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Detect urgency, choose a team, and score severity from the same ticket.
Send a state and typed questions. Get probabilities, a selected option, or a score in one structured response.
Detect urgency, choose a team, and score severity from the same ticket.
Choose the next queue or action and keep the full probability distribution.
Run several atomic checks before your workflow continues.
Use a custom OpenAI-compatible provider to connect OpenCode to ApiClo. Set the endpoint to https://apiclo.com/v1 and keep the key in APICLO_API_KEY. OpenCode v1 and v2 use different configuration keys; check opencode --version before copying a configuration.
Export APICLO_API_KEY in the environment of the OpenCode server. For v2, add providers.apiclo with package @opencode/ai/providers/openai-compatible, env [APICLO_API_KEY] and settings.baseURL https://apiclo.com/v1.
Add a models entry whose modelID is the exact ApiClo model ID, and select apiclo/<alias>. The provider prefix belongs to the OpenCode selector; the upstream model ID remains the ApiClo ID.
If you use v1, follow its provider/npm/options schema instead of mixing it with v2 providers/package/settings. Run text, tool and image checks separately; configure vision only for a model that actually supports it.
export APICLO_API_KEY="YOUR_APICLO_KEY"
$env:APICLO_API_KEY="YOUR_APICLO_KEY"
{
"$schema": "https://opencode.ai/config.json",
"model": "apiclo/sonnet",
"providers": {
"apiclo": {
"env": ["APICLO_API_KEY"],
"package": "@opencode/ai/providers/openai-compatible",
"settings": {"baseURL": "https://apiclo.com/v1"},
"models": {"sonnet": {"modelID": "claude-sonnet-5"}}
}
}
}
Send one small request and inspect ApiClo Logs for the exact model, outcome and charged usage. Test tool calls and images separately. Cache settings in the client do not prove a hit: check actual read/write token counts.
ApiClo is an independent gateway. A client integration does not include that client's subscription or guarantee all its features. Compare ordinary input, cache reads, cache writes and output at current model rates; cache hits and savings are workload-dependent.
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