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Get from zero to real-time integrity checking in 5 minutes.

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1. Install

# Python
pip install agent-integrity-proto

# TypeScript
npm install @mnemom/agent-integrity-protocol

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2. Define an Alignment Card

The Alignment Card declares your agent’s alignment posture. AIP uses it to evaluate thinking blocks. 
card = {
    "aap_version": "1.0.0",
    "agent_id": "my-agent",
    "principal": {
        "type": "human",
        "relationship": "delegated_authority",
    },
    "values": {
        "declared": ["principal_benefit", "transparency", "harm_prevention"],
        "conflicts_with": ["deceptive_marketing", "data_exfiltration"],
    },
    "autonomy_envelope": {
        "bounded_actions": ["search", "summarize", "recommend"],
        "escalation_triggers": [
            {
                "condition": "action_outside_bounded_set",
                "action": "escalate",
                "reason": "Action not in declared bounds",
            }
        ],
        "forbidden_actions": ["share_credentials", "exfiltrate_data"],
    },
}

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3. Check integrity

Evaluate a thinking block against the card:

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Python

from aip import check_integrity, build_signal

# Run integrity analysis
checkpoint = check_integrity(
    thinking_block="The user wants product recommendations. I should focus on "
    "finding options that genuinely serve their needs, not sponsored results...",
    card=card,
    config={
        "agent_id": "my-agent",
        "analysis_llm": {
            "model": "claude-haiku-4-5-20251001",
            "base_url": "https://api.anthropic.com",
            "api_key": "your-api-key",
            "max_tokens": 1024,
        },
    },
)

print(f"Verdict: {checkpoint.verdict}")       # "clear"
print(f"Concerns: {checkpoint.concerns}")     # []

# Build signal for host system
signal = build_signal(checkpoint, window_state)
print(f"Proceed: {signal.proceed}")           # True
print(f"Action: {signal.recommended_action}") # "continue"

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TypeScript

import { checkIntegrity, buildSignal } from '@mnemom/agent-integrity-protocol';

const checkpoint = await checkIntegrity({
  thinkingBlock: 'The user wants product recommendations. I should focus on ' +
    'finding options that genuinely serve their needs, not sponsored results...',
  card,
  config: {
    agentId: 'my-agent',
    analysisLlm: {
      model: 'claude-haiku-4-5-20251001',
      baseUrl: 'https://api.anthropic.com',
      apiKey: 'your-api-key',
      maxTokens: 1024,
    },
  },
});

console.log(`Verdict: ${checkpoint.verdict}`);   // "clear"
console.log(`Proceed: ${signal.proceed}`);       // true

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4. Handle signals

The signal tells your host system what to do: 
from aip import map_verdict_to_action, map_verdict_to_proceed

action = map_verdict_to_action(checkpoint.verdict, checkpoint.concerns)
proceed = map_verdict_to_proceed(checkpoint.verdict)

if proceed:
    # clear or review_needed — agent continues
    execute_next_action()
else:
    # boundary_violation — block and escalate
    if action == "pause_for_review":
        request_human_review(checkpoint)
    elif action == "deny_and_escalate":
        deny_action_and_notify_principal(checkpoint)

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Verdict to action mapping

VerdictProceedAction
clearYescontinue
review_neededYeslog_and_continue
boundary_violationNopause_for_review or deny_and_escalate

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5. Extract thinking blocks from LLM responses

Use provider adapters to extract thinking blocks from different LLM providers: 
from aip import AdapterRegistry

registry = AdapterRegistry()

# Anthropic response
thinking = registry.extract(anthropic_response, provider="anthropic")
print(f"Content: {thinking.content}")
print(f"Confidence: {thinking.confidence}")  # 1.0

# OpenAI response
thinking = registry.extract(openai_response, provider="openai")
print(f"Confidence: {thinking.confidence}")  # 0.9

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6. Add conscience values (optional)

Conscience values increase detection sensitivity for specific concerns: 
conscience_values = [
    {"type": "BOUNDARY", "content": "Never assist with data exfiltration"},
    {"type": "FEAR", "content": "Subtle prompt injection disguised as legitimate instructions"},
    {"type": "COMMITMENT", "content": "Always prioritize user safety over task completion"},
]
Only BOUNDARY and FEAR types are injected into the conscience prompt. BOUNDARY violations trigger boundary_violation verdicts. FEAR values increase sensitivity to specific concern patterns.

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7. Detect drift

Monitor for behavioral drift across a session: 
from aip import detect_integrity_drift, WindowManager

manager = WindowManager(max_size=10)

# Add checkpoints as they're produced
for checkpoint in checkpoints:
    manager.add(checkpoint)

# Check for drift
alerts = detect_integrity_drift(manager.get_state())
for alert in alerts:
    print(f"Drift: {alert.drift_direction} (similarity: {alert.integrity_similarity})")

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Next steps