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deepopen-com/deepopen

  • URL: https://github.com/deepopen-com/deepopen
  • Stars: 1044
  • Language: Python
  • Category: AI & Agents
  • Topics: None

What it does

DeepOpen is an open-source, non-autoregressive decision engine designed for structured decision-making across over 100 languages. It achieves low-latency classification in a single forward pass, eliminating the iterative token generation typical of traditional language models.

Why it's interesting

DeepOpen differentiates itself by providing ultra-low latency and deterministic outputs, addressing common issues like hallucinations and high deployment costs associated with autoregressive models. It supports multilingual capabilities out of the box without the need for additional fine-tuning, making it suitable for diverse applications.

How it works

The architecture includes three optimized checkpoints for different use cases: an English model based on ModernBERT, a multilingual model based on mmBERT, and a typed-decisions model. A built-in intelligent router automatically selects the optimal checkpoint based on the input language and type, achieving inference speeds as low as 33ms for single requests and 7.2ms for batch processing. The system is trained using a reinforcement learning framework (RLCD) to ensure reliable outputs.

Get started in 5 minutes

  1. Install the package via PyPI: bash pip install deepopen
  2. Use the built-in Router to preload models and make predictions: python import deepopen from deepopen import Router router = Router(preload=True) state = {"body": "Your message here."} questions = {"department": {"type": "choice", "criteria": {"billing": "..."}}} res = router.predict(state, questions) print(res)

Watch out for

The repository is relatively new, created in September 2026, which may imply limited community support and maturity. The README does not specify any known issues or security concerns, but as with any open-source project, users should review the code and dependencies for vulnerabilities. The license is Apache 2.0, allowing for permissive use and modification.