First-party app
OpenRouter

OpenRouter

Chat completions, embeddings, and model/usage introspection via OpenRouter's unified LLM API.

stable AI & Machine Learning

About

OpenRouter ships in the w6w first-party pack. It declares 6 actions, 2 health checks, and the host runs its code in a sandbox that never sees the credential.

App id
io.w6w.openrouter
Version
0.1.0
Author
w6w
Licence
MIT
Categories
AI & Machine Learning

Overview

OpenRouter fronts 400+ models from dozens of providers behind one OpenAI-compatible chat-completions endpoint, so a workflow can call openai/gpt-5.2, anthropic/claude-sonnet-4.6 or any other listed model through the same request shape and the same credential — including OpenRouter-only routing controls such as an explicit model-fallback list, provider preferences, and plugins (web search, PDF parsing) layered on top of the normalized request.

Embeddings are exposed the same way, unified across providers behind one endpoint. Models can be listed and filtered (by category, price, context length, supported parameters) to keep a workflow’s model choice explicit and current instead of hardcoded. Because every chat completion is billed and metered centrally, a generation’s exact token counts and cost can be pulled back after the fact by id — useful for auditing spend per workflow run — and the connected key’s own credit limit and usage can be checked before it runs out mid-workflow.

A fit for a workflow that wants model choice as a runtime decision rather than a deploy-time one — routing a step to whichever model is cheapest, fastest, or best for the task — without integrating a separate app per LLM vendor.

Build with OpenRouter

Three routes to the same 6 actions. The Workflow tab is generated from OpenRouter's own manifest and carries its real ids, so it is copy-pasteable; the Code and CLI examples are the same call for any action on any app, so every app-specific value in them is a blank you fill in.

Chat Completion

chat-completion

Generate a chat completion from any model OpenRouter routes to, using one normalized request shape.

Create Embeddings

embeddings

Generate vector embeddings for text (or, on supporting models, images).

Get Credits

get-credits

Get total account credits purchased and used. Requires a Management API key connection (see this app's README) — a regular inference key is rejected here.

Get Generation

get-generation

Fetch cost and usage metadata for a past chat completion or embedding call.

Get Key Info

get-key-info

Get usage, credit limit, and remaining headroom for the connected API key.

List Models

list-models

List models available through OpenRouter, optionally filtered by category or name.

A workflow step names the app and the action, and the editor fills in the connection when you pick one. This is the Step shape from the workflow spec, carrying OpenRouter's real ids.

{
  "manifestVersion": "2",
  "name": "openrouter-example",
  "steps": [
    {
      "id": "chat-completion",
      "uses": {
        "app": "io.w6w.openrouter",
        "action": "chat-completion",
        "connection": "conn_YOUR_CONNECTION_ID"
      },
      "with": {
        "messages": "<messages>"
      }
    }
  ]
}

Here are some of the things you can do

  • Chat Completion

    perform
    chat-completion
  • Create Embeddings

    perform
    embeddings
  • Get Credits

    read
    get-credits
  • Get Generation

    read
    get-generation
  • Get Key Info

    read
    get-key-info

+1 more action available

Every app-specific value here is a blank you have to fill in. An app action is reached through the connection that authenticates it, so the address is a connection id, not the app id — and connections belong to your account, so a public page cannot know yours. Create one for OpenRouter, then fill in the three blanks: conn_YOUR_CONNECTION_ID, the action key, and the parameters that action declares. The call itself is real — the shape is transcribed from the studio's own snippet builder, which prints the same kind of blanks — but nothing in it is specific to OpenRouter. The Workflow tab is where this app's real ids are.

Install
npm install @w6w/sdk
yarn add @w6w/sdk
pnpm add @w6w/sdk
deno add npm:@w6w/sdk
Code
import { W6wClient, isActionRun } from "@w6w/sdk";

// Reads W6W_BASE_URL and W6W_TOKEN from the environment when omitted.
const client = new W6wClient();

const envelope = await client.run({
  urn: "conn_YOUR_CONNECTION_ID",
  action: "chat-completion",
  payload: {
    // model: "<value>",
    messages: "<value>",
    // temperature: "<value>",
    // topP: "<value>",
    // topK: "<value>",
    // frequencyPenalty: "<value>",
    // presencePenalty: "<value>",
    // repetitionPenalty: "<value>",
    // minP: "<value>",
    // topA: "<value>",
    // maxTokens: "<value>",
    // seed: "<value>",
    // stop: "<value>",
    // responseFormat: "<value>",
    // logitBias: "<value>",
    // tools: "<value>",
    // toolChoice: "<value>",
    // parallelToolCalls: "<value>",
    // plugins: "<value>",
    // models: "<value>",
    // route: "<value>",
    // provider: "<value>",
    // user: "<value>",
  },
});

if (isActionRun(envelope)) console.log(envelope.value);
Install the CLI
npm install -g @w6w/cli
CLI
w6w run conn_YOUR_CONNECTION_ID --action chat-completion --payload '{"messages":"<value>"}'

Give an AI agent OpenRouter — without giving it OpenRouter's credentials. One MCP endpoint exposes every app, function and workflow the caller is entitled to, as tools it can discover and run. Access is granted per team while we onboard.

One tool call
{
  "name": "w6w_invoke",
  "arguments": {
    "ref": "app:io.w6w.openrouter#chat-completion",
    "input": {
      "messages": "<messages>"
    }
  }
}

Every tool names its target with a single ref. The app: form above doesn't name a connection at all — the host resolves which of the caller's OpenRouter connections to sign with, and refuses rather than guesses when the answer is ambiguous.

What the agent gets

Credentials it can't read

The token is attached host-side, at the moment of the call. It is never a tool argument, never in the model's context, and never in a transcript — so a prompt injection has nothing to exfiltrate.

A tool surface scoped to the caller

Tools are derived per end user from what that person has actually connected and is entitled to — not one shared bot identity carrying the union of everyone's access.

A durable workflow in one call

Multi-step work runs on the workflow engine and returns a run handle the agent can poll — retries, branching and state survive the conversation that started them.

Health-aware discovery

OpenRouter's declared health checks are on the surface too, so an agent can tell "the vendor is down" from "your credential expired" before it burns a retry on either.

The MCP surface is part of the hosted platform. OpenRouter itself is MIT, and the runtime that executes it is source-available (FSL).

Request MCP access

Health checks

OpenRouter declares its own checks, so its health is a property of the app rather than something the host guesses at.

service

OpenRouter platform status

quota

Credit headroom

This key's own credit limit and remaining headroom, read from GET /key.