Abe

Name: Abe
Author: flowinfosystems-index

@flowinfosystems-indexMCP server · External

Free, local check before an AI agent acts: your rules return ACT, BLOCK or ESCALATE.

AI agent governancev0.2.3Apache-2.0Python

Freeno charge per call

How to install

Runs on your machine from its package; the steps are below.

Watch the demo: an AI agent tries to spend $12,500 twice — From the README

What it does

Reads
Writes
High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

PyPIabe-aiv0.2.3

Run

uvx abe-ai

MCP client config

{
  "mcpServers": {
    "abe": {
      "command": "uvx",
      "args": [
        "abe-ai"
      ]
    }
  }
}

README6 min readView on GitHub

Abe, a friendly traffic light giving a thumbs up

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

bash

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

text

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

Python

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

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

yaml

version: "0.1"
defaults: { unmatched: ESCALATE }           # nothing matched -> needs judgment (fail closed)
rules:
  - id: purchase_hard_limit
    when: { all: [ { field: action.type, op: eq, value: purchase }, { field: action.amount, op: gt, value: 100000 } ] }
    decision: BLOCK
    reason_code: HARD_POLICY_VIOLATION
  - id: purchase_review_limit
    when: { all: [ { field: action.type, op: eq, value: purchase }, { field: action.amount, op: gt, value: 10000 } ] }
    decision: ESCALATE
    reason_code: FINANCIAL_THRESHOLD_EXCEEDED
  - id: routine_purchase
    when: { all: [ { field: action.type, op: eq, value: purchase }, { field: action.amount, op: lte, value: 500 } ] }
    decision: ACT
judgment_required:
  - action.type: terminate_employee

Also: authorization per agent, required evidence, risk and irreversibility thresholds, confidence minimums. BLOCK beats ESCALATE beats ACT. Bad input or a broken rule returns ESCALATE / EVALUATION_FAILURE, never ACT. Full reference: SPEC.md.

Test before you enforce

Replay saved requests through a policy before it touches real money. Label each case with what your people decided and Abe reports where it disagrees; compare against the live policy to see exactly which decisions a change moves.

bash

# cases.jsonl: one request per line, or {"id": "inv-1042", "request": {...}, "expected": "BLOCK"}
abe replay cases.jsonl --policy new-policy.yaml --baseline abe-policy.yaml
#   Replayed 1204 case(s) against ...      ACT 1088   BLOCK 31   ESCALATE 85
#   Changed vs baseline: 3                 inv-1042: ACT -> ESCALATE  [purchase_review_limit]
#   Agreement with expected: 1198/1204 (99.5%)
abe replay cases.jsonl --policy new-policy.yaml --baseline abe-policy.yaml --fail-on-change   # CI: exit 1 if anything moved

Shadow mode runs Abe beside your current approval process. Decisions, results and exit codes are exactly what enforce mode would return, and every record is marked "mode": "shadow". Keep approving the way you do today, report what your people decided, and each record's falsifier shows whether they agreed.

python

from abe.stores import SQLiteStore

abe = Abe("abe-policy.yaml", mode="shadow", store=SQLiteStore("abe.db"))    # or: abe serve --shadow · ABE_MODE=shadow
r = abe.check(action={"type": "purchase", "amount": 48000}, context={"agent_id": "ap-agent"})
# ... your existing approval happens ...
abe.record_outcome(r.records[0], "approved")    # people approved; if Abe said BLOCK, the falsifier triggers

When the agreement rate is where you want it, drop mode="shadow" and Abe enforces.

Every way to run it


Library	`pip install abe-ai` · `npm install abe-ai`
CLI	`abe check · replay · validate-policy · validate-record · conformance · keygen`
Local HTTP sidecar	`abe serve --policy abe-policy.yaml` → `POST http://127.0.0.1:8787/v1/check` (any language)
Docker sidecar	`docker run -e ABE_TOKEN=… -v $PWD:/policy:ro -p 127.0.0.1:8787:8787 ghcr.io/flowinfosystems-index/abe`
MCP server	`pip install "abe-ai[mcp]"` → `abe mcp --policy /abs/abe-policy.yaml` (tool: `fjp_check_action`)
+ Flow on escalations	add `--resolver flow` (or `ABE_RESOLVER=flow`) with `FLOW_API_KEY` to `check`, `serve`, `mcp` or the Docker image

Records you can audit

Every call returns a record with the decision, every matched rule, the exact policy hash, the request hash, and a SHA-256 record_hash (optionally Ed25519-signed with abe keygen). Records never change: outcomes and resolutions are appended as linked records. Each one is a valid FJP-CONF v0.1 Judgment-Grounded Record.

bash

abe check request.json --record-out jgr.json --sign-key abe-signing-key.pem
abe validate-record jgr.json --public-key abe-signing-key.pub.pem

When rules aren't enough: Flow

Abe is deliberately useful without Flow. When it returns ESCALATE, you can hand that one action to Flow's judgment service and get a verb, a reason and a falsifiable record back:

python

from abe import Abe
from abe_flow import FlowResolver                     # pip install abe-flow

abe = Abe("abe-policy.yaml", resolver=FlowResolver(api_key=os.environ["FLOW_API_KEY"]))

No Python? The CLI, sidecar and MCP server take the same resolver (pip install "abe-ai[mcp,flow]"):

json

{"mcpServers": {"abe": {"command": "abe",
  "args": ["mcp", "--policy", "/abs/abe-policy.yaml", "--resolver", "flow"],
  "env": {"FLOW_API_KEY": "fjp_live_..."}}}}

abe serve --resolver flow and docker run -e ABE_RESOLVER=flow -e FLOW_API_KEY=… … work the same way. If Flow is out of credits, the MCP result says so and links to the refill page; the action stays ESCALATE.

ACT and BLOCK never leave your machine. Only escalations are sent (redacted), and if Flow is unreachable the answer simply stays ESCALATE. Human-approval escalations are never auto-resolved.

Conformance

bash

abe conformance --bench        # FJP-CONF v0.1 Gate profile, Level 3 — 160 checks, offline

Repository

SPEC.md                  the normative specification
schemas/                 JSON Schemas (request, response, record, policy)
python/                  abe-ai (PyPI): core, CLI, HTTP, MCP, stores, signing, conformance
typescript/              abe-ai (npm): same API, same records, same hashes
flow-resolver/           abe-flow (PyPI): Gate → Flow for ESCALATE only
conformance/fixtures/    golden decisions + canonical-JSON vectors shared by every SDK
examples/                the $12,500 demo, purchasing, software agent, physical agent, travel (+ Judd), Flow

Apache-2.0. FJP™, Abe™ (Abe) and FJP-CONF™ are trademarks of Flow Information Systems — see TRADEMARKS.md.

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