MAPLE

Model-Agnostic Platform for Laboratory Experiments

MAPLE adds LLM agent capabilities to any MADSci-powered laboratory (v0.5.x) via MCP. Two agents — an Operator for experiment execution and an Overseer for lab monitoring — connect to your lab through configurable MCP servers.

Architecture

MAPLE Architecture

Quick Start

pip install maple-mcp
cd your-experiment/
cp .env.example .env          # Configure MADSci URLs + model provider
maple serve stub              # Start demo (no LLM needed)
maple chat operator           # Open TUI — type anything
maple down                    # Stop all services

For a real LLM experiment:

maple serve operator          # Start with your configured model
maple chat operator           # Run an experiment
maple chat operator --resume  # Pick up where you left off

See examples/block_sorting/ for a complete walkthrough.

Installation

pip install maple-mcp

Requires Python 3.10+ and a running MADSci lab (v0.5.x).

CLI

maple serve {all, operator, overseer, stub, mock} [--dev]
maple chat {operator, overseer} [--resume]
maple down
maple status
maple logs

Configuration

One maple.config.yaml per experiment:

experiment:
  name: My Experiment
  objective: Sort samples by type
  constraints:
    - "Only handle one sample at a time"

operator:
  vision_backend: "vision:MyVision"
  custom_tools:
    - "my_tools:prepare_sample"
  post_action_hooks:
    - node: AnalysisNode
      action: verify_placement

Infrastructure goes in .env (IPs, API keys, model provider).

Extending MAPLE

Extension Point Mechanism Config Key
Vision detection Subclass VisionBackend operator.vision_backend
MCP tools @mcp.tool decorator operator.custom_tools / overseer.custom_tools
Agent hooks extra_hooks param on factory Programmatic
Post-action hooks YAML (no code) operator.post_action_hooks
System prompts Markdown file operator.prompt / overseer.prompt

Supported Models

Provider Environment Variable
OpenAI MODEL_PROVIDER=openai
Anthropic MODEL_PROVIDER=anthropic
Ollama (local, free) MODEL_PROVIDER=ollama

Multi-User

Each device auto-generates a unique identity token. Multiple users can run experiments simultaneously — sessions are isolated automatically.

Programmatic Usage

from maple.operator.agent import create_operator_agent

agent = create_operator_agent("my-session")
result = agent("Sort the colored blocks by color.")

Testing

pytest -m "not integration"                    # Unit tests (no network)
docker compose -f docker-compose.ci.yaml up -d # Start MADSci
pytest -m integration                          # Integration tests

Compatibility

  • Python 3.10+
  • MADSci >=0.5.0, <0.6.0
  • FastMCP 3.x

License

MIT

Project links

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Credits

Author: Gabriel Ferrer

GitHub Statistics

Data verified by PyPI on Aug 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

dev

Tags

agents autonomous-lab llm madsci mcp robotics

Classifiers

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