Claude Code and Codex CLI arrive with sensible defaults: install, authenticate with the model provider they know, start working.
Switching to a different provider is also supposed to be simple. The documentation says so. The endpoints are documented, the setup instructions exist, and plenty of people have done it.
That last part is the problem. Between “documented” and “done” sit the details: which environment variable, which base URL, which wire protocol, which model names are still valid this quarter. Get any of them wrong, and the session fails with a 401, a 404, or an error about a model that no longer exists.
Agent Model Connector exists to close that gap. It generates ready-to-use configurations for connecting AI agents to alternative model providers—deterministically, from official documentation, entirely in your browser.
The problem is a matrix
Every agent exposes configuration differently. Claude Code reads environment variables. Codex CLI reads a TOML file. Every provider exposes a different endpoint, and sometimes a different wire protocol entirely. The result is a matrix of documentation that multiplies, not a single setup guide.
The knowledge also goes stale. On 2026-07-24, DeepSeek retired the model names that most existing guides referenced—deepseek-chat and deepseek-reasoner became deepseek-v4-pro[1m] and deepseek-v4-flash. Any tutorial written before that date now produces a configuration that fails on the first message. Model catalogs churn like this on a regular schedule; documentation does not.
This is why the common response—“just search for a guide”—stops working after a few months. The alternatives are not much better: read three official docs and assemble the pieces yourself, or hand your API key to a third-party generator and hope it assembles them more carefully.
Neither feels right for a task this small.
Generate, don’t guess
The decision behind Agent Model Connector: treat configuration as generated output, not as copy-pasted folklore.
Every configuration is produced by code that reads structured catalogs of agents, providers, and recipes. Those catalogs are maintained against official documentation, and each combination carries an honest status: verified against a live API, or reviewed from documentation without a live test. The site says which one, and when it was last checked. Nothing is presented as official or guaranteed, because none of it is.
The generation itself happens in the browser. No API keys, prompts, or generated output are ever transmitted. There is no backend, no account system, and no analytics. The tool does one job—produce a configuration—and it does that job without the transaction most tools want in return.
What it provides
The homepage is a five-step wizard:
- Choose an agent: Claude Code or Codex CLI.
- Choose a provider: DeepSeek or OpenRouter.
- Choose a model—or, for OpenRouter, enter any model slug, because that catalog changes faster than any static list could mirror.
- Choose your shell: zsh, bash, fish, PowerShell, or CMD, so exported environment variables come out in the form your terminal actually understands.
- Review the result, with pointers to the errors you might hit next.
The output is ready to paste. Claude Code gets the environment variables it needs; Codex gets the config.toml block it expects. No placeholder surgery, no “replace this with your own value” buried inside three nested examples.
Beyond the wizard, the site covers the surrounding questions:
- Four combination pages, one per agent-and-provider pair, each with its own FAQ.
- A compatibility matrix for scanning what works today.
- Troubleshooting pages for six common failure modes—401, 404, unknown model, rate limit, network, and timeout—with fixes written for each agent.
- The whole site in English and Chinese. The commands are identical in both languages; only the explanations change.
Building it the iWebTools way
Agent Model Connector is the fourth project in the iWebTools family, and it follows the same building principles as its predecessors.
The product is static and local-first. Adding a new agent or provider means adding data and tests, not page logic—the site generates its own pages from the catalogs. The interesting work is the catalog itself: keeping model names current, recording verification status honestly, and refusing to overstate what has been tested.
That honesty extends to the tools around the site. A developer-only script can verify a provider against the real API, but it runs locally and never touches the website. When a recipe has been reviewed from documentation but not live-tested, the site says so. “Verified” and “community tested” are not the same word, and the distinction is recorded per combination.
What comes next
The immediate roadmap is breadth: more agents (Continue, Cline, Roo Code) and more providers (Ollama, LM Studio, Azure OpenAI, Gemini, Qwen). Each one is another catalog entry plus tests—the architecture is designed to absorb them without new product code.
Features that would send data anywhere—in-browser API testing, config-file uploads, diagnostics against live endpoints—are deliberately parked behind a privacy review. “Configuration stays in the browser” only stays meaningful if the architecture keeps making it true.
AI agents are new, but the problem they inherit is not: tools change faster than documentation, and the gap between them is where users lose time. Agent Model Connector is our attempt to close that gap with generated, honest, local output—and to let the configuration be the last thing you think about.