Portable desktop/USB app for multi-agent x402 management
agent-manifold is an early-stage Python project in the AI payments / x402 ecosystem. It currently has 0 GitHub stars and 0 forks.
A desktop x402 payment manager for AI agents.
Delegate spending authority to agents without sharing private keys. Implements the full AP2 flow: intent, authorization, settlement, and receipt. Accepts direct A2A x402 as well as HTTP x402.

AI agents need to pay for things, but giving an agent your private key is dangerous as too many recent examples show. No amount of persuasion is guaranteed to convince a free-willed agent to behave as you ask. The Agent Manifold sits between your agents and your wallet, enforcing spending policies and requiring human approval when needed.
Agent hits paywall → 402 + Payment-Required header
↓
Agent calls POST /sign with header
↓
Manifold checks policy (daily limit, domain, etc.)
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Auto-approve OR human approval dialog in app
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Manifold signs EIP-712 authorization
↓
Agent retries request with payment header
↓
Merchant settles via x402 Facilitator
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Agent reports settlement via POST /callback
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Manifold verifies on-chain, stores receipt
Any agent framework can integrate via HTTP to localhost:9402 — Claude, GPT, LangChain, custom agents, or any system that can make HTTP requests. Bearer tokens for simplicity, HMAC-SHA256 for production security. The pattern is intentionally simple: detect paywall → request signature → retry with payment → report settlement.

Each agent can have a signed Intent Mandate — an AP2 VDC documenting:
Mandates are signed with EIP-191 and can be published to the AP2 Registry. Merchants can query the registry by agent code to verify authorization before accepting payment — confirming the agent is backed by a real human with defined spending limits.

Every transaction is logged with agent, amount, domain, timestamp, and on-chain tx_hash. Settled payments are verified against the blockchain. AP2-formatted receipts are available via /receipt/{id}.
{
"type": "AP2Receipt",
"version": "ap2.primer/v0.1",
"intent": {
"agentCode": "XK7M2P",
"policyName": "standard",
"approvalMethod": "human"
},
"authorization": {
"walletAddress": "0x742d35Cc6634C0532925a3b844Bc9e7595f...",
"signedAt": "2025-01-15T14:32:01Z"
},
"payment": {
"amount": "1.50",
"currency": "USDC",
"recipient": "0x8ba1f109551bD432803012645Ac136ddd64...",
"network": "eip155:8453"
},
"settlement": {
"txHash": "0x3a1b2c3d4e5f...",
"status": "verified",
"blockNumber": 12847293
}
}
When a policy violation occurs (limit exceeded, blocked domain, manual rejection), the agent receives a structured error response with a reason code. This allows the agent to understand why the payment was declined and adjust its behavior — choosing a cheaper option, trying a different merchant, or asking the user for guidance.
The demo video shows this in action: an agent exceeds its daily limit and is forced to reconsider its approach.

transferWithAuthorizationVideo: Watch the demo →
The demo shows the full payment flow from agent request to on-chain settlement, including a failure case where a payment limit is exceeded and the agent is forced to consider a new choice.
Or run from source:
pip install -r requirements.txt
python src/app.py
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
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