nidhipati

Nidhipati

Proof of Spend for AI agents. A signed receipt that proves what an agent was authorized to pay, what it actually paid, what it used and what was refunded. Not a token, not custody, not another payment rail.

Рельсы двигают деньги. Nidhipati доказывает, что движение было правильным.

Status: v0.6 — five CLIs, an MCP server, 91 tests, official MCP Registry and Glama listing.

Site: https://mrpkk.github.io/nidhipati/
Free browser audit (no signup): https://mrpkk.github.io/nidhipati/free-spend-audit.html
BusinessMCP directory: https://businessmcp.com/mcp-servers/nidhipati
Private scan request: https://github.com/mrpkk/nidhipati/issues/new?template=free-spend-audit.yml

Reserve and NAV report

For tokenised assets: the issuer's own numbers turned into a NAV with explicit formulas and an evidence hash per line. The verdict is backed, underbacked, or incomplete — and the ratio is refused when any line has no price, because a number computed over half a reserve is not a number.

nidhipati-reserve report examples/reserve.example.json --key keys/private.pem --markdown report.md

NAV = Σ quantity × price × (1 − haircut) − liabilities
ratio = NAV / supply

The statement hash is compared with the file if you provide it. The disclaimer travels inside the signed document. Details and the partner/legal checklist: docs/RESERVE-REPORT.md.

Resolution evidence

When a market resolves and someone disagrees, the question is whether the resolver's sources say what the resolver claims and whether they agree with each other. The report answers with four verdicts — supported, disputed, single_source, insufficient — and never picks a winner.

nidhipati-resolve report examples/resolve.example.json --key keys/private.pem --markdown report.md

Details: docs/RESOLUTION-EVIDENCE.md.

Reconciliation

Invoices on one side, transfers on the other. The report names what is closed, what nobody paid, and what arrived without an invoice — a transfer that duplicates a payment is counted once, never twice.

nidhipati-reconcile run examples/invoices.example.csv examples/transactions.example.csv --csv matches.csv

Details: docs/RECONCILIATION.md.

MCP server (for agents, not shells)

The same five checks over the Model Context Protocol: verify_receipt, audit_spend, reserve_report, resolve_report, reconcile_books. Input is data, never a file path and never a network call; the answers are the same reports the CLIs produce, including the refusal to print a backing ratio when a line has no price.

{"jsonrpc":"2.0","id":1,"method":"tools/call",
 "params":{"name":"reserve_report","arguments":{"reserve":{...}}}}

python3 -m nidhipati_proof.mcp_stdio      # stdio transport
docker build -t nidhipati-mcp . && docker run -i --rm nidhipati-mcp

Five tools, all read-only: no custody, no keys, no chain read.

Official MCP Registry: https://registry.modelcontextprotocol.io/v0/servers?search=nidhipati (io.github.mrpkk/nidhipati, OCI ghcr.io/mrpkk/nidhipati:0.6.0).
Glama listing: https://glama.ai/mcp/servers/mrpkk/nidhipati.

Free scan, and what it costs after

Send records of what your agents spent and get the audit back — no card. The browser audit needs no account; a human review can go by email or Telegram, no GitHub needed: email maxim.pkk@gmail.com or Telegram with the downloaded report (not raw rows). GitHub is optional: open a private scan request; the issue is public, so ask the question only and never paste records or keys. Paid: first 3 audits $1,250; then $2,500 fixed, $1,500/mo retainer, or 20% of verified savings. $5,000 finding guarantee (identified overcharges, not recovery). Full terms, including what is not included: docs/OFFER.md.

In CI, on every run:

- uses: mrpkk/nidhipati@v0.2.0
  with:
    records: billing/agent-spend.csv
    policy: billing/policy.json

Quick start

python3 -m pip install -e .[dev]
python3 -m pytest -q
python3 -m nidhipati_proof.cli verify examples/proof-of-spend.example.json --pubkey <base64url>

What it checks

• schema and canonical JSON;
• Ed25519 signature;
• mandate expiry vs settlement time;
• amount, asset, network, payee;
• usage.quantity × usage.unit_price vs payment;
• refund ≤ payment;
• verdict allow for a settled payment;
• receipt ID/replay (v0.2 store).

Agent spend audit

A signed receipt proves one charge. A bill proves a month. The audit reads the records you already have (CSV or JSON) and names the rows where money left without a reason:

Finding	What it means
duplicate_charge	the same receipt twice, or an equal charge within 120s with no receipt at all
price_escalation	settled above the authorised quote by more than 1%
failed_but_billed	status failed/error and still charged
missing_refund	authorised 10, used 4, nothing returned
runaway_loop	more than 30 calls a minute from one agent
model_overpay	far above this agent's own median for the same tool and model (an estimate)
unknown_asset / unknown_network / missing_payee	the record does not say where the money went
budget_bypass / budget_exceeded	no policy file: the limit lives in a prompt; with a policy, the agent is over it

python3 -m pip install -e .
nidhipati-spend audit examples/spend-sample.csv --policy examples/spend-policy.example.json
nidhipati-spend audit records.csv --json > report.json

Exit code 1 when there is an error — a pipeline can fail on it. The report keeps three amounts apart on purpose: spent without a reason (duplicates, escalation, failed calls, refunds not returned), above the usual price (an estimate, not proof), and over the limit (the money may have been spent on real work).

The sample is synthetic and labelled: it contains every finding once, so you can see what a report looks like before trusting it with real invoices.

Honest limits

• v0.1 does not verify the on-chain transaction; onchain_verified=false is explicit.
• It is not a statutory audit, legal, tax, or investment advice.
• It does not guarantee the service was good; it proves the payment was within mandate.
• No custody, no money transmission, no token.

Docs

• SPEC.md — product, architecture, modules, SEO, monetization, 30/60/90.
• PROGRESS.md — slices, acceptance, metrics.
• docs/PROMPT-AND-IDEAS.md — prompt and decisions for RAG.
• docs/SEO-MONETIZATION.md — keywords, pages, funnel, pricing.
• docs/LAUNCH-KIT.md — Show HN, Reddit, Product Hunt, X/LinkedIn copy.
• docs/DEMO.md — real commands and sample output.
• docs/AGENT-SPEND-AUDIT.md, docs/RESERVE-REPORT.md, docs/RESOLUTION-EVIDENCE.md, docs/RECONCILIATION.md — module rules.
• schemas/proof-of-spend.schema.json — v0.1 schema.
• examples/proof-of-spend.example.json — example.

License

Apache-2.0.