evalt 0.8.9

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

python -m 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-0.8.9-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 product and docs

Production API

from evalt import Evalt

evalt = Evalt(api_key=OPENROUTER_API_KEY)
answer = evalt.run(
    "Classify this support request as billing, account, or technical.",
    ticket,
    route="support-routing",
    test_budget_usd="auto",
)

send(answer.content)
answer.accept()                 # or answer.correct("billing")
print(evalt.route_status("support-routing"))

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. Reasoning configurations receive larger, budget-reserved completion headroom so internal reasoning cannot silently consume the requested visible response size. A transient empty response gets one budget-checked retry. An explicitly truncated response fails that configuration immediately: Evalt does not double a costly reasoning allowance after the provider has already hit the bounded limit. Production cost uses the route's 90th-percentile approved input and output lengths. 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.

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. It emits line-delimited progress events to stderr as each model starts, finishes, or fails, 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": 8,
  "max_parallel_scenarios": 16,
  "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. A prompt that already scores 100% on the validation split skips training replay and rewriting and moves directly to the frozen final test.

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.