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 feedback@contextual.ai.