Social Agent (FUTURE)

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.

This leverages the Eliza agent framework and the X API with its enhanced Actions V2 architecture, allowing modular actions, robust error handling, sophisticated LLM integration, and efficient memory management for advanced, dynamic independent posts & responses.

Key functions (focused on X / Twitter interaction and analysis)

Market Intelligence & Content Generation (“AIXBT for Hyperliquid”)

• Objective: Monitor the Hyperliquid ecosystem and broader market, identify key trends and topics, and generate insightful, engaging content.

• Process (Actions V2 Data Flow):

1. 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.

2. Data Processing

   Filters and sorts collected data based on engagement metrics (likes, retweets, replies).

3. LLM Analysis

   Sends processed data to a Large Language Model (LLM) to identify emerging topics, sentiment, and significant trends within the Hyperliquid ecosystem.

4. 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.

5. 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.

6. Posting/Logging

   Posts the composed tweet to X or logs it if TWITTER_DRY_RUN mode is enabled.

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

• 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

• 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 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—to establish a dynamic and informative presence within the Hyperliquid community directly on social platforms.