kaval 0.2.0

Python SDK for the kaval currentness API — sync client, 30s default timeout, no automatic retries.

kaval (Python SDK)

The freshness gate for AI. Give kaval a belief your system already holds — a cached fact, a CRM field, an agent memory — and it checks the live world and returns a typed freshness gap: current / stale / contradicted / unsupported / conflicting / insufficient.

Install

pip install kaval

Async / concurrency

Sync-only for now. KavalClient is built on httpx.Client (blocking I/O). v0.1.x does not ship an AsyncKavalClient — if you need async/await, call the REST API with httpx.AsyncClient, wrap sync calls in asyncio.to_thread(), or use the Node SDK (@usekaval/kaval). Native async may land in a later release.

Gate a belief before you act on it

from kaval import KavalClient

# Explicit config (always works):
with KavalClient(api_key="kv_live_...") as client:
    decision = client.verify("Acme's CEO is Jane Doe")
    if not decision["act"]:
        ...  # stale / contradicted — re-fetch before relying on it

# Or set env vars and construct with no args (see below):
# export KAVAL_API_KEY=kv_live_...
# export KAVAL_BASE_URL=https://api.usekaval.com   # optional; defaults to prod
with KavalClient() as client:
    ...

verify() returns the verdict plus act — True only when the belief is current and confident (≥ 0.7 by default; override with min_confidence).

Pick a speed/depth tier

verify(belief, mode=...) selects a tier (default auto): instant (cache / graph-prior only, no fetch or LLM), fast (cheap model, origin-only), auto (balanced), or deep (strongest model + a cited explanation). The returned dict echoes tier, and on the deep tier adds explanation:

gap = client.verify("Acme's CEO is Jane Doe", mode="deep")
gap["tier"]                       # "deep"
gap["explanation"]["content"]     # markdown rationale with [n] citations (deep only)
gap["explanation"]["citations"]   # [{"url": ..., "title"?: ...}] — only from gathered evidence
gap["explanation"]["confidence"]  # "high" | "medium" | "low"

Sweep a store for drift

beliefs = ["Acme is on the Enterprise plan", "Jane Doe is VP Eng at Acme"]

report = client.scan_store(beliefs)
for r in report["riskiest"]:
    print(r["belief"], "→", r["status"])

# …or get pushed the newly-stale ones (carry `state` across runs so a still-stale belief
# isn't re-delivered every sweep):
client.monitor(beliefs, webhook="https://your-app.com/hooks/stale")

Pydantic AI guardrail (one line)

Gate a Pydantic AI agent's outputs on belief freshness. Facts the agent is about to return are verified against the live world; a stale / contradicted / unsupported claim raises ModelRetry with the evidence-backed correction, and the agent re-answers with the current fact — verify-and-auto-refresh, no orchestration code:

# pip install "kaval[pydantic-ai]"
from pydantic_ai import Agent
from kaval.pydantic_ai import verify_output

agent = Agent("openai:gpt-5")
agent.output_validator(verify_output())  # <- the guardrail

By default plain-text outputs go through Kaval's claim extractor (extract_and_check). For structured outputs, say which fields are checkable beliefs:

agent.output_validator(
    verify_output(beliefs=lambda out: [f"{out.company}'s CEO is {out.ceo}"], mode="fast")
)

verify_output(...) also takes client= (a configured KavalClient), min_confidence=, and freshness_sla= (e.g. "14d"). Streaming runs are supported — partial chunks pass through and only the complete output is verified. Each retry consumes the run's output-retry budget (Agent(retries={"output": N})). Full runnable example: examples/pydantic_ai_guardrail.py.

Custom base URL

Override the API base URL (e.g. a staging environment or a local proxy):

client = KavalClient(base_url="https://staging.api.usekaval.com", api_key="...")

Environment variables

When omitted, constructor args fall back to:

Variable	Used for	Default
KAVAL_API_KEY	Bearer token	none (unauthenticated)
KAVAL_BASE_URL	API origin	https://api.usekaval.com

The marketing site (apps/web) uses KAVAL_API_URL for its server-side proxy — not KAVAL_BASE_URL. Set both when self-hosting the engine and running the web demo against it.

Explicit api_key= / base_url= always wins over the environment.

Resilience

No automatic retries by default — bring your own. Each API call is a single HTTP round-trip via httpx; transient failures (timeouts, 502s, rate limits) are not retried. Wrap calls in your own retry/backoff (e.g. tenacity) if you need that behavior.

Default timeout: 30 seconds (connect + read), overridable at construction:

# deep verify / scan sweeps may run close to the limit — raise for long-running calls:
client = KavalClient(api_key="...", timeout=60.0)

Timeouts surface as httpx.TimeoutException (not KavalError).

API

verify · check · extract_and_check · scan_store · monitor · report_outcome · kaval · kaval_batch · health. Construct with KavalClient(base_url=?, api_key=?) — base_url defaults to https://api.usekaval.com. The Node/TypeScript client mirrors this surface: npm install @usekaval/kaval.

Test

pip install -e ".[dev]"            # from sdks/python (development)
pytest                             # hermetic contract tests (httpx MockTransport)
KAVAL_BASE_URL=https://api.usekaval.com KAVAL_API_KEY=kv_live_... pytest   # also runs the live test