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Tamper-proof audit trail for AI agents. Signed receipts, offline-verifiable, zero backend.
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Tamper-proof audit trail for AI agents. Signed receipts, offline-verifiable, zero backend.
Security Report
Valid MCP server (2 strong, 3 medium validity signals). 5 known CVEs in dependencies (0 critical, 3 high severity) Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
14 files analyzed · 6 issues found
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How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-burakfenerci5-actionproof": {
"args": [
"-y",
"actionproof"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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 SMTP250— 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.
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