remove support for deepseek

examples/twitter-example/twitter.ts

@@ -6,11 +6,11 @@ import {
   GameWorker,
   LLMModel,
 } from "@virtuals-protocol/game";
-import dotenv from 'dotenv';
-import path from 'path';
+import dotenv from "dotenv";
+import path from "path";
 
 // Load environment variables from the correct location
-dotenv.config({ path: path.join(__dirname, '.env') });
+dotenv.config({ path: path.join(__dirname, ".env") });
 
 const postTweetFunction = new GameFunction({
   name: "post_tweet",
@@ -114,7 +114,7 @@ const agent = new GameAgent(process.env.API_KEY!, {
   goal: "Search and reply to tweets",
   description: "A bot that searches for tweets and replies to them",
   workers: [postTweetWorker],
-  llmModel: LLMModel.DeepSeek_R1, // Optional: Set the LLM model default (LLMModel.Llama_3_1_405B_Instruct)
+  llmModel: LLMModel.Llama_3_1_405B_Instruct, // Optional: Set the LLM model default (LLMModel.Llama_3_1_405B_Instruct)
   // Optional: Get the agent state
   getAgentState: async () => {
     return {

game-starter/src/agent.ts

@@ -4,19 +4,20 @@ import dotenv from "dotenv";
 dotenv.config();
 
 if (!process.env.API_KEY) {
-    throw new Error('API_KEY is required in environment variables');
+  throw new Error("API_KEY is required in environment variables");
 }
 
 export const activity_agent = new GameAgent(process.env.API_KEY, {
-    name: "Activity Recommender",
-    goal: "Help users find the perfect activities based on their location and current weather conditions",
-    description: "You are an agent that gets location of the user and then uses that to get weather information and then uses that to recommend activities",
-    workers: [activityRecommenderWorker],
-    llmModel: LLMModel.DeepSeek_R1 // this is an optional paramenter to set the llm model for the agent. Default is Llama_3_1_405B_Instruct
+  name: "Activity Recommender",
+  goal: "Help users find the perfect activities based on their location and current weather conditions",
+  description:
+    "You are an agent that gets location of the user and then uses that to get weather information and then uses that to recommend activities",
+  workers: [activityRecommenderWorker],
+  llmModel: LLMModel.Llama_3_1_405B_Instruct, // this is an optional paramenter to set the llm model for the agent. Default is Llama_3_1_405B_Instruct
 });
 
 activity_agent.setLogger((agent: GameAgent, msg: string) => {
-    console.log(`🎯 [${agent.name}]`);
-    console.log(msg);
-    console.log("------------------------\n");
-}); 
+  console.log(`🎯 [${agent.name}]`);
+  console.log(msg);
+  console.log("------------------------\n");
+});

src/interface/GameClient.ts

@@ -41,8 +41,6 @@ export interface GameAction {
 export enum LLMModel {
   Llama_3_1_405B_Instruct = "Llama-3.1-405B-Instruct",
   Llama_3_3_70B_Instruct = "Llama-3.3-70B-Instruct",
-  DeepSeek_R1 = "DeepSeek-R1",
-  DeepSeek_V3 = "DeepSeek-V3",
   Qwen_2_5_72B_Instruct = "Qwen-2.5-72B-Instruct",
 }
 

tools/cloud-to-sdk/generateAgent.ts

@@ -1,43 +1,43 @@
 // generateAgent.ts
-import fs from 'fs';
-import path from 'path';
-import { PluginJson } from './types';
+import fs from "fs";
+import path from "path";
+import { PluginJson } from "./types";
 import { GameAgent, LLMModel } from "@virtuals-protocol/game";
 
 export function generateAgent(json: PluginJson, outputPath: string): void {
-    try {
-        // Ensure directory exists
-        const dir = path.dirname(outputPath);
-        if (!fs.existsSync(dir)) {
-            fs.mkdirSync(dir, { recursive: true });
-        }
+  try {
+    // Ensure directory exists
+    const dir = path.dirname(outputPath);
+    if (!fs.existsSync(dir)) {
+      fs.mkdirSync(dir, { recursive: true });
+    }
 
-        const modelMapping: { [key: string]: string } = {
-            'llama_3_1_405b': 'Llama_3_1_405B_Instruct',
-            'llama_3_3_70b': 'Llama_3_3_70B_Instruct',
-            'deepseek_r1': 'DeepSeek_R1',
-            'deepseek_v3': 'DeepSeek_V3',
-            'qwen_2_5_72b': 'Qwen_2_5_72B_Instruct'
-        };
+    const modelMapping: { [key: string]: string } = {
+      llama_3_1_405b: "Llama_3_1_405B_Instruct",
+      llama_3_3_70b: "Llama_3_3_70B_Instruct",
+      qwen_2_5_72b: "Qwen_2_5_72B_Instruct",
+    };
 
-        const modelName = modelMapping[json.gameEngineModel.toLowerCase()] || 'Llama_3_3_70B_Instruct';
+    const modelName =
+      modelMapping[json.gameEngineModel.toLowerCase()] ||
+      "Llama_3_3_70B_Instruct";
 
-        const content = `
+    const content = `
 import { GameAgent, LLMModel } from "@virtuals-protocol/game";
 import { workers } from "./worker";
 
 export const agent = new GameAgent("YOUR_API_KEY", {
-    name: "${json.name || 'name'}",
+    name: "${json.name || "name"}",
     goal: \`${json.goal}\`,
     description: \`${json.description}\`,
     workers: workers,
     llmModel: LLMModel.${modelName}
 });`;
 
-        fs.writeFileSync(outputPath, content);
-        console.log(`✅ Generated agent.ts at ${outputPath}`);
-    } catch (error) {
-        console.error(`❌ Error generating agent.ts:`, error);
-        throw error;
-    }
-}
+    fs.writeFileSync(outputPath, content);
+    console.log(`✅ Generated agent.ts at ${outputPath}`);
+  } catch (error) {
+    console.error(`❌ Error generating agent.ts:`, error);
+    throw error;
+  }
+}