Skip to main content

Heuristic Autonomous Lab

Project description

HAL

Heuristic Autonomous Lab

This tool is supposed to be run in Jupyter notebooks using JupyterLab. Examples are available in the examples folder.

Config

Create a config.json with the following content:

{
  "GEMINI_API_KEY": "your gemini API key",
  "MEMORY_DATA_PATH": "/path/to/the/memory/data.gz",
  "EXEC_IMPORT": "import time, os, sys, json, yaml, scipy\nimport numpy as np\nimport matplotlib.pyplot as plt\n"
}

Or pass config dictionary to init function, check examples/mininum.ipynb

Get Started

import HAL
HAL.init() # initialization: loading memory, setting up display, etc.
# HAL.auto = 3 # auto-execution: HAL will automatically execute up to 3 steps

HAL.reset() # this reset HAL session
HAL("Do something") # main interface: query HAL

HAL() # continue without user input

API

# initialization
HAL.init("Name", _config=None)

# main interface
HAL(query=None)

# properties
HAL.auto = 0
HAL.session = {}

# session operations
HAL.reset()
HAL.save(path="session.json")
HAL.load(path="session.json")

# memory operations
HAL.search(query)
HAL.memorize(content, meta={ "source": HAL.name })

# low-level components
HAL.memory   # knowledge base
HAL.display  # UI rendering for JupyterLab
# Components
HAL.gather_document
HAL.sort
HAL.plan
HAL.answer
HAL.code

Directory Structure

  • HAL/HAL_answer.py: Answer component
  • HAL/HAL_code.py: Develop component (developer)
  • HAL/HAL_gather_document.py: Search agent
  • HAL/HAL_plan.py: Plan component (planner)
  • HAL/HAL_sort.py: Preprocess component
  • HAL/__init__.py: Entrypoint and main interface
  • HAL/memory.py: Knowledge base implementation
  • HAL/display.py: UI rendering for JupyterLab
  • HAL/run.py: Execution runtime
  • HAL/utils.py: Utility functions

Cite this work

https://arxiv.org/abs/2603.08801

@article{li2026large,
  title={Large Language Model-Assisted Superconducting Qubit Experiments},
  author={Li, Shiheng and Miller, Jacob M and Lee, Phoebe J and Andersson, Gustav and Conner, Christopher R and Joshi, Yash J and Karimi, Bayan and King, Amber M and Malc, Howard L and Mishra, Harsh and others},
  journal={arXiv preprint arXiv:2603.08801},
  year={2026}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

clelandlab_hal-0.0.1.tar.gz (12.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

clelandlab_hal-0.0.1-py3-none-any.whl (15.7 kB view details)

Uploaded Python 3

File details

Details for the file clelandlab_hal-0.0.1.tar.gz.

File metadata

  • Download URL: clelandlab_hal-0.0.1.tar.gz
  • Upload date:
  • Size: 12.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for clelandlab_hal-0.0.1.tar.gz
Algorithm Hash digest
SHA256 e012a8244def99519a2e3e46c969589fc9f1e0586184d262260f0ce4838718c5
MD5 e5d1386aed6d4565721a6d774a7590c0
BLAKE2b-256 2be53a76e13ecf7b28424064fcb34c6ee6d34728217acf0410e0cea50c79e25a

See more details on using hashes here.

File details

Details for the file clelandlab_hal-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: clelandlab_hal-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 15.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for clelandlab_hal-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 c2432f836a0ebde84f1f77a5ee43d00bc8ad777f4306fb42945145b58346552d
MD5 9265aedabc07e503f99a701757319324
BLAKE2b-256 f5e051e5bcc04df5b32298b75028889637361f5049ef7fc2e9219a78361e60da

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page