OmniRoute: One API, Many AI Models
Changing the model behind a coding agent should be boring. One URL, one model name, maybe a new credential. In practice every client interprets the format its own way, every provider adds its own fields, and I end up editing environment variables at an hour when I should no longer be making technical decisions.
I use OmniRoute because it attacks exactly that annoyance. It runs a local gateway on port 20128, dashboard included, and exposes an OpenAI-compatible endpoint at http://localhost:20128/v1. On the other side it can talk to commercial providers, or to Ollama running on my own machine. Disclosure up front: I didn't build it and I have no relationship with the project. I'm the tired user on the other end.
The idea fits in one sentence: one API, many models. The interesting part starts when you ask what that layer actually simplifies, and which problems just move somewhere else.
A proxy, but with judgment
If you've never used one, think of a specialized proxy. Your agent doesn't call OpenAI, Anthropic, or Google directly. It calls OmniRoute, which takes the request, decides where it goes, adapts the protocol when needed, and returns a normalized response.
Coding agent
↓ OpenAI-compatible
http://localhost:20128/v1
↓ selection and adaptation
OpenAI | Anthropic | Google | Ollama | othersThat concentrates a decision that used to be scattered everywhere. Instead of spreading keys, URLs, and fallback rules across several clients, all of that logic lives in one local process I can open and read.
The documented install is short:
npm install -g omniroute
omnirouteThe dashboard lands at http://localhost:20128. The version I saw running that day was 3.8.48. And one detail I'd rather find in the docs than in an error at eleven at night: the package requires Node >=22.22.2 <23 || >=24 <27. Node 23 is outside the range on purpose. If your environment sits on the wrong side of that line, check it before installing.
The project is MIT, self-hostable, from diegosouzapw/OmniRoute, and also published as the npm package omniroute. Docker and from-source installs exist too. For trying it out, npm cuts steps. For running it with discipline, a container draws a cleaner boundary around the process, its configuration, and its secrets.
What happens in the middle
The word proxy undersells it. Before any provider is touched there are fault-tolerance layers: circuit breakers that pull a sick route out of rotation, and a per-key cooldown that honors Retry-After when the upstream answers a 429. With agents this matters. They fire a lot of requests, and an ordinary proxy hands you the error back and wishes you luck. This one waits as long as the provider asked and tries another route. It isn't magic. It's retrying with judgment. Though it is one more piece to operate: if the gateway dies, everything loses access even with healthy providers.
On the client side you only need a base URL and a local key:
export OPENAI_BASE_URL=http://localhost:20128/v1
export OPENAI_API_KEY=omniroute-local-keyThen you ask for an explicit model, or an automatic route. The auto routes express intent: auto/coding, auto/cheap, auto/offline. What I like about the alias is that my config stops naming providers: I ask for auto/offline and the work stays on my machine if it can. If I switch providers tomorrow, I change it once in the alias instead of in seven configurations.
The condition: two runs of auto/coding can land on different models. For finishing work, fine. For reproducing an evaluation, no. When I compare results or chase a regression, I pin provider and model by hand. Fallback deserves the same distrust: switching models mid-flow can change tool calling or cost. The circuit keeps you from pounding on a closed door; it can't decide whether the door next to it serves the same purpose.
A stable interface over protocols that aren't
This is the benefit I value most. Clients speak one or two dialects, and an OpenAI-compatible interface loosens the coupling and connects tools that were never designed for each upstream. On the far side it speaks OpenAI Chat Completions, Responses, Anthropic Messages, and Gemini. That sounds like a lot, and mostly it's true. Even so, I wouldn't read “compatible” as “identical”: a normalized response keeps only what the common model can represent, and those formats differ in streaming, tools, and visible reasoning.
That's why I test the full route instead of a curl that returns text: streaming without truncated events, and tool arguments that stay valid. “The chat replied” is too weak a check for something that will edit files or run commands.
Toward providers it supports API keys, OAuth 2.0 with PKCE, and keyless services. Ollama is a first-class citizen, and LM Studio and vLLM are in the docs as well. The case I use most is the local one: same base URL, some tasks routed to Ollama, and a real auto/offline policy becomes viable. The documentation lists Claude Code, Codex CLI, Cursor, OpenCode, Cline, Continue, and Aider as clients, which is almost exactly my daily menu.
Two cautions. None of this is private by default: privacy depends on which route the policy picks and on how logs and credentials are configured; self-hosting the router doesn't seal anything in by itself. And watch the money. OmniRoute itself is $0, no credit card, but costs, limits, and terms belong to each upstream. If an automatic route jumps from a keyless provider to a billed one, I want to know before I read the statement, not while reading it.
The catalog numbers don't add up, and by now it makes me laugh
Three official surfaces, same day, three different inventories. The site says 339 providers, 56 free-forever, 95 MCP integrations, 25k tests. The README says 351 providers, 56 free-forever, 110 MCP integrations, 35 CLI agents, 39k tests. The npm package says 352.
I've stopped trying to reconcile them. The catalog moves fast, and some figures look like promo text updated at different times. But it tells you something about the project: if it can't agree with itself on how many providers it has, I'm not quoting that number anywhere.
For decisions I keep three humbler questions. Is the provider I need there? Does the capability my agent uses actually work? Can I run this version without exposing secrets? One test against my real route is worth more than an argument over whether the true inventory is 339, 351, or 352. Big numbers age badly.
My verdict for now: I keep using it because it makes a part of the work boring again, the way it should be. It is not a transparent abstraction. It is infrastructure with access to my secrets, so I pinned the version and tested the tool calls I actually use before trusting it with anything sensitive. “One API, many models” is a good mental interface as long as you remember what it hides: different contracts, somebody else's quotas, and behaviors the gateway can order but never equalize. It works for me today. If the ecosystem settles, it may stop being necessary; if it keeps fragmenting, it becomes more necessary. I'm in no rush to declare either.