Skip to main content

​
Social Agent: The Interaction & Intelligence Layer

The Neko Social Agent is the primary interface between users and the Neko ecosystem on social platforms like X (formerly Twitter), Discord, and Telegram. It functions as both an interactive touchpoint and an intelligent engine for information gathering, analysis, and dissemination. Built upon the Eliza agent framework and utilizing the dedicated plugin-twitter package with its enhanced Actions V2 architecture, this agent leverages modular actions, robust error handling, sophisticated LLM integration, and efficient memory management to perform its functions.

​
Core Functions & Mechanisms

Social Agent Workflow The Social Agent executes several key functions, primarily focused on Twitter (X) interaction and analysis:
  1. Market Intelligence & Content Generation (“AIXBT for Hyperliquid”):
    • Objective: To monitor the Hyperliquid ecosystem and broader market, identify key trends and topics, and generate insightful, engaging content.
    • Process (Actions V2 Data Flow):
      • Data Collection: Utilizes specific actions like hyperliquidSearchAction (searching keywords) and hyperliquidUsersAction (monitoring ~27 key influencers) to fetch relevant tweets via the Twitter API. Can also leverage data fetched by other specialized actions (e.g., dune_apiAction, hypurrscanAction) via memory.
      • Data Processing: Filters and sorts collected data based on engagement metrics (likes, retweets, replies).
      • LLM Analysis: Sends processed data to a Large Language Model (LLM) to identify emerging topics, sentiment, and significant trends within the Hyperliquid ecosystem.
      • Multi-Source Synthesis (autoAction): Can intelligently merge context from various data sources (e.g., Twitter sentiment, Dune metrics, Hypurrscan activity) retrieved from memory to create a comprehensive analysis.
      • Tweet Composition: Uses a Template System (templates.ts) to structure prompts, incorporating the LLM analysis, data placeholders, agent personality/voice guidelines, and platform constraints. The LLM then generates the final tweet text.
      • Posting/Logging: Posts the composed tweet to X or logs it if TWITTER_DRY_RUN mode is enabled.
  2. User Engagement & Onboarding (Ad Network / Interaction Point):
    • Objective: Facilitate seamless user interaction, drive platform engagement, and simplify the onboarding process onto the Neko application.
    • Process:
      • Enables direct user commands via platform mentions (e.g., @neko_hl gneko on X triggers a tweet linking to the neko.fun application).
      • Plans for future implementation of an interactive iFrame X modal, potentially serving as an innovative ad format or mini-game to drive user acquisition.

​
Data & Intelligence Sources

The agent’s capabilities are fueled by diverse data inputs:
  • Twitter API: Accessed via specific actions (hyperliquidSearchAction, hyperliquidUsersAction) for tweet content and engagement data.
  • Dune API: Accessed via dune_apiAction for on-chain metrics (TVL, volume, etc.).
  • Hypurrscan API: Accessed via hypurrscanAction for detailed transaction data and blockchain metrics.
  • Elfa API: Potentially used for supplementary real-time sentiment analysis (confirm if still primary or augmented by internal LLM analysis).
  • Internal Memory Store: Used by autoAction to retrieve and merge context from different data-fetching actions.
  • User Interactions: Direct commands and mentions serve as explicit triggers.

​
Underlying Technology

Key technological components include:
  • Eliza Agent Framework: The core agent structure.
  • plugin-twitter & Actions V2: Provides modular, robust Twitter interaction capabilities.
  • Large Language Models (LLMs): Used for topic analysis, sentiment detection, and tweet composition.
  • Template System (templates.ts): Ensures consistent voice, formatting, and data integration in generated content.
  • AutoClient (packages/client-auto): Manages scheduled execution of actions (like autoAction) for automated posting.
  • Configuration System: Managed via .env, character JSON files, and runtime secrets (including TWITTER_DRY_RUN control).

​
Outcome

Leveraging a sophisticated architecture (Actions V2) and multiple data sources, the Neko Social Agent transcends simple interaction. It provides valuable, automated market intelligence, generates context-rich content by correlating on-chain data with social sentiment, and offers seamless onboarding pathways functioning partly as an ad network and interaction point. This establishes a dynamic and informative presence within the Hyperliquid community directly on social platforms.