One LLM Gateway for
Coding Agents
Change the base URL — keep the same API key
curl https://api.omniakey.com/v1/chat/completions \ -H "Authorization: Bearer $OMNIAKEY_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-5.5", "messages": [ {"role": "user", "content": "Hello"} ] }'
LLM Gateway for Coding Agents
One OpenAI-compatible API for coding agents, one balance, one usage trail — and the models your tools already expect.
01Setup
An LLM gateway for coding agents means the first setup step is changing the base URL and using the same bearer token everywhere. The protocol can stay OpenAI-compatible, Anthropic-native, or Gemini-native depending on the tool.
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An LLM gateway for coding agents keeps the integration surface small when your stack changes from an IDE extension to a CLI agent, from a local script to a hosted automation, or from a quick prototype to a team workflow. Instead of creating separate keys for OpenAI, Claude, Gemini, and every SDK wrapper, you point the client at OmniaKey and keep the same auth pattern. The model id still decides which provider answers, but the operational surface stays under one account. That is the practical difference between a generic LLM proxy and an LLM gateway for coding agents: the gateway is built around real developer tools, base URL changes, bearer tokens, usage review, and predictable model routing.
02Control
An OpenAI-compatible API for coding agents gives teams a single place to watch quota, usage, spend, and model mix. That matters when agents run repeatedly and small prompts turn into long editing sessions.
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An LLM gateway for coding agents also makes cost review easier. Agentic coding sessions can burn tokens through planning, tool calls, edits, retries, code search, and long context windows. When usage is split across three provider dashboards, the real cost of a coding workflow is hard to see. OmniaKey keeps call metadata, token counts, model mix, latency, and spend in one dashboard, so developers can compare Claude, GPT, and Gemini without rebuilding reporting around each provider. The same OpenAI-compatible API for coding agents works across small experiments and longer team sessions, which keeps billing, refunds, quota, and spend alerts in one place.
03Choice
An LLM gateway for coding agents keeps Claude, GPT, and Gemini available without forcing developers to pick a permanent winner. Select the model per task and keep the surrounding workflow stable.
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An LLM gateway for coding agents does not mean one model for every task. The point is controlled choice. Use Claude when the repo context is complex, GPT when you want strong general reasoning or OpenAI-compatible tooling, and Gemini when long-context work fits the job. OmniaKey routes the request to the model you named. It does not silently swap providers to hide outages or chase margin, because coding agents need predictable behavior more than clever fallback magic. A Claude Code API gateway, a Cursor OpenAI-compatible API, a Cline OpenAI-compatible API, and an OpenCode API gateway all need the same basic promise: the model you configured is the model that runs.
04Billing
A coding-agent gateway keeps top-ups, token spend, and refunds in one account. It is easier to audit than separate cards, separate invoices, and separate provider dashboards.
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An LLM gateway for coding agents is especially useful when a team standardizes a base URL across Claude Code, Codex-style tools, Cursor, Cline, OpenCode, aider, Continue, Zed, shell scripts, and CI jobs. New developers get one setup path. Finance gets one billing trail. Engineering gets model choice without handing every teammate a pile of provider credentials. You can still keep provider-specific habits, but the day-to-day coding surface becomes much simpler. OmniaKey works as an OpenAI-compatible API for coding agents while also keeping Anthropic-native and Gemini-native routes available for tools that prefer those protocols.
05Migration
An LLM gateway for coding agents lets teams add or replace tools without reissuing provider credentials. The base URL can stay stable while the model mix changes.
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An LLM gateway for coding agents also keeps migration reversible. A developer can start with one tool, add another agent later, and keep the same account, balance, and observability. If a model becomes too expensive for a task, switch the model id. If a workflow needs Anthropic-native messages or Gemini-native content generation, use that protocol without rebuilding the surrounding billing and access control. The key point is not hiding providers; it is making provider choice easier to operate. OmniaKey gives teams one OpenAI-compatible API for coding agents, one Claude Code API gateway path, one OpenCode API gateway path, and one usage record for everyday development.
Route Claude, GPT, and Gemini
through one gateway
Same model, no quantizing or swapping
One OpenAI-compatible API key
Built for coding
- Claude Code
- Codex CLI
- Gemini CLI
- Cursor
- Cline
- OpenCode
+ Aider · Continue · Zed · anything OpenAI-compatible
No subscriptions, no tiers,
just usage-based pricing
Every model is a straight discount off the official rate — and your balance never expires.
About billing, models, and integration
Why up to 93% cheaper than the official price?
What about after the launch promo?
Is the model the same model?
What if an upstream provider goes down?
Does OmniaKey work as a Claude Code, Cursor, Cline, and OpenCode API gateway?
/v1, Gemini /v1beta, Anthropic uses the bare URL) and the block highlights the difference. Aider, Continue, and anything OpenAI/Anthropic/Gemini-compatible work the same way.What about latency? Are you adding a hop?
Do you log my prompts?
Where are you based, and who's behind this?
[email protected] — replies come from real humans.How do I pay?
LLM Gateway for Coding Agents
Claude, GPT, and Gemini
Sign up in 30 seconds and prove it on your own code.