RUC-NLPIR/Arbor

Health v2 · maintenance only

Flags: young

Adoption not part of the score

1047 stars · 124 forks observed · 2026-09-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Arbor is a generalist autonomous research agent that takes a benchmark and a goal, then proposes hypotheses, edits code, and runs real experiments while growing a hypothesis tree that preserves failed directions. It ships as a pip-installable Python CLI (`pip install arbor-agent`) and reports beating Claude Code and Codex by 2.5x on matched compute across tasks like BrowseComp, Terminal-Bench, and MLE-Bench.

Use cases

• run autonomous ML experiments that iteratively optimize a benchmark score
• have an agent research, edit code, and test hypotheses without supervision
• automate machine-learning engineering tasks like model tuning and data fixes
• keep a record of failed experiment directions instead of forgetting them
• evaluate an autonomous agent on terminal and web-research benchmarks
• reproduce a published autonomous-research agent from an arXiv paper

When to choose

• you want a hands-off agent that runs and iterates on experiments against a measurable benchmark
• you need optimization loops that only keep gains verified on held-out data
• you want to study or compare against state-of-the-art autonomous research agents

When to avoid

• you need a tightly supervised, human-in-the-loop coding assistant
• you lack the LLM API access or compute budget for long autonomous experiment runs
• you want a deterministic scripted pipeline rather than an exploratory agent

Facets

cli-tool · maturity active

agent-framework workflow-automation machine-learning developer-tools artificial-intelligence large-language-models machine-learning developer-tools python cli cross-platform autonomous-agent hypothesis-tree autoresearch experiment-automation ml-engineering benchmark-optimization tree-search research-paper ai-agents automation

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markdown · JSON · MCP: product_card(name="RUC-NLPIR/Arbor")