keirouter-chat

name	keirouter-chat
description	Chat / code generation via KeiRouter using OpenAI /v1/chat/completions or Anthropic /v1/messages format with streaming + auto-fallback combos. Use when the user wants to ask an LLM, generate code, summarize text, or run prompts through KeiRouter.

KeiRouter — Chat

Requires KEIROUTER_URL (and KEIROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/mydisha/keirouter/main/skills/keirouter/SKILL.md for setup.

Endpoints

• POST $KEIROUTER_URL/v1/chat/completions — OpenAI format
• POST $KEIROUTER_URL/v1/messages — Anthropic format
• POST $KEIROUTER_URL/v1/responses — OpenAI Responses format

Discover

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

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

OpenAI format

curl -X POST $KEIROUTER_URL/v1/chat/completions \
  -H "Authorization: Bearer $KEIROUTER_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.KEIROUTER_URL}/v1`, apiKey: process.env.KEIROUTER_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 $KEIROUTER_URL/v1/messages \
  -H "Authorization: Bearer $KEIROUTER_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{"model":"anthropic/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": "anthropic/claude-opus-4-7",
  "content": [{ "type": "text", "text": "Hello!" }],
  "stop_reason": "end_turn", "usage": { "input_tokens": 8, "output_tokens": 2 } }

Provider quick reference

Provider	model format	Examples
OpenAI	openai/<model>	openai/gpt-5, openai/gpt-4o
Anthropic	anthropic/<model>	anthropic/claude-opus-4-7, anthropic/claude-sonnet-4-6
Claude Code	cc/<model>	cc/claude-opus-4-7
Gemini	gemini/<model>	gemini/gemini-2.5-pro, gemini/gemini-2.5-flash
Groq	groq/<model>	groq/llama-3.3-70b
DeepSeek	ds/<model>	ds/deepseek-chat, ds/deepseek-coder
OpenRouter	openrouter/<model>	openrouter/anthropic/claude-opus-4-7
Mistral	mistral/<model>	mistral/mistral-large-latest
xAI	xai/<model>	xai/grok-3
Ollama Local	ollama-local/<model>	ollama-local/llama3.2
Custom OpenAI	custom-openai/<model>	Any model on your custom endpoint