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Demo harnesses

Use live examples to try a complete task, and scripted lab fixtures to inspect specific runtime mechanisms without model credentials. Each folder contains a spec, supporting assets, and instructions. Change harness.yaml through the validated CLI; code, schemas, and data remain ordinary files.

The current checkout has three active examples in harnesses/. Older examples and scripted labs now live in tests/fixtures/harnesses/; commands using their old harnesses/<name> paths need updating. These are source-checkout assets, not directories installed by the runtime wheel.

Live examples

Example Task and checks Capabilities to inspect
ranked-retrieval Search engineering records and return grounded ranked IDs; measure recall and ranking metrics Enforced tool phases, deterministic search/verification, local evals, a research charter for vocabulary mismatch
ticket-triage Account for every open ticket exactly once, with legal category/priority values and a reason MCP-only data access, parallel reads, validation against the source queue
log-forensics Find facts in a log larger than the inline context Allowlisted shell, optional OS confinement, spilled results and handles, version-scoped run recall

Ticket triage checks completeness, known/open IDs, duplicates, legal labels, and a nonempty reason. It does not establish that the chosen priority or category is the best semantic judgment. Ranked retrieval's larger dataset contains alternative phrasings to expose vocabulary gaps; it is an example to measure, not a promise that a small model will always retrieve correctly.

Follow each folder's setup instructions, then validate and inspect before spending model budget:

hiveloom validate harnesses/ranked-retrieval --json
hiveloom run harnesses/ranked-retrieval \
  --input-text 'Our service keeps running out of database connections.' \
  --dry-run --json

Ticket triage's dry-run starts its declared MCP server to discover tools. The source workbench showcase seeds these examples with real runs and history; it makes paid model calls.

Scripted lab fixtures

These providers and directors are scripts, not general-purpose models. They exercise real tools, validation, journals, and experiment decisions, but cannot establish LLM task quality or model performance.

Fixture What it demonstrates
routing-lab Playbooks change model/tool sets, a pinned plan, forks, and assessed evolution
memory-lab Stored-result transforms, run-scoped notes, exports by handle, durable lessons applied through review
signal-lab Failure location, reflection, a refuted change reverted and a confirmed change kept
delegation-lab Fitness-gated peer handoff, referrals, parent verification, and lineage
research-lab A bounded research program, candidate experiments, sealed confirmation, and an observed ceiling

For example, after installing any dependencies listed in its README:

hiveloom validate tests/fixtures/harnesses/research-lab --json
hiveloom research init tests/fixtures/harnesses/research-lab \
  --name trial --charter tests/fixtures/harnesses/research-lab/research.yaml --json
hiveloom research run tests/fixtures/harnesses/research-lab --name trial --json
hiveloom research report tests/fixtures/harnesses/research-lab --name trial --json

A fresh copy avoids a program-name collision. Foreign code requires the normal trust decision before loading; trust only fixtures you have reviewed.

Other complete fixtures

Fixture What to inspect
quickstart No-tool execution, journal, version hash, guardrails, redaction, packaging and serving
example-summarizer Builtin tools, schema/code validators, retry feedback, and an on-demand house-style skill
article-extractor Custom extraction tool, output hook, and validation against the fetched page

These three use live providers. The site quickstart constructs a smaller summarizer from scratch instead of depending on a fixture path.

Verification and measured results

The repository's scripts/package_e2e.py builds and installs the wheel in a clean environment and exercises the fixtures through the installed CLI, including offline research. Tests verify runtime behavior without credentials or network. Live measurements are separate; see Evaluations for the results, sample sizes, and limitations.