PlanningCopilot

Community AI and agents

Tool-augmented LLM system for the full PDDL planning pipeline, improving reliability without domain-specific training.

Install

The installation steps are in the documentation below. Documentation

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. Getting Started
  2. Dependencies
  3. Usage
  4. How to use the environment:
  5. Citations

Toward PDDL Planning Copilot

Getting Started

We present the Planning Copilot, a chatbot that brings together multiple planning tools and lets users run them using natural language instructions. It’s built on the Model Context Protocol (MCP), which makes it easy for language models to interact with external tools and systems.

The Planning Copilot is modular, so each part can be swapped out, upgraded, or extended without affecting the rest of the system. In the current implementation, Solve uses FastDownward for classical planning and Metric-FF for numeric planning, Verify uses VAL to validate plans, and Execute relies on PDDL_Plus_Parser to simulate and track plan execution.

Dependencies

  1. Make sure that Python 3.10 is installed and active (via virtual environment or conda environment).
  2. Install the latest version of Ollama to run it locally.
  3. Install all project requirements: python -m pip install -r requirements.txt

Usage

How to use the environment:

  1. Update all the paths and settings in the config.py file.
  2. Run the LLM chat with: python app.py
  3. To change the LLM, edit the llm_with_tools.py file.
  4. To add new tools, modify the MCP server in solvers_server.py.

Citations

If you find our work interesting or the repo useful, please consider citing this paper:

@article{benyamin2025toward,
  title={Toward PDDL Planning Copilot},
  author={Benyamin, Yarin and Mordoch, Argaman and Shperberg, Shahaf S and Stern, Roni},
  journal={arXiv preprint arXiv:2509.12987},
  year={2025}
}

Frequently asked questions

What is PlanningCopilot?

Tool-augmented LLM system for the full PDDL planning pipeline, improving reliability without domain-specific training.

How do I install PlanningCopilot?

Follow the installation steps in its documentation, shown on this page and in its repository.

Is PlanningCopilot open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains PlanningCopilot?

It is published by SPL-BGU. AI.tn lists it but does not maintain or endorse it.

From the community awesome-mcp-servers lists and the project's GitHub repository. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.