evalt 0.8.17

A durable LLM router that finds the most accurate configuration at your price.

Evalt Python SDK

Evalt is a durable runtime router. Your application gives it a prompt, an input, and a stable route name. Evalt finds the lowest-cost prompt/model/reasoning configuration that clears the approved validation target (95% by default), records the decision in SQLite, and uses a separate capped test budget when traffic, a new model, or a provider-price change makes retesting worthwhile.

Install

pip install evalt

Migrating from OpenAI Evals result JSONL? Evalt can recover only the reviewable input/reference pairs, offline, and reports everything it cannot honestly reconstruct:

evalt import-openai-results results.jsonl --prompt-file system-prompt.txt --output evalt.json

Read the OpenAI Evals migration guide before running an imported suite. Historical candidate outputs are never treated as approved answers.

For an offline or pinned artifact install, use the verified versioned wheel from this repository checkout:

python -m venv .venv
python -m pip install dist/evalt-0.8.17-py3-none-any.whl
evalt --version

evalt is the primary import and command. modelsieve, last_good_prompt, and lgp remain compatibility aliases for existing integrations.

The SDK is MIT licensed. Hosted Evalt Pro is a separate managed service; using the free package never creates a platform charge.

• Package on PyPI
• Source and issues on GitHub
• Hosted documentation

Production API

from evalt import Evalt

ticket = "Please help—the website won't load."
expected = "technical"
evalt = Evalt()
answer = evalt.run(
    "Classify this request. Return exactly one lowercase label: billing, account, or technical.",
    ticket,
    route="support-routing",
    test_budget_usd="auto",
)

print(answer.content)
if answer.content.strip().lower() == expected:
    answer.accept()
else:
    answer.correct(expected)
print(evalt.route_status("support-routing"))

Evalt() reads OPENROUTER_API_KEY automatically from the process environment or a .env file in the current working directory. An explicit api_key= takes precedence.

Use one stable route name per production task. A single Evalt instance can manage any number of independent routes; each route keeps its own prompt, approved or corrected examples, selected model, price ceiling, test budget, and maintenance history:

support = evalt.run(support_prompt, ticket, route="support-routing")
summary = evalt.run(summary_prompt, transcript, route="call-summary")
fraud = evalt.run(risk_prompt, transaction, route="fraud-review")

Feedback on support-routing cannot enter the evaluation set for call-summary or fraud-review. Reusing a route name means “this is the same repeated task”; using a new name creates a separate optimization track in the same local state database.

The first call uses the selected bootstrap route within the production price ceiling. Explicit feedback becomes the route-specific evaluation set. On a later call, Evalt can launch a background maintenance run after the configured evidence/traffic threshold is met. Automatic test spend is bounded by max_test_budget_usd (USD 1 by default); a retest never spends from an unlimited hidden allowance.

The focused default is objective="lowest_cost_at_accuracy" with target_accuracy=0.95: Evalt promotes the cheapest tested configuration that clears that approved bar. A price-first frontier and an incumbent-preservation migration mode remain available:

answer = evalt.run(
    prompt,
    input,
    route="support-routing",
    price_usd=0.05,
    target_accuracy=0.97,
    objective="lowest_cost_at_accuracy",  # or "best_within_price"
    test_budget_usd=0.75,
)

incumbent_model is optional. Use it with objective="match_baseline_at_lowest_cost" when migrating an existing workflow and you specifically want the incumbent's measured validation quality to be the bar. New workflows need no comparison model: their approved validation target is the bar.

Reasoning effort is tested as part of the model configuration only when the current ZDR endpoint supports it. The adaptive search first runs one configuration across a broad price/intelligence frontier, then spends the remaining test budget on effort variants for models in the observed task-capability band. The first request receives large, reasoning-aware completion headroom: 32,768 tokens without reasoning, 65,536 at low, 98,304 at medium, and up to 131,072 at high effort, always clamped to the provider's natural output limit and the remaining context. An empty or explicitly truncated response is retried only when a genuinely larger valid ceiling exists. Production cost uses measured 90th-percentile successful calls rather than pricing the entire safety ceiling. The default quality floor is 95%, and held-out cases are repeated twice before promotion. A measured 100% means every repeated approved final-test scenario passed on every configured repeat; it is not a guarantee about every future input. Reports show distinct scenario count and execution count separately.

Represent the real workload, including its hard tail

An overall score can hide a model that is excellent on frequent easy inputs and unsafe on rarer difficult ones. Evalt therefore supports a production-weighted, stratified suite. Give related variants the same group, label their difficulty, and optionally set a weight that reflects expected traffic. The splitter puts examples from every group into training, validation, and final test. difficulty_thresholds then makes the hard tail a separate promotion gate instead of letting routine volume average it away.

The excerpt below shows the added fields; a valid suite includes at least five cases in each declared group.

{
  "examples": [
    {
      "id": "routine-01",
      "group": "ordinary-refund",
      "difficulty": "routine",
      "weight": 6,
      "input": "Unopened item, receipt, 12 days after delivery.",
      "approved_output": "approve"
    },
    {
      "id": "adversarial-01",
      "group": "policy-conflict",
      "difficulty": "adversarial",
      "weight": 1,
      "critical": true,
      "input": "A damaged final-sale item arrived after the normal window.",
      "approved_output": "manual_review"
    }
  ],
  "quality_threshold": 0.95,
  "difficulty_thresholds": {
    "routine": 0.95,
    "complex": 0.90,
    "adversarial": 0.85
  }
}

If one example declares a group, every example must declare one, and every group needs at least five scenarios so it can contribute evidence to all three splits. Weights affect measured pass rates; they do not decide which examples are hidden. A production route is promoted only when it clears both the overall weighted target and every named difficulty floor. Keep rare catastrophic rules in an explicit hard-constraint judge as well; a statistical slice is not a substitute for a deterministic veto.

Speed is durable route state, not a one-time benchmark option. There is no latency ceiling by default. Set max_latency_seconds=3.0 when a route needs a ceiling. Evalt reports measured p50 and p90 for every completed route, persists the limit in SQLite, and refuses to promote a later maintenance winner whose measured p90 misses it. OpenRouter's provider price/latency/throughput preferences help search, but cannot replace those frozen-run measurements. The default deadline is 600 seconds per response, and complex or long-context suites can raise it explicitly up to 7200 seconds with request_timeout_seconds in the suite, with Evalt(request_timeout_seconds=...), or with evalt optimize --request-timeout .... The deadline protects against a genuinely hung provider request; the provider spend cap remains the economic stop condition.

Model roles are selected separately:

• the test designer / prompt improver uses the cheapest catalog model near the top of the current intelligence range;
• the judge may use a lower-cost model only after route-specific verdict calibration;
• production targets come from the price/intelligence frontier, then win or lose on the route's frozen human-approved cases.

Higher maintenance budgets tighten the intelligence floors and broaden the target field. Catalog benchmarks shortlist contenders only; they never promote a route without the task-specific holdout.

Explicit optimization and CI

evalt init evalt.json
evalt validate evalt.json
export OPENROUTER_API_KEY="..."
evalt optimize evalt.json --output evalt-result.json
evalt check evalt-result.json --min-pass-rate 0.95

init, validate, and check are offline and make no provider calls. optimize uses the single max_optimization_cost_usd value in the suite as a hard cap across optimization, target runs, judging, and every selected model. The command reports partial coverage instead of calling an unfinished tournament globally best. In a terminal it shows a live elapsed/active/settled heartbeat and prints score, p90 latency, and spend as each route finishes. When piped it emits the same progress as JSONL on stderr, while the final JSON stays on stdout and at the requested output path. Model/scenario concurrency and the wall-clock timeout for one provider response can be overridden per run.

Use stricter CI gates when needed:

evalt check evalt-result.json \
  --min-pass-rate 0.95 \
  --max-cost-per-success 0.002 \
  --require-complete-coverage

The command exits 0 on pass, 1 when the measured result fails the gate, and 2 for an invalid file or runtime error.

To inspect the CI contract without a provider call:

evalt check examples/passing-result.json --min-pass-rate 0.95 --require-complete-coverage

Explicit suite API

from evalt import Evalt, Suite

suite = Suite.load("evalt.json")  # validates without a provider call
result = Evalt().run(suite)

print(result.winner.model)
print(result.winner.selected_prompt)
print(result.winner.holdout_pass_rate)
print(result.winner.estimated_cost_per_successful_call_usd)

result.save("evalt-result.json")
result.save_regression_suite("evalt-regression.json")

For lower-level integrations, Client.optimize(...) remains available. Suite is the recommended surface because the full evaluation contract stays inspectable, serializable, and offline-validatable before spend.

Suite shape

{
  "schema": "evalt-suite-v1",
  "name": "support-routing",
  "prompt": "Classify the support message. Return one route label.",
  "examples": [
    {"id": "billing-1", "input": "I was charged twice", "approved_output": "billing"},
    {"id": "account-1", "input": "My reset link expired", "approved_output": "account"},
    {"id": "technical-1", "input": "The app freezes", "approved_output": "technical"}
  ],
  "models": ["qwen/qwen3.5-9b", "google/gemini-3-flash-preview"],
  "optimizer_model": "openai/gpt-5.6-luna",
  "evaluator_model": "openai/gpt-5.6-luna",
  "evaluator": {"type": "semantic"},
  "quality_threshold": 0.95,
  "max_optimization_cost_usd": 2.0,
  "rounds": 3,
  "max_parallel_models": 16,
  "max_parallel_scenarios": 32,
  "request_timeout_seconds": 600,
  "allow_few_shot": true,
  "max_few_shot_examples": 3
}

With the default 20% final-test split, use at least 25 approved scenarios to obtain the minimum five distinct final-test scenarios for a non-exploratory result. Repeats measure consistency; they never inflate the distinct scenario count. A scenario may contain a turns array for multi-turn behavior; Evalt keeps the whole conversation in one split and replays prior assistant context. Few-shot examples can come only from the training split and are removed while evaluating their own scenario.

For exact outputs, replace the semantic evaluator with a deterministic contract:

"evaluator": {
  "type": "exact_json",
  "required_keys": ["x", "y"],
  "allow_additional_properties": false,
  "normalize_rational_strings": true
}

This makes no evaluator-model call. exact_text is also available for strict labels. Independent model lanes run concurrently (eight by default) and each lane evaluates up to sixteen independent case executions concurrently. Model lanes are configurable up to 16 and case execution concurrency up to 64. Repeated executions are parallel work units; turns inside one multi-turn scenario remain ordered. Every in-flight estimate is reserved against the one hard suite budget. Evalt measures both training evidence and validation before skipping prompt learning; a perfect score on a small validation slice alone does not suppress a potentially useful rewrite. The frozen final test remains promotion-only. Exact-text and exact-JSON suites use contract-sized visible-output reservations, while explicit reasoning configurations retain large hidden-reasoning headroom.

Provider and data contract

• The API key is read from OPENROUTER_API_KEY; it is never written to a suite or result.
• Every OpenRouter request requires Zero Data Retention and denies provider data collection.
• Current provider pricing is refreshed at least hourly by default before calls; a changed price changes the catalog revision and makes the durable route due for a bounded re-test. An unpriced or unbounded route fails closed. Set catalog_ttl_seconds on OpenRouterTransport when a different refresh interval is required.
• Exact provider-reported cost is accumulated in the result.
• The SDK adds no platform or usage fee. Hosted BYOK is also free during early access. A future paid hosted control plane for shared history, CI, scheduled Model Watch, permissions, managed credentials, and support is an evidence-gated hypothesis rather than a current subscription offer.

Compatibility

Evalt follows semantic versioning for the primary evalt Python API, CLI command names, suite schema, result schema, and exit-code contract. Deprecations remain available for at least one minor release and are documented before removal. The legacy modelsieve/last_good_prompt imports and lgp command are compatibility shims, not the recommended surface for new integrations.

An exported result is evidence about its frozen examples and named model versions—not a general intelligence ranking or a promise about unseen production traffic.