CLI: eval

Usage

vally eval [options]

Description

Run stimuli against an agent and grade the results. Stimuli come from one of three sources:

1. --eval-spec / -e — load stimuli from one or more eval spec files.
2. --suite — run a named suite of evals defined in .vally.yaml.
3. --tag — discover and filter eval stimuli by tag.

Options

Flag	Type	Default	Description
--eval-spec, -e <path>	string	—	Path to eval spec file (repeatable)
--skill-dir <path>	string	—	Opt-in: discover and load skills from this directory (same detection as vally lint <path>) for the whole run. A missing/non-directory path fails the command. Omit it to load only skills specified in environment.skills.
--work-dir <path>	string	cwd	Working directory for agent runs
--model <model>	string	From eval spec	Model to use for agent execution. Comma-separated list runs evals against each model (e.g., --model gpt-5.5,claude-opus-4.8).
--timeout <duration>	duration	2m	Timeout per run (e.g. 5m, 300s, 30000ms). Unit suffix required. Falls back to config.timeout from the eval spec, then to 2m.
--threshold <number>	number	From eval spec	Override the eval scoring threshold for this command invocation. Must be between 0 and 1. When no threshold is configured, evals use the binary grader pass/fail verdict.
--judge-model <model>	string	From eval spec	Model for LLM judge graders (overrides config.judge_model in the eval spec)
--grader-plugin <specifier>	string	—	Grader plugin to load (npm package name or local path). Registers external graders before validation and execution.
--executor-plugin <specifier>	string	—	Executor plugin to load (npm package name or local path). Repeatable — pass multiple flags when running eval specs that select different executors via their config.executor field.
--eval-plugin <specifier>	string	—	Eval provider plugin to load (npm package name or local path). Requires --eval-spec; cannot be combined with --suite.
--output-dir <path>	string	./vally-results	Parent directory for eval outputs. Each run is written to a timestamped subdirectory such as <output-dir>/<timestamp>/, containing files like trajectories and eval outcomes.
--output <format>	jsonl	—	Output format. jsonl emits one JSON object per stimulus to stdout.
--junit	boolean	false	Write a JUnit XML report (eval-results.junit.xml) to the run’s output directory.
--workspace <path>	string	—	Workspace directory for the agent (preserved after run).
--runs <n>	number	From eval spec	Number of trials per stimulus (overrides config.runs). ≥ 2 enables multi-trial mode with pass@k metrics.
--max-retries <n>	number	2	Max retries per stimulus on retryable errors (timeouts, rate limits, and transient connection failures). Each retry uses a fresh workspace with exponential backoff. Set to 0 to disable.
--skip-grade	boolean	false	Run agents without grading (grade later with the grade command)
--skip-validate	boolean	false	Skip eval spec validation (validation runs automatically by default)
--workers <n>	number	5	Number of concurrent stimulus sessions
--shutdown-timeout <duration>	duration	5s	Max time to wait for executors to shut down during cleanup, including on Ctrl+C (e.g. 5s, 500ms).
--suite <name>	string	—	Run a named suite of evals from .vally.yaml. Cannot be combined with --eval-spec.
--tag <key=values>	string	—	Filter stimuli by tag (repeatable, e.g., --tag cost=free,low).
--verbose	boolean	false	Increase logging verbosity and show full agent output

Exit codes

Code	Meaning
0	All eval verdicts passed, or verdict failures were suppressed by an override such as --threshold 0
1	One or more eval verdicts failed, or an execution/tooling error occurred

When a threshold is configured via --threshold or scoring.threshold, the eval verdict is based on the aggregate score meeting that threshold. If no threshold is configured, the command uses the binary grader pass/fail verdict. Execution and tooling errors always exit with code 1, even when --threshold 0 is used.

Skills

A run is skill-free by default. Skills load only when you opt in through one of two explicit mechanisms:

• environment.skills (recommended) — list skill directories in an eval spec’s environment block. A top-level block applies to all stimuli in the spec; an individual stimulus can declare its own. Paths resolve relative to the eval file. Each listed directory is copied into the agent’s isolated workspace, so the agent sees exactly the declared skills and nothing else. A missing directory, a directory without a SKILL.md, or an unparseable SKILL.md fails the run fast rather than silently loading nothing.
• --skill-dir <path> (whole run, opt-in) — discover and load skills from <path> (the same detection vally lint <path> uses) for all stimuli in the run. Useful for ad-hoc “load this folder of skills” runs. A missing or non-directory path fails the command.

The two mechanisms don’t stack: when environment.skills is declared (in an eval spec, applying to all its stimuli, or on an individual stimulus), that list is the complete skill set and the --skill-dir base is ignored (replace, not merge). Stimuli with no environment.skills fall back to the --skill-dir set.

Behavioral change

Earlier versions auto-discovered skills from the working directory. They no longer do: omit --skill-dir and declare nothing in environment.skills, and the agent runs with no skills. To restore the old “load skills next to me” behavior, pass --skill-dir . explicitly.

Examples

With eval spec

# Run all stimuli from eval.yaml
vally eval \
  --eval-spec eval.yaml \
  --skill-dir ./skills/test-writer \
  --output-dir ./results \
  --model gpt-5.5

# Run multiple eval spec files
vally eval -e auth.eval.yaml -e perf.eval.yaml \
  --skill-dir ./skills/test-writer

# Compare models side-by-side
vally eval \
  --eval-spec eval.yaml \
  --model gpt-5.5,claude-opus-4.8 \
  --output-dir ./results

# Keep workflow steps running for poor eval performance, but still fail on execution errors
vally eval \
  --eval-spec eval.yaml \
  --threshold 0

Capture without grading

# Capture trajectories now, grade later
vally eval \
  --eval-spec eval.yaml \
  --skip-grade \
  --output jsonl > outcomes.jsonl

# Later: grade with updated graders
vally grade --eval-spec eval.yaml < outcomes.jsonl

Preserve workspace

# Keep agent's workspace for inspection
vally eval \
  --eval-spec eval.yaml \
  --workspace ./debug-workspace \
  --verbose

Trajectory output

Output is saved in a timestamped subdirectory under the output directory (default: ./vally-results/).

Single run or JSONL mode:

results/
└── 2025-01-15T10-30-00-005Z/
    ├── results.jsonl
    ├── eval-results.md
    └── executor-session-logs/
        └── <eval-name>/<model>/<stimulus-name>/trial-0/
            ├── metadata.json
            └── events.jsonl   (best-effort, may be absent)

The executor-session-logs/ directory contains per-run session logs. metadata.json is always written and includes a logSource field ("native", "raw-event-fallback", or "none") indicating what was captured. events.jsonl is best-effort — it is present when the executor emits native session state or when raw SDK events were captured as a fallback, but may be absent for executors that don’t emit events.

Sensitive data

Executor session logs may contain prompts, model outputs, tool-call payloads, and other sensitive data.

Multi-trial (--runs K where K ≥ 2):

results/
└── 2025-01-15T10-30-00-005Z/
    ├── results.jsonl
    ├── eval-results.md
    └── executor-session-logs/
        └── <eval-name>/<model>/<stimulus-name>/
            ├── trial-0/
            │   ├── events.jsonl
            │   └── metadata.json
            └── trial-1/
                ├── events.jsonl
                └── metadata.json

Multi-trial mode

When --runs is ≥ 2 (or config.runs is ≥ 2 in the eval spec), the eval command runs each stimulus multiple times and aggregates results:

vally eval --eval-spec eval.yaml --runs 5 --verbose

Output includes per-trial progress and aggregate metrics:

━━━ basic-test-generation (5 trials) ━━━
  Write unit tests for this function: function add(a, b)...

  Trial 1  ✔ 8.3s
  Trial 2  ✔ 7.1s
  Trial 3  ✘ 9.2s
  Trial 4  ✔ 6.8s
  Trial 5  ✔ 8.0s

  pass@5: 99.2%  pass^5: 41.0%  pass rate: 4/5 (80.0%)
  ⚠ flaky (20.0% minority outcomes)

Trials within a stimulus run sequentially. Parallelism across different stimuli is controlled by --workers.

Workspace caching

When runs ≥ 2 and the stimulus has an environment with setup work (files, skills, or commands), Vally prepares the workspace once and restores it for each trial. This avoids redundant npm install, builds, and other expensive setup.

All trials execute at the same workspace path, so command outputs that embed absolute paths (virtualenvs, build caches, etc.) remain valid. On filesystems that support it (APFS, btrfs, XFS, ReFS), copies use copy-on-write cloning for near-instant restoration.

Workspace caching is skipped for environment.git worktrees (each trial gets its own worktree) and when the executor doesn’t support local workspaces (e.g., sandbox executors).

Three execution modes

runs value	Mode	Behavior
0	Lint-only	Skip execution entirely (print “Skipped”)
1	Single run	Execute once, grade, report (original behavior)
≥ 2	Multi-trial	Run K times, grade each, aggregate with pass@k/pass^k

Multi-model mode

Pass a comma-separated list of models to run every eval against each model independently:

vally eval --eval-spec eval.yaml --model gpt-5.5,claude-opus-4.8

Each eval is executed once per model. With 3 evals and 2 models, that’s 6 total runs. Results are reported separately per model — the console shows which model each eval ran against:

  ✔ my-eval [gpt-5.5]  all graders passed
  ✘ my-eval [claude-opus-4.8]  grader(s) failed

The exit code is 0 only when all model variants pass.

Workspace preservation

--workspace <path> cannot be combined with multiple models in a single command. The workspace path is keyed by (variant, stimulus) and does not include the model, so two models running the same eval would both claim the same workspace directory, which the planner rejects as a collision:

Workspace path collision at '<workspace>/main/<stimulus>': claimed by
'... × gpt-5.5' and '... × claude-opus-4.8'. Rename one of the stimuli
or use distinct --workspace roots.

If you need preserved workspaces with multi-model, run each model separately with its own --workspace root:

vally eval --eval-spec eval.yaml --model gpt-5.5 --workspace ./ws/gpt-5.5
vally eval --eval-spec eval.yaml --model claude-opus-4.8 --workspace ./ws/claude-opus-4.8

Combining with other options

Multi-model works with --runs, --suite, --tag, and --workers.

# 2 models × 5 trials per stimulus
vally eval --eval-spec eval.yaml \
  --model gpt-5.5,claude-opus-4.8 \
  --runs 5

# Suite + multi-model
vally eval --suite regression --model gpt-5.5,claude-opus-4.8