Python SDK

from sakshi import (
    SakshiClient,
    WitnessSession,
    EnforcementDecision,
    SakshiBlocked,
    SakshiSyncReviewRequired,
    SakshiEnforcementUnavailable,
)

pip install sakshi-sdk

from sakshi import SakshiClient

client = SakshiClient(
    base_url="https://sakshi.your-company.internal",
    api_key="sks_...",
)

agent_id = client.register(
    "loan-decision-agent",
    owner_name="Priya Sharma",
    owner_email="priya@your-company.com",
    autonomy_tier="L1",
    description="Approves or refers retail loan applications.",
    org_unit="retail-credit",
)

with client.witness(
    agent_id,
    model={"provider": "openai", "name": "gpt-5.2", "version": "2026-05"},
    context={"applicant_aadhaar": "XXXX-XXXX-1234"},
    client_ref="APP-2026-04471",
) as decision:
    decision.step("retrieve", source="credit-bureau")
    decision.tool("risk_model", output={"band": "low", "score": 0.92})
    decision.human("review", who="analyst-114", note="spot check")
    decision.action(outcome="approved", amount=1_500_000, mode="auto")

from sakshi import (
    SakshiBlocked,
    SakshiSyncReviewRequired,
    SakshiEnforcementUnavailable,
)

try:
    decision = client.enforce(
        agent_id,
        "approve_loan",
        stakes=1_500_000,
        confidence=0.92,
    )
    # decision.allowed is True here: proceed with the action
except SakshiSyncReviewRequired as e:
    # parked for a human before it can run; e.decision has the details
    ...
except SakshiBlocked as e:
    # the envelope, a block rule, or the kill switch stopped it
    ...
except SakshiEnforcementUnavailable:
    # platform unreachable; enforcement fails closed, so do not act
    ...

decision = client.enforce(agent_id, "approve_loan", stakes=1_500_000, raise_on_hold=False)
if decision.allowed:
    ...  # auto or async_review: act now
elif decision.outcome == "sync_review":
    ...  # park for a human before acting
else:  # block
    ...  # do not act

result = client.request_human_handoff(
    agent_id,
    client_ref="APP-2026-04471",
    reason="customer requested a human agent",
)

client.flush()   # drain buffered records
client.close()   # stop pollers and release resources

from sakshi.middleware import watch_openai_compatible

llm = watch_openai_compatible(openai_client)  # also covers Ollama, vLLM, local
with client.witness(agent_id) as decision:
    llm.chat.completions.create(model="gpt-5.2", messages=[...])  # recorded