evalctl

Local-first evals for agents, not just prompts.

evalctl treats eval cases as files, runners as shell commands, and results as durable artifacts. It scores what agents actually do — files written, diffs produced, commands run — on your own machine, with no gateway, dashboard, or SaaS account.

v0.4 writes durable run metadata, supports crash resume, adds local run-state inspection, diagnoses runtime health, produces side-effect-free execution plans, and can optionally delegate runner execution to spoolctl. The standalone synchronous path remains complete and requires no external service. inferctl preflight provenance can be captured before runner execution without changing report scoring.

About

evalctl is an agent-first evaluation harness for local workflows. It runs eval cases as filesystem fixtures, invokes agents through ordinary runner commands, and grades the resulting workspace with deterministic scorers. The core artifact is a portable run directory that another agent can inspect, report on, and re-score without access to the original shell history.

How it differs from promptfoo

promptfoo is the incumbent local eval CLI, and it is prompt/chat-shaped. evalctl is agent-shaped.

Status

Python pre-release. The standalone synchronous path is complete and needs no external service. v0.4 provides:

• Authoring — scaffold (init), validate, and CLI verbs to build suites.
• Running — bounded-parallel run execution, side-effect-free plan, crash resume, and doctor runtime diagnostics.
• Results — durable run metadata, status, report, deterministic local scorers, command scorers, and bounded local jobs inspection.
• Replay — execution replay for failed cases, and artifact replay from a copied run directory.
• Contract — truthful warnings and errors, real schema output, and a machine-readable capabilities surface.
• Optional integrations — spoolctl queueing and inferctl preflight provenance.

contract_version is 1.1.

Install

Requires Python 3.11+ and has no runtime dependencies (standard library only).

pip install evalctl

To use the optional spoolctl queue, install it alongside:

pip install evalctl "spoolctl>=0.4.11"

For unreleased changes, install from the default branch:

pip install "git+https://github.com/evalctl/evalctl.git"

For development, clone and install editable:

git clone https://github.com/evalctl/evalctl.git
cd evalctl
pip install -e .

Quickstart

Scaffold a project, author a suite, run it, and read the report:

export EVALCTL_ACKNOWLEDGE_UNSANDBOXED_RUNNER=1  # or pass --acknowledge-unsandboxed-runner per run
evalctl init --json
evalctl suite add demo --runner-argv "python3 $EVALCTL_WORKSPACE/r.py" --json
evalctl case add demo --task "do X" --workspace fixtures/x --expect-json '{"exact":"ok"}' --json
evalctl scorer add demo --name exact --required --json
evalctl run demo --json
evalctl doctor --json
evalctl plan demo --json
evalctl run demo --inferctl-task code --json
evalctl run --resume <run-id> --json
evalctl jobs list --limit 50 --json
evalctl run demo --queue spoolctl --slots 4 --json
evalctl replay --failed <run-id> --json
evalctl report <run-id> --format json

Durable Runs

Every run writes run.json before executing cases and writes cases/<case_id>/state.json only after the case artifacts needed for reports are complete. manifest.json is finalized from that durable state. If a process is killed mid-run, evalctl run --resume <run-id> --json reuses the original suite snapshot and run parameters, skips terminal cases, deletes partial unfinished case directories, and executes only the remainder.

Reservations are liveness-only .reservation.json files with a TTL and background heartbeat. A live reservation returns E_RUN_BUSY; a stale reservation is reclaimed by explicit --resume. jobs list|get|prune inspects completed, running, stale, and orphaned local run state and safely prunes only with explicit confirmation.

jobs list is bounded by default. Use --limit and --cursor to page through large run directories; the JSON envelope includes pagination metadata and a paste-ready next-page command when more rows are available.

Durability sidecars are operational state. Reports and artifact replay do not require run.json, .reservation.json, .spoolctl.db, state.json, or job.json; report_hash stays based on the report projection. SOURCE_DATE_EPOCH controls created_ts for deterministic manifest parity.

Doctor And Plan

evalctl doctor --json reports runtime, suite root, runs root, reservations, spoolctl, inferctl, and runner-safety state without failing just because a component is degraded. Use --component NAME to scope diagnostics and --fast for PATH-only optional integration checks.

evalctl plan <suite> --json resolves the case set without creating run directories, enqueueing jobs, executing runners, scoring, or writing inferctl artifacts. The plan includes run-id strategy, execution mode, independent-case dependency metadata, parallel tracks, per-case actions, warnings, and paste-ready follow-up commands. --resume, --queue spoolctl, --slots, and --inferctl-task mirror the run surface for planning.

Optional Spoolctl Queue

evalctl run <suite> --queue spoolctl --json delegates only runner execution to spoolctl (>= 0.4.11, speaking contract >= 2). A spoolctl reporting a newer contract is accepted. Evalctl still prepares workspaces, normalizes stdout and stderr, captures workspace diffs, scores cases, and writes terminal markers. If spoolctl is absent or incompatible, queued runs fail explicitly; non-queued runs do not need spoolctl.

The queue database is per-run at .spoolctl.db; v0.4 starts one ephemeral spoolctl work --drain worker per queued run. General externally managed worker fleets are not part of this release.

Inferctl Preflight Provenance

evalctl run <suite> --inferctl-task TASK --json probes inferctl once per run and, when compatible preflight support is available, writes per-case inferctl-preflight.json and inferctl-provenance.json before runner execution. Queued spoolctl runs capture the same artifacts before enqueue. Absence, incompatibility, parse failures, timeouts, and policy/readiness blocks are warnings; the runner still executes and scoring proceeds.

The v0.4 capture mode is preflight only. inferctl route is not called, and report projection is unchanged, so report_hash remains comparable to an equivalent run without inferctl.

Authoring

suite add, case add, and scorer add let agents build a suite without hand-editing suite.json or cases.jsonl. Authoring verbs are idempotent on retry: adding the same canonical object returns created:false; reusing the same key with different content returns E_RUN_CONFLICT.

case add only writes paths under the suite tree. Absolute paths and .. segments are rejected so generated suite files remain portable.

Replay

replay --failed selects failed/errored cases from the source run's recomputed report projection, not from score.json or stored manifest status. It re-runs only those cases against the current suite and writes a fresh partial run whose manifest replayed_from names the source run.

replay --run-id names the destination run. It never resolves the source. Pass --suite <suite-or-path> when replaying a copied run or when the current suite cannot be resolved by manifest suite name.

Command Scorers

scorer add <suite> --name command --id judge --argv "python3 scorer.py" adds an external scorer. The scorer receives EVALCTL_CASE_FILE, EVALCTL_OUTPUT_FILE, and EVALCTL_WORKSPACE, and emits one JSON verdict.

Command-scorer verdicts are captured once under cases/<case_id>/scorers/<id>.json. Reports and artifact replay read that artifact and do not re-execute the scorer binary. Command scorers run arbitrary local code and are covered by the same unsandboxed-runner warning as runners.

License

Apache License 2.0. See LICENSE.

Home

• Site: evalctl.dev
• Repository: github.com/evalctl/evalctl

_{Keywords: agent evals, LLM evaluation, AI agents, local-first, CLI, code review, workspace diff, deterministic scoring, artifact replay, eval harness.}