madcop 0.1.0

madcop — the supply chain cop that goes mad for anomalies. Pluggable LangGraph framework: detect, diagnose, decide.

madcop

mad + cop — the supply chain cop that goes mad for anomalies. Pluggable LangGraph framework: from "detect" to "diagnose" to "decide", with self-evolution.

What is madcop?

madcop is a pluggable framework that turns raw supply chain telemetry (orders, shipments, warehouse readings, contracts) into decision prompts with full causal chains. Where most tools stop at "alert fired", madcop walks the chain back to the human decision that made the anomaly possible.

The name is short for mad cop — a cop that goes mad for anomalies. Not in a punitive sense, but in the sense of "won't let a single anomaly go untraced to its source."

Why?

A typical supply chain alert reads:

⚠️ Cold-chain temperature exceeded threshold at 14:30.

That tells you what happened, not why it could happen. madcop answers the second question:

The temperature breach on SHIP-2026-0615-CG-SH traces back to a BD decision (DEC-2026-03-12-N3) made three months earlier to accept the supplier's "fine equals exemption" concession. That decision shaped CLAUSE-04 — a passive clause that punishes the breach but does not prevent it. The contract was signed with 冷链速运 at Q1 cost-cutting pressure, and the shipment is now exposed to the same failure mode.

The PM framing: an alert without a cause is a notification. An alert with a cause is a decision prompt. madcop's job is to bridge the two.

Architecture (4 layers, 1 graph)

┌──────────────────────────────────────────────────────┐
│  L4  Strategy Router    — zero-code YAML policies     │
├──────────────────────────────────────────────────────┤
│  L3  LangGraph          — detect → diagnose → decide  │
│                         → learn (state machine)        │
├──────────────────────────────────────────────────────┤
│  L2  Anomaly Engine     — rules · RCA · counterfactual│
├──────────────────────────────────────────────────────┤
│  L1  Unified Data Layer — OMS/TMS/WMS/BMS adapters    │
└──────────────────────────────────────────────────────┘

L1 — Unified Data Layer (madcop/event.py, madcop/adapters/) A UnifiedEvent is the lingua franca. Every adapter implements BaseAdapter and yields events with frozen, UTC-validated, severity-rated fields.

L2 — Anomaly Engine (madcop/anomaly/, madcop/rca/) 5 shipped rules + a Detector that orchestrates them. RCA walks a typed property graph from any finding back to a decision.

L3 — LangGraph Orchestrator (madcop/graph/) — planned, W5 A typed state machine that sequences detect → diagnose → decide → learn.

L4 — Strategy Router (madcop/strategy/) — planned, W7 YAML policies + feedback-weighted registry. Self-evolution is real here: weekly reports roll up the week's findings, and policies are ranked by their rolling effectiveness.

What's shipped today (W1 + W2 + W3)

Layer	Component	Status
L1	UnifiedEvent with UTC + severity + source/event_type validation	✅
L1	BaseAdapter contract + WMS mock (cold-chain)	✅
L2	Detector + 5 rules (cold-chain temp / sustained / OMS cancel / TMS lead / BMS score)	✅
L2	KnowledgeGraph + trace() + explain() RCA	✅
L2	Cold-chain seed graph (5 nodes, 4 edges)	✅
L4	Strategy registry, weekly report, LLM backend	🔜 W7

Installation

pip install madcop

Or from TestPyPI (the current published version):

pip install --index-url https://test.pypi.org/simple/ madcop

Quick start

# W1: see the raw event stream
python -m madcop run coldchain

# W2: detect anomalies
python -m madcop run anomalies

# W3: detect + trace each finding to a root cause (the headline feature)
python -m madcop run rca

What run rca looks like

madcop RCA demo — 3 finding(s) on SHIP-2026-0615-CG-SH

━━━ finding 1/3 ━━━
  rule:    wms.coldchain.temperature_breach
  summary: Cold-chain temperature -14.2°C exceeds threshold -15.0°C by 0.8°C
  chain:   5 step(s), root cause:
╭──────────────────────────────── root cause ────────────────────────────────╮
│ decision DEC-2026-03-12-N3 (BD 接受乙方'罚款即免责'让步) (by BD-Lin) —       │
│ rationale: Q1 降本压力 → shaped clause CLAUSE-04 (温控异常通知条款) PASSIVE  │
│ ("温控异常时, 承运商应在 30 分钟内书面通知甲方, 逾期每日扣 0.5% 服务费") →   │
│ under contract CONT-2026-0312 (冷链速运 / 2026 年度框架) → carried by        │
│ 冷链速运 → on shipment SHIP-2026-0615-CG-SH (广州→上海, 冷链, 2026-06-15)    │
╰──────────────────────────────────────────────────────────────────────────────╯

Tests

pip install -e ".[dev]"
pytest

45 tests, all passing. They cover the L1 contract (UTC validation, event type / source system consistency, adapter behavior), the L2 detector (every rule, plus state-machine semantics for windowed rules), and the RCA graph (forward/reverse traversal, empty chain, unknown subject).

Roadmap

See ROADMAP.md. 8 weeks, 1 commit per week. Current: W3 done, ready to push.

Requirements

• Python 3.10+
• rich >= 13.0

Project status

Alpha. The architecture is real, the data layer is real, the anomaly rules are real, and the RCA traces are real. The adapters are mock data today; real wire integrations to OMS / TMS / WMS / BMS systems are scoped for later (see Roadmap).

License

MIT. See LICENSE.

Why "madcop"?

When the user asked for a name for "the agent that goes mad for anomalies", the obvious answer was mad + cop. The product is a cop that goes mad for anomalies — not in a punitive sense, but in the sense of "won't let a single anomaly go untraced to its source."

Contact

Lin Ruihan · [e-mail]