BotMan
The most popular PHP framework for multi-channel chatbot development and orchestration.
The enterprise-grade open-source framework for building modular, multi-skill conversational AI agents.
DeepPavlov is a specialized open-source framework designed for the development of complex, multi-agent conversational systems and NLP pipelines. As of 2026, it remains a critical infrastructure component for enterprises requiring self-hosted, sovereign AI solutions that exceed the capabilities of simple LLM wrappers. Its technical architecture is built on a modular philosophy, allowing developers to orchestrate disparate components—such as Named Entity Recognition (NER), Intent Classification, and Open Domain Question Answering (ODQA)—into a unified 'DeepPavlov Dream' agent. This multi-skill approach enables the creation of assistants that can context-switch between domain-specific knowledge bases and general dialogue. The framework is built on top of PyTorch, TensorFlow, and Hugging Face Transformers, providing a standardized configuration-based approach (JSON/YAML) to model training and deployment. In the 2026 landscape, DeepPavlov distinguishes itself by offering robust support for Knowledge Base Question Answering (KBQA) and entity linking, making it the premier choice for organizations building internal intelligence layers that require high precision and verifiable data retrieval without the privacy risks associated with proprietary third-party APIs.
A microservices-based architecture for building multi-skill AI assistants that can coordinate multiple NLP models simultaneously.
The most popular PHP framework for multi-channel chatbot development and orchestration.
A Deep Domain Conversational AI Platform for Building Industrial-Grade Assistants
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
State-of-the-art pipelines for querying Wikidata or custom SPARQL endpoints using natural language.
Entire NLP pipelines (preprocessing, tokenization, model, post-processing) are defined in human-readable JSON files.
Transfer learning capabilities to perform entity recognition across multiple languages with minimal fine-tuning.
Maps identified mentions in text to specific entries in a formal knowledge graph.
Deep linguistic analysis including part-of-speech tagging and lemmatization for complex languages.
A stateful orchestration layer that manages user session data and context across multiple turns of conversation.
Manually auditing thousands of communication logs for compliance entities is slow and prone to error.
Registry Updated:2/7/2026
Generate structured JSON report of all findings.
Employees cannot find specific information in thousands of technical PDFs and Wikis.
Supporting 10+ languages requires expensive human translation and local support teams.