Python SDK Reference

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from mima_governance import MimaGovernance, AuthorisedBy
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mima = MimaGovernance(
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    api_key="mima_ext_...",          # required
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    system_name="my-ai-pipeline",    # required — matches mima.attest() system_name
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    workspace_id=None,               # resolved automatically from API key
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    base_url="https://api.mima.ai",  # override for self-hosted deployments
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    agent_name=None,                 # defaults to system_name
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    signing_key=None,                # 32-byte hex key for HMAC-SHA256 GRC signing
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    authorised_by=AuthorisedBy(      # default principal for all attestations
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        identity="alice@example.com",
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        role="ml-engineer",
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    ),
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    on_error="warn",                 # "warn" | "raise" | "silent"
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    batch_flush_interval=30.0,       # seconds
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    batch_max_size=100,
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)

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@mima.attest(tool_name="classify_document", model_id="claude-opus-4-6")
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def classify(document: str) -> str:
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    return llm.complete(document)

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@mima.attest(tool_name="generate_summary")
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async def summarise(text: str) -> str:
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    return await async_llm.complete(text)

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import hashlib
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input_hash  = hashlib.sha256(json.dumps(payload).encode()).hexdigest()
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output_hash = hashlib.sha256(json.dumps(result).encode()).hexdigest()
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result = mima.push(
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    "generate_report",
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    input_hash,
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    output_hash,
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    model_id="claude-opus-4-6",
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)

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with mima.trace("run_pipeline") as t:
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    t.set_input(input_docs)
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    output = pipeline.run(input_docs)
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    t.set_output(output)
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    t.set_model_id("gpt-4o")

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with mima.batch() as b:
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    for item in work_queue:
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        result = process(item)
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        b.add("process_item", input=item, output=result)
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# Flushes on exit — single batch API call.

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from mima_governance.approvals import ApprovalDenied, ApprovalTimeout
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try:
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    token = mima.require_approval(
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        action_type="high_risk_decision",
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        context={"affected_users": 1200, "policy": "credit_v3"},
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        timeout_seconds=300,
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        approver="bob@example.com",
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        on_timeout="raise",
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    )
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except ApprovalDenied:
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    return {"status": "rejected"}
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# Use the token to link attestation to the approval record.
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@mima.attest(tool_name="credit_decision", approval_token=token)
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def make_credit_decision(application):
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    return model.predict(application)

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@dataclass
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class GrcResult:
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    record_id: str          # "" on failure
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    record_type: str
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    mapped_controls: list   # e.g. ['EUAIA_ART9', 'ISO42001_6_1']
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    detail: str             # "ok" on success, error message on failure

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mima.ai_risk_assessment(
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    system_name="loan-scorer",
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    risk_tier="high",
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    use_case="Automated credit scoring for consumer loans",
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    intended_purpose="Score loan applications and surface them for human review",
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    impact_domains=["credit", "consumer_finance"],
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    art5_self_assessment=True,   # certifies no Art.5 prohibited practices
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    assessor="alice@example.com",
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    annex_iii_category="essential_services",  # required for high-risk
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    environment="production",
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    system_version="v2.1.0",
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    technical_doc_url="https://docs.internal/loan-scorer/annex-iv",
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)

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mima.model_evaluation(
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    model_id="loan-scorer-v2.1",
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    dataset="holdout-q2-2026",
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    accuracy=0.94,
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    evaluated_by="alice@example.com",
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    evaluation_type="quarterly",   # "initial" | "quarterly" | "triggered"
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    bias_metrics={"demographic_parity": 0.02, "equal_opportunity": 0.01},
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    robustness_score=0.91,
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    passed_threshold=True,
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)

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mima.human_oversight(
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    decision_id="loan-app-48291",
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    ai_recommendation="approve",
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    human_decision="reject",
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    reviewer="bob@example.com",
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    rationale="Application flagged for fraud indicators not in model training data",
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    model_id="loan-scorer-v2.1",
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)
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# override=True is inferred automatically (ai_recommendation != human_decision)

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mima.training_data_governance(
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    model_id="loan-scorer-v2.1",
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    dataset_id="training-set-2026-q1",
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    record_count=2_400_000,
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    bias_checks_performed=True,
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    approved_by="data-governance@example.com",
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    data_sources=["internal_originations", "credit_bureau_x"],
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    data_categories=["financial_history", "demographics"],
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    known_limitations="Underrepresented: self-employed applicants pre-2020",
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)

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mima.incident_report(
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    title="Model output hallucination on edge case inputs",
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    severity="high",   # "critical" | "high" | "medium" | "low"
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    description="Model returned invalid IBAN numbers for 3 users on 2026-06-01",
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    affected_systems=["payment-processor-ai"],
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    detected_at="2026-06-01T14:22:00Z",
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    authority_notified_at="2026-06-01T18:00:00Z",
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)

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mima.access_review(
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    user="contractor@external.com",
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    resource="model-training-pipeline",
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    granted=False,
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    reviewed_by="alice@example.com",
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    review_type="periodic",
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    reason="Contractor engagement ended 2026-05-31",
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)

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mima.change_event(
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    type="prompt_update",
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    by="alice@example.com",
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    description="Updated system prompt to add output format constraints",
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    environment="production",
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    system="customer-support-ai",
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    change_id="CHG-4821",
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)

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mima.vendor_risk(
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    vendor="OpenAI",
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    tier="high",   # "critical" | "high" | "medium" | "low"
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    last_reviewed="2026-06-01",
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    findings=2,
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    contacts=["security@openai.com"],
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)

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mima.policy_acknowledged(
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    policy="AI Use Policy",
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    user="alice@example.com",
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    version="v3.1.0",
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    acknowledgment_type="initial",  # "initial" | "renewal" | "update"
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    policy_url="https://internal.example.com/policies/ai-use/v3.1.0",
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    channel="in-app",
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)

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mima.model_drift_event(
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    model_id="loan-scorer-v2.1",
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    metric="f1_score",
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    baseline=0.94,
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    current=0.88,
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    threshold=0.90,
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    drift_type="performance",   # "performance" | "data" | "concept"
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    detected_by="monitoring-agent",
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    action_taken="Model retraining scheduled for 2026-07-01",
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)

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mima.governance_review(
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    reviewed_by="ciso@example.com",
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    report_type="quarterly",
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    frameworks_reviewed=["EU_AI_ACT", "ISO_42001", "SOC2"],
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    overall_readiness=78,
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    action_items=4,
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    notes="Three open gaps in Art.9 documentation. Drift monitoring not yet wired.",
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)

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from mima_governance.async_client import AsyncMimaGovernance
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mima = AsyncMimaGovernance(
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    api_key="mima_ext_...",
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    system_name="async-pipeline",
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)
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@mima.attest(tool_name="generate")
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async def generate(prompt: str) -> str:
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    return await llm.complete(prompt)
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# GRC methods are also async:
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await mima.ai_risk_assessment(
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    system_name="async-pipeline",
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    risk_tier="limited",
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    ...
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)

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import secrets
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# Generate once and store in secrets manager:
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signing_key = secrets.token_hex(32)
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mima = MimaGovernance(
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    api_key="mima_ext_...",
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    system_name="my-system",
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    signing_key=signing_key,
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)

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from mima_governance.integrations.langchain_callback import MimaLangChainCallback
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handler = MimaLangChainCallback(mima)
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chain.invoke({"question": "..."}, config={"callbacks": [handler]})

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from mima_governance.integrations.llamaindex_handler import MimaLlamaIndexHandler
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Settings.callback_manager.add_handler(MimaLlamaIndexHandler(mima))

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from mima_governance.integrations.autogen_middleware import MimaAutoGenMiddleware
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middleware = MimaAutoGenMiddleware(mima, agent_name="assistant")
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# Pass as a message hook when building your ConversableAgent reply chain.

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from mima_governance import MimaGovernance, MimaAttestationError
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mima = MimaGovernance(api_key="...", system_name="...", on_error="raise")
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try:
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    mima.push("tool", input_hash, output_hash)
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except MimaAttestationError as e:
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    logger.error("Governance record failed: %s", e)

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result = mima.push("tool", input_hash, output_hash)
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if not result.attestation_id:
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    logger.warning("Push failed: %s", result.detail)