rein-ai 1.1.0

Governance layer for autonomous agents: regime detection, circuit breakers, strategy scoring, audit trail

Rein

Runtime kill-switch for autonomous AI agents. Rein gates every action your agent takes, scores it against live performance, and halts the system before things spiral.

pip install rein-ai

from rein_ai import Rein, ReinConfig
brain = Rein(cfg=ReinConfig.from_env()); await brain.start()

@brain.governed(source="llm_agent")
async def send_email(to, body): ...

One decorator. Framework-agnostic — works with LangChain, LlamaIndex, OpenAI Agents SDK, or your own loop.

Use it when: your agent could burn $200 in API calls in four minutes, send 80 emails to the wrong list, or keep trading after the data feed died — and content guardrails won't catch any of it because the outputs were "valid." Rein watches outcomes, not text.

Originally extracted from a production Kalshi trading bot where it caught 12 runaway trades in week one. Origin story.

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The problem

Existing AI safety tooling validates content. Guardrail libraries check whether an LLM's input or output contains PII, profanity, or prompt-injection strings. That's necessary but insufficient. Once an agent is actually taking actions (placing trades, sending emails, calling paid APIs, posting to production), content validation is too late. You need a runtime governor that can cut off a misbehaving agent mid-flight based on observed outcomes, not just text.

Rein is that governor.

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What Rein is

A Python library that sits inline with any autonomous system and gates every action. It:

1. Classifies the current regime (normal / stressed / shock) from live signal inputs.
2. Scores each action source × action type with Bayesian decay. Stale performance gets discounted, recent performance compounds.
3. Halts the system when drawdown, error rate, stale-state, rate-limit storms, or anomaly detection trip a threshold.
4. Writes a tamper-evident audit log (JSONL + cryptographic chain) of every decision for compliance and postmortem.

Three things no other governance library has:

• Natural-language policy compiler. Write rules in English, get an enforceable config.
• Built-in adversarial red-team simulator. Attack your own policy before you ship.
• Regime-sliced scoring. A strategy that works in calm markets but dies in shocks is treated as two different strategies.

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Rein vs alternatives

	Rein	Guardrails AI	NeMo Guardrails	Custom code
Content validation (PII, profanity)	—	✅	✅	DIY
Runtime action governance	✅	—	—	DIY
Regime-aware decisions	✅	—	—	DIY
Natural-language policies	✅	partial	✅	—
Adversarial red-team simulator	✅	—	—	—
Tamper-evident audit log	✅	—	—	DIY
Framework-agnostic	✅	✅	✅	—

Rein and content-guardrail libraries are complementary: use Guardrails/NeMo to validate what the LLM says, use Rein to govern what the agent does.

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60-second quickstart

pip install rein-ai

import asyncio, time
from rein_ai import Rein, ReinConfig

async def main():
    brain = Rein(cfg=ReinConfig.from_env())
    await brain.start()

    # Gate every action
    decision = brain.gate(source="llm_agent", series="send_email")
    if not decision.allowed:
        print(f"blocked: {decision.reason}")
        return

    # ...execute the action...

    # Report outcome so Rein can learn
    await brain.record_fill(
        source="llm_agent", series="send_email",
        ticker="msg-123", filled=True,
        slippage_cents=0.0, attempt_at=time.time(),
    )
    await brain.shutdown()

asyncio.run(main())

Or use the decorator for one-line adoption:

@brain.governed(source="llm_agent")
async def send_email(to, body): ...

⭐ If this is the thing you've been writing yourself, a star helps the next person find it.

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Two things no one else has

1. Natural-language policy compiler

Stop writing YAML. Describe your policy in English:

from rein_ai import compile_policy

policy = compile_policy([
    "Cap each caller at 8 requests per second with bursts of 16",
    "Halt the portfolio when losses exceed 5 percent",
    "Alert when deny rate exceeds 70 percent over a 60 second window",
    "Detect runaway callers at 5x baseline",
])
brain = policy.build_brain(persist_dir="./state")

Each line is parsed into a structured enforcement rule. Review the compiled output before deploy:

for rule in policy.rules:
    print(rule.rule_type, rule.params)

2. Adversarial red-team simulator

Before you trust your policy, attack it:

from rein_ai import run_red_team

report = await run_red_team(brain)
print(report.render())
# Red team report — catch rate: 100%
#   [BLOCKED] runaway_loop       at iter 16
#   [BLOCKED] deny_storm         at iter 9
#   [BLOCKED] enumeration        at iter 17
#   [BLOCKED] portfolio_drain    at iter 0
#   [BLOCKED] cost_bomb          at iter 0

Five registered attack classes out of the box (runaway, deny-storm, enumeration, portfolio-drain, cost-bomb). Ship policies you've already verified catch the obvious exploits.

Full runnable demo: examples/policy_and_redteam/demo.py.

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How it works

         ┌─────────────────────────────────────────────────────────┐
         │                    Your Agent / App                     │
         │   (LLM tool call, trade, email, scraper, RPA step…)     │
         └───────────────────────────┬─────────────────────────────┘
                                     │  brain.gate(source, series)
                                     ▼
         ┌─────────────────────────────────────────────────────────┐
         │                          Rein                          │
         │                                                         │
         │  ┌─────────────┐  ┌─────────────┐  ┌────────────────┐   │
         │  │  Regime     │  │  Strategy   │  │   Rate         │   │
         │  │  Detector   │→ │  Scorer     │  │   Limiter      │   │
         │  │ normal/     │  │  Bayesian,  │  │   token-bucket │   │
         │  │ stress/     │  │  time-decay │  │   per caller   │   │
         │  │ shock       │  │             │  │                │   │
         │  └─────────────┘  └─────────────┘  └────────────────┘   │
         │         │                 │                 │           │
         │         ▼                 ▼                 ▼           │
         │  ┌───────────────────────────────────────────────────┐  │
         │  │              Decision Engine                      │  │
         │  │   GREEN (allow) · YELLOW (shadow) · RED (block)   │  │
         │  └───────────────────────────┬───────────────────────┘  │
         │                              │                          │
         │  ┌─────────────┐  ┌──────────▼──────────┐  ┌──────────┐ │
         │  │  Circuit    │  │   Anomaly           │  │  Audit   │ │
         │  │  Breaker    │  │   Detector          │  │  Log     │ │
         │  │ drawdown/   │  │ deny-storm,         │  │ JSONL    │ │
         │  │ error-rate/ │  │ enumeration,        │  │ hash-    │ │
         │  │ staleness   │  │ runaway callers     │  │ linked   │ │
         │  └─────────────┘  └─────────────────────┘  └──────────┘ │
         └───────────────────────────┬─────────────────────────────┘
                                     │  AllowDecision(allowed, reason)
                                     ▼
                         Execute action  /  Halt  /  Log

Subsystem	Job
RegimeDetector	Classifies state of the world (normal / stressed / shock) from live signals
StrategyScorer	Bayesian scoring per (source, series) with time-decay; learns what works now
CircuitBreaker	Halts the system on drawdown, error rate, or staleness thresholds
RateLimiter	Per-caller token-bucket throttling with burst handling
AnomalyDetector	Deny-storm, enumeration, and runaway-caller detection
AuditLog	Tamper-evident JSONL chain — every decision timestamped and hash-linked

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Integrations

• Anthropic Claude — drop-in GovernedToolRunner auto-governs all tool use

  from rein_ai.adapters.anthropic import GovernedToolRunner
  runner = GovernedToolRunner(brain=brain, source="claude_agent", impls={...})
  result = await runner.execute(tool_use_block)
  

• LangChain — governed wrapper for any BaseTool
• FastAPI admin router — /rein/state, /rein/halt, /rein/resume, /rein/audit endpoints (optional extra: pip install rein-ai[api])
• Framework-agnostic — @brain.governed() decorator works on any async function

See examples/llm_agent_governor/ for non-trading use cases.

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CLI

rein compile --policy policy.txt        # show what a policy expands to
rein redteam --policy policy.txt        # run adversarial simulator
rein attacks                            # list registered attack scenarios

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Configuration

All thresholds are env-overridable. Default prefix REIN_; pass a custom prefix to ReinConfig.from_env(prefix="MYAPP_REIN_") for multi-tenant apps.

Key vars:

Var	Default	Purpose
REIN_ENABLED	true	Master on/off
REIN_SHADOW	true	Observe without blocking (safe default)
REIN_EDGE_RED_P	0.85	Edge-axis probability threshold for RED status
REIN_PORTFOLIO_FLOOR_PCT	-0.05	Drawdown % that halts the portfolio
REIN_REGIME_TICK_SECONDS	30.0	How often the regime detector polls

Full list in src/rein_ai/config.py.

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Status

• 135 tests, all passing
• Running in production (Predbot trading bot, since 2026-04-16)
• Low overhead — gate() mean 1.7 μs, p99 2.7 μs, 500k+ calls/sec on a single core (see scripts/bench.py; 2021 MacBook Pro, Python 3.14)
• Shadow-mode protocol recommended before enforcement — see docs/shadow-protocol.md

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Rein-AI Pro

A production layer for teams running real money, real users, or real compliance surface through their agents — extended attack library, calibrated detector presets, managed cloud, and a commercial AGPL waiver. Details, pricing, and contact: PRO.md.

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License

Dual-licensed:

• AGPL-3.0 (default — see LICENSE). Free for OSS, research, and self-hosted use. If you run Rein as part of a network service, your service must also be released under AGPL-3.0.
• Commercial License (see COMMERCIAL-LICENSE.md) for proprietary / SaaS use without copyleft obligations. Contact [e-mail] for a quote.

Contributors: see CLA.md.

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Created and maintained by John N.W. Hampton Jr ([e-mail]). Copyright © 2026 John N.W. Hampton Jr.