Claw402 is an MCP-native integration layer that enables OpenClaw AI agents to execute autonomous transactions on the x402 protocol. We provide a secure, non-custodial architecture that translates natural language payment instructions into protocol-level blockchain transactions.
How do I get started?
Install the Claw402 provider: pip install claw402 or npm install @claw402/mcp-sdk
Configure your agent wallet (we never access your private keys)
Set up approval policies for autonomous transaction limits
x402 is a payment protocol that enables cryptocurrency payments over HTTP with cryptographic proof validation. It allows AI agents to programmatically initiate and verify blockchain transactions without requiring traditional wallet UI interactions.
Model Context Protocol (MCP) is an open standard for AI agent tool integration. Claw402 implements MCP-native tools that allow agents to:
Initialize payment sessions declaratively
Authorize transactions with approval policies
Verify settlement status in real-time
Query wallet balances and estimate fees
Do I need blockchain knowledge to use Claw402?
No. Claw402 abstracts all blockchain complexity. Your AI agent describes payment intent in natural language (e.g., "Pay invoice #4291 for $250 USDC"), and Claw402 handles transaction construction, signing, and settlement verification.
Which blockchains does Claw402 support?
Claw402 currently supports Solana mainnet-beta and devnet. We handle all blockchain interactions through our MCP tool suite, so agents never interact with RPC nodes directly.
AI Agent Integration
What agent frameworks are compatible with Claw402?
Claw402 supports any MCP-capable agent framework:
OpenClaw (Python) - Native provider integration
Claude Code (TypeScript) - MCP server integration
LangChain (Python/TypeScript) - Custom MCP client
AutoGPT (Python) - MCP tool integration
Custom agents - Standard MCP protocol
How does Claw402 ensure agent transactions are secure?
Claw402 implements a non-custodial security architecture:
HSM isolation: Private keys never enter LLM context windows
Approval policies: Multi-tier transaction limits with human-in-loop workflows
Idempotency: Prevent duplicate transactions from retry logic
Audit logging: Complete transaction history for compliance
# Bad: 10 separate sessions
for invoice in invoices:
await provider.initialize_payment(invoice.amount, "[email protected]")
# Good: 1 aggregated session
total = sum(inv.amount for inv in invoices)
await provider.initialize_payment(total, "[email protected]")