madcop 0.9.0

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

madcop

A personal AI agent that lives in your terminal, talks to any LLM, remembers what you taught it, and gets smarter the more you use it. Runs in one process. Stores everything locally. No cloud, no team, no platform.

What is madcop?

madcop is a personal AI agent for your terminal. You point it at a goal, it walks that goal through a plan-execute-replan loop, dispatches to specialised sub-agents when it helps, and writes what it learned to a local memory store. Next time you ask a similar question, it already knows.

It works with any OpenAI-compatible LLM endpoint (OpenAI, NVIDIA NIM, GLM, DeepSeek, Qwen, local Ollama). It does not need a gateway, a database server, or a cloud account. One pip install, one process, one SQLite file.

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". The framework was originally a supply-chain anomaly detector; v0.6.0 generalised it into a personal agent. The supply-chain skills are still there, but they're no longer the only thing it does.

What's new in v0.6.0 + v0.7.0

v0.5.0	v0.6.0 + v0.7.0
Fixed linear graph (ingest → detect → counterfactual → decision → summarise)	Plan-execute-replan loop with 4 modes (flash / standard / pro / ultra) + sub-agent fan-out
Single LLM call per session	Multi-model orchestration: auto router picks T1/T2/T3 per step, manual override via ~/.madcop/config.yaml
Single LLM provider (OpenAI-compat)	5 default providers, YAML config, env-var resolution, no vendor lock-in
2-layer memory (working + episodic)	4-layer memory: L1 working / L2 episodic / L3 semantic / L4 reflective, cross-layer retriever with time-decay, 3-mechanism self-growth engine
No self-growth	M1 distillation + M2 feedback reflection + M3 meta-pattern mining — the agent gets smarter with use
No CLI for free-text goals	madcop plan "..." --llm runs the new loop end-to-end with a real LLM
No sub-agents	Lead agent dispatches plan steps to general-purpose or bash sub-agents in parallel (capped 3), with context isolation and race-safe state machine. v0.7.2: RoutingStepExecutor lets you write PlanStep(subagent="general-purpose") and the main loop dispatches for you — no more 20-line router_fn hack.
No config file	madcop config init writes a default ~/.madcop/config.yaml; madcop config show resolves it
Ad-hoc eval	EvalRunner v2: cross-run trend tracking, robustness probing, adversarial safety checks
214 tests	564 tests

Quick taste

Plan a goal through the new loop. With a real LLM:

export MADCOP_OPENAI_BASE_URL=https://your-endpoint/v1
export MADCOP_OPENAI_API_KEY=sk-...
export MADCOP_OPENAI_MODEL=your-model
pip install madcop
madcop config init
madcop plan "Why did OMS cancellations spike in the last 24 hours?" --llm --mode pro

Sub-agent fan-out (v0.7.0) — dispatch a plan to a sub-agent:

from madcop.agent import PlanStep, Plan, ExecutionMode, PlanExecuteLoop, TrivialPlanner, FnStepExecutor
from madcop.agent.subagent import SubagentExecutor, ExecutorConfig, LLMRunner
from madcop.llm import OpenAICompatClient

client = OpenAICompatClient()
subagent_executor = SubagentExecutor(
    runner=LLMRunner(client, max_tokens=512, temperature=0.0),
    config=ExecutorConfig(max_concurrent=3),
    parent_tools=("read", "write", "bash"),
)

plan = Plan(steps=[
    PlanStep(name="ingest", action="gather the last 24h of OMS events"),
    PlanStep(name="analyse", action="classify findings by severity", subagent="general-purpose"),
    PlanStep(name="report", action="build a CSV summary", subagent="bash"),
])

loop = PlanExecuteLoop(my_planner, my_inline_executor)
result = loop.run("diagnose OMS cancel spike")

Design philosophy (v0.6.0+)

Five principles shaped v0.6.0 and v0.7.0. They're not features; they're decisions about what kind of software madcop wants to be.

1. Personal-first, not team-first. madcop runs on a laptop. One process, one SQLite file, one operator. No gateway, no Redis, no Kubernetes. If you need a multi-tenant agent platform, you're looking for the wrong tool — and that's fine, those exist.

2. Local-first, no cloud lock-in. Memory is a SQLite file at ~/.madcop/memory.db. Trends are a JSONL file. Eval results are JSON. You can cat everything, grep everything, and back up everything with rsync. There is no Langfuse or LangSmith to log into.

3. Self-growth over time. madcop is the only mainstream AI agent framework (that we know of) where the memory layer is the primary deliverable, not an afterthought. The 3-mechanism 成长 engine means that the longer you use madcop, the more it knows about your domain, your preferences, and your meta-strategies.

4. Cost-aware routing as a first-class concern. Every step of every run can pick a different model. The auto router scores each step on 4 signals (structural / domain / context / user) and picks T1 (reasoning) / T2 (balanced) / T3 (fast). Manual override per provider in ~/.madcop/config.yaml. Built because shipping "always-call-gpt-4" demos is a lie.

5. The harness is small enough to read in one sitting. The whole plan-execute-replan loop is ~90 lines. The router is ~300 lines. The memory layer is 6 modules averaging 200 lines each. The sub-agent executor is ~270 lines. We picked this deliberately — every line of indirection is a line you can't debug.

What's new in v0.7.0

v0.7.0 adds a sub-agent layer to the v0.6.0 plan-execute loop. The lead agent can now dispatch steps to specialised sub-agents that run in parallel, in isolated contexts, and cannot recursively spawn more sub-agents.

The pieces:

• SubagentSpec — a frozen dataclass describing a sub-agent (name, description, system_prompt, tools, disallowed_tools, max_turns, timeout). Two ships with v0.7.0: general-purpose (multi-step reasoning, inherits parent tools) and bash (shell command execution, tools = ("bash",)).
• SubagentResult + SubagentStatus — race-safe state machine with try_set_terminal(). The first writer of a terminal status wins; late writes are no-ops. The four terminal states are COMPLETED, FAILED, CANCELLED, TIMED_OUT.
• SubagentExecutor — runs sub-agents on a ThreadPoolExecutor capped at 3 (clamped to [1, 4]). Each sub-agent gets a deep copy of the parent's context (no leakage back). Cancellation is cooperative: set holder.cancel_event, the runner checks it between LLM calls.
• PlanStep.subagent — set this on any plan step to dispatch the step to a sub-agent instead of running it inline. The lead agent's plan-execute loop routes sub-agent steps through the executor; inline steps go through the v0.6.0 path.
• LLMRunner — a real-LLM-backed Runner that wraps any ChatClient and adapts it to the sub-agent Runner protocol.

Three things we deliberately did not do:

• Sub-agents cannot spawn sub-agents. The task tool is hard-coded as disallowed; this prevents recursive explosions.
• We did not implement custom sub-agents from user config. That's v0.7.1.
• We did not build an async executor. The thread pool is enough for personal use; if you need asyncio, open an issue.

What madcop actually does

Five end-to-end demos ship with the repo. Run them with python -m madcop ... or directly with python examples/...py. The first four use madcop's own supply-chain rule pack; the last one is the new plan-execute loop that you point at any goal.

#	Demo	How to run
1	v0.5.0 LangGraph agent on cold-chain stream	python -m madcop run agent
2	v0.6.0 plan-execute loop on a free-text goal (mock)	python -m madcop plan "your goal"
3	v0.6.0 plan-execute loop on a free-text goal (real LLM)	MADCOP_OPENAI_API_KEY=*** python -m madcop plan "your goal" --llm
4	v0.7.0 sub-agent fan-out (mock)	python examples/v070_subagent_demo.py
5	v0.7.1 real-LLM golden-set (3 scenarios)	python examples/v071_golden_benchmark.py

Demos 2-5 are the new world. They exercise the plan-execute loop, the 4-layer memory, the self-growth engine, and the sub-agent fan-out. They cost real money when you point them at a real LLM (about a cent per demo with the default tier); they cost nothing in mock mode.

Architecture

The codebase is laid out in 6 layers, each independently testable and replaceable. Lower layers are pure-Python; upper layers call out to an LLM.

madcop/
  event.py            # L1: UnifiedEvent contract
  adapters/           # L1: OMS / TMS / WMS / BMS adapters
  anomaly/            # L2: CUSUM detector + 5 anomaly rules
  rca/                # L2: root-cause analysis graph
  counterfactual/     # L2: cost simulation
  decision/           # L2: operator-fatigue diff
  replay/             # L2: historical ROI replay
  llm/                # L3: ChatClient + OpenAICompatClient + MockClient
  strategy/           # L3: ModelRouter + ProviderRegistry + CostTracker + Scratchpad + ContextCompactor (v0.6.0)
  memory/             # L4: 4-layer SQLite+FTS5 memory + 3-mechanism growth (v0.6.0)
  agent/              # L5: LangGraph orchestrator (v0.5.0) + plan_execute (v0.6.0) + sub-agent (v0.7.0)
  config/             # v0.7.1: ~/.madcop/config.yaml loader
  eval/               # EvalRunner v2 with trend / robustness / adversarial (v0.6.0)

Each layer ships with its own tests. The full suite is 453 tests as of v0.7.1.

Installation

pip install madcop

Requires Python 3.10+. langgraph, rich, and openai are bundled as hard dependencies.

Quick start

# 1. Initialise your config (writes ~/.madcop/config.yaml with defaults)
python -m madcop config init
python -m madcop config show    # verify env-var resolution

# 2. Run the v0.5.0 deterministic agent on the cold-chain stream
python -m madcop run coldchain
python -m madcop run anomalies

# 3. Run the v0.6.0 plan-execute loop on a free-text goal
#    (mock client, no LLM, free)
python -m madcop plan "Why did OMS cancellations spike in the last 24h?"

# 4. Same as 3, but with a real LLM
export MADCOP_OPENAI_API_KEY=sk-...
export MADCOP_OPENAI_BASE_URL=https://api.openai.com/v1
export MADCOP_OPENAI_MODEL=gpt-4o-mini
python -m madcop plan "Why did OMS cancellations spike in the last 24h?" --llm

# 5. Sub-agent fan-out (v0.7.0)
python examples/v070_subagent_demo.py

# 6. Real-LLM golden-set benchmark (v0.7.1)
python examples/v071_golden_benchmark.py

Tests

pip install -e ".[dev]"
pytest

564 tests, all passing (Python 3.10–3.12, macOS / Linux). CI runs on every push via GitHub Actions. Coverage:

• L1 event contract (UTC validation, event type / source consistency)
• L2 detector (every rule, windowed-rule state machine, multi-zone bands)
• L2 RCA graph (forward/reverse traversal, empty chain, unknown subject)
• L3 CUSUM (Siegmund ARL₀→h, category baselines, persistent-shift detection)
• L3 LLM client (mock + real-OpenAI-compat, retry, usage tracking)
• L3 strategy (model router scoring, provider registry, cost tracking, scratchpad, compactor)
• L4 memory (4 layers, FTS5 search, time-decay retriever, growth engine)
• L5 plan-execute (4 modes, replan, step outcomes, aggregation)
• L5 sub-agent (spec, race-safe state, builtins, executor, LLMRunner)
• L5 routing executor (mixed inline + sub-agent plan dispatch)
• L6 eval (cases, trend, robustness, adversarial, integration)
• L6 config (loader, env-var resolution, defaults, malformed handling)

Why "madcop"?

The original name came from "the agent that goes mad for anomalies". 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". The framework has since grown past anomaly detection into a general personal AI agent, but the name stuck.

License

MIT. See LICENSE.

Contact

Lin Ruihan · [e-mail]