Metadata-Version: 2.4
Name: a2apay
Version: 1.0.0
Summary: Secure, modular microtransaction framework for agent-to-agent (A2A) and agent-to-resource (MCP) payments.
Home-page: https://github.com/DwirefS/a2a_payments_framework
Author: Dwiref Sharma
Author-email: dwirefz@hotmail.com
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pytest
Requires-Dist: flake8
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# A2A Payments Framework

![build](https://img.shields.io/badge/build-passing-brightgreen)
![license](https://img.shields.io/badge/license-MIT-blue)
![python](https://img.shields.io/badge/python-3.8%2B-blue)

---

## 🚀 Purpose & Philosophy

### 🤝 Why Agent-to-Agent Payments Using Crypto?
As AI agents scale exponentially in number — soon outnumbering humans — they will need to operate autonomously across various ecosystems. Crypto-based payments enable:
- ⚡ Real-time, machine-speed execution
- 🧾 Immutable, tamper-proof logs
- 💸 Microtransactions at fractional costs
- 🔍 Granular cost management at scale
- 🔐 Secure transfers using API-based stablecoins (e.g., RLUSD, USDC)

These capabilities unlock full autonomy: any agent can pay or get paid based on its usefulness, anywhere in the network — even outside its owner's subscription.

The A2A Payments Framework ensures that **as the agent-verse grows**, there is a secure and scalable way for these agents to collaborate economically.

> "Let the machinekind get paid for its work. Autonomy isn't complete until value can flow."


As the adoption of AI agents continues to accelerate, a more organic and autonomous digital ecosystem is emerging. In this new paradigm, agents will often need to collaborate across boundaries — requesting data, services, or compute from other agents or providers that are **outside their originating user's paid subscription environment**.

This introduces a real-world challenge: **How can an agent under User A's account interact with and compensate an agent or DSP that isn't part of User A's billing model?**

The A2A Payments Framework addresses this challenge by offering a secure, modular way for agents to transact using standardized messaging and token-based microtransactions. With this framework, any agent — regardless of its host or subscription tier — can interact with any other agent or resource provider, whether internal or external.

### 🔍 Market Gap
Current agent frameworks (LangChain, AutoGen, DSP prototypes) focus on **communication and orchestration**, but none provide a **built-in payment or value-exchange layer**. This leaves a gap in real-world deployment for commercial or cross-tenant use cases.

The A2A Payments Framework fills that gap by:
- Providing wallet and payment primitives
- Enabling agent-to-agent billing
- Logging and validating transactions
- Allowing integrations with stablecoin platforms (RLUSD, USDC)

This sets the foundation for **economic trust and autonomy** in machine-to-machine interactions.


**What is a DSP?**
A *Decentralized Service Provider (DSP)* is a node, agent, or infrastructure component offering services (APIs, datasets, model inferences) in exchange for payment. DSPs live outside a centralized billing model and are paid per use — ideal for agent-based transactions across ecosystems.

The A2A Payments Framework enables agents to programmatically access and pay DSPs securely, in real time. Imagine:
- An agent using a decentralized model hosted by a GPU DSP
- A learning agent fetching new training data from a knowledge DSP
- A workflow bot outsourcing a task to a translation DSP

This framework allows these interactions to happen **autonomously and fairly.**

**A2A Payments Framework** is designed to be the first plug-and-play microtransaction protocol for autonomous AI agents. It enables machine-to-machine payments between:

- 🤖 Autonomous agents (A2A)
- 🧠 MCP-style resource providers (data, APIs, tools)

The vision: enable agents to **complete tasks, get paid**, and **pay resource providers** without human intervention, across organizational and agentic boundaries. It's built to support **stablecoin payments** (mocked now, with RLUSD/USDC in roadmap), securely and modularly.

> "Just like humans exchange value for work, agents must too. This framework begins that economy."

---

## 🏗️ Architecture Overview

```text
[Agent A] --(task request)--> [Agent B / Resource Provider]
    |                                  |
    |<--(result/response)------------- |
    |                                  |
    |--(validate result & pay)-------->|
```

Agents can:
- Request a task or resource
- Validate successful result
- Trigger secure, verifiable payment

Payments are made using **mock wallets** and logged as receipts, with extensibility for **real APIs** like Ripple RLUSD or Circle USDC.

---

## 📂 Project Structure

See repository file breakdown above.

Each module:
- `agent.py` — Handles task requests/responses
- `wallet.py` — Wallet + transaction logging logic
- `protocol.py` — Message format for tasks/payments
- `transaction.py` — Persistent log for transactions
- `resource.py` — Provides MCP-style static data
- `demo.py` — End-to-end task → result → payment simulation

Tests:
- `test_wallet.py`, `test_agent_flow.py`, `test_resource_flow.py`, `test_transactions.py`

---

## 🧪 How to Run It

```bash
# 1. Install dependencies
pip install -r requirements.txt

# 2. Run the example
python examples/demo.py

# 3. Run tests
pytest tests/
```

---

## 🔐 Security Notes

- Payments only occur **after result validation**
- All transactions use structured `protocol.py` messages
- Every transfer is logged with `uuid`, `timestamp`, and `memo`
- Future: integrate Azure Key Vault, Blob Storage, RLUSD

---

## 🌐 Roadmap

- [ ] DSP integration layer and marketplace interface

- [ ] Real RLUSD integration via API
- [ ] Azure Key Vault + Blob logging
- [ ] Agent reputation scoring + fraud protection
- [ ] Decentralized ledger simulator (optional)
- [ ] LangChain + AutoGen drop-in modules
- [ ] Task escrow, milestone-based payments
- [ ] GitHub Actions + CI/CD for repo

---

## 🤝 Contribute & Collaborate

Want to make this framework better?
- Fork the repo
- Add new modules: RLUSD, real wallets, agents, auth layers
- File issues or open PRs

📫 DM [@DwirefS](https://github.com/DwirefS) or reach out for deeper collabs — this is the **economic infrastructure of the machine world**.

> “One day, your agent will get paid for its work — because you built the system that made it possible.”

---

## 🛠 CI/CD & Quality

This project includes a GitHub Actions workflow for continuous integration:
- ✅ Build and test on push/pull requests to `main`
- 🧪 Run `pytest` across all test modules
- 🧼 Code linting with `flake8`
- 📦 Supports Python 3.8 – 3.10

### 🧰 GitHub Actions Workflow
See `.github/workflows/python-ci.yml` for details.

```yaml
on:
  push:
    branches: ["main"]
  pull_request:
    branches: ["main"]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.8, 3.9, 3.10]
    steps:
    - uses: actions/checkout@v3
    - uses: actions/setup-python@v4
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
        pip install flake8
    - name: Run tests
      run: pytest tests/
    - name: Lint code
      run: flake8 a2a/ examples/ tests/
```

---

## 🔗 License
MIT

---

Happy building. Let agents earn. Let machines trade.
Let the economy evolve.

💡 Powered by SapientEdge x OpenAI
