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Quick Start

To quickly get started with FrameX we recommend using FrameX-Workbench. This allows quick and easy tinkering with the logic engine and the features.

The example

Copy this complete program — four statements, including the world declaration: a world assumption, a fact, a rule, and a question:

world open.
socrates:Human.
?X:Mortal <- ?X:Human.
?- socrates:Mortal.

Read it aloud: Socrates is a human. A person is mortal if that person is human. Is Socrates mortal? The answer is true. Every statement ends with a period; ?X is a variable and world open. declares the world assumption (see World Assumption).

Option 1: FrameX-Workbench

To get started using FrameX we recommend using the FrameX-Workbench. After a quick login you can start modeling the logic directly in the web-tool. You can find FrameX-Workbench here: https://framex.nlp-lab.ai/.

  1. Login to framex.nlp-lab.ai. If you dont have a user, please reach out to the Teaching Assistants.
  2. Start the Workspace using the Start Workspace button on the top right.
  3. Create a new file using the New file button in the FILES tab on the left side of the screen and name it quick-start.fx. If its not visible, you may need to click Show files first to show the colapsed FILES section.
  4. Write your FrameX F-Logic code in the created file. You can use the example from above to start tinkering.
  5. Save the file and press Run program on the top right to evaluate the file.
  6. The result shows the logical answer (true); execution status is reported separately.

Option 2: FrameX-CLI

The engine and Python host are available in the public FrameX-Core repository. Follow Installation and Quick Start to build the engine and put framex on your PATH. You can then run and test .fx files locally.

  1. Name the file quick-start.fx.
  2. Run it from a terminal:
framex run quick-start.fx
  1. Add expect true: socrates:Mortal. to your file, then run the test:
framex test quick-start.fx

Next Steps

Congrats! You have created and run your first FrameX program. 🎉🎉🎉

Continue with the Tutorial to learn the language, or the Python API Exercise to drive a persistent session. To connect models and tools, read Hybrid AI with FrameX and Tool Calling.