Django has the data. LangGraph coordinates the Ai Agents. Permit gives us the guardrails to ensure data is safe.
In this course, I'll take you step-by-step so you can build Ai Agents with Django, LangGraph, and Permit so you can:
✅ Save user-data with minimal overhead
✅ Talk to the data you or your users care about
✅ Integrate third-party rest API services
✅ Turn any Python functions into tools an Ai Agent can run with queries like 'what are my recent documents?' or 'What new movies are out?'
✅ Chat with Django User data through standard Django queries and without the need for vector embeddings (although you can use them)
✅ Easily switch LLMs to upgrade the effectiveness of your agents
✅ Leverage RBAC (role-based access control) within any Django or Python project
✅ Create a Super(visor) Agent that controls other agents
✅ Lock down access to what a User can or cannot do
✅ Add guardrails to ensure an Agent can't do anything it shouldn't (e.g. create, update, read, search, list, share, or delete any data)
✅ and more.
Topics covered:
➕ Integrating Django with LangGraph for building Ai Agents (it's crazy easy)
➕ Django ORM fundamentals
➕ Django Model design basics with database syncing (migrations)
➕ Django Users & Permission Fundamentals
➕ Creating LangChain tools for LangGraph agents
➕ LangGraph Supervisor Agents
➕ Permit.io RBAC for powerful and granular control over user and Ai Agent access
➕ Multi-agent integration
➕ Django + Jupyter integration for rapid prototyping
➕ LangGraph-based lookups to your Django database
