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](https://app.contextual.ai/) and obtain your CONTEXTUALAIAPIKEY. - Add CONTEXTUALAIAPIKEY 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](https://ai.google.dev/gemini-api/docs/api-key)

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](https://docs.contextual.ai/api-reference/agents/create-agent) for more details on agent creation and usage. - If you have feedback or need support, please email feedback@contextual.ai.