Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

MAGMA-BENCH

Evaluate agents on interactive robotic tasks with MAGMA. MAGMA-BENCH loads compiled benchmark episodes, runs them in simulation, communicates with an agent through the MAGMA HTTP protocol, and saves detailed results and metrics.

Version 2.0.0b1 is the first beta of v2. APIs, command-line options, and result formats may change before the stable release. End-to-end validation is still in progress.

Official documentation · Report an issue

Installation

Python 3.12 is required. Simulation installation has been checked on Linux x86_64. GPU simulation and rendering require compatible system drivers.

python -m pip install "magma_bench==2.0.0b1"

This installs magma_core[simulation]>=2.0.0b1,<3.0.0 and magma_scenarios>=2.0.0,<3.0.0 with their Python dependencies. The benchmark files and the agent server are separate inputs and are not bundled with this package.

The version is pinned explicitly because this is a prerelease. To request the latest version, including prereleases, use:

python -m pip install --upgrade --pre magma_bench

Usage

Start a compatible MAGMA agent server, then run a compiled benchmark directory:

magma-bench run \
  --benchmark-root /path/to/compiled-benchmark \
  --agent-address http://127.0.0.1:8888 \
  --run-name experiment-1

The agent must expose /health, /v1/info, and /v1/responses using protocol version 2.0. Configuration can be supplied with --config-path; command-line options override its benchmark settings.

Useful options include:

magma-bench run --help
magma-bench run --benchmark-root /path/to/benchmark --scenarios coffee_comp
magma-bench run --benchmark-root /path/to/benchmark --skip-judge
magma-bench run --benchmark-root /path/to/benchmark --model-logs --videos

Video output uses the optional dependencies:

python -m pip install "magma_bench[video]==2.0.0b1"

Version 2 uses result schema 2.0. Resume a compatible run with --results-path; older result schemas and agent adapters are unsupported.

See the official documentation for benchmark preparation, configuration, agent setup, metrics, and result formats.

Install from source

Install the tagged release from GitHub:

python -m pip install "magma_bench @ git+https://github.com/MAGMA-rob/magma-bench.git@v2.0.0b1"

Dependencies are resolved from PyPI. For local development, clone the repository and run python -m pip install -e ".[dev,video]".

License

BSD 2-Clause.

Metadata

Release files for magma-bench 2.0.0b1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for magma-bench 2.0.0b1
File Size Uploaded
magma_bench-2.0.0b1.tar.gz 69.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for magma-bench 2.0.0b1
File Interpreter ABI Platform
magma_bench-2.0.0b1-py3-none-any.whl Python 3 none any Details

Total release size: 135.3 kB

Release files / magma_bench-2.0.0b1.tar.gz

Download URL magma_bench-2.0.0b1.tar.gz
Size 69.0 kB
Tags Source
SHA-256 checksum
How to use checksums
8f49e57c859e6f55fd79bead43028a1986adb6bfd380b37c078098d256cc1e93
BLAKE2b-256 checksum
How to use checksums
ae0f920fa275264d5db70213f9c010c05c46cfbf783af77350c568e2d3bf0729
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 15, 2026.

Transparency log

Release files / magma_bench-2.0.0b1-py3-none-any.whl

Download URL magma_bench-2.0.0b1-py3-none-any.whl
Size 66.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
eb6c3b5e8a39345ee83f291f7ac81d2324f2a8ed84a3e038f760583490577052
BLAKE2b-256 checksum
How to use checksums
22084d14197b0515fe0352cd2bcbff8d0217a1f298726e792f77d13dba4765d6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 15, 2026.

Transparency log
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page