# deeppavlov/DeepPavlov

An open source library for deep learning end-to-end dialog systems and chatbots.

Repository: https://github.com/deeppavlov/DeepPavlov
Canonical: https://ross.abutalabs.com/products/deeppavlov
Homepage: https://deeppavlov.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: bot, nlp, chatbot, dialogue-systems, question-answering, chitchat, slot-filling, intent-classification, entity-extraction, named-entity-recognition, tensorflow, deep-learning, deep-neural-networks, intent-detection, dialogue-agents, dialogue-manager, artificial-intelligence, ai, nlp-machine-learning, machine-learning
Last push: 2025-08-06T07:11:40+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 35, release rhythm 8, longevity 100
- inputs: {"age_days": 3211, "days_push": 392, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6989, forks 1171 (observed 2026-08-28T04:09:51.921112+00:00)

## What it is
DeepPavlov is an open-source Python NLP library built on PyTorch and Hugging Face transformers for developing, training, and deploying state-of-the-art NLP models via configuration-driven pipelines. It provides pre-trained models for dialog systems, chatbots, question answering, NER, intent classification, and more, with easy REST API serving.

## Use cases
- build a chatbot with python
- named entity recognition on text
- question answering over documents
- intent classification for a support bot
- slot filling for dialogue systems
- serve an NLP model as a REST API
- fine-tune BERT for NLP tasks

## When to choose
- you need pre-trained NLP models (NER, QA, intent classification) without deep ML expertise
- you want configuration-driven training and inference pipelines
- you need to quickly expose an NLP model as a REST or socket API
- you are building end-to-end dialog systems or conversational assistants

## When to avoid
- you need a lightweight general-purpose LLM agent framework rather than classic NLP task models
- you work outside Python or need GPU-free tiny deployments
- you want cutting-edge LLM fine-tuning features found in newer frameworks
- you need real-time streaming chat interfaces out of the box

## Facets
- artifact type: library
- maturity: active
- function: nlp, machine-learning, deep-learning, chatbot, rag, llm-inference, api-framework
- domain: chatbots, machine-learning, deep-learning, artificial-intelligence
- platform: python, windows, cli
- tags: conversational-ai, named-entity-recognition, question-answering, intent-classification, slot-filling, bert, transformers, pytorch, pretrained-models, rest-api-serving, natural-language-processing, linux, macos, docker

## Member repositories
- deeppavlov/DeepPavlov (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:51.921112+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:41:07.514409+00:00, confidence not recorded.
  - readme: https://github.com/deeppavlov/DeepPavlov (fetched 2026-08-28T04:09:51.921112+00:00, sha a3c65b3bc2de)
  - homepage: https://deeppavlov.ai (fetched 2026-08-29T08:36:58.621173+00:00, sha da8adf673a4d)
  - site_page: http://docs.deeppavlov.ai/en/master (fetched 2026-08-29T08:36:58.630610+00:00, sha 8337130446f8)
  - site_page: http://deeppavlov.ai/about-us (fetched 2026-08-29T08:36:58.632831+00:00, sha e7d84ec0ab87)
  - site_page: http://docs.deeppavlov.ai/en/master/integrations/rest_api.html (fetched 2026-08-29T08:36:58.636257+00:00, sha fa9c0c5e1d43)
  - site_page: https://deeppavlov.ai/about-us (fetched 2026-08-29T08:36:58.639171+00:00, sha 7d15dd5e3f2b)
  - registry_pypi: https://pypi.org/pypi/deeppavlov/json (fetched 2026-08-29T08:36:58.641305+00:00, sha fea397e708aa)
- Data as of 2026-08-30T08:39:29.467469+00:00.