braintrust-harbor 0.1.0a4

Harbor-first, Braintrust-backed evaluation helpers for coding-agent tooling.

Braintrust Harbor

Harbor-first, Braintrust-backed evaluation helpers for coding-agent tooling.

Use this package when you want to evaluate how coding agents interact with your developer tools, CLIs, MCP servers, skills, repositories, or harness-specific instructions.

What This Does

This package is the bridge between Harbor and Braintrust:

• Harbor runs the agent trials in sandboxes.
• Braintrust stores experiment rows, traces, scores, comparisons, and analysis.
• bt-harbor runs one Harbor job or imports an existing Harbor job, then maps each Harbor trial to one Braintrust experiment row.

The intended flow is:

Harbor tasks + JobConfig
  -> harbor run
  -> Harbor job artifacts
  -> bt-harbor import
  -> Braintrust experiment

Prerequisites

You need:

• Python 3.11 or newer.
• The Harbor CLI available on PATH.
• A Harbor-supported sandbox backend, usually Docker for local examples.
• Model provider API keys required by the Harbor agents you run.
• BRAINTRUST_API_KEY only when using --upload. Local preview imports work without uploading.

Install this package from a checkout:

uv tool install -e .

Check the CLIs:

bt-harbor --help
harbor --version

After publishing, the package can also be run with:

uvx braintrust-harbor --help

Quickstart

Start with the prompt + repo + harness/model + tooling-version workflow:

bt-harbor init harness-model-demo --template harness-model-matrix
cd harness-model-demo

Edit eval-input.json to choose:

• prompt: the user request the agent should complete.
• repo.url and repo.ref: optional repository to clone into the sandbox.
• agents: Harbor harness/model pairs.
• tooling_versions: labels and optional install commands for the tooling versions you want to compare.

Generate a Harbor suite for one tooling version:

python scripts/materialize.py \
  --input eval-input.json \
  --tooling-version current \
  --out generated/current

Run Harbor and write a local Braintrust import preview:

cd generated/current
bt-harbor run harbor-job.json \
  --project "agent-tooling-demo" \
  --suite-artifacts suite-artifacts.json \
  --metadata metadata.json \
  --scorer "scorers:summary_present" \
  --scorer "scorers:used_demo_tool" \
  --scorer "scorers:no_harbor_exception"

Add --upload to publish the imported rows to Braintrust:

bt-harbor run harbor-job.json \
  --project "agent-tooling-demo" \
  --suite-artifacts suite-artifacts.json \
  --metadata metadata.json \
  --scorer "scorers:summary_present" \
  --scorer "scorers:used_demo_tool" \
  --scorer "scorers:no_harbor_exception" \
  --upload

Core Concepts

Harbor concepts:

• A task is a directory with instructions, an environment, and verifier tests.
• A trial is one agent attempt at a task.
• A job is a collection of trials across tasks, datasets, agents, and models.

Braintrust concepts:

• An experiment is a comparable eval run.
• Each row represents one eval case. In this package, one Harbor trial becomes one Braintrust row.
• Scores are numeric checks added by Harbor rewards or Braintrust-compatible scorers.
• Traces preserve the trial trajectory, tool calls, command logs, and scorer spans when those artifacts are available.

This package does not replace Harbor's JobConfig, agent adapters, sandbox management, or task format. It also does not impose your product's scoring policy. Suites should keep product-specific prompts, fixtures, verifiers, and scorers in their own task directories.

Common Workflows

Run a Harbor job and import it:

bt-harbor run harbor-job.json \
  --project "agent-tooling-demo" \
  --suite-artifacts suite-artifacts.json

Import a job that another system already ran:

bt-harbor import jobs/nightly-agent-tooling-run \
  --project "agent-tooling-demo" \
  --suite-artifacts suite-artifacts.json \
  --upload

Add suite scorers:

bt-harbor import jobs/nightly-agent-tooling-run \
  --project "agent-tooling-demo" \
  --suite-artifacts suite-artifacts.json \
  --scorer "my_suite.scorers:summary_present" \
  --scorer "my_suite.scorers:tool_order_quality" \
  --upload

Run on a schedule:

1. Keep the Harbor execution in CI, cron, or your scheduler.
2. Install the latest version of your tooling in the task environment.
3. Run bt-harbor run ... --upload, or run Harbor separately and then bt-harbor import ... --upload.
4. Compare Braintrust experiments by metadata such as tooling_version, agent, model, scenario, and variant.

Examples

See examples/README.md for the full examples guide.

• harness-model-matrix: best first example for evaluating one prompt across optional repos, harness/model pairs, and tooling versions.
• minimal-cli-tool: smallest runnable template for a toy CLI, command logging, artifacts, and a verifier.
• skill-vs-no-skill: task layout pattern for comparing a skill, MCP server, rules file, or guide against a control variant.
• import-existing-harbor-job: import-only pattern for teams that already run Harbor elsewhere.

Customization Checklist

For a real suite, customize these pieces:

• harbor-job.json: agents, models, concurrency, datasets, attempts, and environment backend.
• tasks/*/instruction.md: the user-facing task prompt.
• tasks/*/environment/: install your CLI, MCP server, skill, repo, or service.
• tasks/*/tests/: verify the agent produced the required behavior and reward.
• .agent-tooling-eval.json: row input, expected output, and metadata labels.
• suite-artifacts.json: extra trial artifacts to attach to Braintrust rows.
• scorers.py: Braintrust-compatible scorers for trajectory and output quality.

Useful metadata dimensions are tooling_version, scenario, variant, agent, model, repo_ref, and any feature flag names you are comparing.

External Documentation

Harbor:

• Core concepts
• Task structure
• Run jobs
• Agents

Braintrust:

• Evaluate systematically
• Run experiments
• Interpret evaluation results
• Examine traces

Project docs:

• Architecture
• Scorers
• Compatibility
• Releasing

Library API

from braintrust_harbor import (
    HarborBatchConfig,
    SuiteArtifactConfig,
    import_harbor_job_to_braintrust,
    run_harbor_batch,
)

The recommended integration point is still Harbor's native JobConfig:

Harbor JobConfig -> one Harbor run -> one Braintrust experiment

If your suite needs extra dimensions such as "with skill" vs. "without skill", generate ordinary Harbor task directories or datasets for those variants, then point the Harbor job config at them.

Status

This is an alpha extraction. The Harbor job import path and Braintrust upload path are usable, but the CLI and normalized trace/event helpers should be treated as evolving APIs until a stable release.