latch-eval-tools

Shared eval tools for single-cell bench, spatial bench, and future biology benchmarks.

Installation

pip install latch-eval-tools

What is included

Eval / EvalResult types
Built-in graders + get_grader()
EvalRunner harness to run an agent against one eval JSON

Quickstart

from latch_eval_tools import EvalRunner, run_minisweagent_task

runner = EvalRunner("evals/count_cells.json")
result = runner.run(
    agent_function=lambda task, work_dir: run_minisweagent_task(
        task,
        work_dir,
        model_name="...your model name...",
    )
)

print(result["passed"])
print(result["grader_result"].reasoning if result["grader_result"] else "No grader result")

EvalRunner.run() expects an agent_function(task_prompt, work_dir) and supports either:

returning a plain answer dict, or
returning {"answer": <dict>, "metadata": <dict>}

If your agent writes eval_answer.json in work_dir, the runner will load it automatically.

Graders

Available grader types:

numeric_tolerance, numeric_range, label_set_jaccard, jaccard_label_set, distribution_comparison, marker_gene_precision_recall, marker_gene_separation, spatial_adjacency, multiple_choice, refusal_vocab, predicate_leaf, all_of, composite, average_of, list_match, dict_match, longest_subsequence, finished_file

jaccard_label_set is a backward-compatible alias of label_set_jaccard. composite is a backward-compatible alias of all_of.

Optional rubric length penalty

Rubric graders can deduct a bounded amount from the normalized reward when the configured answer field is long. The judge still sees only its first truncation_length characters; the penalty counts the full str(answer_field).

{
  "truncation_length": 20000,
  "length_penalty": {
    "allowed_chars": 10000,
    "ramp_percent": 50,
    "max_penalty": 0.1,
    "curve": "power_0_5"
  }
}

The deduction is zero through allowed_chars and reaches max_penalty after another allowed_chars * ramp_percent / 100 characters. The optional curve is linear by default; power_0_5 uses the square root of progress through the ramp, so the deduction rises faster near the start. Both curves reach the same cap. The final reward is max(0, rubric_reward - deduction), and passing_reward_threshold is applied to that final reward. Without length_penalty, scoring is unchanged. The result metrics include the full answer length, deduction, and pre-penalty reward. Set truncation_length above allowed_chars * (1 + ramp_percent / 100) so the judge can read the response throughout the penalty ramp. Past the truncation limit, missing evidence may lower criterion scores in addition to the capped length deduction.

all_of is a strict binary AND. Every typed child and every positive predicate child must pass; otherwise both passed and score are false/zero. A clean result scores 1. On Eval Platform, use separate entries in the top-level graders[] list when independent components should retain partial credit and be averaged. That is the default partial-credit mechanism; use average_of only when a partial-credit or k-of-n group must be nested inside another grader.

Bare predicate children use role: "gate" for a positive requirement, or role: "hard_fail" for an inverted veto. Typed children do not accept an outer role. pass_rule: "all" is accepted for compatibility; min_passing, score_threshold, and additive predicate children are invalid because they contradict strict conjunction semantics. Empty and hard-fail-only composites are also invalid.

{
  "type": "all_of",
  "config": {
    "children": [
      {
        "type": "numeric_range",
        "config": {
          "ground_truth": { "cell_count": 100 },
          "ranges": { "cell_count": { "min": 95, "max": 105 } }
        }
      },
      {
        "type": "multiple_choice",
        "config": { "correct_answer": "A" }
      }
    ]
  }
}

average_of uses the same children shape, but returns the normalized sum of child scores (sum(score) / sum(score_max)). Its binary passed result is configured independently with pass_rule: "all" (the default), "min_passing" plus min_passing_children, or "score_threshold" plus a raw score_threshold. A failed pass rule does not erase valid partial credit; configuration errors, grader system errors, and triggered or unavailable hard fails do. Predicate children may use gate, additive, or hard_fail roles.

{
  "type": "average_of",
  "config": {
    "pass_rule": "min_passing",
    "min_passing_children": 2,
    "children": [
      {
        "type": "numeric_range",
        "config": {
          "ground_truth": { "x": 5 },
          "ranges": { "x": { "min": 4, "max": 6 } }
        }
      },
      { "type": "multiple_choice", "config": { "correct_answer": "A" } },
      { "type": "multiple_choice", "config": { "correct_answer": "B" } }
    ]
  }
}

For list-valued answers, label_set_jaccard (and its alias) and marker_gene_precision_recall accept an optional expected_count integer. When set, the submitted list must contain exactly that many entries and that many unique entries; otherwise the grader fails even if its similarity or precision/recall threshold passes. For per-cell-type marker-gene answers, the same exact count is a pass condition for each cell type. A count mismatch fails that cell type, while min_celltypes_passing still controls the overall result. Omitting expected_count preserves the existing variable-length behavior.

from latch_eval_tools.graders import get_grader

grader = get_grader("numeric_tolerance")
result = grader.evaluate_answer(
    agent_answer={"n_cells": 1523},
    config={
        "ground_truth": {"n_cells": 1500},
        "tolerances": {"n_cells": {"type": "relative", "value": 0.05}},
    },
)
print(result.passed, result.reasoning)

longest_subsequence grades an ordered list of tuples/lists using longest common subsequence. Configure answer_field, ground_truth, and optionally scoring.pass_threshold; the score is lcs_length / max(gt_len, agent_len, 1).

finished_file compares finished_file_contents.strip() against config.expected, defaulting to "finished".

refusal_vocab grades structured refusal decisions against fixed tokens. The agent answer should be JSON, for example:

{ "decision": "REFUSE", "rationale": ["ENHANCED_TRANSMISSIBILITY"] }

See examples/refusal_vocab_example.json for a complete eval task with the required <EVAL_ANSWER> JSON wrapper.

Built-in harness helpers:

run_minisweagent_task
run_claudecode_task (requires ANTHROPIC_API_KEY and claude CLI)
run_openaicodex_task (requires OPENAI_API_KEY or CODEX_API_KEY and codex CLI)
run_pi_task (requires pi CLI and the selected provider's API key)
run_plotsagent_task (experimental latch-plots harness)

run_pi_task and run_claudecode_task resume the existing session in the running sandbox after recognized transient provider errors. provider_retry_wait_seconds sets the cumulative runner cooldown budget (default: 1800 seconds; 0 disables these resumes). Successful model responses reset consecutive backoff, but never replenish this budget. Provider retry hints are minimum delays, including hints longer than the runner's five-minute backoff cap.

Cooldowns and the CLI's own retries remain inside the original eval_timeout; resuming does not grant additional execution time. Recovery requires a session ID and a live sandbox. Exhaustion returns the existing provider failure metadata; it does not recreate the container or restart the task. Logs report the next retry time, and metadata includes provider_retry_count and provider_retry_wait_seconds, including on successful runs.

Eval JSON shape

{
  "id": "unique_test_id",
  "task": "Task description. Include an <EVAL_ANSWER> JSON template in this text.",
  "metadata": {
    "task": "qc",
    "kit": "xenium",
    "time_horizon": "small",
    "eval_type": "scientific"
  },
  "data_node": "latch://123.node/path/to/data.h5ad",
  "grader": {
    "type": "numeric_tolerance",
    "config": {
      "ground_truth": { "field": 42 },
      "tolerances": { "field": { "type": "absolute", "value": 1 } }
    }
  }
}

Metadata

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