Running the standalone Arbor CLI (upstream tool)

This skill normally runs HTR natively — Claude is the coordinator and subagents are executors. That's the recommended path: no extra install, no separate API keys, and you stay in the loop to read evidence between cycles.

But the paper's authors also ship a full implementation as a CLI. Use it instead when the user explicitly wants to run the published system (e.g. to reproduce paper results), wants Arbor to run fully unattended for many hours via its own live dashboard, or wants its built-in report/web-UI tooling.

Source: https://github.com/RUC-NLPIR/Arbor

Install

Requires Python ≥ 3.10 and Git.

git clone https://github.com/RUC-NLPIR/Arbor.git
cd Arbor
python -m venv .venv && source .venv/bin/activate
pip install -e .
arbor doctor      # verify install, PATH, git, API keys

Configure provider/model/keys

arbor setup       # writes ~/.arbor/config.yaml (provider, model, base URL, keys)

Supported backends: Anthropic, OpenAI / OpenAI-compatible Responses API, and LiteLLM (DeepSeek, Gemini, Qwen, vLLM, Ollama, local gateways). Keys can also be set via environment variables.

Run

1. Prepare a benchmark directory: an initial artifact under a clean git repo plus an evaluation script (your E_dev / E_test).
2. Author a project research_config.yaml (task description, coordinator settings — max cycles, depth, merge thresholds — executor max turns, UI mode). See examples/research_config.example.yaml in the repo.
3. Start the interactive session:
   bashCopy

   arbor
   

   Arbor runs an intake conversation, forms a Research Contract, then a live dashboard takes over. Each experiment runs in an isolated git worktree; verified improvements merge into a per-run trunk.
4. Outputs land in .arbor/sessions/ with REPORT.md, the event log, and results. Re-render a past session's report with arbor report <session>.

Key CLI commands

Codebase map (for the curious / for debugging)

The implementation lives under src/ (a src-layout; the CLI installs as arbor). The package directories are:

• core/ — ReAct loop, tools, LLM providers, context management
• coordinator/ — coordinator agent, the tree, orchestrator, coordinator tools
• executor/ — executor agent and CLI
• cli/ — intake, live dashboard, setup, doctor, config
• events/ — typed event bus and payloads
• report/, webui/ — report generation and read-only run monitor
• search_agent/ — the minimal ReAct search harness (the M_0 for the BrowseComp / search-agent tasks)
• plugins/ — domain plugins (e.g. mle_kaggle.yaml)
• skills/ — on-demand markdown playbooks

(top-level src/ also has dashboard.py, run.py, review.py.)

Naming note: the paper and this skill call the persistent state the hypothesis tree; the tool's code and dashboard call the same structure the Idea Tree. They are the same thing. The depth convention also matches the native skill: root/depth 0 = objective + global insights, depth 1 = research directions, depth 2+ = concrete tested methods.

Native vs. upstream — quick guide

• Native (this skill): best default. Lower setup, transparent, you read and steer between cycles, reuses your existing Claude Code session and worktrees.
• Upstream CLI: choose for paper reproduction, long unattended runs with the official dashboard, or when the user specifically asks for the arbor tool.