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9router-chat

9router-chat

github.com/decoluaAgent workflows31.6k140 installsUpdated today
Repository

About 9router-chat

9router-chat is a skill for calling chat models through 9Router, for workflows that need a unified interface to multiple providers. It offers OpenAI- and Anthropic-format endpoints and automatic fallback for model combos.

Key features

  1. 01OpenAI-format endpoint
  2. 02Anthropic-format endpoint
  3. 03Discover models and metadata
  4. 04Support streaming responses
  5. 05Automatic fallback for model combos

Use cases

  1. 01Call chat models through a unified interface
  2. 02Handle streamed replies in an application
  3. 03Check model context windows and parameters
  4. 04Switch models when a provider is unavailable

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/decolua/9router --skill 9router-chat

After installing, type/9router-chatin Claude Code to confirm it loaded.

Details

From the project README

9Router — Chat

Requires NINEROUTER_URL (and NINEROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/decolua/9router/refs/heads/master/skills/9router/SKILL.md for setup.

Endpoints

  • POST $NINEROUTER_URL/v1/chat/completions — OpenAI format
  • POST $NINEROUTER_URL/v1/messages — Anthropic format

Discover

curl $NINEROUTER_URL/v1/models | jq '.data[].id'
# Per-model metadata (contextWindow, params)
curl "$NINEROUTER_URL/v1/models/info?id=openai/gpt-4o"

Combos (e.g. vip, mycodex) auto-fallback through multiple providers.

OpenAI format

curl -X POST $NINEROUTER_URL/v1/chat/completions \
  -H "Authorization: Bearer $NINEROUTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/gpt-5","messages":[{"role":"user","content":"Hi"}],"stream":false}'

JS (OpenAI SDK):

import OpenAI from "openai";
const client = new OpenAI({ baseURL: `${process.env.NINEROUTER_URL}/v1`, apiKey: process.env.NINEROUTER_KEY });
const res = await client.chat.completions.create({
  model: "openai/gpt-5",
  messages: [{ role: "user", content: "Hi" }],
  stream: true,
});
for await (const chunk of res) process.stdout.write(chunk.choices[0]?.delta?.content || "");

Anthropic format

curl -X POST $NINEROUTER_URL/v1/messages \
  -H "Authorization: Bearer $NINEROUTER_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{"model":"cc/claude-opus-4-7","max_tokens":1024,"messages":[{"role":"user","content":"Hi"}]}'

Response shape

OpenAI (/v1/chat/completions):

{ "id": "chatcmpl-...", "object": "chat.completion", "model": "openai/gpt-5",
  "choices": [{ "index": 0, "message": { "role": "assistant", "content": "Hello!" }, "finish_reason": "stop" }],
  "usage": { "prompt_tokens": 8, "completion_tokens": 2, "total_tokens": 10 } }

Streaming (stream:true) emits SSE: data: {choices:[{delta:{content:"..."}}]}\n\n ... data: [DONE]\n\n.

Anthropic (/v1/messages):

{ "id": "msg_...", "type": "message", "role": "assistant", "model": "cc/claude-opus-4-7",
  "content": [{ "type": "text", "text": "Hello!" }],
  "stop_reason": "end_turn", "usage": { "input_tokens": 8, "output_tokens": 2 } }

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