One LLM API gateway for multiple model providers

What a unified LLM API gateway does

An LLM API gateway sits between your application and model providers. Instead of maintaining separate credentials, request formats and billing integrations, your backend calls one gateway and selects a model in the request. This is useful for SaaS products, internal tools, support automation, coding agents and teams that compare several model families.

A multi-model API does not make every provider identical. Context limits, tool semantics, streaming events and supported parameters still differ. CLODEX exposes compatible routes while keeping model-specific behavior visible, so applications can validate capabilities instead of assuming that every model supports the same feature set.

OpenAI-compatible and Anthropic-compatible access

Use https://clodex.xyz/v1 as the base URL for OpenAI-compatible clients. Anthropic SDK and Claude Code use https://clodex.xyz as the base URL because the client appends /v1/messages itself. Always use an exact model identifier returned by the authenticated model catalog.

from openai import OpenAI

client = OpenAI(
    api_key="clodex_YOUR_KEY",
    base_url="https://clodex.xyz/v1",
)

response = client.responses.create(
    model="gpt-5.6-sol",
    input="Review this service architecture",
)
print(response.output_text)

Routing, fallbacks and LLM API management

Production routing should be explicit. Pick a primary model for a workload, define which failures are retryable and decide whether a fallback may change quality, latency or cost. Never replace a model silently inside an active stream. Record the requested model, actual model, request ID and terminal status for every important operation.

CLODEX API keys can be separated by application, environment or customer role. This reduces the blast radius of a leaked key and makes usage attribution clearer. Add client-side budgets and concurrency controls as well; a gateway is one layer of control, not a replacement for your own authorization and business limits.

• Use separate keys for development, staging and production.
• Retry only classified transient errors with bounded backoff.
• Treat EOF without a terminal stream event as an incomplete response.
• Validate tool arguments and user permissions before side effects.

Migration checklist for one API across multiple LLMs

Start with one tested model and one endpoint. Confirm authentication, a non-streaming response, a streaming response and error parsing before enabling tools or fallbacks. Compare results on real prompts rather than synthetic one-line examples. The best gateway setup is the one whose reliability, output quality and unit economics are measured for your workload.

Keep the model catalog and pricing outside application source code. Availability and billing modes can change, so deployment checks should verify required models before traffic is shifted. If a model disappears or a protocol feature is unavailable, fail with a clear operational message instead of returning a different model unexpectedly.