Running the upstream Arbor CLI

This skill's bundled tree.py is a standard-library bookkeeping helper driven by the current host agent. The separate upstream runtime supplies its own coordinator, executors, checkpoints, dashboard and Git/evaluation tools. Their state files are not interchangeable.

Reviewed on 2026-09-30 against arbor-agent 0.1.4 and upstream commit 7cdaf1fa. The Python distribution is arbor-agent; arbor on PyPI is an unrelated neuroscience simulator. Both use the import name arbor, so keep them in separate environments.

Install

Python 3.10+ and Git are required. The built-in alphaXiv search dependency requires Python 3.12+, so use 3.12+ when that feature is needed. The native runtime requires model access; the bundled helper requires no provider key.

uv tool install arbor-agent==0.1.4
arbor version
arbor doctor

The equivalent isolated verification command is uv run --isolated --no-project --with arbor-agent==0.1.4 arbor version. For source development, clone RUC-NLPIR/Arbor, record the commit, and install it into a dedicated virtual environment.

doctor checks local installation, Git and credential/config presence. It does not authenticate a key or probe model availability; even a missing-key warning can accompany exit code 0. See its implementation.

Configure a native run

arbor setup

This writes ~/.arbor/config.yaml. Current setup choices include auto, openai-responses, openai-chat and anthropic; auto may probe an endpoint and make a model request. Use the provider's configured environment credential (such as OPENAI_API_KEY or ANTHROPIC_API_KEY) or the setup flow, without committing credentials. Compatibility with a proxy depends on its supported protocol; a base URL alone does not establish Responses/tool support.

The upstream prose configuration guide still lists legacy names such as openai and litellm. For this release, prefer the current setup implementation and example configuration. No provider endpoint was exercised during this skill review.

Prepare a clean Git project with a runnable evaluator and explicit development, gate and final-assessment protocols. A small project research_config.yaml can use the following shape (parsed locally with 0.1.4; the actual experiment and evaluator are supplied through intake):

max_cycles: 3
executor_max_turns: 10
ui:
  interaction_mode: review

max_cycles limits completed/skipped/failed experiments in the upstream CLI; it is different from the bundled helper's coordinator-cycle counter. --max-turns caps coordinator turns, while executor_max_turns controls an executor. Do not interpret merge_threshold: 5.0 as an enforced 5% held-out gain: the current config describes it as an LLM guideline, and the paper's Table 6 describes a development-gain threshold for invoking the gate. Verify the actual candidate/baseline gate evidence before reporting admission.

arbor --cwd ./benchmark --config ./research_config.yaml --max-cycles 3

This starts intake and can launch a real, paid research run after the native runtime's contract flow; it is not a dry run. Set evaluator, protected files, metric direction, resource limits and Git destination in the contract. The runtime keeps per-experiment worktrees and a per-run trunk. Promotion of that trunk into the project's main branch is a separate Git operation.

Commands and outputs

Default native outputs are under <project>/.arbor/sessions/<run_name>/: REPORT.md, events.jsonl, checkpoint/tree data, and a redacted resolved configuration at .coordinator/config_snapshot.yaml. See the output documentation. A report can render from incomplete or empty session data; its existence is not evidence that any experiment succeeded.

Upstream also offers a keyless host integration (arbor install plus optional arbor mcp) that uses the host agent's model rather than making its own model calls. Installing that suite changes the host's skill inventory; it is optional and does not replace this collection's helper automatically.

Verification boundaries

The isolated installed release passed command help/version checks, parsed the configuration above, rendered a synthetic partial report, and exported the bundled replay to HTML. Model setup, autonomous experiment dispatch, live provider requests, MCP registration and runtime Git merges were source-reviewed only, not executed. This skill does not claim reproduction of the paper.