mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-15 17:00:59 +00:00
38ac29acfb
- Created shared wrapTextWithAnsi() function in utils.ts - Handles word-based wrapping while preserving ANSI escape codes - Properly tracks active ANSI codes across wrapped lines - Supports multi-byte characters (emoji, surrogate pairs) - Updated Markdown and Text components to use shared wrapping - Removed duplicate wrapping logic (158 lines total)
455 lines
No EOL
13 KiB
TypeScript
455 lines
No EOL
13 KiB
TypeScript
#!/usr/bin/env tsx
|
|
|
|
import { writeFileSync } from "fs";
|
|
import { join, dirname } from "path";
|
|
import { fileURLToPath } from "url";
|
|
import { Api, KnownProvider, Model } from "../src/types.js";
|
|
|
|
const __filename = fileURLToPath(import.meta.url);
|
|
const __dirname = dirname(__filename);
|
|
const packageRoot = join(__dirname, "..");
|
|
|
|
interface ModelsDevModel {
|
|
id: string;
|
|
name: string;
|
|
tool_call?: boolean;
|
|
reasoning?: boolean;
|
|
limit?: {
|
|
context?: number;
|
|
output?: number;
|
|
};
|
|
cost?: {
|
|
input?: number;
|
|
output?: number;
|
|
cache_read?: number;
|
|
cache_write?: number;
|
|
};
|
|
modalities?: {
|
|
input?: string[];
|
|
};
|
|
}
|
|
|
|
async function fetchOpenRouterModels(): Promise<Model<any>[]> {
|
|
try {
|
|
console.log("Fetching models from OpenRouter API...");
|
|
const response = await fetch("https://openrouter.ai/api/v1/models");
|
|
const data = await response.json();
|
|
|
|
const models: Model<any>[] = [];
|
|
|
|
for (const model of data.data) {
|
|
// Only include models that support tools
|
|
if (!model.supported_parameters?.includes("tools")) continue;
|
|
|
|
// Parse provider from model ID
|
|
let provider: KnownProvider = "openrouter";
|
|
let modelKey = model.id;
|
|
|
|
modelKey = model.id; // Keep full ID for OpenRouter
|
|
|
|
// Parse input modalities
|
|
const input: ("text" | "image")[] = ["text"];
|
|
if (model.architecture?.modality?.includes("image")) {
|
|
input.push("image");
|
|
}
|
|
|
|
// Convert pricing from $/token to $/million tokens
|
|
const inputCost = parseFloat(model.pricing?.prompt || "0") * 1_000_000;
|
|
const outputCost = parseFloat(model.pricing?.completion || "0") * 1_000_000;
|
|
const cacheReadCost = parseFloat(model.pricing?.input_cache_read || "0") * 1_000_000;
|
|
const cacheWriteCost = parseFloat(model.pricing?.input_cache_write || "0") * 1_000_000;
|
|
|
|
const normalizedModel: Model<any> = {
|
|
id: modelKey,
|
|
name: model.name,
|
|
api: "openai-completions",
|
|
baseUrl: "https://openrouter.ai/api/v1",
|
|
provider,
|
|
reasoning: model.supported_parameters?.includes("reasoning") || false,
|
|
input,
|
|
cost: {
|
|
input: inputCost,
|
|
output: outputCost,
|
|
cacheRead: cacheReadCost,
|
|
cacheWrite: cacheWriteCost,
|
|
},
|
|
contextWindow: model.context_length || 4096,
|
|
maxTokens: model.top_provider?.max_completion_tokens || 4096,
|
|
};
|
|
models.push(normalizedModel);
|
|
}
|
|
|
|
console.log(`Fetched ${models.length} tool-capable models from OpenRouter`);
|
|
return models;
|
|
} catch (error) {
|
|
console.error("Failed to fetch OpenRouter models:", error);
|
|
return [];
|
|
}
|
|
}
|
|
|
|
async function loadModelsDevData(): Promise<Model<any>[]> {
|
|
try {
|
|
console.log("Fetching models from models.dev API...");
|
|
const response = await fetch("https://models.dev/api.json");
|
|
const data = await response.json();
|
|
|
|
const models: Model<any>[] = [];
|
|
|
|
// Process Anthropic models
|
|
if (data.anthropic?.models) {
|
|
for (const [modelId, model] of Object.entries(data.anthropic.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "anthropic-messages",
|
|
provider: "anthropic",
|
|
baseUrl: "https://api.anthropic.com",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Process Google models
|
|
if (data.google?.models) {
|
|
for (const [modelId, model] of Object.entries(data.google.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "google-generative-ai",
|
|
provider: "google",
|
|
baseUrl: "https://generativelanguage.googleapis.com/v1beta",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Process OpenAI models
|
|
if (data.openai?.models) {
|
|
for (const [modelId, model] of Object.entries(data.openai.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "openai-responses",
|
|
provider: "openai",
|
|
baseUrl: "https://api.openai.com/v1",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Process Groq models
|
|
if (data.groq?.models) {
|
|
for (const [modelId, model] of Object.entries(data.groq.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "openai-completions",
|
|
provider: "groq",
|
|
baseUrl: "https://api.groq.com/openai/v1",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Process Cerebras models
|
|
if (data.cerebras?.models) {
|
|
for (const [modelId, model] of Object.entries(data.cerebras.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "openai-completions",
|
|
provider: "cerebras",
|
|
baseUrl: "https://api.cerebras.ai/v1",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Process xAi models
|
|
if (data.xai?.models) {
|
|
for (const [modelId, model] of Object.entries(data.xai.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "openai-completions",
|
|
provider: "xai",
|
|
baseUrl: "https://api.x.ai/v1",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Process xAi models
|
|
if (data.zai?.models) {
|
|
for (const [modelId, model] of Object.entries(data.zai.models)) {
|
|
const m = model as ModelsDevModel;
|
|
if (m.tool_call !== true) continue;
|
|
|
|
models.push({
|
|
id: modelId,
|
|
name: m.name || modelId,
|
|
api: "anthropic-messages",
|
|
provider: "zai",
|
|
baseUrl: "https://api.z.ai/api/anthropic",
|
|
reasoning: m.reasoning === true,
|
|
input: m.modalities?.input?.includes("image") ? ["text", "image"] : ["text"],
|
|
cost: {
|
|
input: m.cost?.input || 0,
|
|
output: m.cost?.output || 0,
|
|
cacheRead: m.cost?.cache_read || 0,
|
|
cacheWrite: m.cost?.cache_write || 0,
|
|
},
|
|
contextWindow: m.limit?.context || 4096,
|
|
maxTokens: m.limit?.output || 4096,
|
|
});
|
|
}
|
|
}
|
|
|
|
console.log(`Loaded ${models.length} tool-capable models from models.dev`);
|
|
return models;
|
|
} catch (error) {
|
|
console.error("Failed to load models.dev data:", error);
|
|
return [];
|
|
}
|
|
}
|
|
|
|
async function generateModels() {
|
|
// Fetch models from both sources
|
|
// models.dev: Anthropic, Google, OpenAI, Groq, Cerebras
|
|
// OpenRouter: xAI and other providers (excluding Anthropic, Google, OpenAI)
|
|
const modelsDevModels = await loadModelsDevData();
|
|
const openRouterModels = await fetchOpenRouterModels();
|
|
|
|
// Combine models (models.dev has priority)
|
|
const allModels = [...modelsDevModels, ...openRouterModels];
|
|
|
|
// Add missing gpt models
|
|
if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5-chat-latest")) {
|
|
allModels.push({
|
|
id: "gpt-5-chat-latest",
|
|
name: "GPT-5 Chat Latest",
|
|
api: "openai-responses",
|
|
baseUrl: "https://api.openai.com/v1",
|
|
provider: "openai",
|
|
reasoning: false,
|
|
input: ["text", "image"],
|
|
cost: {
|
|
input: 1.25,
|
|
output: 10,
|
|
cacheRead: 0.125,
|
|
cacheWrite: 0,
|
|
},
|
|
contextWindow: 128000,
|
|
maxTokens: 16384,
|
|
});
|
|
}
|
|
|
|
if (!allModels.some(m => m.provider === "openai" && m.id === "gpt-5.1-codex")) {
|
|
allModels.push({
|
|
id: "gpt-5.1-codex",
|
|
name: "GPT-5.1 Codex",
|
|
api: "openai-responses",
|
|
baseUrl: "https://api.openai.com/v1",
|
|
provider: "openai",
|
|
reasoning: true,
|
|
input: ["text", "image"],
|
|
cost: {
|
|
input: 1.25,
|
|
output: 5,
|
|
cacheRead: 0.125,
|
|
cacheWrite: 1.25,
|
|
},
|
|
contextWindow: 400000,
|
|
maxTokens: 128000,
|
|
});
|
|
}
|
|
|
|
// Add missing Grok models
|
|
if (!allModels.some(m => m.provider === "xai" && m.id === "grok-code-fast-1")) {
|
|
allModels.push({
|
|
id: "grok-code-fast-1",
|
|
name: "Grok Code Fast 1",
|
|
api: "openai-completions",
|
|
baseUrl: "https://api.x.ai/v1",
|
|
provider: "xai",
|
|
reasoning: false,
|
|
input: ["text"],
|
|
cost: {
|
|
input: 0.2,
|
|
output: 1.5,
|
|
cacheRead: 0.02,
|
|
cacheWrite: 0,
|
|
},
|
|
contextWindow: 32768,
|
|
maxTokens: 8192,
|
|
});
|
|
}
|
|
|
|
// Add missing OpenRouter model
|
|
if (!allModels.some(m => m.provider === "openrouter" && m.id === "openrouter/auto")) {
|
|
allModels.push({
|
|
id: "openrouter/auto",
|
|
name: "OpenRouter: Auto Router",
|
|
api: "openai-completions",
|
|
provider: "openrouter",
|
|
baseUrl: "https://openrouter.ai/api/v1",
|
|
reasoning: true,
|
|
input: ["text", "image"],
|
|
cost: {
|
|
// we dont know about the costs because OpenRouter auto routes to different models
|
|
// and then charges you for the underlying used model
|
|
input:0,
|
|
output:0,
|
|
cacheRead:0,
|
|
cacheWrite:0,
|
|
},
|
|
contextWindow: 2000000,
|
|
maxTokens: 30000,
|
|
});
|
|
}
|
|
|
|
// Group by provider and deduplicate by model ID
|
|
const providers: Record<string, Record<string, Model<any>>> = {};
|
|
for (const model of allModels) {
|
|
if (!providers[model.provider]) {
|
|
providers[model.provider] = {};
|
|
}
|
|
// Use model ID as key to automatically deduplicate
|
|
// Only add if not already present (models.dev takes priority over OpenRouter)
|
|
if (!providers[model.provider][model.id]) {
|
|
providers[model.provider][model.id] = model;
|
|
}
|
|
}
|
|
|
|
// Generate TypeScript file
|
|
let output = `// This file is auto-generated by scripts/generate-models.ts
|
|
// Do not edit manually - run 'npm run generate-models' to update
|
|
|
|
import type { Model } from "./types.js";
|
|
|
|
export const MODELS = {
|
|
`;
|
|
|
|
// Generate provider sections
|
|
for (const [providerId, models] of Object.entries(providers)) {
|
|
output += `\t${providerId}: {\n`;
|
|
|
|
for (const model of Object.values(models)) {
|
|
output += `\t\t"${model.id}": {\n`;
|
|
output += `\t\t\tid: "${model.id}",\n`;
|
|
output += `\t\t\tname: "${model.name}",\n`;
|
|
output += `\t\t\tapi: "${model.api}",\n`;
|
|
output += `\t\t\tprovider: "${model.provider}",\n`;
|
|
if (model.baseUrl) {
|
|
output += `\t\t\tbaseUrl: "${model.baseUrl}",\n`;
|
|
}
|
|
output += `\t\t\treasoning: ${model.reasoning},\n`;
|
|
output += `\t\t\tinput: [${model.input.map(i => `"${i}"`).join(", ")}],\n`;
|
|
output += `\t\t\tcost: {\n`;
|
|
output += `\t\t\t\tinput: ${model.cost.input},\n`;
|
|
output += `\t\t\t\toutput: ${model.cost.output},\n`;
|
|
output += `\t\t\t\tcacheRead: ${model.cost.cacheRead},\n`;
|
|
output += `\t\t\t\tcacheWrite: ${model.cost.cacheWrite},\n`;
|
|
output += `\t\t\t},\n`;
|
|
output += `\t\t\tcontextWindow: ${model.contextWindow},\n`;
|
|
output += `\t\t\tmaxTokens: ${model.maxTokens},\n`;
|
|
output += `\t\t} satisfies Model<"${model.api}">,\n`;
|
|
}
|
|
|
|
output += `\t},\n`;
|
|
}
|
|
|
|
output += `} as const;
|
|
`;
|
|
|
|
// Write file
|
|
writeFileSync(join(packageRoot, "src/models.generated.ts"), output);
|
|
console.log("Generated src/models.generated.ts");
|
|
|
|
// Print statistics
|
|
const totalModels = allModels.length;
|
|
const reasoningModels = allModels.filter(m => m.reasoning).length;
|
|
|
|
console.log(`\nModel Statistics:`);
|
|
console.log(` Total tool-capable models: ${totalModels}`);
|
|
console.log(` Reasoning-capable models: ${reasoningModels}`);
|
|
|
|
for (const [provider, models] of Object.entries(providers)) {
|
|
console.log(` ${provider}: ${Object.keys(models).length} models`);
|
|
}
|
|
}
|
|
|
|
// Run the generator
|
|
generateModels().catch(console.error); |