PALACE

PALACE is an open benchmark format for LLM evaluation, with native support for agentic tasks.

Benchmarks should be portable. A PALACE benchmark is a self-contained folder with two JSON files: info.json (metadata and evaluation config) and tasks.json (the actual tasks). No framework lock-in, no Python decorators, no code to run. Just data that any tool can read.

palace-eval is the reference implementation.

Quick start

1. Install:

uv tool install palace-eval   # recommended
# or: pip install palace-eval

2. Configure your API:

palace config set url https://api.openai.com/v1
palace config set key sk-your-api-key
palace config set judge_model gpt-4o

3. Run your first evaluation:

palace download SimpleQA
palace run SimpleQA -m gpt-4o -l 10

That's it. Results go to ~/.cache/palace/results/.

Run palace config anytime to see your current settings.

Why another benchmark format?

Most benchmarks today are tied to specific frameworks. LM-eval-harness tasks are Python code. Inspect-AI uses @task decorators. If you want to run a benchmark, you need their infrastructure.

PALACE takes a different approach:

• Self-contained: everything travels together (tasks, expected outputs, judge criteria, evaluation logic)
• HuggingFace-distributed: download with palace download, standard hosting and discovery
• Agentic-native: tool-using agents are a first-class task type
• Framework-agnostic: palace-eval runs them, but so could anything else

We ship 27+ benchmarks covering reasoning, knowledge, safety, multilingual, multimodal, and agentic capabilities.

The format

A PALACE benchmark is just two JSON files:

SimpleQA/
├── info.json      # what kind of benchmark, how to evaluate
└── tasks.json     # the actual tasks

info.json defines the benchmark:

{
  "name": "SimpleQA",
  "id": "openai/SimpleQA", 
  "task_type": "QA",
  "category": "Knowledge"
}

tasks.json contains the tasks:

[
  {"id": "q1", "objective": "What is the capital of France?", "expected": "Paris"},
  {"id": "q2", "objective": "Who wrote Romeo and Juliet?", "expected": "Shakespeare"}
]

The format supports five task types (QA, Classification, Criteria Evaluation, Instruction Following, and Agentic), each with its own verification logic. See the full spec.

Configuration

Palace stores settings in ~/.config/palace/config.yaml. The quick start covers the essentials.

For CI/Docker, use environment variables instead. Run palace config env to see the mapping.

Included benchmarks

palace list                    # see all available
palace download MMLU           # download one
palace download --all          # download everything

Running evaluations

palace run MMLU -m gpt-4o
palace run MMLU -m gpt-4o -l 50           # limit to 50 tasks
palace run "GPQA Diamond" -m claude-sonnet
palace run SWE-bench -m o3-mini --agentic # agentic benchmark

Python API (for integration):

from palace import evaluate

evaluate(
    run_name="my-eval",
    url="https://api.openai.com/v1",
    token="sk-...",
    name="gpt-4o",
    tasklist="SimpleQA",
    limit=100,
)

Works with any OpenAI-compatible endpoint: OpenAI, Anthropic, Azure, Mistral, local deployments, etc.

Agentic evaluation

For benchmarks where the model uses tools (GAIA, AssistantBench, etc.), you need Vivarium, a sandboxed Docker runtime for agents.

pip install vivarium-ai

Vivarium starts automatically when you run an agentic tasklist. Requires Docker 24+.

Note: Vivarium is currently pending open-source release (expected within weeks). Contact massimiliano.altieri@ec.europa.eu for early access.

Creating your own benchmarks

palace init my-benchmark      # interactive wizard
palace validate my-benchmark  # check for errors
palace publish my-benchmark   # publish to HuggingFace

See Your First Benchmark for a walkthrough.

Documentation

Full docs at palace.pages.code.europa.eu/palace-eval.

Contributing

Issues and PRs welcome. See CONTRIBUTING.md.

License

Copyright 2025 European Union. Licensed under EUPL-1.2.

Developed by the European Commission Joint Research Centre.

Citation

@software{palace2025,
  title = {PALACE: An Open Benchmark Format for LLM Evaluation},
  author = {Altieri, Massimiliano},
  year = {2025},
  institution = {European Commission Joint Research Centre},
  url = {https://code.europa.eu/palace/palace-eval},
  license = {EUPL-1.2}
}

Contact

• Issues: GitHub or GitLab
• Email: massimiliano.altieri@ec.europa.eu