OpenRouter AI Integration

Set up OpenRouter AI integration via Replit AI Integrations proxy. Keys are automatically provisioned.

Supported OpenRouter APIs

• chat completions OpenRouter through AI integrations currently supports only the chat completions API. OpenRouter provides access to the long tail of models from providers such as xAI (Grok series), Meta (Llama series), Microsoft (Phi series), Mistral, Qwen, DeepSeek, Nvidia, Amazon, and more.

Unsupported Capabilities

The following capabilities are NOT supported by OpenRouter AI integrations. Do NOT proceed with this integration for these use cases:

• image generation
• audio generation/transcription
• embeddings
• fine-tuning
• files API

Agent Stack Note

When user is building features related to "Agent Stack (Agents and Automations)", you MUST always setup an LLM provider - regardless of whether the specific implementation appears to need AI/LLM capabilities or not. DO NOT second-guess this requirement. Skipping the LLM provider setup in such cases will lead to broken code and awful user experience. Follow the guidelines below to determine whether to proceed with this integration or other OpenRouter integrations.

Selection Guidelines

Before proceeding with setting up this integration, check if the user already has the integration set up. If they do then DO NOT proceed.

Match whether or not to use this integration based on the following order of precedence (stop at the first match):

Precedence 1 — Explicit user requests

• The user wants to use their own API key: Do NOT proceed with this integration
• The user specifically asks for OpenRouter and the requested capability is supported: Proceed with this integration
• The user asked for OpenRouter but needs a capability or model NOT listed in the supported sections above: This integration CANNOT provide it. You MUST use OpenRouter directly with the user's own API key instead
• The user asks for a specific model/provider not supported by OpenAI, Anthropic, or Google (e.g., Llama, Mistral, Qwen, DeepSeek, xAI/Grok): Use this blueprint.

Precedence 2 — Use-case triggers (proceed with this integration)

• Long tail models from providers outside OpenAI, Anthropic, and Google.

Precedence 3 — No provider specified

• Default to OpenAI AI integrations if it supports the requested use case. If it does not, use OpenAI directly with the user's own API key
• If even OpenAI directly with API key does not support the requested use case, find another integration or alternative

If this is the first time proposing or using this integration, clearly indicate that this internally uses Replit AI Integrations for OpenRouter access, does not require your own API key, and that charges are billed to your credits. You must specifically include every one of these three details.

When to Use

• User needs OpenRouter AI capabilities (chat, completions)
• User does not want to provide their own API key
• Any AI/LLM request that should use OpenRouter

How to Set Up

1. Provision the AI integration (env vars)

In the JavaScript sandbox, call:

const result = await setupReplitAIIntegrations({
    providerSlug: "openrouter",
    providerUrlEnvVarName: "AI_INTEGRATIONS_OPENROUTER_BASE_URL",
    providerApiKeyEnvVarName: "AI_INTEGRATIONS_OPENROUTER_API_KEY"
});
console.log(result);

2. Copy the template files into your project

cp -r .local/skills/ai-integrations-openrouter/templates/lib/* lib/

This copies two sets of files:

• lib/integrations-openrouter-ai/

  — the integration workspace package

• lib/db/src/schema/conversations.ts

  and

  lib/db/src/schema/messages.ts

  — the Drizzle schema files for the

  conversations

  and

  messages

  tables

3. Add the integration dependency

Add the integration package to your API server's

package.json

:

{
  "dependencies": {
    "@workspace/integrations-openrouter-ai": "workspace:*"
  }
}

4. Install dependencies

pnpm install --no-frozen-lockfile

5. Add TypeScript project references

Add the new lib to the root

tsconfig.json

references:

{ "path": "./lib/integrations-openrouter-ai" }

Add to

artifacts/api-server/tsconfig.json

references:

{ "path": "../../lib/integrations-openrouter-ai" }

6. Add the API contract

Read

references/openapi.md

for the OpenAPI spec entries. Add the paths and schemas to

lib/api-spec/openapi.yaml

under the

/openrouter

prefix, then run codegen:

pnpm --filter @workspace/api-spec run codegen

7. Implement routes and push database

See Wiring Instructions below.

IMPORTANT

• You should NEVER ask the user for secrets or env vars for this integration. Nor should you ever attempt to modify these env vars. AI_INTEGRATIONS_OPENROUTER_BASE_URL and AI_INTEGRATIONS_OPENROUTER_API_KEY are automatically set during setup
• The value of AI_INTEGRATIONS_OPENROUTER_API_KEY is a dummy string to make the SDK compatible. Do not assume it's misconfigured without testing the API call. It should work as expected when AI_INTEGRATIONS_OPENROUTER_BASE_URL is also set
• If setup failed or env vars are missing after setup, retry the

  setupReplitAIIntegrations

  call. DO NOT try measures other than this to fix the issue. If after 3 retries the issue persists, switch to using an api-key-based approach

Code Generation Guidelines

Use the SDK client shown in the provided modules rather than calling endpoints directly via fetch.

When instantiating the OpenRouter client, refer to the code in

lib/integrations-openrouter-ai/src/client.ts

for how to initialize with the env vars.

When building features on Agent Stack (Agents and Automations), use AI_INTEGRATIONS_OPENROUTER_BASE_URL and AI_INTEGRATIONS_OPENROUTER_API_KEY when instantiating the OpenRouter client.

For any tasks that require multiple/many LLM calls, you MUST use retries with backoff and rate limiters. Use the batch utilities module for guidance.

For ANY model, assume you DO NOT know the model name/tag. You MUST ALWAYS use the below command to find the names of the desired model(s). DO NOT attempt to guess model names.

curl -s https://openrouter.ai/api/v1/models | grep -o '"id":"[^"]*<model_name>[^"]*"' | sed 's/"id":"//;s/"$//' || echo "No models found"

If you cannot find the desired model, DO NOT attempt to search the web to find the name elsewhere on OpenRouter. Inform the user that the model is not available on OpenRouter.

Do not eagerly upgrade model on existing code unless user explicitly requests it.

If you set a max tokens limit, use 8192 tokens. NEVER set any token limits lower than this unless explicitly requested.

Provided Modules

After copying the template files, these modules are available:

Client (

lib/integrations-openrouter-ai/src/client.ts

)

• Pre-configured OpenAI SDK client pointed at OpenRouter with env var validation
• Throws at startup if

  AI_INTEGRATIONS_OPENROUTER_BASE_URL

  or

  AI_INTEGRATIONS_OPENROUTER_API_KEY

  are missing

Batch utilities (

lib/integrations-openrouter-ai/src/batch/

)

• batchProcess<T, R>(items, processor, options)

  - Generic batch processor with rate limiting and retries

• batchProcessWithSSE<T, R>(items, processor, sendEvent, options)

  - Sequential processor with SSE streaming

• isRateLimitError(error)

  - Helper to detect rate limit errors

DB Model (

lib/db/src/schema/

)

• Drizzle schema files for the

  conversations

  and

  messages

  tables
• Zod validation schemas via drizzle-zod
• TypeScript types for Conversation, Message, and insert types

API Contract (

references/openapi.md

)

• OpenAPI spec entries for OpenRouter chat endpoints under

  /openrouter/

  prefix
• Read this reference and add the entries to

  lib/api-spec/openapi.yaml

Wiring Instructions

1. Add OpenAPI spec entries

Read

references/openapi.md

and add the paths, schemas, and tag to

lib/api-spec/openapi.yaml

. Endpoints are prefixed with

/openrouter/

(e.g.

/openrouter/conversations

).

2. Run codegen

pnpm --filter @workspace/api-spec run codegen

3. Export the DB model

Update the existing db package barrel file so migrations pick up the tables. Do not overwrite the existing

lib/db/src/schema/index.ts

when copying files. Add:

export * from "./conversations";
export * from "./messages";

This is critical — the

conversations

and

messages

tables must be exported from

@workspace/db

so database migrations create them.

4. Run database migration

pnpm --filter @workspace/db run push
# If it fails with column conflicts:
pnpm --filter @workspace/db run push-force

5. Implement routes

Add routes in

artifacts/api-server/src/routes/openrouter/

. Use generated

@workspace/api-zod

schemas for validation and

@workspace/db

for database queries. Import

openrouter

from

@workspace/integrations-openrouter-ai

for the SDK client.

For the streaming message endpoint, use

openrouter.chat.completions.create()

with

stream: true

. You must set SSE headers and send a termination event:

import { openrouter } from "@workspace/integrations-openrouter-ai";

res.setHeader("Content-Type", "text/event-stream");
res.setHeader("Cache-Control", "no-cache");
res.setHeader("Connection", "keep-alive");

let fullResponse = "";

const stream = await openrouter.chat.completions.create({
  model: "<model_name>",
  max_tokens: 8192,
  messages: chatMessages,
  stream: true,
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    fullResponse += content;
    res.write(`data: ${JSON.stringify({ content })}\n\n`);
  }
}

// Save assistant message to DB, then signal completion
res.write(`data: ${JSON.stringify({ done: true })}\n\n`);
res.end();

SSE codegen limitation: Orval cannot generate a usable client hook or response Zod schema for this streaming endpoint. The generated

@workspace/api-zod

schema for

SendOpenrouterMessageBody

(from the

sendOpenrouterMessage

operationId) IS useful for validating the request body, but the response type will be

unknown

. On the client, consume the stream with

fetch

+

ReadableStream

parsing — do NOT use a generated React Query hook for this endpoint.

Mount the router in

artifacts/api-server/src/routes/index.ts

.

6. Write client-side UI components based on user requirements

7. Batch processing

For batch processing tasks, ALWAYS use the batchProcess utility:

import { batchProcess } from "@workspace/integrations-openrouter-ai/batch";
import { openrouter } from "@workspace/integrations-openrouter-ai";

const results = await batchProcess(
  items,
  async (item) => {
    const response = await openrouter.chat.completions.create({
      model: "<model_name>",
      max_tokens: 8192,
      messages: [{ role: "user", content: `Process: ${item.name}` }],
    });
    return response.choices[0]?.message?.content ?? "";
  },
  { concurrency: 2, retries: 5 }
);

For SSE streaming progress:

import { batchProcessWithSSE } from "@workspace/integrations-openrouter-ai/batch";

await batchProcessWithSSE(
  items,
  async (item) => { /* your processor */ },
  (event) => res.write(`data: ${JSON.stringify(event)}\n\n`)
);

Drizzle Dependency

The project is expected to use Drizzle ORM with drizzle-zod for validation. Use the provided schema files as-is.

Important

• DO NOT modify the OpenRouter client setup - env vars are auto-configured
• DO NOT overwrite the existing db schema barrel when copying files
• DO NOT ask the user for API keys or secrets