parallax-evals 2.0.0.dev8

A reproducible benchmark for agent harnesses, graded on what they did to the world.

Most agent benchmarks score a model inside one fixed scaffold. In production, though, the model runs inside a product. That product is the harness, and it decides the prompts, tools, memory, retries, approvals and model routing. Two harnesses on the same model can differ more than two models in the same harness. Parallax measures the harness and model together, end to end, and records every run in Langfuse:

• Same tasks, any harness. Every harness gets the same seeded services (mail, chat, calendar, drive, sheets, payments, a scheduler, a seeded web, and more) over one MCP server. A task is a directory of data, not code written for one harness.
• Graded on world state, not self-report. Checks read four things: the final service state, the server's own call log, the workspace snapshot and a trusted test run. What the harness says it did is kept for diagnosis only.
• Every task is proven gradeable. A scripted oracle must pass every task. Two null agents must fail it: one claims success and does nothing, the other asks a question and does nothing. Seeded near-miss mutants must be caught.
• Scores, reliability and cost. pass@1 comes with task-clustered confidence intervals, pass^k measures reliability, and cost comes from a metering proxy when available. Unknown cost stays unknown, never $0.
• Regressions, not just scores. parallax compare pairs a run with an earlier one task by task and reports the pass@1 delta with a 95% confidence interval, so a pull request can be gated on it.

Quick start

You need Docker, Python 3.13, uv and a Langfuse project (Langfuse Cloud, or self-hosted v4.59.0 or later). Install the CLI:

uv tool install parallax-evals

Point it at Langfuse, the judge and a model provider:

export LANGFUSE_BASE_URL=https://cloud.langfuse.com
export LANGFUSE_PUBLIC_KEY=pk-lf-...
export LANGFUSE_SECRET_KEY=sk-lf-...
export PARALLAX_LANGFUSE_PROJECT=my-evals    # the project id or name results belong in
export AI_GATEWAY_API_KEY=...                # the Jev judge
export PARALLAX_UPSTREAM_OPENAI_KEY=...      # the meter's OpenAI-wire key (OpenRouter by default)

Run a suite against a harness, then compare it with the previous run:

parallax run lite --harness hermes --model openai/gpt-5-mini
parallax compare

A run uploads itself to Langfuse when it finishes and prints a link to each experiment. The quickstart walks through the whole workflow, including the task controls, show, and gating pull requests with parallax ci init. Troubleshooting covers common setup failures.

Agent harnesses

Parallax focuses on general-purpose agents: connected services, multi-step work, persistent memory and delegation. The in-tree harnesses are hermes and openclaw, plus the oracle, null-claim and null-refuse controls. The harness matrix records what has been verified for each. Docker is the default isolation boundary.

Desktop tasks declare desktop and run seeded GTK apps in a private X11 device box. A harness that declares desktop controls them through its own computer-use tools; grading reads the apps' saved workspace files. Other harnesses are NA. See the desktop recipe for image building, offline GUI smoke tests, and task authoring.

Suites

Each suite's suite.toml names its owner and purpose; a subset suite such as lite lists its task ids there, with within naming the suite they come from. parallax check --lint-only checks every listed id.

A selection can mix suites, task ids, globs, paths and filters, for example parallax run core area=safety requires<=mcp,multi_turn .... Plugin packages add suites through the parallax.suites entry point, and private task roots are added with --tasks DIR; a draft under such a root, such as review/drafts, is selectable by id. A plugin suite's first root holds its suite.toml.

Adding a harness

Most harnesses need no Python. A TOML manifest gives the launch argv, how the world MCP server is mounted, how the harness reaches the metering proxy, and which of the closed set of output parsers applies. Anything else, such as an SDK, a remote API or a bridge, implements a four-method protocol and registers through the parallax.harnesses entry point. See docs/adapters.md for the protocol, the manifests and the in-tree harnesses.

Adding a task

A task is a Harbor-compatible directory. It holds task.toml, one instruction per turn, a seed for the world, checks.yaml, mutations.yaml, and an oracle. It ships only when parallax check passes. See docs/authoring.md for the format, the check kinds and the lint rules, and docs/taxonomy.md for the failure modes. New tasks start as proposals in review/drafts/.

Methodology

docs/methodology.md covers these topics:

• verdicts, and the precedence NA > INVALID > FAIL > PASS;
• the statistics;
• cost provenance;
• web isolation;
• judge calibration;
• contamination controls (canaries and a private holdout);
• the task maturity lifecycle;
• a self-assessment against the Agentic Benchmark Checklist.

docs/tracking.md describes what a run records in Langfuse and how comparisons pair runs.

Status

2.0.0.dev8. The task format, world, checks, controls, statistics and Langfuse recording are in place. No pilot has been run, so no cross-harness results are published yet.

Contributing and licence

See CONTRIBUTING.md, SECURITY.md and CODE_OF_CONDUCT.md.

Code is licensed under MIT. Task data under tasks/ is licensed under CC BY 4.0. Third-party fixtures keep their own licences, which are listed in THIRD_PARTY_DATA.md.