trapstreet-cli 0.1.0

tp — non-invasive CLI testing framework for AI workflows. Submits results to trapstreet.run.

trap — CLI

Lives at trapstreet-mvp/cli/ — part of the trapstreet monorepo. The standalone repo AntiNoise-ai/trap is retained for history only; all active development happens here.

Install (from PyPI — recommended):

uv tool install trapstreet-cli
# also works via pipx / pip

From git (latest main, no PyPI release needed):

uv tool install "git+https://github.com/AntiNoise-ai/trapstreet-mvp.git#subdirectory=cli"

The command name is tp. Releases are git-tagged cli-vX.Y.Z; tagging triggers PyPI publish via GitHub Actions.

trap is a non-invasive CLI testing framework for AI prompts, agents, and workflows. It treats the program under test (the "solution") as a black box: it invokes the solution as a subprocess, captures stdout/stderr/files, then optionally pipes that output through a "judge" (per-case scorer) and a "grader" (overall aggregator) — also subprocesses, also language-agnostic.

The framework knows nothing about how the solution is implemented. Python, shell scripts, compiled binaries, agentic pipelines — anything that can be invoked from a shell works.

---

Core idea: solution and task are decoupled

Two roles, two repos (or two directories), connected only by a small IO contract:

Role	Owns	Configures
Solution author	trap.yaml, the solution code	how to invoke the solution, which inputs to feed it, which outputs it produces
Task author	traptask.yaml, judge.py, grader.py, inputs/, expected/	the test cases, scoring logic, expected outputs

The solution doesn't need to import trap or know it exists. It reads paths from two environment variables (INPUTS, OUTPUTS) and runs.

inputs/{case_id}/   ──[INPUTS env var]──▶  solution  ──[OUTPUTS env var]──▶  .trap/{task}/{ts}/{case_id}/
expected/{case_id}/                                                                 │
       │                                                                            │
       └──────────────────────── judge  ◀──────────────────────────────────────────┘
                                   │
                            {metrics: any JSON}
                                   │
                  [collect all cases, hand to grader]
                                   │
                                grader
                                   │
                            {passed, score, ...}

---

Install

Requires Python >=3.14 and uv.

git clone https://github.com/AntiNoise-ai/trap
cd trap
uv sync

The installed entry point is tp (not trap), declared in pyproject.toml:

uv run tp --help

There is no PyPI release yet, so use uv run tp … from a clone, or run it from a wheel you build locally (uv build).

---

Quick start — the echo example

The repo ships two complete worked examples under examples/. Walk through examples/echo/ to see the moving pieces.

1. Solution side — examples/echo/solution/

echo.py reads JSON from stdin and prints message to stdout (or errors with exit 1 if message is missing):

import json, sys
data = json.load(sys.stdin)
if "message" not in data:
    print("error: missing 'message' field", file=sys.stderr)
    sys.exit(1)
print(data["message"])

trap.yaml tells trap how to run it and where the task lives:

tasks:
  test:
    description: Echo solution — reads stdin JSON, writes it back to stdout
    cmd: uv run python echo.py
    traptask: ../task          # path to the task directory (relative to trap.yaml)
    inputs:
      stdin: input.json        # pipe inputs/{case_id}/input.json into stdin

2. Task side — examples/echo/task/

traptask.yaml lists the cases and points at the judge/grader:

dirs:
  inputs: inputs/         # optional; this is the default
  expected: expected/     # optional; this is the default

cases:
  - id: contains_basic
    description: stdout contains the substring (case-insensitive)
    tags: [smoke]
  - id: exit_code_failure
    description: exit code is 1 when message field is missing
  - id: skipped_example
    skip: true
    tags: [wip]

judge:
  cmd: .venv/bin/python judge.py     # optional — omit for output-only mode

grader:
  cmd: .venv/bin/python grader.py    # optional — omit to skip aggregation

Each case has a directory under inputs/{id}/ (and optionally expected/{id}/) holding whatever files that case needs.

judge.py reads the payload from TRAPTASK_PAYLOAD, evaluates one case, prints a JSON metric to stdout:

import json, os, re
from pathlib import Path

data = json.loads(os.environ["TRAPTASK_PAYLOAD"])
stdout = Path(data["outputs"]["case_stdout"]).read_text().strip()
exit_code = json.loads(Path(data["outputs"]["case_meta.json"]).read_text())["exit_code"]
expected = json.loads(Path(data["expected"]["expected.json"]).read_text())

# … compute results …
print(json.dumps({"score": score}))

grader.py receives the list of all case results and emits the overall verdict:

import json, os
results = json.loads(os.environ["TRAPTASK_PAYLOAD"])
passed = all(r["metrics"]["score"] == 1.0 for r in results)
print(json.dumps({"passed": passed, "score": avg_score}))

3. Run it

From examples/echo/solution/:

uv run tp run                    # run the first task in trap.yaml
uv run tp run test               # run the named task
uv run tp run -t smoke           # only run cases tagged `smoke`
uv run tp run --output json      # print machine-readable JSON instead of a rich table
uv run tp run --fail-fast        # stop on first case whose judge score < 1.0

Trap writes per-run artifacts under .trap/{task}/{timestamp}/ and updates a latest symlink alongside it.

---

CLI reference

tp run [TASK] [OPTIONS]      # execute a task
tp report [TASK] [RUN]       # re-print the report for a stored run (defaults to `latest`)
tp init                      # scaffold trap.yaml + traptask.yaml — NOT YET IMPLEMENTED

tp run options

Flag	Default	Purpose
TASK (positional)	first task in trap.yaml	which task to run
--config / -c	trap.yaml	path to the trap config
--tag / -t	(none)	filter cases by tag; repeatable
--output / -o	rich	report renderer: rich or json
--fail-fast	false	stop after the first case whose judge score < 1.0
--workspace / -w	.trap	where to write run artifacts

tp report options

Re-renders a previously stored run from disk. Same --config / --output / --workspace flags; the RUN argument is the timestamp directory name, or latest (default).

Exit codes

• 0 — every case exited 0, and (if a grader ran) metrics.passed is not False.
• 1 — at least one case had a non-zero exit code, or the grader returned {"passed": false}.

---

Configuration reference

trap.yaml (solution author)

tasks:
  test:                        # task name; arbitrary
    description: optional      # shown in the report
    cmd: uv run python solution.py
    traptask: ../task          # path to the task dir (contains traptask.yaml)
    inputs:                    # optional
      stdin: input.txt         # filename in inputs/{case_id}/ to pipe as stdin
      files:                   # optional: filenames to assert exist before running
        - config.json
    file_outputs:              # files the solution promises to write
      - result.json
    timeout: 30                # seconds; default 30
    inputs_envvar: INPUTS      # override the env var name if you want
    outputs_envvar: OUTPUTS

  run:                         # second task; same traptask, different cmd or inputs
    cmd: uv run python solution.py
    traptask: ../task
    inputs:
      stdin: input.txt
    file_outputs:
      - result.json

• tasks: is a mapping; each key is a task name you can pass to tp run.
• traptask is required for every task — it points at the directory containing traptask.yaml.
• cmd is parsed via shlex.split, run with the trap.yaml's directory as cwd.

traptask.yaml (task author)

The entire file is optional. If traptask.yaml is absent, trap scans inputs/ and treats each subdirectory as a case in output-only mode (no judge, no grader, no expected). With it:

dirs:
  inputs: inputs/                # default
  expected: expected/            # default

cases:
  - id: contains_basic           # must match an inputs/<id>/ directory
    description: optional
    tags: [smoke]                # for `tp run -t smoke`
  - id: skipped_example
    skip: true                   # case is not executed

judge:                           # optional; omit for output-only mode
  cmd: .venv/bin/python judge.py
  payload_envvar: TRAPTASK_PAYLOAD   # default; override if you must

grader:                          # optional; omit to skip aggregation
  cmd: .venv/bin/python grader.py

judge.cmd and grader.cmd run with cwd set to the task directory.

---

The IO contract

Trap injects environment variables at three points. Values are always JSON strings (not file paths) so consumers can json.loads(os.environ[…]) directly.

Solution-side: INPUTS and OUTPUTS

Before running each case, trap injects:

// INPUTS — every file in inputs/{case_id}/
{
  "input.json":  "/abs/path/task/inputs/contains_basic/input.json",
  "config.json": "/abs/path/task/inputs/contains_basic/config.json"
}

// OUTPUTS — every filename declared in trap.yaml `file_outputs`
{
  "result.json": "/abs/path/.trap/test/2026-05-09T14:30:00/contains_basic/result.json"
}

Keys are full filenames with extension. Values are absolute paths. The solution reads INPUTS["foo.json"], writes to OUTPUTS["result.json"]. If you have nothing to read or write via files you can still receive content on stdin via inputs.stdin in trap.yaml.

stdout, stderr, and meta.json are captured automatically — the solution never writes to OUTPUTS["case_stdout"] itself.

Judge-side: TRAPTASK_PAYLOAD

For each case, the judge receives a JSON string with three namespaces:

{
  "inputs":   { "input.json":      "/abs/path/task/inputs/case1/input.json"      },
  "outputs":  {
    "case_stdout":     "/abs/path/.trap/test/.../case1/case_stdout",
    "case_stderr":     "/abs/path/.trap/test/.../case1/case_stderr",
    "case_meta.json":  "/abs/path/.trap/test/.../case1/case_meta.json"
  },
  "expected": { "expected.json":   "/abs/path/task/expected/case1/expected.json" }
}

Note the captured outputs are keyed case_stdout, case_stderr, case_meta.json (prefixed) — that's what the runner writes to disk and what the example judge.py reads. case_meta.json contains {"exit_code": N, "duration": seconds}.

The judge prints free-form JSON to stdout. Trap stores it verbatim as CaseResult.metrics. Convention: include a numeric score field if you want --fail-fast to be meaningful (it checks metrics.score < 1.0).

Grader-side: TRAPTASK_PAYLOAD

The grader receives the full list of per-case results as JSON:

[
  {"case_id": "contains_basic", "exit_code": 0, "duration": 0.12, "metrics": {"score": 1.0}, "skipped": false},
  {"case_id": "exact_match",    "exit_code": 0, "duration": 0.11, "metrics": {"score": 0.5}, "skipped": false}
]

It prints free-form JSON to stdout. Convention: include passed: bool (used for the exit-code check) and score: float (used in the report header).

---

The .trap/ workspace

.trap/
└── {task_name}/
    ├── latest -> 2026-05-09T14:30:00/
    └── 2026-05-09T14:30:00/
        ├── {case_id}/
        │   ├── case_stdout
        │   ├── case_stderr
        │   ├── case_meta.json           # {"exit_code": 0, "duration": 0.12}
        │   ├── judge_stdout             # raw judge output (if judge ran)
        │   ├── judge_stderr
        │   ├── judge_meta.json
        │   └── {any declared file_outputs}
        ├── grader_stdout                # raw grader output (if grader ran)
        ├── grader_stderr
        ├── grader_meta.json
        └── report.json                  # the rendered/JSON report for this run

Use tp report to re-display a stored run without re-executing the solution.

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Three running modes

Choose by what you put (or don't put) in traptask.yaml:

Mode	judge	grader	Pass/fail signal
Output-only	absent	absent	any case exit_code != 0 → exit 1
Per-case scoring	present	absent	same as above
Full evaluation	present	present	grader's metrics.passed == false → exit 1

In output-only mode you can even omit traptask.yaml entirely — trap will discover cases by scanning inputs/ subdirectories.

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Current limitations

• tp init is a stub; scaffolding is not implemented yet.
• Cases run sequentially (the TaskRunner._iter generator is deliberately left as a seam for future parallelization).
• No PyPI release; install from source.
• Python 3.14 minimum.