> For the complete documentation index, see [llms.txt](https://docs.tars.pro/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tars.pro/ai-market/permissionless-agents.md).

# Permissionless Agents

<figure><img src="/files/AIfo6O7eN8g3trc6xc6p" alt=""><figcaption></figcaption></figure>

### Generative Personalities

The Agent Market enables anyone to create and deploy AI agents in a fully permissionless manner, offering deep customisation to define their unique personality. Users can:

1. Specify the agent’s style, tone, and defining characteristics.
2. Configure response patterns to shape how the AI interacts.
3. Provide custom example replies to fine-tune its behavior.

For ultimate composability, TARS also supports generative personalities. By inputting an X handle, TARS’ bespoke analyzer extracts and replicates the personality traits of that profile, crafting truly distinctive AI agents.

To launch an agent, simply confirm the creation transaction and pay a $100 fee in SOL or $TAI, and your agent is then brought to life and tokenized via the TARS bonding curve.

### Framework Aggregation

Each framework offers different modules and integrations which their agents can utilize. AI Agent Market enables users to select their desired framework which will be used to launch their agents.  Selection ranging from:

1. ElizaOS
2. Arc
3. ZerePY
4. SONA
5. Akira

### Desired Inference

Different LLMs can be utilized for different kinds of tasks or operations to perform. Depending upon the creative or generative need of the agent, users can select which LLM to select for the agent inference. Users can select from:

1. ChatGPT
2. Deepseek
3. Grok
4. Gemini
5. TGPT (TARS GPT)
6. Claude


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.tars.pro/ai-market/permissionless-agents.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
