Automate Document Q&A with Multi-Agent RAG Orchestration using Contextual AI & Gemini

PROBLEM

Managing multiple RAG AI agents can be complex when each has its own purpose and vector database.

• Manually tracking agents and deciding which one to query wastes time.
• LLMs often struggle to determine which agent best fits a user’s request.

This workflow enables automated multi-agent orchestration, dynamically selecting and querying the correct agent using Contextual AI Query Tool and Gemini 2.5 Flash.

How it works

• A form trigger allows users to create new agents by specifying a name, description, datastore, and uploading files.
• A new agent is created with the provided information and files are ingested in the datastore
• We get the status of file ingestion every 30 seconds until the ingestion process is complete
• When users send queries, the Agent Orchestrator identifies the most relevant agent to generate grounded, context-aware responses.

Note: The document ingestion process is asynchronous and may take a few minutes before your agent has the document fully available in the datastore for querying.

How to set up

• Create a free Contextual AI account and obtain your CONTEXTUALAI_API_KEY.
• Add CONTEXTUALAI_API_KEY as an environment variable in n8n.
• For the baseline model, we have used Gemini 2.5 Flash Model, you can find your Gemini API key here

How to customize the workflow

• Replace the Form Trigger with a Webhook Trigger or manual input to integrate with custom systems.
• Swap Gemini 2.5 Flash with another LLM provider
• Update the wait time as per user requirement
• Modify the system prompt to fine-tune how the orchestration logic selects and queries agents.
• You can check out this Contextual AI API reference for more details on agent creation and usage.
• If you have feedback or need support, please email [email protected].