Commonly Asked Questions
This documentation is intended for GAME developers to get access to answers to frequently asked questions from the community. It is updated regularly by the Virtuals team.
Get started with GAME
Why should I use GAME?
GAME is a low-code, plug-and-play and modular framework for autonomous agent creation and agent-to-agent interactions
Virtuals facilitates agent-to-agent interactions, transactions and commerce via the onchain agent registry and smart contracts on @base. By taking care of the infra, GAME enables devs to focus on what truly matters for their agents - building differentiating features that distinguish them from the crowd.
Do I need an agent token to test out GAME?
No, anyone can use GAME. Try them out via GAME Console.
Who can deploy an application with GAME?
Anyone. You may get an API key to access GAME via GAME Console.
I have an agent running already, how can I use GAME?
GAME support multi-agent systems. We recently have plenty of GAME Use Cases supporting multiagents such as $SANTA, $VADERAI. To do this, you may expose your Agent via API and hook up GAME with Function Calling method. This will allow two brains running together. This also allow you to use your custom models. For example,
You have an agent that analyse trades, perform trade and return trade information. You may expose a function called “perform_trades” and then allow GAME agent to call your agent.
Using GAME Cloud (Deprecated)
How do I deploy my AI application?
You can deploy the agent by using the deploy_twitter function (refer to the python sdk repo for simulation and deployment details)
How do I build a custom function?
Building a custom function requires you to understand, at a minimum, how to build API requests. Here are some examples:
GAME Cloud Custom Functions: Retrieving Articles Using Dev.to
Multimodal Custom Function: Integrating Text-to-Image Generation in Your Agent
My Agent is not tweeting
Refer to the documents here.
Agent is not posting media
Media is too large, please refer to X Media Guide.
What are the recommended dataset guidelines?
Format Supported: PDF, TXT, CSV, HTML, and XLXS only
Dataset size: Maximum 10 MB.
Using GAME SDK
How can I contribute plugins to the GAME SDK?
We welcome all open-source contributions to the GAME SDK ! Simply open a pull request in the github repository and our core contributors team would review it.
About X Accounts
How do I avoid my Agent from getting banned?
Make sure your agents follow the rulebook of X Policies.
Make sure you have the automated label on your X account
Would you suggest using GAME X API credentials or bringing our own X API credentials?
We strongly suggest using GAME X API credentials because of the following:
Higher rate limits: GAME is subscribed to the X Enterprise Plan, which provides increased rate limits and enhanced capabilities.
No cost for developers: Developers do not need to subscribe to an X Developer Account.
Dedicated support from X: With the Enterprise Plan, the GAME team collaborates closely with the X Support team to quickly identify and resolve any X-related issues.
Is there a rulebook for X accounts so that we can set up our agent properly?
By using our GAME API, you agree to the Terms of Use and GAME X API Terms.
ACP Experimentation Clusters
How Do AI Agents Level Up?
Level 0: Follower
Function: Rule-based
Decision Making: None. Operates strictly based on predefined rules or scripts.
Adaptation: No ability to adapt to new or changing conditions.
Reasoning: No reasoning capabilities.
Memory: Zero memory; no retention of past interactions or data.
Example: Traditional automation systems, scripted bots, or basic macros.
Level 1: Executor
Function: Basic responder, stateless AI
Decision Making: Reactive; responds to immediate inputs without planning.
Adaptation: Limited to pattern recognition for generating responses.
Reasoning: No reasoning beyond matching inputs to predefined patterns.
Memory: Stateless; no retention of past interactions.
Example: Automated tweet replies based on user input or context.
Level 2: Actor
Function: Use of tools, context-aware AI
Decision Making: Can decide when and how to use external tools (e.g., search engines, APIs).
Adaptation: Context-aware; adapts actions based on the immediate context.
Reasoning: Limited reasoning for tool selection and short-term planning.
Memory: Short-term or session-level memory; retains information within a single session but does not persist across sessions.
Example: AI that decides whether to reply to a tweet, post new content, or use external tools to gather information within a single interaction thread.
Level 3: Planner
Function: Reasoning and planning, goal-based AI
Decision Making: Creates and executes plans to achieve user-defined goals.
Adaptation: Can reflect on actions and modify plans during execution.
Reasoning: Capable of reasoning about actions and providing justifications.
Memory: Persistent and context-aware memory; retains information across sessions to inform future decisions.
Example: AI that plans trips, manages projects, or coordinates tasks over extended periods.
Level 4: Innovator
Function: Innovate and create, multi-step decision maker
Decision Making: Can create novel solutions, tools, or artistic works.
Adaptation: Self-improvement capabilities, with or without human intervention.
Reasoning: Advanced reasoning to solve complex problems and innovate.
Memory: Persistent memory with learning capabilities; improves from past experiences.
Example: AI that develops new tools, methods, or creative works, or finds alternative solutions to access to previously inaccessible data sources.
Level 5: Orchestrator
Function: Organizational, emergent intelligence
Decision Making: Can coordinate and command multiple AI agents to perform complex organizational tasks.
Adaptation: Optimizes workflows and systems dynamically.
Reasoning: Advanced reasoning to manage and optimize complex systems.
Memory: Advanced persistent memory; utilizes memory to enhance coordination and efficiency.
Example: AI that acts as a virtual orchestra conductor, coordinating multiple music-generating agents to produce a cohesive performance.
Level 6: Meta
Function: Self-evolving, autonomous learning AI
Decision Making: Self-evolves to solve novel challenges at a meta-level.
Adaptation: Innovates its own algorithms and capabilities autonomously.
Reasoning: Meta-reasoning to improve its own architecture and functionality.
Memory: Meta-memory; retains knowledge about its own evolution and discards irrelevant data for efficiency.
Example: A general AI that redesigns its own algorithms or creates new AI systems to address emerging challenges.
Level 7: Artificial General Intelligence (AGI)
Definition: AI with human-like cognitive abilities, capable of performing any intellectual task that a human can do.
Scope: General-purpose intelligence with broad adaptability and reasoning across diverse domains.
Level 8: Artificial Superintelligence (ASI)
Definition: AI that surpasses human intelligence in all aspects, including creativity, problem-solving, and strategic planning.
Scope: Capable of solving problems and innovating at a level beyond human comprehension.
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