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title LabAgent: An Open-Source AI-Guided Framework for Automated Scientific Measurements
tags
Python
laboratory automation
artificial intelligence
instrument control
measurement planning
authors
name orcid affiliation
Anai Guo
0000-0000-0000-0000
1
affiliations
name index
Independent Researcher
1
date 13 April 2026
bibliography paper.bib

Summary

LabAgent is an open-source Python framework that connects large language models (LLMs) to laboratory instruments for automated scientific measurements. The framework provides AI-guided measurement planning, instrument discovery and classification, safety boundary validation, data analysis with domain-specific interpretation, and experiment memory — covering 46 measurement types across 9 scientific disciplines and ~50 instrument models across 15+ manufacturers.

Statement of Need

High-precision laboratory measurements in physics, chemistry, biology, and materials science require complex instrument orchestration, parameter optimization, and data analysis. Researchers currently face a choice between expensive proprietary software (e.g., LabVIEW) or writing custom measurement scripts from scratch for each experiment. Both approaches are time-consuming, error-prone, and lack safety guarantees.

While instrument control libraries such as PyVISA [@pyvisa] and PyMeasure [@pymeasure] provide foundational drivers, and measurement suites like PICA [@pica] offer hardcoded GUIs for specific instruments, no existing tool combines AI reasoning with instrument control in a unified, extensible framework.

LabAgent addresses this gap by placing AI at every step of the measurement workflow: from literature-informed protocol discovery to adaptive measurement planning with three-tier safety validation, to AI-generated data analysis with domain-specific scientific interpretation.

Architecture

The framework follows an agent harness architecture inspired by OpenHarness [@openharness], with four core subsystems:

  1. Tool Registry: Eight standardized tools (scan, classify, propose, validate, literature search, analyze, memory recall, healthcheck), each following a BaseTool abstract interface with Pydantic input validation.

  2. Agent Engine: An asynchronous query loop that processes user messages, invokes LLMs via litellm [@litellm] for model-agnostic routing, detects tool calls, executes them with permission checks, and streams results back to the user.

  3. Permission System: A three-layer safety model with immutable absolute limits (e.g., maximum current 10 A), boundary checks loaded from YAML policies, and interactive user confirmation for potentially dangerous operations.

  4. Multiple Interfaces: A Model Context Protocol (MCP) server for AI IDE integration, a command-line interface with 11 subcommands, a FastAPI web GUI with adaptive measurement forms, and a Textual-based terminal panel for interactive sessions.

Measurement Coverage

The framework includes 46 YAML-based measurement templates spanning:

  • Electrical characterization: IV curves, R-T, delta mode, high resistance, FET transfer/output, breakdown voltage
  • Electrochemistry: Cyclic voltammetry, EIS, chronoamperometry, potentiometry
  • Semiconductor: Solar cell IV, DLTS, photocurrent spectroscopy, C-f analysis, tunneling spectroscopy
  • Optics and photonics: Optical power measurement, UV-Vis absorbance, photoresponse transients
  • Biological / analytical: Plate-reader absorbance / fluorescence / luminescence, biosensor impedance, mass weighing, pH and ISE readout
  • Environmental and sensor: Gas sensor, humidity response, strain gauge, fatigue, mass flow, pressure control
  • Thermoelectric: Seebeck coefficient, thermal conductivity
  • Magnetic and transport (condensed-matter specialty): Hall effect, magnetoresistance, anomalous Hall effect, FMR, hysteresis loops, magnetostriction, Nernst effect, SOT loop shift
  • Superconductivity: Tc transition, critical current density
  • Dielectric / ferroelectric: P-E loop, pyroelectric current
  • Quantum Design systems: PPMS R-T / MR / Hall / heat capacity, MPMS M-H / M-T with MultiPyVu integration

AI Capabilities

The framework provides eight distinct AI capabilities:

  1. Instrument Classification: Dictionary-based lookup with LLM fallback for unknown instruments
  2. Parameter Optimization: LLM suggests optimal sweep ranges based on sample description and literature
  3. Safety Advisory: LLM explains why limits exist and suggests safer alternatives
  4. Script Generation: LLM creates custom analysis scripts for arbitrary measurement types
  5. Result Interpretation: LLM provides domain-specific scientific insights on extracted values (physics, electrochemistry, spectroscopy, etc.)
  6. Skill Generation: LLM creates new measurement protocols from existing examples
  7. Agent Chat: Multi-turn conversation with autonomous tool calling
  8. Experiment Memory: SQLite with FTS5 full-text search, frozen snapshots injected into agent context

Acknowledgements

Measurement procedures reference patterns from the PICA project [@pica]. Agent architecture is inspired by OpenHarness [@openharness] and Hermes Agent [@hermes]. The framework uses litellm [@litellm] for model routing and PyVISA [@pyvisa] for instrument communication.

References