> For the complete documentation index, see [llms.txt](https://docs.aspis.finance/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aspis.finance/whitepaper/aidao/creation-flow.md).

# Creation Flow

## **1. Overview: Agent Creation Flow**

From the screenshots, you can see two main components:

1. **Workflow Input Trigger & Data Prep**
   * This is where the system fetches relevant environment variables, user inputs, and any scheduled tasks (e.g., a daily trigger).
   * The data includes on-chain info, crypto price feeds, or user parameters (like risk tolerance).
2. **Agent Configuration & Prompt Merging**
   * Here, you define your AI agent’s **persona** (style, niche, inspiration), plus the “base prompt” (the core instructions for how it behaves).
   * The system merges these elements—**promptBase**, **promptNotes**, **promptOutputFormat**—into one final set of instructions that shape your agent’s behavior.

Essentially, you’re **assembling** both the **functional** side (where does it get data, how does it execute trades, etc.) and the **personality** side (tone, style, comedic flair) into a single “agent recipe.”

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## **2. Defining the Agent: Persona & Style**

In the **“Twitter Persona”** or **“Persona”** blocks, you’ll see:

* **getNiche** / **editNiche**: Where you decide the agent’s domain focus, e.g., “AI trading,” “DeFi strategies,” or “meme coin alpha.”
* **getStyle** / **editStyle**: The agent’s communication style—funny, sarcastic, professional, or a mix.
* **getInspiration** / **editInspiration**: Additional references that shape the agent’s voice (like referencing your knowledge base or other role models).
* **mergePersona**: All these elements fuse into a single “persona” object.

**Why This Matters**:

* You can create an agent that’s purely serious and data-driven, or comedic and approachable like AIDAO.
* The persona system ensures **consistency** in how the agent speaks and interacts across various channels (Twitter, Telegram, etc.).

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## **3. Setting Up Data Sources & Tools**

Looking at the pipeline around **aspisManager** and **aspisReporter**, you can see references to **Onchain Data Collectors** and **Aspis Tools**:

#### **3.1 Onchain Data Collectors**

* Examples: **getETHUSDTInflow**, **getBTCUSDTInflow**, **getFearGreed**.
* These “tools” query block explorers, analytics APIs, or aggregator sites to gather real-time data—like token inflows/outflows or fear/greed indices.
* The system stores these results so the agent can interpret them before making decisions or generating messages.

#### **3.2 Aspis Tools**

* **getAspisRates**: Retrieve real-time yield, performance, or vault metrics from the Aspis platform.
* **aspisExecute**: The function that can place trades, rebalances, or other on-chain calls under your vault’s rules.
* **aspisGetFundInfo** / **getAspisVaultBalance**: Query the vault’s details—how many assets are allocated, current stake, etc.

**Why This Matters**:

* An agent’s intelligence hinges on the **breadth** of data. The more tools it has, the better it can reason (like checking inflows, computing fear/greed signals, or verifying your vault’s current state).

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## **4. Merging Prompts & Generating Output**

After the system merges:

1. **Base Prompt** (the agent’s overall mission/instructions).
2. **Persona** (its style, tone, domain).
3. **Aspis Info** (vault data, on-chain stats).

…the agent effectively has everything it needs to produce **an answer**, a **trade decision**, or a **tweet**.

You’ll notice steps like **promptAspis**, **promptBase**, **mergePrompt**, etc. This is where each piece of text or data merges into the final “prompt” fed to your chosen LLM (e.g., GPT-4, Claude). The LLM then returns a structured response, which can either be:

* **A tweet**: If you’re driving social media updates.
* **A Telegram message**: If you’re chatting with the agent in real time.
* **An on-chain command**: If the agent is rebalancing the Aspis vault.

***

## **5. Output & Execution**

Once the agent’s output is ready, a few modules handle the final steps:

* **aspisReporter**: Formats the final text into the style you want—maybe a short comedic post or a detailed explanation of the vault’s rebalancing.
* **sendTelegramReport** or **sendSMMTelegram**: Delivers the message to your chosen Telegram channels or your social feed.
* **aspisExecute** (in the Aspis Tools set): If the agent decided to actually **place a trade** or modify a vault, it calls this function to do so on-chain, respecting the vault’s constraints.

**Why This Matters**:

* The system doesn’t just produce text; it can follow through with real actions (assuming you allow it).
* Logging all these outputs (via **saveTweetPostgres** or similar modules in your pipeline) ensures you have a record of what the agent said or did.

***

## **6. Lifecycle: From Creation to Daily Operation**

1. **Create / Configure Agent**
   * Define persona, niche, style.
   * Select which data sources and on-chain tools the agent can access.
2. **Schedule or Trigger**
   * Decide how often the agent runs: daily for a summary, or on-demand if a user messages it.
   * Optionally set a “Schedule Trigger” for routine tasks like “9 AM daily market recap.”
3. **Data Gathering & Prompt Assembly**
   * The pipeline fetches environment variables, user input (“What’s my vault’s status?”), plus any aggregator data (on-chain, price feeds, social insights).
   * Merges base instructions + persona + fresh data into a single prompt.
4. **AI Reasoning**
   * LLM (GPT-4, Claude, etc.) processes it, decides on an answer or action.
   * For trading, it might propose “Buy $TOKEN with 5% vault allocation, rebalance from stablecoins,” or for SMM, it might produce a tweet like “Market mania is brewing…”
5. **Report & Execution**
   * The system either posts that tweet, sends a Telegram message, or calls **aspisExecute** to do the on-chain transaction.
   * All relevant logs and responses are saved for reference.
6. **User Feedback**
   * You or your community can chat with the agent, refine strategies, or change its persona settings at any time.
   * The cycle continues automatically, letting the agent handle day-to-day DeFi tasks or marketing updates.

***

## **7. Putting It All Together**

* **Agent Creation**: Use the **Behavior Pattern Forge** to shape how it responds to volatility or what style it uses in social posts.
* **Data Connectors**: Link up the **Onchain Data Collectors**, news feeds, sentiment APIs, so your agent remains well-informed.
* **Action Tools**: Provide the agent with the ability to execute vault trades or post tweets, so it’s not just generating text but truly automating your DeFi ops and marketing.
* **Monitoring & Logging**: Keep track of everything through the logs, Postgres saving, and Telegram updates. This gives full visibility into what your agent decided and why.

**Result**: A fully integrated flow where your AI agent is **born** (with a persona + dataset), **gathers** wide-ranging data, **reasons** about market moves, **communicates** that reason via Telegram or Twitter, and can even **deploy** on-chain trades, all under the secure governance of the **Aspis** vault infrastructure.

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