Configuration Guide

This guide walks you through the essential configuration steps to get AI workflows running in your Open edX installation.

Note

These instructions are written for site operators. You’ll need access to your site backend; it is recommend to use Tutor.

Prerequisites

Before configuring the plugin, ensure you have:

• Completed the plugin installation

• Access to your Tutor configuration files (config.yml)

• An API key from a supported LLM provider (OpenAI, Anthropic, or a local model server)

• Django admin access to your Open edX instance

Core Concepts

The plugin uses three main configuration concepts:

Provider

Handles authentication and model routing. Defines which AI service to use and how to connect to it.

Scope

Defines the where - the context in which an AI workflow will be visible and usable (LMS/CMS, specific course, location).

Profile

Defines the what - what the AI will be instructed to do and which information it will have access to.

Configuring Providers

Using Tutor Configuration (Recommended)

The recommended approach for production environments is to configure providers in your Tutor config.yml file. This keeps API keys secure and separate from your workflow configurations.

Add the following to your config.yml:

AI_EXTENSIONS:
  provider:
    API_KEY: "sk-proj-your-api-key"
    MODEL: "provider/your-model"

Depending on your selected provider, the configuration should look like this:

AI_EXTENSIONS:
  openai:
    API_KEY: "sk-proj-your-api-key"
    MODEL: "openai/gpt-4o-mini"
  anthropic:
    API_KEY: "sk-ant-api-abc123"
    MODEL: "anthropic/claude-3-haiku-20240307"
  ollama:
    API_BASE: "http://ollama:11434"
    MODEL: "ollama/llama3.2:1b"

Configuration Parameters:

• API_KEY: Your authentication key for the provider

• MODEL: The model identifier in format provider/model-name

• API_BASE: (Optional) Custom API endpoint for self-hosted solutions

• Additional LiteLLM parameters can be passed as needed (see LiteLLM documentation)

Then enable the plugin:

PLUGINS:
  - openedx-ai-extensions

After updating your configuration:

tutor config save
tutor local launch

If you haven’t built the images yet, run these commands first:

tutor images build openedx
tutor images build mfe

Using the Provider in Workflow Profiles

Reference your configured provider in workflow profiles using a patch. The default provider is the first one in the list. If you have only one provider, you can skip this.

{
  "processor_config": {
    "LLMProcessor": {
      "provider": "openai"
    }
  }
}

The provider name must match one of the keys defined in AI_EXTENSIONS.

Direct Configuration in Profiles (Testing Only)

For testing and development, you can configure the model and API key directly in the workflow profile. This approach is convenient for quick tests but is not recommended for production.

Warning

Security Risk: API keys stored in profiles are visible to users with Django admin access. Use Tutor configuration for production environments.

Add the following to your profile configuration:

{
  "processor_config": {
    "LLMProcessor": {
      "options": {
        "MODEL": "openai/gpt-4o-mini",
        "API_KEY": "sk-proj-put-your-api-key-here"
      }
    }
  }
}

Example Profile

The plugin includes an example profile at backend/openedx_ai_extensions/workflows/profiles/base/standalone_config.json that demonstrates this approach. You can access this example in the Django admin interface at /admin/.

Self-Hosted Models

For organizations that prefer to run their own models, the plugin has been tested with CPU-based Ollama deployments. However, for production workloads, we recommend using a proper LLM inference engine.

Tested Solutions

• Ollama (CPU, development/testing): See deployment guide

• vLLM (GPU, production recommended): vLLM Project

Ollama Configuration Example

AI_EXTENSIONS:
  local-llama:
    API_BASE: "http://ollama:11434"
    MODEL: "ollama/llama3.2:1b"

vLLM Configuration Example

See the vLLM project website or their GitHub project page for more information on setting up vLLM.

Troubleshooting

Provider Connection Issues

If you encounter connection errors:

• Verify API keys are correct and have proper permissions

• For self-hosted solutions, ensure the API_BASE URL is accessible from the Open edX containers

• Check network connectivity and firewall rules

Profile Not Appearing

If configured profiles don’t appear in the UI:

• Verify the scope configuration matches your current context

• Check that the profile is active in Django admin

• Ensure the provider referenced in the profile exists in your configuration

For additional support, visit the GitHub Issues page.

See also

Usage Guide

Setting Up a Local MCP Example Server

Model Context Protocol (MCP) Integration