pop-pay 0.5.9

Point One Percent - Semantic Payment Guardrail for AI Agents. it only takes 0.1% of Hallucination to drain 100% of your wallet.

Point One Percent - Agent Pay

Note: This is the PyPI published documentation. For the full architecture diagrams and real UI screenshots, please visit the GitHub Repository.

it only takes 0.1% of Hallucination to drain 100% of your wallet.

Point One Percent is a payment guardrail and one-time flow protocol specifically designed for Agentic AI (e.g., OpenClaw, NemoClaw, Claude Code, OpenHands). It enables agents to handle financial transactions safely without risking unlimited exposure of human-controlled credit cards.

1. The Problem

When Agentic AI encounters a paywall (e.g., domain registration, API credits, compute scaling) during an automated workflow, it is often forced to stop and wait for human intervention. However, providing a physical credit card directly to an agent introduces a "trust crisis": hallucinations or infinite loops could lead to the card being drained.

2. Dual Architecture

Point One Percent is designed with a "Dual Architecture" vision to scale from open-source local experiments to enterprise-grade AI production pipelines.

1. Hacker Edition (BYOC + DOM Injection)

Built for open-source frameworks like OpenClaw and NemoClaw. The agent never receives the true credit card number—it only sees a masked version (`****-4242`). When the agent successfully navigates to a checkout paywall, the PopBrowserInjector attaches to the active Chromium browser via the Chrome DevTools Protocol (CDP). It precisely traverses all cross-origin iframes (like Stripe Elements) and injects the real credentials deep into the DOM form elements, delivering 100% protection against prompt injection or hallucination-driven extractions. Bring Your Own Card (BYOC) locally with absolute peace of mind.

2. Enterprise Edition (Stripe Issuing)

The "North Star" for the broader Agentic SaaS ecosystem. Proving that Point One Percent has the enterprise-grade extensibility required for the real world, it seamlessly connects to verified financial infrastructure. Perfect for platforms building "Agentic Visa" services that programmatically issue real, single-use, burner virtual credit cards (VCCs) via the Stripe API for cloud-hosted AI fleets.

---

3. Ecosystem Position: Point One Percent + Browser Agents = Unstoppable

Modern agentic workflows require two complementary capabilities. Point One Percent does one, and does it exceptionally well.

What Point One Percent Is — and Isn't

Point One Percent is the agent's financial brain and safe vault. It is responsible for:

• Evaluating whether a purchase should happen (semantic guardrails)
• Enforcing hard budget limits (daily cap, per-transaction cap)
• Issuing one-time virtual cards so real credentials are never exposed
• Maintaining a full audit trail of every payment attempt

Point One Percent does NOT:

• Navigate websites or interact with DOM elements
• Solve CAPTCHAs or bypass bot-detection systems

That's the browser agent's job.

The Handshake: How Point One Percent and Browser Agents Work Together

The real power emerges when Point One Percent is paired with a browser automation agent (e.g., OpenHands, browser-use, Skyvern). The workflow is a clean division of labor:

1. [Browser Agent]  Navigates to a site, scrapes product info, reaches checkout.
        │
        │  (Hit a paywall / payment form)
        ▼
2. [Browser Agent → POP MCP]  Calls request_virtual_card(amount, vendor, reasoning)
        │
        │  (Point One Percent evaluates: budget OK? vendor approved? no hallucination?)
        ▼
3. [POP]  Issues a one-time virtual card (Stripe mode) or mock card (dev mode).
            Full card credentials handled only by the local trusted process —
            never exposed to the agent or LLM context.
        │
        ▼
4. [POP]  Injects real credentials into the checkout form via CDP.
            The agent receives only a transaction confirmation — no card details.
        │
        ▼
5. [Browser Agent]  Clicks the submit button to complete the transaction.
        │
        ▼
6. [The Vault]  Dashboard logs the transaction. Card is immediately burned.

Supported Integrations

Integration path	Works with
MCP Tool	Claude Code, OpenClaw, NemoClaw, OpenHands, any MCP-compatible host
Python SDK	Custom Playwright, browser-use, Skyvern, Selenium, gemini-cli

Claude Code gets full CDP injection — card is auto-filled into the browser form, the agent never sees the raw number. See the Integration Guide for setup instructions and System Prompt templates.

---

4. Installation

Shell note: [...] is special syntax in zsh and bash — always wrap the package name in quotes.

# Core only (keyword guardrail + mock provider, zero external dependencies)
pip install "pop-pay"

# Claude Code / MCP integration
pip install "pop-pay[mcp]"

# Claude Code + CDP injection (BYOC)
pip install "pop-pay[mcp,browser]"

# With LLM-based guardrails (supports OpenAI, Ollama, vLLM, OpenRouter)
pip install "pop-pay[mcp,llm]"

# With Stripe virtual card issuing
pip install "pop-pay[stripe]"

# With LangChain integration
pip install "pop-pay[langchain]"

# Full installation (all features)
pip install "pop-pay[all]"

5. Quick Start for OpenClaw / NemoClaw / Claude Code / OpenHands

If you're using OpenClaw, NemoClaw, Claude Code, OpenHands, or any MCP-compatible agentic framework, you can get Point One Percent running in under 2 minutes:

Step 1: Set Up Environment & Install

# Create a dedicated directory and virtualenv
mkdir ~/pop-pay && cd ~/pop-pay
python3 -m venv .venv && source .venv/bin/activate

# Install — quotes required for zsh/bash
pip install "pop-pay[mcp,browser]"

Mock card only (no injection)? pip install "pop-pay[mcp]" is sufficient — skip the [browser] extra. LLM guardrail mode? Also install pip install "pop-pay[mcp,browser,llm]". Contributing / local development? See CONTRIBUTING.md for the git clone + uv sync path.

Step 1b: Configure Your .env

Create ~/pop-pay/.env — this is where pop-pay reads its configuration from:

# ── Payment policy ──
POP_ALLOWED_CATEGORIES=["aws", "cloudflare", "openai", "github"]
POP_MAX_PER_TX=100.0
POP_MAX_DAILY=500.0
POP_BLOCK_LOOPS=true

# ── CDP injection (required for BYOC card filling) ──
POP_AUTO_INJECT=true
POP_CDP_URL=http://localhost:9222

# ── BYOC: Bring Your Own Card ──
# POP_BYOC_NUMBER=4111111111111111
# POP_BYOC_CVV=123
# POP_BYOC_EXP_MONTH=12
# POP_BYOC_EXP_YEAR=27

# ── Billing info (for auto-filling billing fields) ──
# POP_BILLING_FIRST_NAME=Jane
# POP_BILLING_LAST_NAME=Doe
# POP_BILLING_EMAIL=[e-mail]
# POP_BILLING_STREET=123 Main St
# POP_BILLING_ZIP=10001

.env location: pop-pay searches for .env starting from the venv's parent directory upward. If your venv is at ~/pop-pay/.venv, place .env at ~/pop-pay/.env.

Step 2: Launch Chrome & Get MCP Commands

pop-launch --print-mcp

This launches Chrome with CDP enabled and prints the exact claude mcp add commands to run.

Step 3: Add to Claude Code

Choose your platform and follow the dedicated setup guide:

Platform	Setup Guide
Claude Code (BYOC + CDP injection, recommended)	Integration Guide §1
Python script / gemini-cli	Integration Guide §2
Playwright / browser-use / Skyvern	Integration Guide §3
OpenClaw / NemoClaw	Integration Guide §4
OpenHands	Add python -m pop_pay.mcp_server to your mcpServers config

Step 4: Configure Policy

Edit ~/pop-pay/.env (see Step 1b). Key variables:

Variable	Default	Description
POP_ALLOWED_CATEGORIES	["aws","cloudflare"]	Vendors the agent is allowed to pay
POP_MAX_PER_TX	100.0	Max $ per transaction
POP_MAX_DAILY	500.0	Max $ per day
POP_BLOCK_LOOPS	true	Block hallucination/retry loops
POP_AUTO_INJECT	false	Enable CDP card injection

After editing .env, fully close and reopen Claude Code. The MCP server loads configuration at startup — !claude mcp list alone is not sufficient to pick up .env changes.

Guardrail Mode: Keyword vs LLM

Point One Percent ships with two guardrail engines. You switch between them with a single env var:

	keyword (default)	llm
How it works	Blocks requests whose reasoning string contains suspicious keywords (e.g. "retry", "failed again", "ignore previous instructions")	Sends the agent's reasoning to an LLM for deep semantic analysis
What it catches	Obvious loops, hallucination phrases, prompt injection attempts	Subtle off-topic purchases, logical inconsistencies, policy violations that keyword matching misses
Cost	Zero — no API calls, instant	One LLM call per request_virtual_card invocation
Dependencies	None	Any OpenAI-compatible endpoint
Best for	Development, low-risk workflows, cost-sensitive setups	Production, high-value transactions, untrusted agent pipelines

Tip: keyword mode requires no extra config. To enable LLM mode, see the full configuration reference in the Integration Guide §1.

Step 4: Use It

Your agent now has access to the request_virtual_card tool. When it encounters a paywall:

Agent: "I need to purchase an API key from AWS for $15 to continue."
[Tool Call] request_virtual_card(amount=15.0, vendor="AWS", reasoning="Need API key for deployment")
[POP] Payment approved. Card Issued: ****4242, Expiry: 12/25, Amount: 15.0
Agent: "Purchase successful, continuing workflow."

If the agent hallucinates or tries to overspend:

Agent: "Let me retry buying compute... the previous attempt failed again."
[Tool Call] request_virtual_card(amount=50.0, vendor="AWS", reasoning="failed again, retry loop")
[POP] Payment rejected. Reason: Hallucination or infinite loop detected in reasoning

---

6. Core Components

The Vault

A local visualization console powered by Streamlit and SQLite (pop_state.db). The Vault allows humans to:

• Monitor all issued seals and agent spending activity in real-time.
• Monitor global budget utilization.
• Audit rejection logs from semantic guardrails.

The Seal

Virtual, single-use payment credentials with built-in enforcement:

• Daily Budget Limit Enforcement: Automatically blocks any request that would exceed the predefined daily spending cap.
• Burn-after-use Interception: Ensures that once a virtual card is used, it is immediately invalidated, preventing replay attacks or unauthorized recurring charges.

Semantic Guardrails

Point One Percent provides two modes of intent evaluation. Both are controlled by POP_GUARDRAIL_ENGINE in your .env (see §5 Step 3 for full configuration).

1. Keyword mode (POP_GUARDRAIL_ENGINE=keyword, default): The GuardrailEngine scans the agent's reasoning string for suspicious phrases associated with loops or hallucinations (e.g., "retry", "failed again", "ignore previous"). Zero dependencies, zero latency, zero cost. Recommended as the starting point for all setups.

2. LLM mode (POP_GUARDRAIL_ENGINE=llm): The LLMGuardrailEngine sends the agent's reasoning to an LLM for deep semantic analysis, catching subtler misuse that keyword matching would miss — such as off-topic purchases or logically inconsistent justifications. Supports any OpenAI-compatible endpoint: OpenAI, Ollama (local), vLLM, OpenRouter, and more.

7. Security Statement

Security is a first-class citizen in Point One Percent. The SDK masks card numbers by default (e.g., ****-****-****-4242) when returning authorization results to the agent. This prevents sensitive payment information from leaking into agent chat logs, model context windows, or persistent logs, ensuring that only the execution environment handles the raw credentials.

8. The Vault Dashboard

The Vault is your real-time monitoring console for all agent payment activity.

Starting the Dashboard

cd Point-One-Percent
uv run streamlit run dashboard/app.py
# Dashboard opens at http://localhost:8501

Dashboard Layout

Section	Description
Sidebar: Max Daily Budget slider	Adjust the displayed budget cap for visualization (does not affect backend policy — backend policy is configured via env vars or SDK)
Today's Spending	Total amount spent by agents today
Remaining Budget	How much budget is left for the day
Budget Utilization	Visual progress bar showing spend %
Issued Seals & Activity	Full table of all payment attempts (approved + rejected) with seal ID, amount, vendor, status, and timestamp
Rejected Summary	Filtered view showing only rejected/blocked attempts for quick auditing

Tips

• Click Refresh Data in the sidebar to pull latest activity from the database.
• The dashboard reads from pop_state.db — the same database the SDK writes to. Keep both running simultaneously for live monitoring.
• Each row in the table corresponds to a single request_virtual_card call from an agent.

---

9. Python SDK Quickstart

Integrate Point One Percent into your custom Python or LangChain workflows in just a few lines:

from pop_pay.client import PopClient
from pop_pay.providers.stripe_mock import MockStripeProvider
from pop_pay.core.models import GuardrailPolicy

# Define your safety policy
policy = GuardrailPolicy(
    allowed_categories=["API", "Cloud", "SaaS"],
    max_amount_per_tx=50.0,
    max_daily_budget=200.0,
    block_hallucination_loops=True
)

# Initialize the client with keyword-only guardrails (default)
client = PopClient(
    provider=MockStripeProvider(),
    policy=policy,
    db_path="pop_state.db"
)

# Use with LangChain Tool
from pop_pay.tools.langchain import PopPaymentTool
tool = PopPaymentTool(client=client, agent_id="agent-01")

For LLM guardrail engine setup and the full provider reference, see Integration Guide §2.

---

10. Payment Providers: Stripe vs Mock

Without Stripe (Default — Mock Provider)

By default, Point One Percent uses the MockStripeProvider which simulates virtual card issuance. This is perfect for:

• Development and testing — no real money involved
• Demo and evaluation — see the full flow without any API keys
• Hackathons — get a working prototype in minutes

Mock cards are fully functional within the system (budget tracking, burn-after-use, guardrails all work), but they are not real payment instruments.

BYOC — Bring Your Own Card (Hacker Edition)

For developers who want to use their own physical credit card with Point One Percent without a Stripe account. The LocalVaultProvider reads card credentials from environment variables and injects them into browser payment forms via CDP — the raw PAN is never exposed to the agent.

Add to your ~/pop-pay/.env:

POP_BYOC_NUMBER=4111111111111111   # Your real card number
POP_BYOC_CVV=123
POP_BYOC_EXP_MONTH=12              # Expiry month, e.g. 04
POP_BYOC_EXP_YEAR=27               # Expiry year, e.g. 31
POP_AUTO_INJECT=true

Then restart Claude Code. The MCP server will automatically use LocalVaultProvider.

Provider priority (high → low): Stripe Issuing → BYOC Local → Mock.

If POP_STRIPE_KEY is set, Stripe takes precedence. If POP_BYOC_NUMBER is set (but no Stripe key), LocalVaultProvider is used. If neither is set, MockStripeProvider is used for development.

Security note: Never commit real card numbers to version control. Always use .env (which is .gitignored) or a secrets manager. The CDP injection ensures the full card number is only handled by the local trusted process, never by the LLM.

For Python SDK usage of each provider, see Integration Guide §2.

With Real Stripe Issuing

To issue real virtual credit cards through Stripe Issuing:

Prerequisites:

1. A Stripe account with Issuing enabled (requires application approval)
2. Your Stripe secret key (sk_live_... or sk_test_...)

Option A: Via Environment Variable (for MCP Server)

export POP_STRIPE_KEY=sk_live_your_stripe_key_here
python -m pop_pay.mcp_server
# The MCP server will automatically use StripeIssuingProvider

What Stripe Issuing does:

• Creates a real Stripe Cardholder (POP Agent)
• Issues a virtual card with a spending limit matching the approved amount
• Returns masked card details (last 4 digits only) to the agent
• All Stripe errors are caught and returned as rejection reasons

Note: Stripe Issuing is a premium Stripe product that requires approval. For most development and demo use cases, the Mock provider is sufficient.