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Protocol for secure, interoperable AI-driven payments
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This repository provides code samples and demonstrations for the Agent Payments Protocol (AP2), designed to build a secure and interoperable future for AI-driven payments. It targets developers creating AI agents for commerce, offering a foundational protocol to enable secure and standardized payment interactions.
How It Works
The Agent Payments Protocol (AP2) establishes standards for secure and interoperable AI-driven financial transactions. While the provided samples leverage Google's Agent Development Kit (ADK) and Gemini 2.5 Flash for demonstration, the protocol itself is designed to be tool-agnostic, allowing developers to integrate with any preferred agent framework or large language model. This approach promotes flexibility and broad adoption across diverse AI agent ecosystems.
Quick Start & Requirements
uv pip install git+https://github.com/google-agentic-commerce/AP2.git@main
for the AP2 Types package.GOOGLE_API_KEY
either as an environment variable (export GOOGLE_API_KEY=your_key
) or within a .env
file at the repository root.samples/python/scenarios/your-scenario-name/
) and run the provided run.sh
script, which handles dependency installation and agent startup. Follow individual scenario READMEs for detailed instructions.Highlighted Details
Maintenance & Community
No specific details regarding maintainers, community channels, or project roadmaps are provided in the available documentation.
Licensing & Compatibility
The license type is not explicitly stated in the provided information.
Limitations & Caveats
The repository primarily offers code samples and demonstrations; the protocol's full implementation details may require further development. While the protocol is tool-agnostic, the provided samples rely on specific technologies like ADK and Gemini 2.5 Flash, which might influence initial understanding or setup. The AP2 Types package is currently distributed directly from GitHub, with a PyPI package planned for future release, indicating potential for evolving distribution methods.
1 day ago
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