Guardrail

📄 Read the white paper

A policy firewall for AI agent tool calls.

Your agent wants to run a shell command, send an email, or move money. Guardrail checks that request against rules you wrote, before it happens, and either lets it through, asks a human, or blocks it — with a plain- English reason every time.

60-second quickstart

git clone <this repo> && cd agent-guardrail
pip install -r requirements.txt

python3 cli.py check --agent trading-agent-001 --tool wallet.transfer \
  --args '{"amount": 9999, "to": "0xabc"}'

Or pip install guardrail-mcp gives you a guardrail command directly — same output, no repo checkout required (falls back to the policy bundled in the package if you don't point --policy at your own file):

guardrail check --agent trading-agent-001 --tool wallet.transfer \
  --args '{"amount": 9999, "to": "0xabc"}'

{
  "decision": "BLOCK",
  "matched_rules": [
    {"rule": "numeric_cap_exceeded", "severity": "BLOCK",
     "message": "amount=9999.0 exceeds cap 5 for 'wallet.transfer' (unknown agent)"}
  ]
}

That's it — no server, no account, no API key. policies/default.yaml is the file that decided this; open it and change the numbers to match your own rules.

Related MCP server: Agent Identity MCP Server

Why this, not another "AI risk scoring" tool

Most "AI agent security" projects (including an earlier project of mine) lean on statistical risk scores computed from data nobody can actually verify at build time — wallet age, "reputation," contract "risk" — which either requires paid data feeds you don't have yet, or quietly becomes mock data pretending to be real. Fine for prototyping, dishonest to ship.

Guardrail only makes claims it can back up. Every check is a deterministic rule — a blocklist entry, a regex match, a numeric cap, a rate limit — evaluated against a policy file you write and can audit yourself, backed by a real, persistent audit log (SQLite) you can query. Nothing here pretends to know something it doesn't.

It's also not blockchain-specific. Shell execution, email, HTTP requests, file deletion, database writes, crypto transactions — same engine, same policy file, same rules.

Four ways to use it

1. CLI — for testing a policy by hand

Shown above. No setup, instant feedback while you write rules.

2. MCP server (mcp_server.py) — the easy on-ramp, advisory

Exposes guardrail_check, guardrail_record_outcome, and guardrail_agent_history as MCP tools any MCP-compatible agent (Claude Desktop, Claude Code, custom MCP clients) can call.

{
  "mcpServers": {
    "guardrail": {
      "command": "python3",
      "args": ["/absolute/path/to/agent-guardrail/mcp_server.py"],
      "env": { "GUARDRAIL_POLICY": "/absolute/path/to/agent-guardrail/policies/default.yaml" }
    }
  }
}

Then tell your agent (in its system prompt) to always call guardrail_check before spending money, deleting data, messaging someone externally, or running code.

Be clear-eyed about its limit: like any MCP tool, nothing stops the calling model from just not invoking it. This only helps if the agent is instructed to always check first — for a guarantee it can't skip, see #3.

3. guardrail.decorator.enforce — the real guarantee

Wraps the actual Python function that performs a tool's side effect. The check runs in your code, before that function executes — the model never gets a chance to call the real function directly.

from guardrail.decorator import enforce, BlockedActionError

@enforce(engine, tool_name="send_email")
def send_email(agent_id: str, to: str, subject: str, body: str):
    ...  # only runs if the decision is ALLOW, or WARN-and-confirmed

Use this if you're building your own agent loop (LangChain, CrewAI, a custom MCP host, a Slack bot with tool access). Run python3 examples/example_agent_usage.py to see it block a real function call.

4. guardrail.mcp_enforced_server.EnforcedGuardrailMCPServer — the real guarantee, over MCP

The MCP server in #2 above is honest about being advisory: the model gets a guardrail_check tool, but nothing stops it from calling the actual tool (exposed by some other MCP server, or by the model's own direct access) without checking first, or checking one thing and doing another. If the model talks to your infrastructure only over MCP - no Python decorator possible - this is the same #3 guarantee for that case: the operator registers real action executors (the code that holds real credentials and performs the real side effect) as the only way the model can invoke that action at all.

from guardrail.mcp_enforced_server import EnforcedGuardrailMCPServer

def do_transfer(request):
    wallet = get_wallet_for(request.agent_id)  # real credentials, held here - never exposed to the model
    tx_hash = wallet.transfer(to=request.arguments["to"], amount=request.arguments["amount"])
    return {"tx_hash": tx_hash}

server = EnforcedGuardrailMCPServer(policy_path="policies/default.yaml")
server.register_action(
    "wallet.transfer", "Transfer funds from the agent's wallet.",
    input_schema={"type": "object", "properties": {"to": {"type": "string"}, "amount": {"type": "number"}}, "required": ["to", "amount"]},
    executor=do_transfer,
)
server.serve_stdio()

The model is given exactly one MCP tool named wallet.transfer - there is no separate, unguarded way to move funds through this server. A BLOCK decision means do_transfer never runs. Both this and enforce() share one implementation of "check, maybe route WARN to a human, run only if not blocked, report the real outcome back" (guardrail/enforcement.py) - not two independently-maintained copies of the same guarantee.

Getting a human to actually confirm a WARN

on_warn is the hook — Guardrail ships two ready-made implementations:

Local web UI (guardrail/confirmation/web_ui.py) — a tiny built-in server (stdlib only, no Flask) with Approve/Reject buttons. The wrapped function blocks until someone clicks one, or times out (fails closed — timeout means reject, not "allow by default").

from guardrail.confirmation.web_ui import ConfirmationServer

confirmation = ConfirmationServer(port=8787, timeout_seconds=300)
confirmation.start(open_browser=True)

@enforce(engine, tool_name="wallet.transfer", on_warn=confirmation.request_confirmation)
def transfer(...): ...

Try it live: python3 examples/example_web_confirmation.py, then open http://localhost:8787.

Terminal prompt (guardrail/confirmation/cli_ui.py) — for scripts and local testing where a browser is overkill:

from guardrail.confirmation.cli_ui import cli_confirm

@enforce(engine, tool_name="wallet.transfer", on_warn=cli_confirm)
def transfer(...): ...

Neither is required — on_warn is just a function (decision) -> bool, so a Slack message, a ticket, or anything else you already use works too.

Writing a policy

Policies are plain YAML — see policies/default.yaml for a real, working starting point (11 confirmation-gated tools, 10 destructive-pattern checks, numeric caps, domain rules, rate limits, all commented).

numeric_caps limits each tool independently — wallet.transfer capped at 1000/day and wallet.approve capped at 1000/day separately means an agent using both can still move 2000/day combined. aggregate_caps closes that: every tool listed in the same group draws from one shared running total, e.g.

aggregate_caps:
  daily_money_movement:
    tools:
      wallet.transfer: amount
      wallet.approve: amount
    window_seconds: 86400
    max_unknown_agent: 5
    max_known_agent: 1000

Only confirmed spend counts toward the total: a BLOCKed request never adds anything, and a request that's provisionally recorded (because its own check passed) is refunded if the real action later turns out not to have succeeded — engine.record_outcome(request_id, "error"), called automatically by both enforce() and the enforced MCP server (they share one implementation of this, guardrail/enforcement.py) when the real executor raises, or when a WARN a human rejects results in a BlockedActionError. Real enforcement of this therefore has the same caveat as everything else that depends on record_outcome being called: it works fully under enforce() and the enforced MCP server (see below); under the advisory-only MCP server (#2 above), a provisionally-recorded amount just stays recorded, since nothing ever reports back whether the action actually happened. See guardrail/storage/aggregate_spend.py's module docstring for the full picture.

No code changes needed to adjust any of this — edit the YAML, restart the process (or the MCP server).

Running the tests

pip install -r requirements.txt
PYTHONPATH=. python3 -m unittest discover -s tests -v

134 tests: rule evaluation, the full engine pipeline (real SQLite-backed rate limiting, aggregate spend tracking, and audit persistence), the enforce decorator and the enforced MCP server (both proving a BLOCK genuinely prevents the real action from running, sharing one implementation of that guarantee), the advisory MCP server's JSON-RPC handling, the confirmation web UI over real HTTP requests against a live server, and a dedicated suite that checks the shipped policies/default.yaml — not just synthetic test policies — actually catches what it claims to.

What's honestly still missing

• Single-process SQLite by default. Fine for one agent process; for multiple replicas sharing rate limits/audit history, point every process at the same file on shared storage, or swap in a real database (the storage classes are small and easy to re-target).

• Secrets/PII redaction in the audit log is on by default. AuditLog redacts values whose key looks sensitive (password, api_key, authorization, ...) and a couple of high-confidence value shapes (PEM private key blocks, JWT-shaped strings) regardless of key name, recursing into nested dicts/lists - see guardrail/storage/redaction.py for exactly what is and isn't caught, and why general-purpose entropy heuristics were deliberately left out (too many false positives on ordinary UUIDs/hashes). Pass AuditLog(redact=False) to store arguments as-submitted, or extra_sensitive_keys={...} to redact additional field names specific to your tools.

• The default policy is a reasonable starting point, not a complete threat model. It catches well-known destructive shell/SQL patterns and obvious credential formats — extend argument_patterns for whatever your agents actually touch.

• The confirmation web UI has no auth. It binds to 127.0.0.1 by design (not exposed on the network), but anyone with local access to that port can approve/reject. Fine for a single developer's machine; put it behind your own auth if multiple people share the host.

None of these are mocked or faked — they're just not built yet, and they're the honest next steps if you adopt this.

Publishing this / getting people to actually use it

See PUBLISHING.md for a concrete checklist: MCP directories to submit to, what a listing needs, and what "done" looks like.

Related projects

Same author, same principle applied elsewhere:

• agentic-wallet-guardian-v3 - a security decision layer for AI agents transacting on-chain. MIT, 112 tests.

• x402-attest - cryptographically signed (Ed25519), independently verifiable attestations for agent-to-agent payment policy decisions. Early proof of concept.

• open-agent-attestation - vendor-neutral open spec (JWT+EdDSA) for signing agent policy decisions, verifiable by anyone. x402-attest above uses a custom format; this is the generalized version. Draft v0.1.

Project layout

guardrail/
    __main__.py            CLI implementation — also the `guardrail` console command
    mcp_server.py            MCP stdio server — also the `guardrail-mcp-server` console command
    core/
        models.py               ActionRequest, RuleMatch, GuardrailDecision (stdlib only)
        policy.py                 Policy loader (the one place PyYAML is used)
    rules.py                    Deterministic rule evaluators
    storage/
        rate_limiter.py           SQLite-backed sliding-window rate limiter
        audit.py                    SQLite-backed persistent audit log
    engine.py                    GuardrailEngine — orchestrates rules + rate limit + audit
    decorator.py                 enforce() — the unbypassable integration point
    confirmation/
        web_ui.py                    Local web UI for human approve/reject (stdlib http.server)
        cli_ui.py                      Terminal-prompt confirmation
    policies/default.yaml           Copy of the default policy bundled into the installed package
policies/default.yaml       Canonical, editable default policy (git-clone workflow)
cli.py                      Thin shim -> guardrail/__main__.py (for `python3 cli.py`)
mcp_server.py                Thin shim -> guardrail/mcp_server.py (for `python3 mcp_server.py`)
pyproject.toml               Package metadata — `pip install .` gives you `guardrail` + `guardrail-mcp-server`
.github/workflows/ci.yml      Runs the test suite + policy validation + package build on every push
examples/
    example_agent_usage.py       Decorator basics
    example_web_confirmation.py    Real browser-based approve/reject, live
tests/                       46 unit tests, all runnable with just PyYAML installed
CONTRIBUTING.md              How to add a rule type, ground rules
CHANGELOG.md                  Version history
PUBLISHING.md                 How to actually get this in front of people
landing/index.html             Static one-page site (open directly or host on GitHub Pages)