LIVE
Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|Google's AI gives evasive answers about who won the 2020 US election28/10/26 · Google|NVIDIA introduces Nemotron 3.5 Transcribe, a promptable multilingual transcription model28/10/26 · NVIDIA|OpenAI and ServiceNow deepen partnership on enterprise workflows28/10/26 · OpenAI|Failed startups' data finds a second life training AI models28/10/26|Anthropic expands AI for Science program to 20 foundations and research institutions28/10/26 · Anthropic|Anthropic shares early results on using Claude to accelerate drug development27/10/26 · Anthropic|GEPA-Curator: Filtering Failures to Better Train LLM Agents27/10/26|Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|Google's AI gives evasive answers about who won the 2020 US election28/10/26 · Google|NVIDIA introduces Nemotron 3.5 Transcribe, a promptable multilingual transcription model28/10/26 · NVIDIA|OpenAI and ServiceNow deepen partnership on enterprise workflows28/10/26 · OpenAI|Failed startups' data finds a second life training AI models28/10/26|Anthropic expands AI for Science program to 20 foundations and research institutions28/10/26 · Anthropic|Anthropic shares early results on using Claude to accelerate drug development27/10/26 · Anthropic|GEPA-Curator: Filtering Failures to Better Train LLM Agents27/10/26|
Toolofficial publisher

PayPal Agent Toolkit

Tool : A precise action the AI can call · requires paypal

PayPal orders, invoices, disputes, and subscriptions for agents.

Same rule as Stripe: never without a human in the loop.

Overall score
5.7 /10
Security: serious reservations
Utility
7.0
Reliability
6.0
Security
Serious reservations
Ease
Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Developers who want to integrate PayPal operations into an AI agent face the complexity of PayPal's REST APIs: OAuth authentication, endpoint management, and error handling. The PayPal Agent Toolkit solves this by providing ready-to-use functions that bridge AI agent frameworks and PayPal APIs.

In practice, the library installs with a single command (npm or pip), is configured with a client ID and secret from the PayPal Developer Dashboard, and exposes tools the agent can invoke via function calling. It covers 31 operations across 7 domains: orders and payments, invoices, subscriptions, catalog management, disputes, shipment tracking, and transaction reporting.

This tool is aimed at developers building automated commerce workflows: a customer service agent that creates an order when a user confirms a purchase, a billing assistant that generates and sends invoices by email, or a subscription agent that manages the full lifecycle of a recurring plan.

It is not suitable for use without human oversight on sensitive operations: PayPal does not enforce a mandatory confirmation gate before every tool call. Refunds, subscription cancellations, invoice sends, and dispute acceptances must be protected by application-level controls that the developer must build themselves.

What it offers

  • ·Order and payment management: create, capture, and refund orders via ready-to-use functions.
  • ·Intelligent invoicing: create invoices with line items, custom color themes, and a PAY_BY_BANK option, send by email, and monitor payment status.
  • ·Recurring invoice series: automatically generate and send invoices on a defined schedule.
  • ·Full subscription management: create products, set up billing plans, and process recurring payments.
  • ·Dispute management: summarize and accept dispute claims directly from the agent.
  • ·Shipment tracking: check the status of shipments linked to PayPal orders.
  • ·Merchant reporting and insights: retrieve transaction data to support data-driven decisions.
  • ·Multi-framework compatibility: works with Amazon Bedrock, CrewAI, LangChain, MCP, OpenAI Agents SDK, and Vercel AI SDK, in TypeScript and Python.

In practice

Price
Free
Commercial use
Commercial use: yes
Available on
sdk-python, sdk-ts, mcp
Host
paypal
FR interface
No

Security and data

Rating
Security: serious reservations
Capabilities
readswritesexecutesnetwork
Your data
Data with third parties
Maintenance
Active

Links and repository checked on 25 October 2026 · added on 1 April 2025

How to install PayPal Agent Toolkit

1. Install Node.js version 18 or later (for TypeScript) or Python 3.11 or later (for Python). 2. Install the toolkit: npm install @paypal/agent-toolkit (TypeScript) or pip install paypal-agent-toolkit (Python). 3. Create an application on the PayPal Developer Dashboard (developer.paypal.com) and retrieve your Client ID and Secret. 4. Store these credentials in environment variables (a .env file); never hard-code them in source files. 5. In your code, instantiate the toolkit with your credentials and an actions object limited to only the operations you need, enabling sandbox: true for testing. 6. Security note: before going to production, build human-confirmation controls for all destructive operations (refunds, cancellations, disputes), as PayPal does not enforce any automatically.
npm install @paypal/agent-toolkit

Official documentation ↗

In the same category

Whole category Automation & agents →