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Basic Information

This repository provides a developer-focused Django extension and example code to integrate large language models and AI assistants into Django applications. It is designed so developers can combine LLM capabilities with Django's productivity to build intelligent, agent-like application features. The project enables AI assistants to call server-side Django methods so assistants can perform actions, query application state, and interact with users through application logic. It supports AI Tool Calling and retrieval-augmented generation (RAG) workflows to build assistants that use external tools and application data. The README and repo signals point to documentation, a short demo video, continuous integration and coverage badges, contribution guidelines, community channels, and an option for commercial support from the maintaining company. The primary audience is developers building AI-enabled features inside Django projects.

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Features
Integration with large language models and a focus on combining LLMs with Django productivity. Support for AI Tool Calling so assistants can invoke server-side Django methods and application logic. Support for retrieval-augmented generation (RAG) to incorporate application or external data into assistant responses. Documentation and example/demo materials that show how to get started quickly. Continuous integration and coverage badges indicating CI and testing infrastructure. Community and contribution resources including a Discord server and contributing guide. Maintained by a company that offers commercial support. The repo emphasizes developer tooling and patterns to build stateful, capability-rich assistants within Django projects.
Use Cases
This project helps developers accelerate building intelligent features by providing patterns and tooling to connect LLMs with Django backends. Allowing assistants to call Django methods reduces the need to hand-write glue code and makes it easier to expose application capabilities to AI-driven workflows. RAG support helps improve relevance by letting assistants access application data or documents. Included documentation, a demo video, CI indicators, and contribution guidance lower onboarding friction for teams. Community channels provide a place to ask questions and share work, and commercial support is available from the maintainers for teams needing consulting or integration help. Overall it streamlines adding conversational or agent-like behavior to Django applications.

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