AutoRubric

A Python library for evaluating text outputs against weighted criteria using AI judges such as LLMs and decision models.

  @misc{rao2026autorubric,
        title={Autorubric: A Unified Framework for Rubric-Based LLM Evaluation},
        author={Delip Rao and Chris Callison-Burch},
        year={2026},
        eprint={2603.00077},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2603.00077},
  }

Installation

pip install autorubric

For decision-model judges, install the TypeSafe extra and set its key:

pip install 'autorubric[typesafe]'
export TYPESAFE_API_KEY=your_key_here

Quick Example

import asyncio
from autorubric import Rubric, LLMConfig, DecisionModelConfig
from autorubric.graders import CriterionGrader

async def main():
    grader = CriterionGrader(judge_model_config=LLMConfig(model="openai/gpt-5.6-luna"))
    # Or let a decision model grade the whole rubric in one request (pip install 'autorubric[typesafe]'):
    # grader = CriterionGrader(judge_model_config=DecisionModelConfig(model="jev-latest"))

    rubric = Rubric.from_dict([
        {"weight": 10.0, "requirement": "States NMC cell-level energy density in the 250-300 Wh/kg range"},
        {"weight": 8.0, "requirement": "Identifies LFP thermal runaway threshold (~270°C) as higher than NMC (~210°C)"},
        {"weight": 6.0, "requirement": "States LFP cycle life advantage (2000-5000 cycles vs 1000-2000 for NMC)"},
        {"weight": -15.0, "requirement": "Incorrectly claims LFP has higher gravimetric energy density than NMC"}
    ])

    result = await rubric.grade(
        to_grade="""NMC cathodes (LiNixMnyCozO2) achieve 250-280 Wh/kg at the cell level,
        while LFP (LiFePO4) typically reaches 150-205 Wh/kg. However, LFP offers superior
        thermal stability with decomposition onset at ~270°C compared to ~210°C for NMC,
        and delivers 2000-5000 charge cycles versus 1000-2000 for NMC.""",
        grader=grader,
        query="Compare NMC and LFP cathode materials for EV battery applications.",
    )

    # result.score is `float | None` (None if the grade failed or no criterion was left
    # to score); guard before formatting.
    print(f"Score: {result.score:.2f}" if result.score is not None else "Score: n/a")
    for criterion in result.report:
        print(f"  [{criterion.final_verdict}] {criterion.criterion.requirement}")

asyncio.run(main())

Documentation

Full documentation, API reference, and a cookbook with several dozen recipes are available at autorubric.org.

Using AutoRubric in Claude Code, Codex, Gemini CLI, and other coding agents

AutoRubric's documentation is indexed by Context7, so you can give your coding agent live access to the current API reference and cookbook. With it connected, the agent writes correct, idiomatic AutoRubric code — right imports, the async grading API, weighted criteria, ensemble and multi-choice config — instead of guessing from stale memory.

1. Connect the Context7 MCP server

Get a free API key at context7.com/dashboard, then add the server to your agent (swap in your key):

Claude Code

claude mcp add --scope user --header "CONTEXT7_API_KEY: YOUR_API_KEY" \
  --transport http context7 https://mcp.context7.com/mcp

Codex CLI

codex mcp add context7 -- npx -y @upstash/context7-mcp --api-key YOUR_API_KEY

Gemini CLI — add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "context7": {
      "httpUrl": "https://mcp.context7.com/mcp",
      "headers": { "CONTEXT7_API_KEY": "YOUR_API_KEY" }
    }
  }
}

Other clients (Cursor, VS Code, Windsurf, Zed, …): run npx ctx7 setup, or see the full client list. The underlying package is @upstash/context7-mcp.

2. Point the agent at AutoRubric

Reference the library in your prompt so Context7 loads the right docs — AutoRubric's library ID is /websites/autorubric:

Write an AutoRubric grader that scores answers against weighted criteria with an ensemble of two judges. use context7, library /websites/autorubric

Context7 exposes a resolve-library-id tool (to find the library) and a docs-query tool (to fetch version-specific docs and examples); your agent invokes these automatically.

Make it the default — drop this into your CLAUDE.md / AGENTS.md

To get high-quality AutoRubric code on every task (not just when you remember to ask), add a block like this to your agent's instruction file — CLAUDE.md for Claude Code, AGENTS.md for Codex, Gemini CLI, and most other agents:

## Writing AutoRubric code

When writing or editing code that uses AutoRubric:

- Load the current docs via Context7 (library `/websites/autorubric`) first — rely on
  the real API, not prior memory or guesswork.
- The grading APIs are async: `await` `Rubric.grade`, `Grader.grade`, and `EvalRunner`,
  and treat `result.score` as `float | None` (guard before formatting).
- Use the feature that fits the task: weighted (±) criteria, `CriterionGrader` with LLM
  judges (`LLMConfig`) or decision-model judges (`DecisionModelConfig`), ensemble judging
  with aggregation strategies, confidence cascades (`EscalationConfig`), multi-choice
  (ordinal/nominal) criteria, batch `EvalRunner` with checkpointing, agreement metrics and
  bootstrap CIs, YAML configs, or meta-rubric improvement.
- Verify exact signatures and types against the API reference
  (https://autorubric.org/docs/api/) and reuse patterns from the cookbook
  (https://autorubric.org/docs/cookbook/).
- Ensure provider keys (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, …) are set in the
  environment or present in the project's `.env`; AutoRubric reaches 100+ providers via
  LiteLLM.
- If `TYPESAFE_API_KEY` is set in the environment or present in the project's `.env`
  (check without printing its value), ask the user which judges to use before writing
  the grader (decision models need `pip install 'autorubric[typesafe]'`):
  - a cheaper decision model (`DecisionModelConfig`, one request per item);
  - an LLM judge, or a panel of LLM judges (`judges=[JudgeSpec(...), ...]`);
  - a mixed panel, in which a decision model votes alongside LLM judges;
  - a cascade (`EscalationConfig`), in which the decision model grades first and
    escalates uncertain criteria to LLM judges.

Features

License

MIT License - see LICENSE file for details.

Acknowledgments

This research was developed with funding from the Defense Advanced Research Projects Agency’s (DARPA) SciFy program (Agreement No. HR00112520300). The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.