AI Gateway
MCP
Prerequisites
- Hermes installed (desktop app or CLI)
- Active Orq.ai account
- An Orq.ai API key
AI Gateway
Hermes is OpenAI-compatible, so it routes through the AI Gateway by pointing the OpenAI provider at the router. Configure it in the desktop app or the CLI. Changes made in the desktop app apply to the CLI as well, since both share the same configuration.Setup
- Desktop app
- CLI
Open provider settings
Set the OpenAI API key and base URL

Overriding the OpenAI provider base URL with the Orq.ai router in Hermes
Select a model
Verification
Send a prompt in Hermes. The response appears in Hermes and the request appears in Orq.ai Traces with the selected model identifier.MCP
The Orq MCP server exposes the Orq.ai platform as MCP tools. Hermes’s built-in MCP client connects to it and uses those tools to manage Agents, run experiments, and query Traces from natural language.Installation
- Desktop app
- CLI
Open MCP settings
Add the Orq MCP server
orq and paste the following into the Server JSON field, then click Save server:YOUR_ORQ_API_KEY with an Orq.ai API key.
Adding the Orq MCP server in the Hermes desktop app
Reload MCP
Available Commands
Once connected, ask Hermes to perform operations across the workspace:Agents
Agents
Create an agent with custom instructions and toolsGet agent configuration for [agent-key]Update agent [agent-key] with new instructions or modelConfigure agent with evaluators and guardrailsInvoke agent [agent-key] with input [message]Retrieve agent response [response-id]
Deployments
Deployments
Create a deployment called [deployment-key]Get deployment configuration for [deployment-key]
Skills
Skills
Create a skill called [skill-key]List all skills in my workspaceGet skill [skill-key]Update skill [skill-key]Delete skill [skill-key]
Analytics
Analytics
Get analytics overview for my workspaceShow me workspace metrics for the last 7 daysQuery analytics filtered by deployment ID
Datasets
Datasets
Create a dataset called "customer-queries"List all datapoints in dataset [dataset-key]Add datapoints to dataset [dataset-key]Update datapoint [datapoint-id]Delete specific datapoints in dataset [dataset-key]Delete dataset [dataset-key]
Experiments
Experiments
Create an experiment from dataset [dataset-key]List all experiment runsExport experiment run [run-id] as CSVRun experiment and auto-evaluate results
Evaluators
Evaluators
Get evaluator configuration for [evaluator-key]Create an LLM-as-a-Judge evaluator for toneCreate a Python evaluator to check response lengthAdd evaluator to experiment [experiment-key]Update evaluator [evaluator-key] with a new promptUpdate Python evaluator [evaluator-key] with revised code
Traces
Traces
List traces from the last 24 hoursShow me traces with errorsGet span details for trace [trace-id]Find the slowest traces from todayShow all traces for thread [thread-id]
Models
Models
List all available chat modelsList all available embedding modelsInvoke model [model-id] with prompt [message]
Search
Search
Search for datasets named "customer"Find experiments in project [project-id]List directories in project [project-id]
Documentation
Documentation
Search the Orq.ai docs for [topic]
Managing Entities
Managing Entities
Delete agent [agent-key]Delete experiment [experiment-key]Delete evaluator [evaluator-key]Delete prompt [prompt-key]Delete knowledge base [knowledge-base-key]
delete_dataset to delete a dataset along with all its datapoints.