mcp.json env block (e.g. "ZHIPU_API_KEY": "${ZHIPU_API_KEY}") and export it in your shell. ANTHROPIC_API_KEY, OPENAI_API_KEY, and BROWSER_USE_API_KEY already flow through — only custom providers need this step.