django-ai-sdk 0.1.1a2

Django AI SDK is a library that provides tools and utilities for building AI-powered applications with Django. It includes features such as natural language processing, machine learning, and integration with popular AI frameworks.

Django AI SDK

A Django SDK for building AI-powered applications with support for multiple LLM providers, RAG (Retrieval-Augmented Generation), and streaming responses.

Project Status: Read This First

This is an early preview. We're actively iterating on the API and learning from real usage. Here's what that means for you:

• Expect breaking changes: APIs will shift as we find better patterns.
• Migrations might be reset: Don't rely on database schema stability between versions.
• Not for production: Use this for experimentation, prototypes, and side projects. Keep critical workloads elsewhere.
• Watch the repo: Things change quickly. Star & watch to stay in the loop.
• Your feedback shapes the SDK: Break things, open issues, tell us what hurts.

We'd love to have you along for the ride, just keep your seatbelt on.

Install

pip install django-ai-sdk

Or with uv:

uv add django-ai-sdk

Quick Start

1. Add to INSTALLED_APPS

# settings.py
INSTALLED_APPS = [
    ...
    "django_ai_sdk",
]

Then run python manage.py migrate.

2. Define your assistant

# assistants.py
from django_ai_sdk import Assistant

class HelpDeskAssistant(Assistant):
    name = "Help Desk"
    model = "gpt-4o"
    instructions = "You are a helpful support assistant."

3. Return a streaming response

# views.py
from .assistants import HelpDeskAssistant

assistant = HelpDeskAssistant()

@router.post("/chat")
async def chat(request, payload: ChatRequest):
    return await assistant.as_view(
        payload.messages,
        thread_id=payload.thread_id,
    )

Features

• RAG Pipelines: BM25, ChromaDB, and Qdrant hybrid search with query expansion.
• Streaming Responses: Built-in SSE streaming. Works with Vercel AI SDK protocol.
• Conversation Storage: Automatic message persistence. Thread-based history out of the box.
• Tool Calling: MCP, memory, and custom tools — all managed by your Assistant.
• Reindexing: Hot-reload documents. Cached embeddings with simple refresh API.

Documentation

Full documentation and examples: github.com/django-ai-sdk/django-ai-sdk