Contract-first policy decision CLI for deterministic decision + reason + trace.
Project description
Kernite
Kernite is an AI-operable policy engine for write-path enforcement. Use it as a Python library or run the OSS decision server.
It is purpose-built for one job: return a reproducible and machine-operable decision contract before any mutation call such as create, update, delete, or associate is executed.
Why Kernite
- Reason by Design: decision reasons are structured for programmatic remediation, not only human logs.
- Contract-First:
decision,reason_codes,reasons,trace_hashare required integration surfaces. - Reproducible tracing: canonical input and policy context produce deterministic
trace_hashoutput.
Design Philosophy
- Zero overhead: no runtime dependencies (
dependencies = []). - Zero DSL: enforce your policy without learning a new language.
- Zero bypass: every write-mutation gets reviewed, every time.
See docs/design-philosophy.md for more information on design principles.
Compared with OPA/Cedar for AI Write Paths
| Topic | Kernite | OPA/Cedar |
|---|---|---|
| Primary optimization | Machine remediation in write-path gating (reason_codes support fix-and-retry loops). |
Human-authored policy ecosystems and policy-language tooling. |
| Decision contract | Required structured contract (decision, reason_codes, reasons, trace_hash). |
Explanations exist, but the remediation contract is not standardized across integrations (codes/reasons/evidence vary by setup). |
| Reproducible evidence | Built-in deterministic evidence surface (trace_hash) plus conformance vectors for replay. |
Strong policy engines; replay determinism depends on integration and policy/data pipeline discipline. |
While OPA/Cedar/others are strong options for centralized policy-language workflows, Kernite is purpose-built for app-embedded, machine-operable, write-path enforcement with a stable response contract.
Non-goals
- A general-purpose policy language / DSL runtime.
- Proxying or routing traffic (Kernite returns decisions; your app performs the write).
- Replacing your domain logic. Kernite governs whether a write may proceed and why.
Install and Start
uvx kernite start
If you get invalid choice: 'start', your local CLI is an older release. Use one of:
uvx --from git+https://github.com/kerniteio/kernite kernite start
or:
uv tool install --upgrade kernite
kernite start
kernite start scaffolds a starter bundle at ./kernite/:
policy.jsonexecute-request.denied.jsonexecute-request.approved.jsonguard.pyREADME.md
kernite scaffold is an alias for the same command.
Run the OSS server:
uvx kernite serve
Run the starter flow:
cd kernite
curl -sS http://127.0.0.1:8000/v1/execute \
-H 'content-type: application/json' \
-H 'Idempotency-Key: readme-denied-001' \
-d @execute-request.denied.json
curl -sS http://127.0.0.1:8000/v1/execute \
-H 'content-type: application/json' \
-H 'Idempotency-Key: readme-approved-001' \
-d @execute-request.approved.json
Apply this write-path rule:
- mutate only when
data.decision == "approved" - when denied, branch on
data.reason_codesfor remediation/retry - persist
ctx_id,data.trace_hash,data.idempotency_key
Server endpoints:
GET /healthPOST /executePOST /v1/executePOST /validate/executePOST /v1/validate/execute
/v1/execute is the versioned stability surface; /execute maps to the latest stable surface (currently v1 in this OSS server).
/execute and /v1/execute are the public decision endpoints. Each request always goes through:
- payload validation and normalization
- deterministic policy evaluation
Library Quick Start (Python)
from kernite import evaluate_execute
request = {
"workspace_id": "workspace-demo",
"principal": {"type": "token", "id": "api:ops-bot"},
"object_type": "document",
"operation": "create",
"payload": {"title": "Q1 Plan"},
}
result = evaluate_execute(request, idempotency_key="req-001")
decision = result["data"]["decision"]
trace_hash = result["data"]["trace_hash"] # persist with your write/audit record
reason_codes = result["data"]["reason_codes"]
Production Notes (OSS Reference Server)
- Authentication/authorization: the OSS reference server does not include built-in authn/authz. Run it behind your trusted boundary (for example mTLS, JWT verification, internal network policy, or API gateway auth).
- Request size/timeouts: the OSS reference server does not define built-in max body size or per-request timeout controls. Enforce size limits and timeouts at ingress/runtime (reverse proxy, gateway, or process supervisor).
- Logging/metrics: the OSS reference server is minimal by design (startup log only, request access logs suppressed by default). Add structured logs/metrics in your service wrapper or edge layer, and persist
ctx_id/trace_hashfrom responses for audit evidence.
Quick Start: Execute
Request (governed scope with one policy, missing required title):
In v1, an allow policy is approved only if all its rules pass; any rule failure produces a denied decision with structured reasons.
{
"workspace_id": "workspace-demo",
"principal": {
"type": "token",
"id": "api:ops-bot"
},
"object_type": "document",
"operation": "create",
"payload": {},
"policy_context": {
"governed": true,
"selected_policies": [
{
"policy_key": "document_create_default",
"policy_version": 1,
"effect": "allow",
"rules": [
{
"rule_key": "require_title",
"rule_definition": {
"type": "required_fields",
"fields": ["title"]
},
"reason_code": "missing_required_fields",
"reason_message": "title is required."
}
]
}
]
}
}
Response shape:
{
"ctx_id": "ctx_...",
"message": "Denied by governance policy.",
"data": {
"decision": "denied",
"reason_codes": ["missing_required_fields"],
"reasons": [
{
"code": "missing_required_fields",
"message": "title is required.",
"rule_key": "require_title",
"field_path": "payload.title",
"details": {
"missing_fields": ["title"]
}
}
],
"policy_selection_reason_code": "policy_selected_workspace_default",
"policy": {
"policy_key": "document_create_default",
"policy_version": 1
},
"trace_hash": "sha256:...",
"idempotency_key": "..."
}
}
Happy-path response example (approved):
{
"ctx_id": "ctx_...",
"message": "Approved by governance policy.",
"data": {
"decision": "approved",
"reason_codes": [],
"reasons": [],
"policy_selection_reason_code": "policy_selected_workspace_default",
"policy": {
"policy_key": "document_create_default",
"policy_version": 1
},
"trace_hash": "sha256:...",
"idempotency_key": "..."
}
}
Contract invariants (v1):
- Required response fields:
ctx_id,message,data,data.decision,data.reason_codes,data.reasons,data.policy_selection_reason_code,data.policy,data.trace_hash,data.idempotency_key. data.decisionenum values are onlyapprovedordenied.messageis human-readable (best-effort); integrations should branch ondata.*fields, not message text.data.reason_codesanddata.reasonsmay be empty arrays whendata.decisionisapproved.- When
data.decisionisdenied, at least onedata.reason_codesentry is present. data.trace_hashis stable across v1.x for the same canonicalized input and policy context.- Canonicalization rule: Kernite deterministically canonicalizes all hash-participating arrays (for example sorted
reason_codesand canonical policy-match entries used for hashing). This makes order-only differences inselected_policies/rulesnot changedata.trace_hash.
Policy Context Model
policy_context is optional, but recommended for production integrations.
Kernite evaluates the policies you provide in policy_context.selected_policies; in OSS mode it does not fetch policies from storage.
Main fields:
governed(bool): whether this request must be enforced as governed scope.selected_policies(array): policies selected by your resolver.governed_scopes(array): optional scope list (object_type+operation) to infer governed status.policy_selection_reason_code(string): explicit selection reason, if already known.
Default behavior:
- governed + no selected policy =>
deniedwithno_matching_policy - not governed + no selected policy =>
approvedwithout_of_scope_phase1
PARC Request Model
Kernite uses a Cedar-style PARC shape:
- principal
- action (
operation) - resource
- context
This keeps policy evaluation explicit and stable for relationship operations like associate.
See docs/parc-model.md for details and examples.
Use Cases (AI and Non-AI)
- AI-assisted actions: gate tool calls and use
reason_codesfor automatic retry/remediation. - Internal APIs: apply one deterministic write guard across UI/API/workers.
- SaaS multi-tenant systems: enforce tenant-scoped write decisions and persist evidence.
See docs/use-cases.md for details and examples.
Compatibility and Conformance
- Contract policy:
docs/compatibility.md - Conformance vectors:
docs/conformance/v1/execute_vectors.json - Reason code semantics:
docs/conformance/v1/reason_codes_v1.json
Objective Performance Check (Python)
Kernite includes a dependency-free benchmark harness.
uv run python benchmarks/benchmark_execute.py --iterations 20000
This gives p50/p95 latency and throughput from your actual environment so language/runtime decisions are based on measured data.
Latest measured snapshot is tracked in docs/performance.md.
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