> For the complete documentation index, see [llms.txt](https://docs.neko.fun/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.neko.fun/architecture/agent-system/social-agent-future.md).

# 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.

<figure><img src="https://684504343-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fy6KZK4gpJ1V4VuGXtQO5%2Fuploads%2Ffh10THZXr3fF8UNv5UaL%2Fsocial_agent.png?alt=media&amp;token=de42fe8f-f34d-4f31-9401-549307bb8281" alt=""><figcaption></figcaption></figure>

## 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.