Abe MCP server

Abe

Act. Block. Escalate.

Abe is a free, open-source guardrail that checks every AI agent action before it happens.

Your agent is about to pay an invoice, buy servers, delete a table or email a customer. Abe checks the action against your rules in under a millisecond, on your machine, and answers ACT, BLOCK or ESCALATE, with a tamper-evident record of why.

▶ Watch the 40-second demo: an AI agent tries to spend $12,500 twice. Rules decide the easy calls; Flow catches the duplicate and approves the real renewal. (Run it yourself)

Free. Really.

Apache-2.0. No account, no API key, no network, no model, no usage limits. Every check runs on your machine, at no cost, forever.

Why teams put Abe in front of their agents

• Stops expensive mistakes: duplicate payments, over-budget purchases, force-pushes to main, DROP TABLE in production.
• Same answer every time: plain YAML rules you can read, test, and replay against past decisions before going live.
• Proof for auditors: every decision gets a hash-verified record (optionally Ed25519-signed) showing which rule fired and why.
• Works with any agent: Python, TypeScript, a local HTTP sidecar, Docker, or MCP (Claude, Cursor and other MCP clients).
• No-risk rollout: shadow mode runs beside your current approval process and shows where Abe would agree.
• Fails closed: bad input or a broken rule returns ESCALATE, never ACT.

Pay only for the close calls (optional)

When rules can't decide, Abe returns ESCALATE. By default that goes to a person. Add --resolver flow and only that action is sent to Flow (redacted), which returns a decision, a reason, and what would prove it wrong, for about $0.03 to $0.05 each. ACT and BLOCK never leave your machine. Details below.

Quick start

pip install abe-ai            # or: npm install abe-ai
abe init                      # writes abe-policy.yaml + request.json
abe check request.json

Decision: ESCALATE
Reason: FINANCIAL_THRESHOLD_EXCEEDED
Rules: purchase_review_limit
Risk: MEDIUM
Record: jgr_01M3Q4...

Python

from abe import Abe

abe = Abe("abe-policy.yaml")

result = abe.check(
    action={"type": "wire_transfer", "amount": 50000},
    context={"agent_id": "finance-agent"},
)

if result.decision == "ACT":
    ...                                   # execute exactly as evaluated
elif result.decision == "BLOCK":
    ...                                   # do not execute; tell the user result.reason_code
else:  # ESCALATE
    ...                                   # hold; ask a human, or let a resolver decide

result.record.to_dict()                   # the Judgment-Grounded Record (store it, ship it to your SIEM)

TypeScript

import { Abe } from "abe-ai";

const abe = new Abe({ policy: "./abe-policy.yaml" });
const result = await abe.check({
  action: { type: "purchase", amount: 12500, currency: "USD" },
  context: { agent_id: "procurement-agent", principal_id: "user_123" },
});
console.log(result.decision);             // "ESCALATE"

A policy

Shortened. The full README is on GitHub.