📦 deps(thirdparty): update snapshots
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name: routerbase-model-gateway
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description: "Integrate RouterBase as an OpenAI-compatible model gateway for routing GPT, Claude, Gemini, media, audio, and embedding requests."
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category: ai-ml
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risk: safe
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source: community
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source_repo: zenlee123/routerbase-agent-skills
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source_type: community
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date_added: "2026-07-07"
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author: zenlee123
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tags: [routerbase, llm-routing, openai-compatible, model-gateway]
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tools: [claude, cursor, gemini, codex, antigravity]
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license: "MIT-0"
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license_source: "https://github.com/zenlee123/routerbase-agent-skills/blob/main/LICENSE"
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---
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# RouterBase Model Gateway
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## Overview
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Use [routerbase](https://routerbase.com/) when an application needs one OpenAI-compatible API surface for model routing across GPT, Claude, Gemini, image, video, audio, and embedding workloads. This skill helps agents migrate existing OpenAI SDK calls, document model-selection tradeoffs, and produce safe implementation snippets without exposing credentials.
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RouterBase model availability, pricing, and provider capabilities can change, so treat examples as starting points and verify current catalog data before production recommendations.
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## When to Use This Skill
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- Use when migrating an OpenAI-compatible client to RouterBase by changing the base URL and model ID.
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- Use when selecting primary and fallback models for chat, reasoning, vision, media generation, audio, or embeddings.
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- Use when debugging RouterBase request setup, headers, environment variables, streaming, tool calls, JSON mode, or multimodal payloads.
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- Use when documenting an internal model-routing plan that balances cost, latency, quality, and provider redundancy.
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## How It Works
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### Step 1: Classify the Workload
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Identify the modality and hard constraints before choosing a model:
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- Modality: chat, vision, image, video, audio, embeddings, or mixed.
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- Quality target: draft, production, high-stakes review, or automated background task.
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- Runtime constraints: latency budget, context length, streaming, JSON mode, tool calling, and retry tolerance.
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- Business constraints: price ceiling, provider preference, regional requirements, and fallback rules.
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### Step 2: Configure the OpenAI-Compatible Client
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Keep the RouterBase API key server-side in an environment variable such as `ROUTERBASE_API_KEY`. Do not put keys in browser, mobile, or public repository code.
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```python
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import os
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from openai import OpenAI
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client = OpenAI(
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api_key=os.environ["ROUTERBASE_API_KEY"],
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base_url="https://routerbase.com/v1",
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)
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response = client.chat.completions.create(
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model="google/gemini-2.5-flash",
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messages=[{"role": "user", "content": "Write one sentence about model routing."}],
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)
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print(response.choices[0].message.content)
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```
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```js
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import OpenAI from "openai";
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const client = new OpenAI({
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apiKey: process.env.ROUTERBASE_API_KEY,
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baseURL: "https://routerbase.com/v1",
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});
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const response = await client.chat.completions.create({
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model: "google/gemini-2.5-flash",
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messages: [{ role: "user", content: "Write one sentence about model routing." }],
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});
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console.log(response.choices[0].message.content);
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```
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### Step 3: Validate Model IDs and Capabilities
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When credentials and network access are available, check the live catalog before locking in a model ID or price-sensitive recommendation.
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```bash
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curl "https://routerbase.com/api/v1/models?task=chat" \
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-H "Authorization: Bearer $ROUTERBASE_API_KEY"
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```
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Confirm feature assumptions with a small request fixture:
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- Streaming works when `stream: true` is set.
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- Tool calling accepts the exact schema used by the app.
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- JSON mode returns parseable output and still passes application validation.
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- Vision or media payloads use the expected OpenAI-compatible content shape.
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### Step 4: Design Fallbacks Conservatively
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Use explicit application-level fallbacks unless the user's RouterBase account already has a smart-routing policy configured.
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```js
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const modelPlan = [
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"anthropic/claude-sonnet-4-6",
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"google/gemini-2.5-flash",
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];
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for (const model of modelPlan) {
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try {
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return await client.chat.completions.create({ model, messages });
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} catch (error) {
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if (!isRetryableRouterBaseError(error)) throw error;
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}
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}
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```
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Treat transient network errors, timeouts, rate limits, and server errors as candidates for retry. Do not blindly retry authentication failures, invalid model IDs, validation errors, or policy refusals.
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## Examples
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### Migration Checklist
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When converting an existing OpenAI SDK integration:
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1. Change the base URL to `https://routerbase.com/v1`.
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2. Read `ROUTERBASE_API_KEY` from server-side environment configuration.
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3. Replace the model name with a RouterBase model ID that matches the task.
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4. Preserve standard OpenAI request fields unless RouterBase documentation says otherwise.
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5. Run one minimal smoke test before shipping.
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### Routing Plan Format
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Use this table when recommending a model strategy:
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| Use case | Primary model | Fallback model | Reason | Validation |
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| --- | --- | --- | --- | --- |
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| Support chat | Provider/model ID | Provider/model ID | Low latency and acceptable quality | Streaming smoke test |
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| Deep analysis | Provider/model ID | Provider/model ID | Strong reasoning, higher cost acceptable | Eval prompt plus human review |
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## Best Practices
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- Do keep RouterBase keys in server-side environment variables or secret managers.
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- Do verify current model availability and pricing before production decisions.
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- Do document primary and fallback model assumptions in the code or runbook.
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- Do validate structured outputs with application schemas.
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- Do not paste, log, commit, or screenshot real API keys.
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- Do not hard-code model pricing or provider availability as permanent facts.
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- Do not expose RouterBase keys in client-side JavaScript, mobile apps, or public repos.
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## Limitations
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- This skill does not replace RouterBase account configuration, live model catalog checks, or production observability.
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- Some model features are provider-specific and must be tested with the exact selected model.
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- High-stakes outputs still require human review and domain-specific evaluation.
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## Security & Safety Notes
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- Treat RouterBase credentials as production secrets.
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- Mask tokens in logs and support tickets.
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- Ask for explicit user approval before running live API calls that consume credits.
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- Use placeholders such as environment variables in examples; never invent or include realistic secret strings.
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## Common Pitfalls
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- **Problem:** The code works with one provider but fails after switching models.
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**Solution:** Re-test tool calling, JSON mode, streaming, and multimodal payloads for each selected model.
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- **Problem:** Fallback logic retries non-retryable errors.
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**Solution:** Retry only transient failures and fail fast on authentication, validation, and invalid model errors.
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- **Problem:** A model recommendation becomes stale.
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**Solution:** Re-check the RouterBase catalog and pricing page before finalizing the plan.
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## Related Skills
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- `@api-analyzer` - Use when the task is only to validate one API request shape.
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- `@langfuse` - Use when the task needs production LLM observability, tracing, and evaluation.
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