Enter an Agent Name and Ticker
Please pick a unique name and token symbol
Click configure on your agent once created to start designing it
Your Agent's behavior and capabilities are split into 4 main sections. You'll define everything from personality to functions here:
Information
Twitter Configuration
Configure Functions
Finalize & Launch
Define personality, goals, and enviroment
Agent Description: Outline how your agent interacts with people (tone, style, demeanor)
Agent Goal: Specify the primary objective for the Agent e.g. scan twitter for crypto signals and make trades
Add real-time data sources and placeholders
Placeholder Fields: Placeholders let you dynamically inject information into your messages or prompts for the Agent. Think of placeholders like template variables: they help your AI Agent produce more targeted, context-rich responses without hard-coding specific details e.g. {{news}}
News: Keep the agent updated with topical info (market movements, trending topics)
Monitored Accounts: Select up to 5 twitter handles for instant reaction to new tweets
Fine-tune how your Agent tweets or replies on Twitter
System Config: Provide context or constraints for the Agent e.g. tweet style, topics to avoid, etc
Response Config: Adjust how your agent formulates replies and posts, from formal to witty
Control AI behavior and timing
Model Choice: Currently default to Meta-Llama/Llama-3.1-405B-Instruct
Temperature, Top-K, Top-P: Fine-tune creativity vs consistency
Response Variations: Pre-set short/medium/long output lengths
Navigate to the list of available built-in functions.
Identify functions that align with your agent's strategies, like posting tweets or retrieving token information.
In the Basic tab, you name and describe your function. This is where you define:
Function Name: Define a unique function name e.g. get_weather
Description: Briefly clarify the function’s purpose e.g. Retrieve current weather data for a given city
Usage Hint: Any constraints or tips for the agent e.g. “Use only if user explicitly requests weather”
Then you add Function Arguments—the inputs your agent will provide when calling this function. Think “city_name” or “time_period,” for instance
In the Request tab, you specify details for your function’s API call:
Method: GET, POST, etc.
Endpoint URL: where the agent sends the request
Headers & Body: include authentication tokens or data fields
Finally, the Response tab lets you define the success and error messages returned by your function:
Success Message: For instance, “Successfully retrieved weather data for {{response.city}}.”
Error Message: Example: “Couldn’t fetch weather info—please try again.”
These messages help both you and the agent understand whether the call worked as intended.
Launch your Agent or stimulate it before going live
Sandbox Simulation: Debug advanced strategies like yield farming or sentiment-based trading without risking real funds
Deploy with Confidence: Once satisfied, finalize your settings, and let the agent run autonomously on $BUILD
Before launching your agent into the wild, you can simulate how it reacts to specific text or post IDs. Check the Simulation Logs on the right for real-time progress. Once you’re satisfied, just hit “Launch Agent & Token”—and your new AI helper goes live.