wendell 0.1.27

Wendell CLI for playbook-generated agent test suites and run uploads

Wendell CLI

Wendell is a Python CLI for running production agents against hosted Wendell test suites.

The CLI is advisory by default: it reports scores, captures traces, and returns a successful process exit unless the project explicitly enables blocking gates. In blocking mode, wendell run returns a nonzero exit code when gates fail.

Intended split

• Wendell system: turns reviewed Playbooks into hosted test suites, owns rubrics, stores traces, and reports regressions.
• Wendell CLI runner: runs in a repo or CI job, fetches scenario work from a published suite, invokes the customer's agent adapter, uploads turns/results, and prints a concise summary.

Install

Install the latest published CLI:

uv tool install --force wendell

Alternative installers:

pipx install --force wendell
python3 -m pip install --user --upgrade wendell

The packaged CLI does not require an OpenAI, OpenRouter, Anthropic, or other LLM provider key to install, register, create Playbook drafts, or run hosted suites. Wendell's hosted service generates suites from reviewed Playbooks. Your own agent_command may need provider credentials if your agent uses an LLM.

For unreleased changes from current main:

python3 -m pip install "git+https://github.com/croppia/wendellai.git#subdirectory=wendell-ci"

For release validation from this monorepo, run:

scripts/smoke_wendell_production_app_install.sh

That smoke creates a clean app directory, installs the packaged wendell CLI into a fresh virtualenv, and exercises the hosted-suite contract path against a local mock API without using repo-local PYTHONPATH. The smoke adapter asserts the customer-facing payload uses wendell.agent_input.v1 and does not expose rubrics, hidden facts, source lineage, terminal outcomes, or success/failure criteria. The smoke also asserts the public package does not ship the internal worldsim package.

The package installs the wendell command only. Customer-facing examples and CI jobs should use wendell run, not compatibility aliases.

Local development

cd wendell-ci
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest
wendell --help

Login

For a first-time Wendell runner, install and register the CLI:

curl -fsSL https://www.wendellai.com/install | bash
wendell register

Registration creates a short-lived runner session, asks for email/password and agent details, and stores a scoped local runner credential.

For local development with an existing InkPass API key, store it once:

wendell login --api-key-stdin --validate
wendell auth status

Credentials are stored at ~/.config/wendell/credentials.json by default, or $WENDELL_CONFIG_HOME/credentials.json when set. The directory is created with 0700 permissions and the credentials file with 0600 permissions.

CI should continue to use WENDELL_INKPASS_API_KEY; environment variables take precedence over stored credentials.

Minimal config

project = "support-agent"
mode = "advisory"
agent = "support_agent"
agent_command = "python scripts/wendell_agent_adapter.py"
agent_timeout_seconds = 120
upload_traces = true

[gates]
suite_min_score = 0.80
scenario_min_score = 0.75
critical_failures_allowed = 0

When you run a hosted suite with wendell run --suite, Wendell resolves the hosted world, version, scenario pack, and scoring contract from the published suite.

Production app test loop

Use wendell run as the local and CI entry point for production agents.

wendell suites configure --suite refund-agent-regression --project support-agent
wendell doctor --config wendell.toml --validate
wendell run --suite refund-agent-regression --config wendell.toml

Your adapter receives a production-facing JSON payload. Wendell does not send the scoring rubric, hidden facts, expected outcomes, or success/failure criteria to the agent process.

{
  "schema_version": "wendell.agent_input.v1",
  "task": "Respond as a support agent for Support Agent.",
  "policies": ["Inspect the request before completing the workflow."],
  "transcript": [{"speaker": "customer", "text": "I need help with this request."}],
  "available_tools": [
    {
      "name": "workflow_console.inspect_request",
      "arguments": {"case_id": "str"},
      "description": "inspect request"
    }
  ],
  "case": {"case_id": "case_123", "request": "I need help with this request."},
  "scenario": {"id": "case_123", "kind": "realistic"}
}

The adapter must print JSON:

{
  "message": "I inspected the request and recorded the required evidence.",
  "tool_calls": [
    {"name": "workflow_console.inspect_request", "args": {"case_id": "case_123"}}
  ],
  "metrics": {}
}

wendell suites configure creates:

• wendell.toml: the hosted-suite run config
• scripts/wendell_agent_adapter.py: the adapter boundary for your production agent

The generated adapter is intentionally not a fake passing agent. It requires WENDELL_APP_AGENT_COMMAND or replacement code that calls your production agent and maps its real actions back to Wendell tool names. Wendell no longer generates a passing example adapter because that makes validation look real without testing the customer's agent.

Wendell parses agent_command and WENDELL_APP_AGENT_COMMAND into command-line arguments and executes them directly, without a shell. Keep those values to an executable plus arguments. Put pipes, redirects, command chaining, or environment setup inside your adapter script or CI environment instead.

Create, review, and publish hosted suites with the wendell playbook and wendell suites commands documented in the SDK docs. New customer projects should use hosted suite creation and wendell run; the public CLI package does not ship the internal local compiler/runtime.

For CI, set mode = "blocking" so failed gates return a nonzero exit code:

project = "support-agent"
mode = "blocking"
agent = "support_agent"
agent_command = "python scripts/wendell_agent_adapter.py"
agent_timeout_seconds = 120
upload_traces = true

[gates]
suite_min_score = 0.90
scenario_min_score = 0.85
critical_failures_allowed = 0

Failure output includes the failed scenario, gate reason, incomplete workflow steps, Playbook rule id, assertion id, trajectory event indexes, and fix prompts when the suite provides assertions.

agent_timeout_seconds controls the maximum duration for one adapter invocation. The default is 120 seconds; increase it for production agents that need more time for hosted tools or long-running workflow turns.

What to commit

Commit these files:

• wendell.toml
• your production adapter, usually scripts/wendell_agent_adapter.py

Do not commit secrets, raw customer transcripts, API keys, or generated run outputs. Keep production credentials in your CI secret store.

GitHub Actions

name: Wendell Agent Tests

on:
  pull_request:
  push:
    branches: [main]

jobs:
  wendell:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.13"
      - name: Install Wendell
        run: |
          python -m pip install --upgrade pip
          python -m pip install wendell
      - name: Run Wendell
        env:
          WENDELL_INKPASS_API_KEY: ${{ secrets.WENDELL_INKPASS_API_KEY }}
          WENDELL_APP_AGENT_COMMAND: python scripts/run_my_agent.py
        run: |
          set +e
          wendell run \
            --suite refund-agent-regression \
            --config wendell.toml \
            --json | tee wendell-run.json
          status=${PIPESTATUS[0]}
          set -e

          python - <<'PY'
          import json
          import os
          from pathlib import Path

          summary_path = os.environ.get("GITHUB_STEP_SUMMARY")
          if not summary_path:
              raise SystemExit(0)

          payload = json.loads(Path("wendell-run.json").read_text(encoding="utf-8"))
          remote = payload.get("remote") if isinstance(payload.get("remote"), dict) else {}
          suite = payload.get("suite") if isinstance(payload.get("suite"), dict) else {}

          lines = [
              "## Wendell report",
              "",
              f"- Gate: `{payload.get('decision', 'unknown')}`",
              f"- Run: `{remote.get('run_id', 'unknown')}`",
          ]
          if suite.get("suite_score") is not None:
              lines.append(f"- Score: `{suite['suite_score']}`")
          if remote.get("private_report_url"):
              lines.append(f"- Private report: {remote['private_report_url']}")
          with Path(summary_path).open("a", encoding="utf-8") as summary:
              summary.write("\n".join(lines) + "\n")
          PY

          exit "$status"

For hosted Wendell reporting, keep upload_traces = true and provide WENDELL_INKPASS_API_KEY from CI secrets. Add api_url only when targeting a staging, preview, or local API. The JSON output includes remote.private_report_url when Wendell can create a private dashboard handoff link for the run.

For a newly published hosted suite, create the repo-local config and adapter boundary first:

wendell suites show refund-agent-regression
wendell suites configure --suite refund-agent-regression --project refund-agent

After a hosted run uploads, inspect its private status and report:

wendell runs watch run_abc123
wendell runs report run_abc123

The intended production flow is:

1. wendell run runs on the developer machine or CI worker.
2. It fetches pinned public scenario work and tool schemas from Wendell.
3. It invokes the agent locally through an adapter such as agent_command.
4. It captures local traces and uploads agent turns/results back to Wendell for server-side scoring.
5. Advisory mode exits 0; blocking mode exits nonzero only when explicitly enabled.

Remote uploads authenticate with an InkPass API key sent as X-API-Key. The key needs at least wendell:worlds:read, wendell:runs:create, and wendell:runs:read.