ActionProof

A tamper-proof audit trail for AI agents. Verifiable observability: every action your agent takes gets a cryptographically signed receipt you can verify offline, anywhere — zero backend.

Observability tools (LangSmith, Langfuse, Arize) show you what your agent reportedly did — traces recorded inside their platform, on their word. But those logs are self-asserted: an agent, a bug, or an attacker can write anything into them, and you can't prove after the fact that the record wasn't edited.

ActionProof adds the missing layer: verifiable observability. Each action — email sent, form filed, payment made — gets a tamper-evident, Ed25519-signed receipt capturing what was done, by which agent, when, and on whose authority. Edit any field and verification fails. It's an audit trail you (or an auditor, a user, or a counterparty) can trust without trusting the agent, the vendor, or us.

Built for the compliance floor that's coming — the EU AI Act (Article 12) and ISO 42001 require traceable, tamper-evident logs for automated decisions. ActionProof produces exactly that, as a portable primitive rather than a walled-garden platform.

Install

npm install actionproof      # TypeScript / JavaScript
pip install actionproof      # Python

Receipts are cross-compatible: one signed in TypeScript verifies in Python, and vice-versa.

Quick start (TypeScript)

import { attest, verify, generateKeypair } from "actionproof";

const agent = generateKeypair();               // agent's identity = its key (did:key)

const receipt = attest(agent, {
  type: "email.send",
  summary: "Sent renewal quote to jane@acme.com",
  params: { to: "jane@acme.com", amount: 4200 }, // hashed, not stored in clear
  result: { smtp: 250 },
  outcome: "ok",
});

verify(receipt);            // -> { valid: true, agent: "did:key:z6Mk..." }

Quick start (Python)

from actionproof import attest, verify, generate_keypair

agent = generate_keypair()

receipt = attest(
    agent,
    type="email.send",
    summary="Sent renewal quote to jane@acme.com",
    params={"to": "jane@acme.com", "amount": 4200},  # hashed, not stored in clear
    result={"smtp": 250},
    outcome="ok",
)

verify(receipt)             # -> VerifyResult(valid=True, agent="did:key:z6Mk...")

Edit any field of that receipt and verify returns invalid. That's the whole idea.

Where it fits: the verifiable layer of agent observability

ActionProof complements your observability stack rather than replacing it. Keep using LangSmith / Langfuse / Arize for rich traces, latency, and cost — then attach an ActionProof receipt to the actions that matter (the ones that move money, change state, or touch a user's data) so that part of your trail is tamper-evident and independently verifiable.

	Observability platforms	ActionProof
Recording	traces/logs inside the vendor	signed receipts you hold
Trust model	trust the platform's stored record	verify cryptographically, trust no one
Tamper-evidence	editable by whoever has DB access	any edit breaks the signature
Portability	lives in the vendor	offline, cross-language, anywhere
Cost at scale	metered per event	~$0 (local signing, zero backend)

It's a proof, not just a log entry — the difference between "our dashboard says the agent did this" and "here's a signed receipt anyone can verify."

Design principles

• Offline & zero-backend. The agent brings its own Ed25519 key. Signing and verification use only native crypto — no server, no account, no network. (This is also why it costs ~nothing to run at any scale.)
• Privacy-preserving. Sensitive inputs/outputs are stored as SHA-256 hashes; you can later prove a value matches without ever putting it in the receipt.
• Composable, not competitive. ActionProof is the receipt envelope. Bind stronger evidence into result_hash — an x402 settlement, an AP2 mandate reference, a DKIM-signed SMTP 250 — to make a receipt as strong as its counterparty evidence.
• Identity with no registry. Agent identity is a did:key (self-describing public key). Who you trust is your policy (pinned keys, an allow-list, or the optional log below).

See SPEC.md for the wire format.

Use it as an MCP server (no code)

The fastest way to give an agent receipts: run ActionProof as an MCP server and add it to Claude Desktop / Cursor. Your agent gets three tools — attest_action, verify_receipt, get_identity — and can emit a receipt right after it does something.

Add to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "actionproof": {
      "command": "npx",
      "args": ["-y", "actionproof-mcp"]
    }
  }
}

The server mints a stable Ed25519 identity on first run (stored at ~/.actionproof/agent.key.pem, override with ACTIONPROOF_KEY_PATH). Every receipt it signs is attributable to that one agent did:key.

Auto-emit receipts (framework wrappers)

You don't have to call attest by hand after every action — wrap the tool once and every call emits a receipt.

TypeScript (framework-agnostic; works with LangChain.js, Mastra, Vercel AI SDK):

import { withReceipts, generateKeypair } from "actionproof";

const agent = generateKeypair();
const send = withReceipts(agent, rawSendEmail, {
  type: "email.send",
  onReceipt: (r) => store(r),   // called with a signed receipt on every call
});

Python (@attest_action decorator, or a LangChain/CrewAI callback):

from actionproof import attest_action, ActionProofCallbackHandler

@attest_action(agent, type="email.send", on_receipt=store)
def send_email(to, body): ...

# or attest every tool a framework agent runs, no per-tool code:
handler = ActionProofCallbackHandler(agent, on_receipt=store)
agent_executor.invoke(input, config={"callbacks": [handler]})

Develop locally

git clone https://github.com/Burakfenerci5/actionproof
cd actionproof && npm install
npm run demo     # full sign → verify → tamper loop
npm test         # TS suite (9 tests)
npm run mcp      # start the MCP server over stdio

cd python && pip install -e ".[dev]" && pytest   # Python suite (7 tests, incl. TS↔Python interop)

Roadmap

• Now (shipped): TypeScript library + MCP server + framework wrapper, and the Python package with a decorator and LangChain/CrewAI callback. Receipts interoperate across both.
• Next: first-class LlamaIndex / CrewAI plugins; exporters that attach receipts to spans in your existing observability stack (OpenTelemetry, LangSmith, Langfuse).
• Later (optional, hosted): a verifiable audit dashboard — a searchable, shareable, tamper-evident timeline of what your fleet of agents did, backed by an append-only log, for teams that need compliance-grade evidence (EU AI Act / ISO 42001) without building it themselves. The library and MCP server stay free and offline forever; only the hosted dashboard is a paid service.

License

MIT.