Tenet SDK — cloud judge client and local server client. Framework-agnostic.
Project description
tenet-client
Pure-Python SDK for the Tenet cloud judge service and local Tenet server. No framework dependencies — works inside LangChain, LlamaIndex, FastAPI, plain scripts, anywhere.
For LangChain-specific middleware/callback adapters, install
tenet-langchain instead — it depends on this
package and ships the LangChain integration classes.
Install
pip install tenet-client
Quickstart — cloud judge
The cloud judge runs at https://api.tenetlabs.com/v1/judge/evaluate.
Your customer credentials (M2M client_id / client_secret) are issued
by Tenet at provisioning time.
from tenet_client import TenetCloudJudgeClient, JudgeUnavailableError
client = TenetCloudJudgeClient(
client_id="<your-m2m-client-id>",
client_secret="<your-m2m-client-secret>",
)
# Recommended: warm the client at process start so the first user-facing
# call doesn't pay the Auth0 token-mint cold path.
client.warmup()
try:
verdict = client.evaluate(
phase="tool_pre",
tool_name="search_resumes",
tool_input={"query": "5+ years Python experience"},
)
if verdict.decision == "block":
# Integrator decides what to do — return an error to the agent,
# surface a safe message to the user, log + halt, etc.
...
except JudgeUnavailableError as e:
# Cloud judge unreachable. The SDK does NOT have a fail_open flag —
# it's a policy decision. Catch and either retry, halt, or proceed
# depending on your environment.
...
Or read credentials from env. from_env() requires TENET_CLIENT_ID
and TENET_CLIENT_SECRET; optional tuning vars are TENET_JUDGE_ID and
TENET_JUDGE_TIMEOUT_SECONDS. (TENET_CLOUD_URL, TENET_AUTH0_AUDIENCE,
and TENET_AUTH0_ISSUER_URL exist as escape hatches for dev / staging
but should not be set in production.)
client = TenetCloudJudgeClient.from_env()
Async
Every method has an _async counterpart:
from contextlib import asynccontextmanager
from fastapi import FastAPI
from tenet_client import TenetCloudJudgeClient
@asynccontextmanager
async def lifespan(app: FastAPI):
app.state.judge = TenetCloudJudgeClient.from_env()
await app.state.judge.warmup_async()
yield
await app.state.judge.aclose()
app = FastAPI(lifespan=lifespan)
@app.post("/run-tool")
async def run_tool(req: ...):
verdict = await app.state.judge.evaluate_async(
phase="tool_pre", tool_name=..., tool_input=...,
)
...
Reading multi-verdict responses
The judge returns a per-domain findings list alongside the top-level
verdict. Simple callers can keep reading just the top level; callers who
need to dispatch on a specific domain (EEO, disclosure, prompt
injection, ...) read findings.
verdict = client.evaluate(phase="tool_pre", tool_input={"q": "..."})
# 1) Top-level still works. Backwards-compat is a guarantee: existing
# code reading `verdict.verdict` / `verdict.decision` keeps working.
if verdict.verdict == "route_to_human":
...
# 2) Per-domain detail — the EEO finding is always present (the engine
# synthesizes one from the top-level on legacy responses).
eeo = verdict.eeo_finding
print(eeo.verdict, eeo.severity, eeo.rule_ids, eeo.evidence_span)
# Index by name when you care about a specific domain.
disclosure = verdict.findings_by_domain.get("disclosure")
if disclosure is not None:
...
# Convenience helpers read well in agent code.
if verdict.blocked_by_domain("eeo"):
return safe_message
span = verdict.evidence_for("eeo") # str | None
Audit-only vs enforce
Every finding carries an enforcement field:
- enforce — this finding's verdict participated in the merged
top-level verdict (
most-restrictive-winsacross all enforcing findings). - audit_only — wire-visible for observability but never gates the
top-level verdict. A new domain ships in audit-only until its
promotion gate; an audit-only
blockdoes not block the caller.
verdict.enforcing_findings and verdict.audit_only_findings
partition the list. blocked_by_domain(name) returns True only for
an enforcing blocking finding — audit-only blockers return False
on purpose; that's the semantic difference.
Which domains exist today
- EEO — live,
enforce. Alwaysfindings[0]. - Additional domains (disclosure, prompt injection, ...) append to
findingsas they ship, each starting inaudit_onlyand graduating toenforceper its promotion gate.
Out-of-vocab verdicts normalize to route_to_human for the merged
top-level value; the per-finding verdict is preserved verbatim so an
audit can reconstruct what each detector actually returned.
Streaming (verdict-first)
The judge exposes an SSE surface that emits the verdict ~4× sooner than the full response. Two opt-in paths:
judge = TenetCloudJudgeClient.from_env() # or use_stream=True / TENET_USE_STREAM=1
async def decide(prompt: dict):
# 1. Drop-in: route through the stream, identical return type.
verdict = await judge.evaluate_stream_to_response(
phase="tool_pre", tool_input=prompt
)
# 2. Verdict-first: act on the verdict event before the body streams in.
async for event in judge.evaluate_stream(phase="tool_pre", tool_input=prompt):
if event.event_type == "verdict":
... # dispatch your routing decision at ~TTV
return verdict
Streaming is async-only; the sync evaluate() stays on the non-streaming
route. The aggregated response additionally carries canonical_id,
client_action, safe_message, explanation, and rewrite.
When a per-domain finding tightens the merged verdict after the initial
verdict event, the server emits verdict_revised carrying the same
shape plus a domain field identifying which finding drove the
revision. The aggregator takes the latest revision as terminal; verdict-
first consumers can either ignore verdict_revised (act at the first
verdict) or re-dispatch on it. The domain key may be absent on
older deployments — tolerate it.
Multi-turn history
Pass prior recruiter turns so the stateless judge sees the conversation
context (oldest first); they become extra {"role": "user"} messages ahead of
the latest turn. Never send assistant turns.
async def decide_with_history(judge):
return await judge.evaluate_async(
phase="agent_input",
tool_input="now only the recent grads",
prior_user_turns=["find backend engineers", "ones who can start now"],
)
Session grouping
Pass a session_id — a conversation id — so the server groups every judge call
in one conversation into a single Langfuse session. Use
a stable id per conversation (e.g. your LangGraph thread_id):
async def decide_in_conversation(judge, conversation_id: str):
return await judge.evaluate_async(
phase="agent_input",
tool_input="now only the recent grads",
session_id=conversation_id,
)
The server tenant-namespaces and length-caps the id, so send it raw; omit it (don't send empty) for an ungrouped, standalone trace. Works on the sync, async, and streaming paths.
Support correlation — canonical_id
Each streamed decision carries a 32-hex canonical_id (also on the
X-Tenet-Canonical-Id response header) — the same key the server emits to
Langfuse, Metronome, and OTel. Log it on every call so support can grep one key
across all three; never show it to the end user.
@judged — gate any function
from tenet_client import TenetCloudJudgeClient, JudgeBlocked, judged
judge = TenetCloudJudgeClient.from_env()
judge.warmup()
@judged(judge, fail_open=False)
def search_resumes(query: str) -> list[dict]:
return _real_search(query)
try:
hits = search_resumes("5+ years Python")
except JudgeBlocked as e:
# e.reason / e.phase / e.tool_name / e.judge_id available
...
The decorator gates the function at tool_pre (before the body runs)
and tool_post (after it returns). Works on sync and async functions —
the wrapper auto-detects via inspect.iscoroutinefunction.
Integration recipes
For LangChain agents, install tenet-langchain —
it ships CloudJudgeMiddleware, the wrap() one-call helper, and
re-exports @judged. For other frameworks (LlamaIndex, FastAPI, raw
loops), see the cloud judge recipes
for copy-pasteable wiring patterns.
License
Apache-2.0.
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