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PyLabRobot

Free

Automate laboratory workflows with precision and safety.

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Free · Opens the source repo

What PyLabRobot does

PyLabRobot is a Python-based framework designed for developing and reviewing lab automation resources, particularly focusing on liquid handling and offline simulations. It provides a hardware-agnostic interface that allows users to create and manage automation protocols while ensuring that physical equipment is operated safely and under strict human oversight. The skill is particularly useful for researchers and laboratory technicians who need to streamline their workflows without compromising safety or accuracy.

The skill leverages a structured resource tree and device-specific backends to facilitate the creation of liquid-handling plans and simulation scenarios. Users can validate their manifests and check deck geometries before executing any physical operations. This offline-first approach ensures that all planning and validation steps are performed without the risk of inadvertently activating hardware, making it ideal for environments where safety is paramount.

PyLabRobot supports various liquid handling backends, including the STAR, Vantage, and Opentrons OT2, among others. It allows for detailed bookkeeping and tracking of resources, but it is crucial to note that the skill does not provide real-time sensing capabilities. Instead, it relies on explicit confirmations from trained operators before any physical actions are taken. This ensures that all parameters, such as device identity, calibration, and safety protocols, are thoroughly checked and approved before executing any automation tasks.

Overall, PyLabRobot is a valuable tool for those involved in laboratory automation, offering a comprehensive suite of resources for planning and managing liquid handling tasks while maintaining a strong focus on operator safety and protocol validation.

When to use it

Use PyLabRobot when you need to develop, validate, and simulate lab automation workflows safely without direct hardware interaction.

When not to use it

This skill is not suitable for real-time monitoring or control of physical devices, as it strictly prevents any automatic commands to hardware.

What you can build with it

Developing Liquid Handling Plans

Use PyLabRobot to create detailed liquid handling plans, ensuring all parameters are validated before execution.

Simulating Lab Workflows

Run offline simulations to visualize and validate lab workflows without any risk to physical equipment.

Integrating with Laboratory Equipment

Easily integrate various hardware backends to streamline automation processes while maintaining safety protocols.

How to install PyLabRobot

View source

1. Install with the skills CLI

npx skills add k-dense-ai/scientific-agent-skills/pylabrobot --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by k-dense-ai

PyLabRobot

Use PyLabRobot's hardware-agnostic frontends, resource tree, trackers, and device-specific backends to develop laboratory automation. Default to local manifest validation, bookkeeping, and the software-only chatterbox backend.

Verified snapshot

  • PyPI stable: PyLabRobot==0.2.1, released 2026-03-23.
  • Upstream requirement: Python >=3.9. This skill uses Python 3.11 for its reproducible smoke tests.
  • /stable/ documentation identifies itself as 0.2.1. /dev/ and repository main describe unreleased work and must not be assumed available in 0.2.1.
  • Stable liquid-handler backends include STARBackend, VantageBackend, EVOBackend, OpentronsOT2Backend, and the offline LiquidHandlerChatterboxBackend.
  • PyLabRobot's GitHub Releases page has no 0.2.x software release entry; use the PyPI history, v0.2.1 tag, and changelog as release evidence.

Non-negotiable hardware boundary

Never connect to, initialize, home, move, heat, shake, spin, pump, open/close, or otherwise command physical equipment automatically. Do not turn a simulation plan into a live backend merely by changing an environment variable, config value, or import.

Before any separately authorized live run, require a trained human to:

  1. Explicitly confirm the exact backend, device identity, firmware, transport, deck, and protocol revision.
  2. Reconcile the physical deck against the resource tree, including carriers, adapters, lids, plates, tip racks, waste, labware orientation, barcodes, and every occupied coordinate.
  3. Verify calibration, teaching, motion envelopes, collision risks, gripper or channel clearances, and all aspiration/dispense coordinates.
  4. Review source identity and actual fill volume, dead volume, destination capacity, tip type/capacity/filter compatibility, channel mapping, units, heights, rates, liquid class, blowout/mixing, and contamination boundaries.
  5. Confirm guards, doors, waste capacity, containment, emergency stop readiness, PPE, biosafety/chemical controls, and a safe abort/recovery procedure.
  6. Approve a slow dry run or nonhazardous commissioning run when anything is new or changed.

Tracker state is bookkeeping, not sensing. It cannot prove that liquid or a tip is physically present. The Visualizer renders resource/tracker events; it does not model physics. Chatterbox prints planned operations; it does not prove calibration, reachability, collision freedom, liquid behavior, or device state.

Required intake

Do not guess any of these:

  • Exact device model, installed options, firmware, computer/OS, and transport.
  • Stable PyLabRobot version and required extras.
  • Deck/deck origin, carriers, adapters, resource definitions, dimensions, coordinates, orientations, and motion clearances.
  • Plate/tube/reservoir capacities and dead volumes; initial physical volumes.
  • Tip model, filter, fitting, capacity, rack state, channel count, and channel mapping.
  • Transfer units (uL, mm, uL/s, s), heights, rates, mixing, air gaps, blowout, liquid properties, and validated vendor liquid class.
  • Contamination policy, controls, waste handling, operator interventions, acceptance criteria, and recovery procedure.

If information is missing, produce an assumptions/blockers list and an offline draft only.

Reproducible install

For offline API inspection and chatterbox simulation:

uv venv --python 3.11 .venv-pylabrobot
uv pip install --python .venv-pylabrobot/bin/python "PyLabRobot==0.2.1"

On Windows, use .venv-pylabrobot\Scripts\python.exe. Do not install hardware extras until the user names the device and explicitly approves its transport dependencies. Then inspect the matching stable device page before considering a pin such as "PyLabRobot[serial]==0.2.1" or "PyLabRobot[usb]==0.2.1".

Offline-first workflow

Run from the repository root. Every bundled CLI uses strict, bounded UTF-8 JSON/CSV, local non-symlink paths, fixed allowlists, and JSON output. None can select a live backend.

python3 skills/pylabrobot/scripts/validate_manifest.py \
  --input tests/pylabrobot/fixtures/protocol_manifest.json

python3 skills/pylabrobot/scripts/check_deck_geometry.py \
  --input tests/pylabrobot/fixtures/protocol_manifest.json

python3 skills/pylabrobot/scripts/plan_transfers.py \
  --manifest tests/pylabrobot/fixtures/protocol_manifest.json \
  --transfers tests/pylabrobot/fixtures/transfers.csv

python3 skills/pylabrobot/scripts/generate_simulation_plan.py \
  --manifest tests/pylabrobot/fixtures/protocol_manifest.json \
  --transfers tests/pylabrobot/fixtures/transfers.csv

python3 skills/pylabrobot/scripts/inspect_backends.py \
  --expected-version 0.2.1 --strict

The geometry checker uses conservative static axis-aligned boxes; it is not a motion planner. The transfer planner requires one new tip per row and checks source/dead/destination volumes, tip capacity, wells, channels, heights, rates, units, and allowlists. Review assets/protocol-manifest.schema.json and the synthetic fixtures before making a project-specific manifest.

Verified software-only example

The exact backend below is software-only. Do not substitute a hardware backend.

from pylabrobot.liquid_handling import LiquidHandler
from pylabrobot.liquid_handling.backends import LiquidHandlerChatterboxBackend
from pylabrobot.resources import (
    Cor_96_wellplate_360ul_Fb,
    PLT_CAR_L5AC_A00,
    TIP_CAR_480_A00,
    hamilton_96_tiprack_1000uL_filter,
    set_tip_tracking,
    set_volume_tracking,
)
from pylabrobot.resources.hamilton import STARLetDeck

set_tip_tracking(True)
set_volume_tracking(True)

deck = STARLetDeck()
tip_carrier = TIP_CAR_480_A00(name="tip_carrier")
tips = hamilton_96_tiprack_1000uL_filter(name="tips")
tip_carrier[0] = tips
plate_carrier = PLT_CAR_L5AC_A00(name="plate_carrier")
source = Cor_96_wellplate_360ul_Fb(name="source")
destination = Cor_96_wellplate_360ul_Fb(name="destination")
plate_carrier[0] = source
plate_carrier[1] = destination
deck.assign_child_resource(tip_carrier, rails=3)
deck.assign_child_resource(plate_carrier, rails=15)
source.get_well("A1").tracker.set_volume(100.0)  # planned state, not sensing

lh = LiquidHandler(backend=LiquidHandlerChatterboxBackend(), deck=deck)
await lh.setup()  # safe here only because the backend above is software-only
try:
    await lh.pick_up_tips(tips["A1"])
    await lh.aspirate(source["A1"], vols=[10.0])
    await lh.dispense(destination["A1"], vols=[10.0])
    await lh.return_tips()
finally:
    await lh.stop()

API rules that prevent stale code

  • Current names are STARBackend, VantageBackend, EVOBackend, and OpentronsOT2Backend; do not use stale STAR, TecanBackend, OpentronsBackend, or ChatterboxBackend imports.
  • Use LiquidHandlerChatterboxBackend for generic offline liquid-handler testing. ChatterBoxBackend is a separate legacy-named export; do not conflate the two.
  • Visualizer(resource=...) is valid, followed by await vis.setup() and await vis.stop(); it starts localhost HTTP/WebSocket servers and may open a browser.
  • There is no generic from pylabrobot.liquid_handling import LiquidClass in 0.2.1. Stable liquid classes are vendor-specific, for example pylabrobot.liquid_handling.liquid_classes.hamilton.HamiltonLiquidClass.
  • Most frontend methods are async. Backend kwargs and capabilities are vendor/model specific; a shared frontend does not imply identical behavior.

References

  • Liquid handling — operations, tips, tracking, liquid classes, units, and validation.
  • Resources — decks, coordinates, plates, tip racks, collisions, state, and serialization.
  • Hardware backends — verified names, support levels, capabilities, and live-run gate.
  • Analytical equipment — plate readers and scales.
  • Material handling — pumps, heaters, shakers, temperature control, storage, and centrifuges.
  • Visualization — chatterbox, Visualizer, localhost services, and simulation limits.

Dated upstream sources

Checked 2026-07-23:

Frequently asked questions about PyLabRobot

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