Trialmark

Trialmark is an open-source framework for evaluating language models and acceptance-testing them for your task. Test models on your own cases, fix the pass mark before you see the results, and get a reproducible record of accuracy, cost and speed, with a gate your pipeline can enforce.

Evaluation measures how models perform. Acceptance testing decides whether a model is good enough for the task, against a rule written down before the results are seen, and records that decision so anyone can check it. Trialmark does both.

Choosing a language model is a trade between accuracy, cost and speed. Trialmark makes that choice systematic:

1. Your own test set. Cases from your own work, each with a reference answer written by a person and tagged by kind, such as "factual" or "multi-part". The set is hashed, so a later run can prove it used exactly the same cases.
2. A pass mark set before the run. For example: "choose the cheapest model within 3 points of the best on correctness, as long as at least 95% of its answers are grounded". The rule is stored with its hash and the time it was set, so an accidental change after the results are in is caught. Signed records, planned, will also make a deliberate change detectable.
3. Repeats, with the range shown. Models do not give the same answer every time. Each model runs several times, and the report shows the lowest and highest result so that a difference inside the range is read as no difference.
4. A report by kind of case. A small model may be as good as a large one on single facts and worse on multi-part questions. The breakdown by tag shows where.
5. A run record and a release gate. Every run writes one JSON file with the test set's hash, the models and their settings, the judge, the pass mark and every result. trialmark gate exits with 0 or 1, so a pipeline can refuse a model change that fails the pass mark.

Trialmark runs on your own machines. Your test sets, reference answers and records stay with you.

Where it comes from

Trialmark was spun out of the model benchmark in Forge, a governed layer through which organisations delegate work to AI. That benchmark compares models on a fixed set of business-document questions, through Forge's own answer path, with three repeats and accuracy, cost and speed side by side. Trialmark makes the same kind of test available to any developer or team, with or without Forge.

Status

Trialmark is at an early stage.

Install

pip install trialmark

Python 3.10 or later. The core has no dependencies. To call models directly, add the provider's SDK as an extra:

pip install "trialmark[anthropic]"
pip install "trialmark[openai]"

Try it

The examples folder holds a five-question test set and a configuration that compares three stand-in models, so a run needs no API key. Run it from the repository's root folder:

trialmark validate examples/testset.json
trialmark run examples/config.json --by-tag
trialmark report examples/config-run-record.json --format markdown
trialmark gate examples/config-run-record.json --model perfect-model
trialmark plugins

examples/run-record.json is an illustrative record of three made-up models whose scores are invented to show the format. They are not measurements of any real model.

Compare models on your own questions

This configuration compares two Claude models, gives each the whole source document, and has a third model grade every answer against your reference answers:

{
  "test_set": "my-questions.csv",
  "pass_mark": {
    "metric": "correct", "tolerance_points": 3, "floors": { "grounded": 0.95 }, "choose": "cheapest"
  },
  "repeats": 3,
  "concurrency": 4,
  "models": [
    { "id": "claude-sonnet-5-5", "runner": "anthropic",
      "settings": { "documents": ["contract.md"], "pricing": { "input": 2.0, "output": 10.0, "cache_read": 0.2 } } },
    { "id": "claude-haiku-5-5", "runner": "anthropic",
      "settings": { "documents": ["contract.md"], "pricing": { "input": 0.1, "output": 0.5 } } }
  ],
  "scorers": [
    { "name": "judge", "options": { "model": "claude-opus-5-5", "documents": ["contract.md"] } }
  ],
  "output": "run-record.json"
}

export ANTHROPIC_API_KEY=...
trialmark run config.json --by-tag

Every run calls the models for real and costs money: here, two models, three repeats and every question, plus one judge call per answer.

A few things to know:

• Prices are yours to give, in US dollars per million tokens, because they change. Check your provider's current prices. A model without prices reports a cost of zero, with a note on each result.
• The judge works best when it is not one of the models under test. Check it against a person's grades on part of the set before relying on it. Its model, the documents it read and a hash of its rules are kept in the record. rules_file replaces the built-in rules with your own.
• OpenAI models use "runner": "openai", and the judge takes "provider": "openai".
• Your own system, such as your own retrieval pipeline, plugs in through the python runner: write a function that takes the model's name, the question and the settings, and returns the answer, or a trialmark.RunOutput with its cost and tokens. Name it in the model's settings as "callable": "my_system.py:answer".
• Exact match ("scorers": ["exact-match"]) is enough for short answers such as a number or a date, and costs nothing.

Use it from Python

from trialmark import ModelEntry, PassMark, load_with, make_scorer, render, run_trial

record = run_trial(
    load_with("my-testset.csv"),
    PassMark(metric="correct", tolerance_points=3, floors={"grounded": 0.95}, choose="cheapest"),
    [ModelEntry(id="model-a", runner="mine"), ModelEntry(id="model-b", runner="mine")],
    runners={"mine": MyRunner()},          # any object with run(model, case) -> RunOutput
    scorers=[make_scorer("exact-match"), MyGroundednessScorer()],
    repeats=3,
    concurrency=4,
)
record.save("run-record.json")
print(render(record, "text", by_tag=True))

Customising Trialmark

There are four customisation points: runners (call a model), scorers (turn an answer into metrics), loaders (read a test set from a file format) and report writers. ARCHITECTURE.md gives each interface and explains what stays fixed in the core and why.

You do not need to fork Trialmark to customise it. Install it from PyPI and add plugins:

• For a one-off model call, use the python runner, as above.
• From Python, pass your own runner and scorer objects to run_trial.
• To share a plugin, publish it as its own package.

Writing a plugin

A plugin package declares an entry point in one of the groups trialmark.runners, trialmark.scorers, trialmark.loaders or trialmark.reports:

# trialmark_acme/__init__.py
from trialmark import RunOutput

class AcmeRunner:
    def __init__(self, endpoint="https://api.acme.example"):
        self.endpoint = endpoint

    def run(self, model, case):
        reply = call_acme(self.endpoint, model.id, case.input, **model.settings)
        return RunOutput(output=reply.text, cost_usd=reply.cost)

# pyproject.toml of the plugin package
[project.entry-points."trialmark.runners"]
acme = "trialmark_acme:AcmeRunner"

Once it is installed, trialmark plugins lists it, and a configuration uses it with "runner": "acme", with options under "runners": {"acme": {"endpoint": "..."}}. A plugin cannot take the name of a built-in plugin.

Formats

Test set

{
  "name": "contract-questions",
  "version": "2026-11-05",
  "cases": [
    { "id": "q1", "input": "What is the notice period?", "reference": "Sixty days' written notice.", "tags": ["factual"] }
  ]
}

The hash covers the cases (their ids, inputs, references and tags), not the name or version. It is the SHA-256 of canonical JSON under RFC 8785, so other languages can compute the same hash. Hashes written before 0.3 start with sha256: and are still checked. trialmark schema testset, passmark and run-record print the JSON Schema of each format.

Pass mark

Metrics are whatever your scorer produces, each between 0 and 1. A model's score on a metric is the mean within each repeat; the report shows the mean of those and their range.

Licence

Apache-2.0. See LICENSE.