predicatelabs 1.0.5

Python SDK for Sentience AI Agent Browser Automation

Predicate Python SDK

A verification & control layer for AI agents that operate browsers

Predicate is built for AI agent developers who already use Playwright / CDP / browser-use / LangGraph and care about flakiness, cost, determinism, evals, and debugging.

Often described as Jest for Browser AI Agents - but applied to end-to-end agent runs (not unit tests).

The core loop is:

Agent → Snapshot → Action → Verification → Artifact

What Predicate is

• A verification-first runtime (AgentRuntime) for browser agents
• Treats the browser as an adapter (Playwright / CDP / browser-use); AgentRuntime is the product
• A controlled perception layer (semantic snapshots; pruning/limits; lowers token usage by filtering noise from what models see)
• A debugging layer (structured traces + failure artifacts)
• Enables local LLM small models (3B-7B) for browser automation (privacy, compliance, and cost control)
• Keeps vision models optional (use as a fallback when DOM/snapshot structure falls short, e.g. <canvas>)

What Predicate is not

• Not a browser driver
• Not a Playwright replacement
• Not a vision-first agent framework

Install

pip install predicatelabs
playwright install chromium

Conceptual example (why this exists)

In Predicate, agents don’t “hope” an action worked.

• Every step is gated by verifiable UI assertions
• If progress can’t be proven, the run fails with evidence (trace + artifacts)
• This is how you make runs reproducible and debuggable, and how you run evals reliably

Quickstart: a verification-first loop

This is the smallest useful pattern: snapshot → assert → act → assert-done.

import asyncio

from predicate import AgentRuntime, AsyncPredicateBrowser
from predicate.tracing import JsonlTraceSink, Tracer
from predicate.verification import exists, url_contains


async def main() -> None:
    tracer = Tracer(run_id="demo", sink=JsonlTraceSink("trace.jsonl"))

    async with AsyncPredicateBrowser() as browser:
        page = await browser.new_page()
        await page.goto("https://example.com")

        runtime = await AgentRuntime.from_sentience_browser(
            browser=browser,
            page=page,
            tracer=tracer,
        )

        runtime.begin_step("Verify homepage")
        await runtime.snapshot()

        runtime.assert_(url_contains("example.com"), label="on_domain", required=True)
        runtime.assert_(exists("role=heading"), label="has_heading")

        runtime.assert_done(exists("text~'Example'"), label="task_complete")


if __name__ == "__main__":
    asyncio.run(main())

PredicateDebugger: attach to your existing agent framework (sidecar mode)

If you already have an agent loop (LangGraph, browser-use, custom planner/executor), you can keep it and attach Predicate as a verifier + trace layer.

Key idea: your agent still decides and executes actions — Predicate snapshots and verifies outcomes.

from predicate import PredicateDebugger, create_tracer
from predicate.verification import exists, url_contains


async def run_existing_agent(page) -> None:
    # page: playwright.async_api.Page (owned by your agent/framework)
    tracer = create_tracer(run_id="run-123")  # local JSONL by default
    dbg = PredicateDebugger.attach(page, tracer=tracer)

    async with dbg.step("agent_step: navigate + verify"):
        # 1) Let your framework do whatever it does
        await your_agent.step()

        # 2) Snapshot what the agent produced
        await dbg.snapshot()

        # 3) Verify outcomes (with bounded retries)
        await dbg.check(url_contains("example.com"), label="on_domain", required=True).eventually(timeout_s=10)
        await dbg.check(exists("role=heading"), label="has_heading").eventually(timeout_s=10)

SDK-driven full loop (snapshots + actions)

If you want Predicate to drive the loop end-to-end, you can use the SDK primitives directly: take a snapshot, select elements, act, then verify.

from predicate import PredicateBrowser, snapshot, find, click, type_text, wait_for


def login_example() -> None:
    with PredicateBrowser() as browser:
        browser.page.goto("https://example.com/login")

        snap = snapshot(browser)
        email = find(snap, "role=textbox text~'email'")
        password = find(snap, "role=textbox text~'password'")
        submit = find(snap, "role=button text~'sign in'")

        if not (email and password and submit):
            raise RuntimeError("login form not found")

        type_text(browser, email.id, "[e-mail]")
        type_text(browser, password.id, "password123")
        click(browser, submit.id)

        # Verify success
        ok = wait_for(browser, "role=heading text~'Dashboard'", timeout=10.0)
        if not ok.found:
            raise RuntimeError("login failed")

Capabilities (lifecycle guarantees)

Controlled perception

• Semantic snapshots instead of raw DOM dumps
• Pruning knobs via SnapshotOptions (limit/filter)
• Snapshot diagnostics that help decide when “structure is insufficient”

Constrained action space

• Action primitives operate on stable IDs / rects derived from snapshots
• Optional helpers for ordinality (“click the 3rd result”)

Verified progress

• Predicates like exists(...), url_matches(...), is_enabled(...), value_equals(...)
• Fluent assertion DSL via expect(...)
• Retrying verification via runtime.check(...).eventually(...)

Scroll verification (prevent no-op scroll drift)

A common agent failure mode is “scrolling” without the UI actually advancing (overlays, nested scrollers, focus issues). Use AgentRuntime.scroll_by(...) to deterministically verify scroll had effect via before/after scrollTop.

runtime.begin_step("Scroll the page and verify it moved")
ok = await runtime.scroll_by(
    600,
    verify=True,
    min_delta_px=50,
    label="scroll_effective",
    required=True,
    timeout_s=5.0,
)
if not ok:
    raise RuntimeError("Scroll had no effect (likely blocked by overlay or nested scroller).")

Explained failure

• JSONL trace events (Tracer + JsonlTraceSink)
• Optional failure artifact bundles (snapshots, diagnostics, step timelines, frames/clip)
• Deterministic failure semantics: when required assertions can’t be proven, the run fails with artifacts you can replay

Framework interoperability

• Bring your own LLM and orchestration (LangGraph, AutoGen, custom loops)
• Register explicit LLM-callable tools with ToolRegistry

ToolRegistry (LLM-callable tools)

Predicate can expose a typed tool surface for agents (with tool-call tracing).

from predicate.tools import ToolRegistry, register_default_tools

registry = ToolRegistry()
register_default_tools(registry, runtime)  # or pass a ToolContext

# LLM-ready tool specs
tools_for_llm = registry.llm_tools()

Permissions (avoid Chrome permission bubbles)

Chrome permission prompts are outside the DOM and can be invisible to snapshots. Prefer setting a policy before navigation.

from predicate import AsyncPredicateBrowser, PermissionPolicy

policy = PermissionPolicy(
    default="clear",
    auto_grant=["geolocation"],
    geolocation={"latitude": 37.77, "longitude": -122.41, "accuracy": 50},
    origin="https://example.com",
)

async with AsyncPredicateBrowser(permission_policy=policy) as browser:
    ...

If your backend supports it, you can also use ToolRegistry permission tools (grant_permissions, clear_permissions, set_geolocation) mid-run.

Downloads (verification predicate)

If a flow is expected to download a file, assert it explicitly:

from predicate.verification import download_completed

runtime.assert_(download_completed("report.csv"), label="download_ok", required=True)

Debugging (fast)

• Manual driver CLI (inspect clickables, click/type/press quickly):

predicate driver --url https://example.com

• Verification + artifacts + debugging with time-travel traces (Predicate Studio demo):

If the video tag doesn’t render in your GitHub README view, use this link: sentience-studio-demo.mp4

• Predicate SDK Documentation: https://predicatelabs.dev/docs

Integrations (examples)

• Browser-use: examples/browser-use
• LangChain: examples/lang-chain
• LangGraph: examples/langgraph
• Pydantic AI: examples/pydantic_ai