AI Features

Coding Agents

VectorAxis is an OpenAI-compatible gateway, so any AI coding tool that accepts a custom base_url routes through it with a single configuration change. no plugin, no wrapper. Your whole engineering team gets cost tracking, provider fallbacks, caching, budgets, and guardrails without changing how the tools work.

The one change: point the tool’s base_url (sometimes called apiBase or OpenAI API Base) at https://api.vectoraxis.ai/v1 and set its API key to a virtual key (vk-…). Everything else stays the same.

One endpoint, any model: including Claude

Because the gateway is OpenAI-compatible, you send OpenAI-format chat requests and VectorAxis translates them to whatever provider the key points at. So a coding tool that only speaks “OpenAI” can still use Anthropic, Groq, and more: create a virtual key for that provider, pick a model it serves (e.g. claude-haiku-4-5), and VectorAxis handles the format under the hood, with no separate Anthropic endpoint needed.

A note on Claude Code. Claude Code talks to Anthropic’s native Messages API, which VectorAxis does not expose as a public endpoint, so it can’t be pointed at VectorAxis today. To use Claude through VectorAxis, use any OpenAI-compatible tool (below) with an Anthropic virtual key.

Configuring popular tools

Tool	How to point it at VectorAxis
Cursor	Settings → Models → OpenAI API Key: enable “Override OpenAI Base URL”, set it to https://api.vectoraxis.ai/v1, and paste a virtual key as the API key.
Continue.dev	In ~/.continue/config.json, set each model’s apiBase and apiKey (see example below).
Cody (Sourcegraph)	Add a custom OpenAI-compatible provider in Cody settings with the VectorAxis base URL and a virtual key.
Aider	Set OPENAI_API_BASE=https://api.vectoraxis.ai/v1 and OPENAI_API_KEY=vk-… (or the matching CLI flags).
Any OpenAI SDK / tool	Set base_url + api_key (a virtual key slug).

Quick start: the OpenAI SDK

python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.vectoraxis.ai/v1",
    api_key="vk-1a2b3c4d5e6f7a8b",   # your VectorAxis virtual key
)

# model + everything else stays the same
client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain this function"}],
)

Continue.dev config example

Two models, each behind its own virtual key: a GPT key and a Claude key. Both use the openai provider type (that just means “OpenAI-compatible protocol”); VectorAxis routes to the real provider based on the key.

json: ~/.continue/config.json

{
  "models": [
    {
      "title": "VectorAxis -> GPT-4o",
      "provider": "openai",
      "model": "gpt-4o",
      "apiBase": "https://api.vectoraxis.ai/v1",
      "apiKey": "vk-1a2b3c4d5e6f7a8b"
    },
    {
      "title": "VectorAxis -> Claude",
      "provider": "openai",
      "model": "claude-haiku-4-5",
      "apiBase": "https://api.vectoraxis.ai/v1",
      "apiKey": "vk-c7d4e1f2a3b95a8b"
    }
  ]
}

What you get

Capability	How it helps with coding agents
Cost visibility	See exactly what each developer, team, or project spends on AI, broken down by model and day, in Analytics.
Provider fallback	If a provider has a bad minute mid-session, retry & fallback keeps the IDE responding instead of erroring out.
Caching	Repeated prompts (“explain this function”, autocomplete) can be served from the cache, cutting latency and cost.
Budget controls	Give each dev or team a virtual key with a credit limit and rate limits, so no one can blow the monthly budget.
Guardrails	Block or redact secrets and PII in a snippet before it ever leaves your network with guardrails.
Model flexibility	Send cheap tasks to a small model and hard ones to a frontier model with a routing config, with no tool change.

Best practice: create a separate virtual key per developer or team so spend is tracked individually in Logs and Analytics, and set a credit_limit_usd on each to enforce monthly budgets automatically.

VectorAxis is built by GK Labs, a member of NVIDIA Inception.