LLMFlows provides components for building language model applications with explicit workflows.
Open source
24 alternativesDeepPavlov
Build dialogue systems and chatbots with DeepPavlov, an open-source NLP framework using PyTorch and transformer models.
About DeepPavlov
DeepPavlov is an open-source NLP framework built on PyTorch and transformer models, intended for building end-to-end dialogue systems and chatbots. It supports modular, configuration-driven development of a wide range of NLP model applications. The framework includes pre-trained NLP models, and offers both command line interface and Python API for training, evaluating, and running inference on models. It supports Linux, macOS, and Windows 10+ (via WSL/WSL2), runs on Python 3.6 through 3.10, and requires between 4 and 16 GB of RAM depending on the selected model.
Key features
- Open-source NLP framework built on PyTorch and transformers
- Modular, configuration-driven development of NLP models
- Pre-trained NLP models available for immediate use
- Tools for building end-to-end dialogue systems and chatbots
- Command line interface and Python API for model operations
- Option to deploy models via a REST API server
Best for: It is best suited for practitioners with limited NLP/ML expertise building dialogue systems and chatbots.
Good for
- Open-source and freely available for use and modification
- Cross-platform support for Linux, macOS, and Windows 10+ (via WSL/WSL2)
- Designed for users with limited NLP/ML knowledge
Watch out for
- Windows support is limited to WSL/WSL2, with no native Windows compatibility
- GPU usage requires CUDA compatibility with the installed PyTorch version
- RAM requirements range from 4 to 16 GB depending on the selected model
DeepPavlov at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Linux, macOS, Windows
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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DeepPavlov FAQ
What operating systems and Python versions does DeepPavlov support?
It supports Linux, macOS, and Windows 10+ (via WSL/WSL2), and is compatible with Python 3.6 through 3.10.
What hardware is required to run DeepPavlov models?
RAM needs range from 4 to 16 GB depending on the model; GPU use requires a Pascal or newer GPU with 4+ GB VRAM and CUDA compatibility with the required PyTorch version.
How can I run inference on a pre-trained DeepPavlov model?
You can use the command line interface with `python -m deeppavlov interact ` or the Python API by building the model with `build_model()`.
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