platform-adapters/
├── Recurr/               # Tier 2 — standing-authority recurring payments (per-chain
│                         #          wallet-side flows + chain-agnostic merchant examples).
│                         #          Lives alongside the language adapters which carry
│                         #          the merchant HTTP wrappers (native-python v1.2.0+).
├── woocommerce/          # WooCommerce plugin (single PHP file)
├── opencart/             # OpenCart 4 extensions (hosted + wallet)
├── prestashop/           # PrestaShop 8 modules (hosted + wallet)
├── shopware/             # Shopware 6 plugin (Symfony handlers)
├── native-php/           # Framework-free PHP adapter
├── native-python/        # Stdlib-only Python adapter
├── native-go/            # Stdlib-only Go package
├── native-rust/          # Zero-crate Rust library
├── native-typescript/    # Single-file TS adapter (Node 18+/Bun/Deno/browsers/Edge)
├── magento2/             # Magento 2 / Adobe Commerce module (PHP)
├── amazon-mws/           # Amazon SP-API webhook adapter (Python)
├── tiktok-shop/          # TikTok Shop Open Platform adapter (Python)
├── squarespace/          # Squarespace Commerce webhook adapter (Python)
├── wix/                  # Wix Payment Provider SPI (Velo)
├── mpp-adapter/          # MPP server middleware (Python)
├── ap2-adapter/          # AP2 server middleware (Python)
├── shopify-app/          # Private — Shopify payment app (Cloudflare Pages, not distributed)
├── x402-widget/          # Embeddable payment widget (Web Component)
│
│   — Live-tested Python webhook adapters (4 original chains verified 2026-04-14; Base + Solana added 2026-04-22/23; Tempo added 2026-04-23) —
├── allegro/              # Allegro marketplace (Poland / CEE)
├── bigcommerce/          # BigCommerce webhook adapter (partial — see note)
├── bolcom/               # Bol.com (Netherlands / Belgium)
├── cdiscount/            # Cdiscount (France / Belgium)
├── cex/                  # CeX (webstore operator bypass)
├── discord/              # Discord interactions payment adapter (Ed25519 — needs real app keypair)
├── ebay/                 # eBay Platform Notifications adapter
├── ecwid/                # Ecwid / Lightspeed E-Series adapter
├── etsy/                 # Etsy webhook adapter
├── faire/                # Faire B2B wholesale adapter (docs only — API approval required)
├── flipkart/             # Flipkart Seller API adapter (India)
├── freshbooks/           # FreshBooks invoice payment adapter
├── instagram-shops/      # Instagram & Facebook Shops adapter
├── jumia/                # Jumia seller adapter (docs only — no webhook endpoint)
├── lazada/               # Lazada open platform adapter (SE Asia)
├── mercadolibre/         # Mercado Libre adapter (Latin America)
├── myob/                 # MYOB AccountRight poll-based adapter
├── onbuy/                # OnBuy marketplace adapter (UK)
├── printful/             # Printful print-on-demand adapter
├── printify/             # Printify print-on-demand adapter (docs only — no webhook endpoint)
├── quickbooks-online/    # QuickBooks Online invoice adapter
├── rakuten/              # Rakuten marketplace adapter
├── sage-business-cloud/  # Sage Business Cloud invoice adapter
├── shopee/               # Shopee open platform adapter (SE Asia)
├── telegram/             # Telegram Bot payment adapter
├── tokopedia/            # Tokopedia seller adapter (Indonesia)
├── truelayer/            # TrueLayer open banking adapter (ES512 — needs real signing key)
├── walmart/              # Walmart Marketplace adapter
├── wave/                 # Wave Accounting invoice adapter
├── whatsapp/             # WhatsApp Business API adapter
├── wormhole/             # Wormhole cross-chain bridge adapter
├── x402-ai-agents/       # x402 autonomous AI agent payment adapter
├── ai-adapters/
│   ├── openai/           # Payment-gated OpenAI / compatible API wrappers (MPP + AP2 + x402)
│   ├── claude/           # Payment-gated Anthropic Claude wrappers (MPP + AP2 + x402)
│   ├── gemini/           # Payment-gated Google Gemini wrappers (MPP + AP2 + x402)
│   ├── bedrock/          # Payment-gated Amazon Bedrock Converse API wrappers (MPP + AP2 + x402)
│   ├── cohere/           # Payment-gated Cohere ClientV2 wrappers (MPP + AP2 + x402)
│   ├── xai/              # Payment-gated xAI Grok wrappers (MPP + AP2 + x402)
│   └── mistral/          # Payment-gated Mistral AI wrappers (MPP + AP2 + x402)
├── ai-agent-frameworks/
│   ├── langchain/        # LangChain gate — any ChatModel, LCEL chain, RAG pipeline, or ReAct agent
│   ├── llamaindex/       # LlamaIndex gate — QueryEngine, ChatEngine, RAG pipeline, or ReAct agent
│   ├── crewai/           # CrewAI gate — crew.kickoff() + BaseTool for multi-agent crews
│   ├── huggingface/      # Hugging Face gate — InferenceClient, transformers pipeline, smolagents tool
│   ├── autogen/          # AutoGen gate — initiate_chat() + callable tool (0.2.x + 0.4.x)
│   ├── semantic-kernel/  # Semantic Kernel gate — chat completion, KernelFunction, SK plugin
│   ├── pydantic-ai/      # Pydantic AI gate — any Agent, deps injection, provider:model strings
│   ├── dspy/             # DSPy gate — any Predict / ChainOfThought / ReAct / compiled program
│   ├── vercel-ai-sdk/    # Vercel AI SDK gate — generateText, streamText, tool() — TypeScript
│   ├── a2a/              # Google A2A gate — A2A v1.0 REST (6 routes), agent card + extended card, task store
│   ├── langgraph/        # LangGraph gate — StateGraph invoke/stream, ToolNode, create_react_agent
│   └── agno/             # Agno gate — pre_hooks, ASGI middleware (AgentOS), run_agent + arun_agent
├── no-code/              # No-code / automation adapters (Zapier, Make, n8n, X) — v1.0.0, 225+ tests
│   ├── zapier/           #   AlgoVoiZapier — ZapierActionResult, webhook bridge, action handlers
│   ├── make/             #   AlgoVoiMake — Make bundle dict, module handlers
│   ├── n8n/              #   AlgoVoiN8n — n8n item dict, operation handlers
│   └── x/                #   AlgoVoiX — X (Twitter) webhook adapter; auto-posts payment confirmations & checkout links via OAuth 1.0a
├── mcp-server/           # MCP server — 13 AlgoVoi tools for Claude Desktop / Claude Code / Cursor / Windsurf
│   ├── typescript/       #   @algovoi/mcp-server (npm) — `npx -y @algovoi/mcp-server`
│   └── python/           #   algovoi-mcp (PyPI) — `uvx algovoi-mcp`
├── drupal-commerce/      # Drupal 10/11 + Commerce 2/3 payment gateway module
├── easy-digital-downloads/ # EDD 3.2+ WordPress plugin (digital downloads, licensing)
├── ghost/                # Ghost 5.x paid-membership grant-on-payment adapter
├── xero/                 # Xero invoice payment adapter
├── yapily/               # Yapily open banking adapter
├── zoho-books/           # Zoho Books invoice adapter
│
├── {platform}.md         # Integration guides (45+ platforms)
└── README.md

from mpp_algovoi import AlgoVoiMppAI

gate = AlgoVoiMppAI(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    networks          = ["algorand_mainnet"],   # or voi_mainnet / hedera_mainnet / stellar_mainnet
    amount_microunits = 10000,                  # 0.01 USDC per call
    resource_id       = "ai-chat",
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    result = gate.check(dict(request.headers))
    if result.requires_payment:
        return result.as_flask_response()   # 402 + WWW-Authenticate: Payment challenge
    # result.receipt.payer, .tx_id, .amount available
    return jsonify({"content": gate.complete(request.json["messages"])})

from ap2_algovoi import AlgoVoiAp2AI

gate = AlgoVoiAp2AI(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    networks          = ["algorand-mainnet", "voi-mainnet"],
    amount_microunits = 10000,                  # 0.01 USDC per call
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()   # 402 + X-AP2-Cart-Mandate header
    # result.mandate.payer_address, .network, .tx_id available
    return jsonify({"content": gate.complete(body["messages"])})

from claude_algovoi import AlgoVoiClaude

gate = AlgoVoiClaude(
    anthropic_key     = "sk-ant-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",               # "mpp" | "ap2" | "x402"
    network           = "algorand-mainnet",
    amount_microunits = 10000,               # 0.01 USDC per call
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from gemini_algovoi import AlgoVoiGemini

gate = AlgoVoiGemini(
    gemini_key        = "AIza...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",               # "mpp" | "ap2" | "x402"
    network           = "algorand-mainnet",
    amount_microunits = 10000,               # 0.01 USDC per call
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from bedrock_algovoi import AlgoVoiBedrock

gate = AlgoVoiBedrock(
    aws_access_key_id     = "AKIA...",       # or set AWS_ACCESS_KEY_ID env var
    aws_secret_access_key = "wJal...",       # or set AWS_SECRET_ACCESS_KEY env var
    aws_region            = "us-east-1",
    algovoi_key           = "algv_...",
    tenant_id             = "your-tenant-uuid",
    payout_address        = "YOUR_ALGORAND_ADDRESS",
    protocol              = "mpp",               # "mpp" | "ap2" | "x402"
    network               = "algorand-mainnet",
    amount_microunits     = 10000,               # 0.01 USDC per call
    model                 = "amazon.nova-pro-v1:0",
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from cohere_algovoi import AlgoVoiCohere

gate = AlgoVoiCohere(
    cohere_key        = "...",                     # Cohere API key
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",                     # "mpp" | "ap2" | "x402"
    network           = "algorand-mainnet",
    amount_microunits = 10000,                     # 0.01 USDC per call
    model             = "command-r-plus-08-2024",
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from xai_algovoi import AlgoVoiXai

gate = AlgoVoiXai(
    xai_key           = "xai-...",                  # xAI API key
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",                      # "mpp" | "ap2" | "x402"
    network           = "algorand-mainnet",
    amount_microunits = 10000,                      # 0.01 USDC per call
    model             = "grok-4",
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from mistral_algovoi import AlgoVoiMistral

gate = AlgoVoiMistral(
    mistral_key       = "...",                        # Mistral API key
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",                        # "mpp" | "ap2" | "x402"
    network           = "algorand-mainnet",
    amount_microunits = 10000,                        # 0.01 USDC per call
    model             = "mistral-large-latest",
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from langchain_algovoi import AlgoVoiLangChain

gate = AlgoVoiLangChain(
    openai_key        = "sk-...",          # OpenAI key (or pass llm= to use any ChatModel)
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",             # "mpp" | "ap2" | "x402"
    network           = "algorand-mainnet",
    amount_microunits = 10000,             # 0.01 USDC per call
)

@app.route("/ai/chat", methods=["POST"])
def chat():
    body   = request.get_json(silent=True) or {}
    result = gate.check(dict(request.headers), body)
    if result.requires_payment:
        return result.as_flask_response()
    return jsonify({"content": gate.complete(body["messages"])})

from langchain_anthropic import ChatAnthropic

gate = AlgoVoiLangChain(
    algovoi_key    = "algv_...",
    tenant_id      = "...",
    payout_address = "...",
    llm            = ChatAnthropic(model="claude-opus-4-7"),
)

chain = ChatPromptTemplate.from_template("Answer: {question}") | ChatOpenAI() | StrOutputParser()

result = gate.check(headers, body)
if not result.requires_payment:
    output = gate.invoke_chain(chain, {"question": body["question"]})

tool = gate.as_tool(resource_fn=lambda q: my_handler(q), tool_name="premium_kb")

from langchain.agents import create_react_agent, AgentExecutor
agent    = create_react_agent(llm, tools=[tool], prompt=prompt)
executor = AgentExecutor(agent=agent, tools=[tool])

from llamaindex_algovoi import AlgoVoiLlamaIndex

gate = AlgoVoiLlamaIndex(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
)

# Gate a LlamaIndex QueryEngine (RAG pipeline)
result = gate.check(headers, body)
if not result.requires_payment:
    answer = gate.query_engine_query(query_engine, body["query"])

# Gate a ChatEngine (multi-turn)
if not result.requires_payment:
    reply = gate.chat_engine_chat(chat_engine, body["message"])

from llama_index.core.agent import ReActAgent

tool  = gate.as_tool(resource_fn=my_handler, tool_name="premium_kb")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)

from llama_index.llms.anthropic import Anthropic

gate = AlgoVoiLlamaIndex(
    algovoi_key    = "algv_...",
    tenant_id      = "...",
    payout_address = "...",
    llm            = Anthropic(model="claude-opus-4-7"),
)

from crewai_algovoi import AlgoVoiCrewAI
from crewai import Agent, Task, Crew, LLM

gate = AlgoVoiCrewAI(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
)

# Gate a crew.kickoff() call
result = gate.check(headers, body)
if not result.requires_payment:
    output = gate.crew_kickoff(my_crew, inputs={"topic": body["topic"]})

# Or use flask_guard with an inputs extractor
@app.route("/ai/research", methods=["POST"])
def research():
    return gate.flask_guard(my_crew, inputs_fn=lambda b: {"topic": b.get("topic", "")})

tool = gate.as_tool(resource_fn=my_handler, tool_name="premium_kb")

researcher = Agent(
    role      = "Research Analyst",
    goal      = "Use premium_kb to answer questions.",
    backstory  = "Expert researcher.",
    tools     = [tool],
    llm       = LLM(model="openai/gpt-4o"),
)

from huggingface_algovoi import AlgoVoiHuggingFace

gate = AlgoVoiHuggingFace(
    hf_token          = "hf_...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
    model             = "meta-llama/Meta-Llama-3-8B-Instruct",
)

# Gate InferenceClient.chat_completion()
result = gate.check(headers, body)
if not result.requires_payment:
    reply = gate.complete(body["messages"])

# Gate a transformers pipeline
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", token="hf_...")
if not result.requires_payment:
    answer = gate.inference_pipeline(pipe, body["messages"])

from smolagents import ToolCallingAgent, InferenceClientModel

tool  = gate.as_tool(resource_fn=my_handler, tool_name="premium_kb")
model = InferenceClientModel(model_id="meta-llama/Meta-Llama-3-8B-Instruct")
agent = ToolCallingAgent(tools=[tool], model=model)
agent.run("Use premium_kb to answer my question.")

from autogen_algovoi import AlgoVoiAutoGen

gate = AlgoVoiAutoGen(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
)

# Build agents from gate.llm_config
from autogen import AssistantAgent, UserProxyAgent
assistant  = AssistantAgent("assistant",  llm_config=gate.llm_config)
user_proxy = UserProxyAgent("user_proxy", human_input_mode="NEVER",
                             max_consecutive_auto_reply=3,
                             code_execution_config=False)

# Gate a conversation
result = gate.check(headers, body)
if not result.requires_payment:
    output = gate.initiate_chat(assistant, user_proxy, body["message"], max_turns=5)

tool = gate.as_tool(resource_fn=my_handler, tool_name="premium_kb")

@user_proxy.register_for_execution()
@assistant.register_for_llm(description=tool.description, name=tool.name)
def premium_kb(query: str, payment_proof: str = "") -> str:
    return tool(query=query, payment_proof=payment_proof)

from autogen_core.tools import FunctionTool
fn_tool = FunctionTool(tool, description=tool.description, name=tool.name)

from semantic_kernel_algovoi import AlgoVoiSemanticKernel

gate = AlgoVoiSemanticKernel(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
    model             = "gpt-4o",
)

# Gate SK chat completion (sync wrapper around async SK API)
result = gate.check(headers, body)
if not result.requires_payment:
    reply = gate.complete(body["messages"])

# Gate any KernelFunction
output = gate.invoke_function(kernel, summarise_fn, input=body["text"])

plugin = gate.as_plugin(resource_fn=my_handler, plugin_name="premium_kb")
kernel.add_plugin(plugin, plugin_name="premium_kb")
# The LLM can select plugin.gate() via function calling (auto-invocation)

from pydanticai_algovoi import AlgoVoiPydanticAI

gate = AlgoVoiPydanticAI(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
    model             = "openai:gpt-4o",     # any Pydantic AI provider:model string
)

# Gate chat completion
result = gate.check(headers, body)
if not result.requires_payment:
    reply = gate.complete(body["messages"])

# Gate any pre-built Agent (with optional dependency injection)
from pydantic_ai import Agent
my_agent = Agent("anthropic:claude-opus-4-7", system_prompt="Be concise.")
output   = gate.run_agent(my_agent, body["prompt"], deps=my_deps)

from pydantic_ai.tools import Tool

tool  = gate.as_tool(resource_fn=my_handler, tool_name="premium_kb")
agent = Agent("openai:gpt-4o", tools=[Tool(tool, name=tool.name, description=tool.description)])

from dspy_algovoi import AlgoVoiDSPy

gate = AlgoVoiDSPy(
    openai_key        = "sk-...",
    algovoi_key       = "algv_...",
    tenant_id         = "your-tenant-uuid",
    payout_address    = "YOUR_ALGORAND_ADDRESS",
    protocol          = "mpp",
    network           = "algorand-mainnet",
    amount_microunits = 10000,
    model             = "openai/gpt-4o",   # DSPy provider/model string (slash, not colon)
)

# Gate any DSPy module
import dspy

class QA(dspy.Signature):
    """Answer the question."""
    question: str = dspy.InputField()
    answer:   str = dspy.OutputField()

result = gate.check(headers, body)
if not result.requires_payment:
    answer = gate.run_module(dspy.ChainOfThought(QA), question=body["question"])

# Works with any compiled / optimised DSPy program (Predict, ChainOfThought, ReAct, MIPROv2…)
result = gate.run_module(my_compiled_program, question=body["question"])

tool  = gate.as_tool(resource_fn=my_handler, tool_name="premium_kb")
react = dspy.ReAct(QA, tools=[tool])

lm = gate._ensure_lm()
with dspy.context(lm=lm):
    out = react(question=body["question"])

import { openai } from "@ai-sdk/openai";
import { AlgoVoiVercelAI } from "./vercel_ai_algovoi";

const gate = new AlgoVoiVercelAI({
  algovoiKey:       "algv_...",
  tenantId:         "your-tenant-uuid",
  payoutAddress:    "YOUR_ALGORAND_ADDRESS",
  protocol:         "mpp",
  network:          "algorand-mainnet",
  amountMicrounits: 10_000,
  model:            openai("gpt-4o"),   // any @ai-sdk/* provider
});

// Next.js App Router — one-liner
export const POST = (req: Request) => gate.nextHandler(req);

export async function POST(req: Request) {
  const body = await req.json();
  const result = await gate.check(req.headers, body);
  if (result.requiresPayment) return result.as402Response();
  return gate.streamText(body.messages).toDataStreamResponse();
}

const tool = gate.asTool(
  async (query) => myPremiumHandler(query),
  { toolName: "premium_kb", toolDescription: "Access premium knowledge base." }
);
// Use in generateText({ tools: { premium_kb: tool } })

from a2a_algovoi import AlgoVoiA2A

gate = AlgoVoiA2A(
    algovoi_key="algv_...",
    tenant_id="your-tenant-uuid",
    payout_address="YOUR_ALGORAND_ADDRESS",
    protocol="mpp",
    network="algorand-mainnet",
    amount_microunits=10_000,
    agent_name="My AlgoVoi Agent",
)

# Flask A2A server
from flask import Flask, jsonify
app = Flask(__name__)

@app.route("/a2a", methods=["POST"])
def a2a_endpoint():
    return gate.flask_agent(lambda text: my_llm(text))

@app.route("/.well-known/agent-card.json")
def card():
    return jsonify(gate.agent_card("https://myhost.com/a2a"))

# A2A client — call another agent
response = gate.send_message("https://other-agent.example.com/a2a", "hello", payment_proof="proof")
task = response["result"]  # {"id": "...", "status": {"state": "completed"}, "artifacts": [...]}

# Payment tool for A2A pipelines
tool = gate.as_tool(lambda q: fetch_kb(q), tool_name="premium_kb")

from zapier_algovoi import AlgoVoiZapier

handler = AlgoVoiZapier(
    algovoi_key    = "algv_...",
    tenant_id      = "your-tenant-uuid",
    payout_algorand= "YOUR_ALGORAND_ADDRESS",
    webhook_secret = "whsec_...",                          # optional
    zapier_hook_url= "https://hooks.zapier.com/hooks/catch/XXXX/YYYY/",  # optional
)

# Create a payment link (Zapier action)
result = handler.action_create_payment_link({
    "amount":   5.00,
    "currency": "USD",
    "label":    "Premium access",
    "network":  "algorand_mainnet",
})
# result.success, result.data["checkout_url"], result.data["token"]

# Verify payment status
result = handler.action_verify_payment({"token": "tok_..."})
# result.data["paid"] → True/False, result.data["status"] → "active"|"paid"|...

# Receive and forward an AlgoVoi webhook to a Zapier Catch Hook
result = handler.receive_and_forward(raw_body=..., signature=...)

# Generate MPP / x402 / AP2 challenge
result = handler.action_generate_challenge({
    "protocol":          "mpp",        # "mpp" | "x402" | "ap2"
    "resource_id":       "/api/premium",
    "amount_microunits": 10_000,
    "network":           "algorand_mainnet",
})

from make_algovoi import AlgoVoiMake

handler = AlgoVoiMake(
    algovoi_key     = "algv_...",
    tenant_id       = "your-tenant-uuid",
    payout_algorand = "YOUR_ALGORAND_ADDRESS",
    payout_stellar  = "G...",    # optional — add any chain
)

# Create a payment link (Make module)
bundle = handler.module_create_payment_link({
    "amount":   10.00,
    "currency": "USD",
    "label":    "API access — 30 days",
    "network":  "stellar_mainnet",
})
# bundle["data"]["checkout_url"], bundle["data"]["token"]
# on error: bundle["error"]["message"], bundle["error"]["code"]

# Verify payment
bundle = handler.module_verify_payment({"token": "tok_..."})
# bundle["data"]["paid"], bundle["data"]["status"]

# Generate challenge (MPP / x402 / AP2)
bundle = handler.module_generate_challenge({
    "protocol":          "x402",
    "resource_id":       "/api/v1/data",
    "amount_microunits": 500_000,
    "network":           "stellar_mainnet",
})

# Receive AlgoVoi webhook
bundle = handler.receive_webhook(raw_body=..., signature=...)

from n8n_algovoi import AlgoVoiN8n

handler = AlgoVoiN8n(
    algovoi_key   = "algv_...",
    tenant_id     = "your-tenant-uuid",
    payout_hedera = "0.0.XXXXX",   # set whichever chains you support
)

# Create a payment link (n8n Code node operation)
item = handler.execute_create_payment_link({
    "amount":   2.50,
    "currency": "USD",
    "label":    "Agent service call",
    "network":  "hedera_mainnet",
})
# item["json"]["success"], item["json"]["checkout_url"], item["json"]["token"]

# Verify payment
item = handler.execute_verify_payment({"token": "tok_..."})
# item["json"]["paid"], item["json"]["status"]

# Generate challenges
item = handler.execute_generate_mpp_challenge({
    "resource_id": "/api/premium", "amount_microunits": 10_000, "network": "algorand_mainnet"
})
item = handler.execute_generate_x402_challenge({
    "resource_id": "/api/data",    "amount_microunits": 500_000, "network": "algorand_mainnet"
})
item = handler.execute_generate_ap2_mandate({
    "resource_id": "/service",     "amount_microunits": 2_000_000, "network": "voi_mainnet"
})

# Receive and verify an AlgoVoi webhook
item = handler.receive_webhook(raw_body=..., signature=...)
item = handler.execute_verify_webhook_signature({"raw_body": ..., "signature": ...})

{
  "mcpServers": {
    "algovoi": {
      "command": "npx",
      "args": ["-y", "@algovoi/mcp-server"],
      "env": {
        "ALGOVOI_API_KEY":         "algv_...",
        "ALGOVOI_TENANT_ID":       "...",
        "ALGOVOI_PAYOUT_ALGORAND": "<your-algorand-address>",
        "ALGOVOI_PAYOUT_VOI":      "<your-voi-address>",
        "ALGOVOI_PAYOUT_HEDERA":   "0.0.<your-account>",
        "ALGOVOI_PAYOUT_STELLAR":  "G<your-stellar-address>",
        "ALGOVOI_PAYOUT_BASE":     "0x<your-evm-address>",
        "ALGOVOI_PAYOUT_SOLANA":   "<your-solana-address>"
      }
    }
  }
}

<script type="module" src="https://your-domain.example/widget.js"></script>
<algovoi-x402
  amount="29.99"
  chains="ALGO,VOI,XLM,HBAR,ETH,SOL"
  tenant-id="your-tenant-id"
  api-key="algv_your-api-key">
</algovoi-x402>

Customer places order and selects chain (Algorand / VOI / Hedera / Stellar / Base / Solana)
            ↓
Plugin creates payment link via POST /v1/payment-links
            ↓
Hosted: redirect to AlgoVoi checkout page
Extension: pay in-page via AlgoVoi browser wallet
            ↓
Customer pays on-chain
            ↓
AlgoVoi verifies the transaction on-chain
            ↓
Webhook fires → order marked as paid in your platform