NumProof

The deterministic numeric truth layer for AI agents and spreadsheets.
Your agent writes "gross margin improved from 42.1% to 44.8%" or "the workbook is internally
consistent" — NumProof tells you, deterministically, whether that number is VERIFY, REFUTE,
or ABSTAIN, with a counterexample, cell/formula provenance, and a signed, machine-checkable
audit bundle. It's exact arithmetic and symbolic math — not an LLM judging another LLM.
- ✅ Verify a single math/finance claim, or batch thousands in CI
- ✅ Audit
xlsx/csv rows: footing, cross-footing, balance-sheet ties, margins, formula cells — with provenance
- ✅ Diff two report versions; evaluate covenant rule packs (DSCR, Debt/EBITDA, current ratio, …)
- ✅ Signed evidence bundle (JSON + HTML/PDF/ZIP) anyone can re-verify offline
- ✅ API · CLI · MCP server · optional x402 pay-per-call
This repo is the open-source client (SDK + MCP). The verification engine runs as a hosted
service — pip install numproof, point it at the API, done. (Same shape as stripe-python:
the SDK is open, the engine is the service.)
Live demo (no key): https://numproof.com · Docs: https://numproof.com/docs
30-second start
pip install numproof
from numproof import NumProof
np = NumProof.from_env()
print(np.verify("120 + 90 + 340 + 15 == 565"))
print(np.verify("a 50% loss needs a 100% gain to break even"))
print(np.verify("two 10% raises equal a 21% total increase"))
print(np.verify("operating margin is 18% when EBIT is 180 and revenue is 1000"))
No install? It's just HTTP:
curl -s https://numproof.com/demo -H 'Content-Type: application/json' \
-d '{"claim":"gross margin is 60% when gross profit is 600 and revenue is 1000"}'
Free API key:
curl -s https://numproof.com/signup -X POST -H 'Content-Type: application/json' -d '{}'
Audit a spreadsheet (with provenance)
rows = [["Revenue", 1000], ["COGS", 400], ["Gross Profit", 600], ["Gross Margin", "60%"]]
print(np.audit_rows(rows)["verdict"])
print(np.covenant_rows(
[["EBITDA", 500], ["Debt Service", 300], ["Debt", 1200]],
rule_pack="credit_covenants_basic",
)["verdict"])
Every audit/diff/covenant result can be returned as a signed bundle + human-readable HTML/PDF
report (format="zip"). Recipients verify it without trusting you or NumProof:
curl -s https://numproof.com/audit/verify -H 'Content-Type: application/json' -d @bundle.json
Use it from an AI agent (MCP)
NumProof ships an MCP server so Claude / OpenAI-style agents can call it as a tool — gate every
numeric claim before it reaches a user, report, or auditor.
python -m numproof.mcp
{ "mcpServers": { "numproof": { "command": "python", "args": ["-m", "numproof.mcp"] } } }
Or point any MCP client at the hosted descriptor: https://numproof.com/mcp.json.
See examples/ for runnable scripts (verify, audit, covenants, agent-gate, MCP).
Drop-in guardrails for agent frameworks
Verify the numbers your agent emits before it acts, in the framework you already use
(numproof/integrations/ — each lazily imports its framework, so the numproof client stays stdlib-only):
from agents import Agent
from numproof.integrations.openai_agents import numproof_output_guardrail
agent = Agent(name="...", instructions="...", output_guardrails=[numproof_output_guardrail()])
- OpenAI Agents SDK —
numproof.integrations.openai_agents (output guardrail / tripwire)
- Pydantic AI —
numproof.integrations.pydantic_ai (output validator; raises ModelRetry with the counterexample so the model self-corrects)
- LangChain —
numproof.integrations.langchain (a NumProof Tool + an output checker)
- DeepEval (Confident AI) —
numproof.integrations.deepeval (a deterministic NumProofMetric: VERIFY → score 1.0, REFUTE → score 0.0 with the counterexample on .reason)
- Guardrails AI —
numproof.integrations.guardrails (a Hub Validator: REFUTE → FailResult with the counterexample, so your on_fail action — reask/fix/exception — fires)
VERIFY → pass · REFUTE → block/retry with the counterexample · ABSTAIN → pass-through (configurable). Runnable examples in examples/; install only the framework you use.
Independently re-checkable receipts (the part you can't fake)
Any verdict can be returned as a signed Verification Receipt — and you re-check it offline,
trusting neither the transport nor NumProof:
pip install "numproof[verify]"
numproof-verify receipt.json --signer 0x<published-NumProof-signer>
It recovers the EIP-191 signer (tamper-evident) and, for value/agg/identity/sequence
claims, independently re-derives the verdict with stdlib Fraction + sympy. A tampered
field, a wrong signer, or a verdict that doesn't actually hold all fail loudly — even a receipt
NumProof itself mis-signed is caught by the re-derivation. An agent can recompute a number for
itself; it cannot issue an independent, signed attestation a second party will accept. That
independence — not the arithmetic — is the product. Format + spec: RECEIPT_FORMAT.md.
Why deterministic (and why it matters)
Generic "AI guardrails" use a model to grade a model — probabilistic, and itself can hallucinate.
NumProof recomputes the math exactly (rational arithmetic + symbolic identity checking) and
returns a reproducible verdict with a trace. When it can't prove something it says ABSTAIN
rather than guess. For finance, regulated, and agent workflows, "the number is provably right"
beats "another model thinks it looks right." Full table: comparison.md.
Pricing
| Sandbox | $0 | web demo + free credits |
| x402 PAYG | $0.005 / call | agent-to-tool, no subscription |
| Builder | $29/mo | API + MCP + CLI, 2k credits |
| Pro | $99/mo | batch, webhooks, CI, signed exports, 10k credits |
| Finance Team | $299/mo | 5 seats, version diff, covenant packs, branded exports |
What's in this repo
The numproof Python SDK (NumProof client), the MCP server, and runnable examples — all thin
HTTP clients to the hosted API. MIT licensed. The verification engine, finance audit logic,
formal (Lean) proof tier, and signing are the hosted service and are not in this repo.
Found a wrong verdict? Open an issue with the exact claim — correctness is the whole product.