Merge pull request game-by-virtuals#69 from stevensf1998/feat/allow-selection-of-gameEngine

Feat/allow selection of game engine

Author: celesteanglm

src/game_sdk/hosted_game/README.md

@@ -3,11 +3,12 @@
 This is a Python SDK for the Virtuals Twitter Agent. It allows you to configure and deploy agents that can interact with the Twitter/X platform. This SDK/API allows you to configure your agents powered by the GAME architecture. This is similar to configuring your agent in the [Agent Sandbox](https://game-lite.virtuals.io/) on the [Virtuals Platform](https://app.virtuals.io/).
 
 ## Create an API key
+
 Open the [Virtuals Platform](https://app.virtuals.io/) and create/get an API key from the Agent Sandbox by clicking `SDK/API Access`
 
 ![getGAMEApi](./../../../docs/imgs/accesskey.jpg)
 
-Store the key in a safe location, like a `.bashrc` or a `.zshrc` file. 
+Store the key in a safe location, like a `.bashrc` or a `.zshrc` file.
 
 ```bash
 export VIRTUALS_API_KEY="your_virtuals_api_key"
@@ -16,12 +17,14 @@ export VIRTUALS_API_KEY="your_virtuals_api_key"
 Alternatively, you can also use a `.env` file ([`python-dotenv` package](https://github.com/theskumar/python-dotenv) to store and load the key) if you are using the Virtuals Python SDK.
 
 ## Usage (GAME)
+
 The Virtuals SDK current main functionalities are to develop and configure agents powered by GAME. Other functionalities to interact with the Virtuals Platform will be supported in the future. This GAME SDK can be used for multiple use cases:
 
 1. Develop, evaluate and update the existing Agent in Twitter environment.
-2. Build on other platforms and application using GAME (Task-based Agent). 
+2. Build on other platforms and application using GAME (Task-based Agent).
 
 ### Update the existing Agent in Twitter environment
+
 The SDK provides another interface to configure agents that is more friendly to developers rather than through a UI. This is similar to configuring your agent in the [Agent Sandbox](https://game-lite.virtuals.io/).
 
 ```python
@@ -35,7 +38,8 @@ agent = Agent(
     world_info="Virtual crypto trading environment where 1 DOGE = 1 DOGE"
 )
 ```
-You can also initialize the agent first with just the API key and set the goals, descriptions and world information separately and check the current agent descriptions if needed. 
+
+You can also initialize the agent first with just the API key and set the goals, descriptions and world information separately and check the current agent descriptions if needed.
 
 ```python
 agent = Agent(api_key=VIRTUALS_API_KEY)
@@ -54,9 +58,15 @@ agent.set_world_info("Virtual crypto trading environment where 1 DOGE = 1 DOGE")
 agent.get_goal()
 agent.get_description()
 agent.get_world_info()
+
+# set game engine model
+# Available models: llama_3_1_405b, deepseek_r1, llama_3_3_70b_instruct, qwen2p5_72b_instruct, deepseek_v3
+agent.set_game_engine_model("llama_3_1_405b")
+
 ```
 
 ### Functions
+
 By default, there are no functions enabled when the agent is initialized (i.e. the agent has no actions/functions it can execute). There are a list of available and provided functions for the Twitter/X platform and you can set them.
 
 ```python
@@ -67,6 +77,7 @@ agent.use_default_twitter_functions(["wait", "reply_tweet"])
 ```
 
 You can then equip the agent with some custom functions. Because the agent is hosted, custom functions need to be wrapped in API calls and can then be defined as follows:
+
 ```python
 
 search_function = game.Function(
@@ -93,12 +104,14 @@ agent.add_custom_function(search_function)
 ```
 
 ### Evaluate with Simulate, Deploy
+
 You can simulate one step of the agentic loop on Twitter/X with your new configurations and see the outputs. This is similar to the simulate button on the [Agent Sandbox](https://game-lite.virtuals.io/).
 
 ```python
 # Simulate one step of the full agentic loop on Twitter/X from the HLP -> LLP -> action (NOTE: supported for Twitter/X only now)
 response = agent.simulate_twitter(session_id="123")
 ```
+
 To more realistically simulate deployment, you can also run through the simulate function with the same session id for a number of steps.
 
 ```python
@@ -118,13 +131,15 @@ response = agent.react(
 ```
 
 Once you are happy, `deploy_twitter` will push your agent configurations to production and run your agent on Twitter/X autonomously.
+
 ```python
 # deploy agent! (NOTE: supported for Twitter/X only now)
 agent.deploy_twitter()
 ```
 
 ## Build on other platforms using GAME
-`simulate_twitter` and `deploy_twitter` runs through the entire GAME stack from HLP → LLP→ action/function selected. However, these agent functionalities are currently for the Twitter/X platform. You may utilize Task-based Agent with Low-Level Planner and Reaction Module to develop applications that are powered by GAME. The Low Level Planner (LLP) of the agent (please see [documentation](https://www.notion.so/1592d2a429e98016b389ea26b53686a3?pvs=21) for more details on GAME and LLP) can separately act as a decision making engine based on a task description and event occurring. This agentic architecture is simpler but also sufficient for many applications. 
+
+`simulate_twitter` and `deploy_twitter` runs through the entire GAME stack from HLP → LLP→ action/function selected. However, these agent functionalities are currently for the Twitter/X platform. You may utilize Task-based Agent with Low-Level Planner and Reaction Module to develop applications that are powered by GAME. The Low Level Planner (LLP) of the agent (please see [documentation](https://www.notion.so/1592d2a429e98016b389ea26b53686a3?pvs=21) for more details on GAME and LLP) can separately act as a decision making engine based on a task description and event occurring. This agentic architecture is simpler but also sufficient for many applications.
 
 We are releasing this simpler setup as a more generalised/platform agnostic framework (not specific to Twitter/X). The entire GAME stack along with the HLP will be opened up to be fully configurable and platform agnostic in the coming weeks.
 
@@ -150,19 +165,23 @@ response = agent.react(
 ## Arguments Definition
 
 ### Session ID
-The session ID is an identifier for an instance of the agent. When using the same session ID, it maintains and picks up from where it last left off, continuing the session/instance. It should be split per user/ conversation that you are maintaining on your platform. For different platforms, different session ID can be used. 
+
+The session ID is an identifier for an instance of the agent. When using the same session ID, it maintains and picks up from where it last left off, continuing the session/instance. It should be split per user/ conversation that you are maintaining on your platform. For different platforms, different session ID can be used.
 
 ### Platform Tag
+
 When adding custom functions, and when calling the react agent (i.e. LLP), there is a platform tag that can be defined. This acts like a filter for the functions available that is passed to the agent. You should define the platform when passing in the events.
 
 ### Task Description
+
 Task description serves as the prompt for the agent to respond. Since the reaction can be platform-based, you can define task description based on the platforms. In the task description, you should pass in any related info that require agent to make decision. That should include:
+
 - User message
 - Conversation history
 - Instructions
 
-
 ## Importing Functions and Sharing Functions
+
 With this SDK and function structure, importing and sharing functions is also possible. Looking forward to all the different contributions and functionalities we will build together as a community!
 
 ```python

src/game_sdk/hosted_game/agent.py

@@ -164,7 +164,6 @@ class ContentLLMTemplate:
     top_p: float = 0.7
     repetition_penalty: float = 1.0
     type: str = None
-    taskDescription: str = None
     isSandbox: bool = False
 
     def _validate_fields(self):
@@ -190,11 +189,7 @@ def _validate_fields(self):
             self.type = self.template_type
             self.isSandbox = False
             self.userPrompt = self.user_prompt or ""
-            
-            # Additional settings for REPLY only
-            if self.template_type == "REPLY":
-                self.taskDescription = self.taskDescription or "Process incoming tweet. Ignore if it is boring or unimportant. Ignore if the conversation has gone too long."
-                
+                            
         elif self.template_type in ["TWITTER_START_SYSTEM_PROMPT", "TWITTER_END_SYSTEM_PROMPT", "SHARED"]:
             if not self.system_prompt:
                 raise ValueError("system_prompt is required")
@@ -259,7 +254,8 @@ def __init__(
         world_info: str = "",
         main_heartbeat: int = 15,
         reaction_heartbeat: int = 5,
-        task_description: str = ""
+        task_description: str = "",
+        game_engine_model: str = "llama_3_1_405b"
     ):
         self.game_sdk = sdk.GameSDK(api_key)
         self.goal = goal
@@ -272,7 +268,7 @@ def __init__(
         self.templates: List[ContentLLMTemplate] = []
         self.tweet_usernames: List[str] = []
         self.task_description: str = task_description
-        
+        self.game_engine_model: str = game_engine_model
 
     def set_goal(self, goal: str):
         self.goal = goal
@@ -298,8 +294,16 @@ def set_task_description(self, task_description: str):
         self.task_description = task_description
         return True
 
+    def set_game_engine_model(self, game_engine_model: str):
+        # Available models: llama_3_1_405b, deepseek_r1, llama_3_3_70b_instruct, qwen2p5_72b_instruct, deepseek_v3
+        self.game_engine_model = game_engine_model
+        return True
+    
     def get_task_description(self) -> str:
         return self.task_description
+    
+    def get_game_engine_model(self) -> str:
+        return self.game_engine_model
 
     def get_goal(self) -> str:
         return self.goal
@@ -378,7 +382,10 @@ def deploy_twitter(self):
             self.enabled_functions,
             self.custom_functions,
             self.main_heartbeat,
-            self.reaction_heartbeat
+            self.reaction_heartbeat,
+            self.tweet_usernames,
+            self.templates,
+            self.game_engine_model
         )
 
     def export(self) -> str:

src/game_sdk/hosted_game/sdk.py

@@ -90,7 +90,7 @@ def react(self, session_id: str, platform: str, goal: str,
 
         return response.json()["data"]
 
-    def deploy(self, goal: str, description: str, world_info: str, functions: list, custom_functions: list, main_heartbeat: int, reaction_heartbeat: int, tweet_usernames: list = None, templates: list = None, task_description: str = None):
+    def deploy(self, goal: str, description: str, world_info: str, functions: list, custom_functions: list, main_heartbeat: int, reaction_heartbeat: int, tweet_usernames: list = None, templates: list = None, game_engine_model: str = "llama_3_1_405b"):
         """
         Simulate the agent configuration
         """
@@ -104,9 +104,9 @@ def deploy(self, goal: str, description: str, world_info: str, functions: list,
                 "mainHeartbeat": main_heartbeat,
                 "reactionHeartbeat": reaction_heartbeat,
             },
-            "taskDescription": task_description
+            "gameEngineModel": game_engine_model
         }
-        
+            
         if tweet_usernames is not None:
             payload["tweetUsernames"] = tweet_usernames