OpenDecision

OpenDecision answers typed questions about application state and documents. It runs a local natural language inference model and returns structured values.

Doom demo

OpenDecision chooses actions for a bot in ViZDoom's Deadly Corridor. This is the Skill 5 recording.

Watch Skill 1 | Watch Skill 3 | Run the demo

Install

With pip:

pip install OpenDecision

With uv:

uv add OpenDecision

Continue to the get started guide

What it provides

Type	Use	Result
Choice	Select one option.	Option name and probabilities
Noul	Test one statement.	A score from 0 to 1
Score	Use an ordered scale.	Weighted score and probabilities
Relation	Compare evidence with a statement and its opposite.	supports, contradicts, unknown, or conflicted
Document decisions	Ask questions about text or JSON.	Answers and source passages

See code examples for each primitive

Start here

• Get started: install the package, run a Python example, and start the API.
• Primitives: use Choice, Noul, Score, and Relation.
• Document decisions: ask questions about long text or JSON and select a yes/no mode.
• Evidence and rules: rank evidence and combine facts with rules.
• Examples: run the Doom demo and review the insurance and GDPR examples.

Interfaces

Interface	Use
Python	Call OpenDecision in the same process as the application.
POST /v1/systemone	Send state and typed questions to the API.
POST /v1/documents/decide	Send a document and typed questions to the API.
TypeSafe-compatible endpoint	Use a compatible TypeSafe SDK client with a local server.

Basic example

from opendecision import OpenDecisionEngine

engine = OpenDecisionEngine()

result = engine.choice(
    state="The customer was charged twice.",
    instructions="Which team should handle this request?",
    criteria={
        "billing": "Payments, invoices, refunds, and duplicate charges",
        "technical": "Software bugs",
        "sales": "Pricing and purchases",
    },
)

print(result["choice"])
# billing