> For the complete documentation index, see [llms.txt](https://docs.cod3x.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cod3x.org/terminal-overview/settings.md). # Settings ## Settings ![](/files/51YoFzkQinH7wlSRS1w2)\ The **Settings** tab is where you define, refine, and fine-tune how your agent behaves—both as a trader and as an AI personality.
*** ### Agent Status & Early Access At the top of the page you’ll see the **Agent Status** card: * Shows whether your agent is **Active** or **Deactivated** * Displays your **CDX balance** * Lets early supporters with **100,000 CDX on Base** claim **free early-access credits** to run the app This is the operational control center for starting or pausing your agent. *** ### Core Personality Shape how your agent communicates and responds: * **Personality Description** – The fundamental character (e.g., professional, degen, humorous). * **Communication Tone** – Formal, conversational, or anything in between. * **Response Length** – Concise or more elaborate, depending on your needs. Adjust these to give your trading agent a unique voice and presence. *** ### Thinking & Learning Define the way your agent reasons and improves: * **Direct** – Fast, simple tool calling. Best for straightforward tasks like checking a price or closing a position. * **ReAct** – Think → Act → Observe loop. The agent reasons, takes an action, observes the result, then decides the next step. Great for multi-step tasks. * **Chain of Thought** – Analyze → Plan → Execute. Sequential reasoning where each step builds on the last, producing a clear logical chain. Ideal for complex analytical tasks. * **Graph of Thought** – Multi-perspective branching analysis. The agent explores the problem from multiple angles simultaneously, then synthesizes the best insights into a final answer. Best for nuanced decisions. *** ### Trading Approach This is where you control the **guardrails for every trade**.\ The trading style and strategy defined during agent creation are stored here for easy editing: * Update risk parameters and execution logic * Select your preferred exchange * Refine trigger commands * Enforce TPs and SLs * Adjust growth targets and trading philosophy > **Important:** If you want to modify your agent’s trading strategy later, this is the place to do it. *** ### Optimizations Cod3x lets you fine-tune how each goal uses compute resources.\ These **Credit Usage Optimization** settings help reduce costs without affecting overall performance. * **Skip Final Chat Formatting**\ Returns raw model output instead of formatted text. much simpler output. * **Skip Step Completion Assessment**\ Bypasses additional verification logic after each step. This slightly increases speed and lowers credit use at the cost of skipping LLM self-checks. * **Enable LLM Model Auto-Selection**\ Allows Cod3x to automatically choose the most efficient model for each task based on complexity — using smaller models for simple steps and larger ones when needed. * **Enable AI Commit Messages**\ Allows AI to generate commit messages when making code changes, improving consistency accross versions. *** ### Advanced Parameters The **Advanced Parameters** section lets you fine-tune how your selected model behaves when running a goal.\ Cod3x supports hundreds of LLMs across providers like Anthropic, OpenAI, and xAI — each optimized for different types of reasoning and execution.
If you have **Enable LLM Model Auto-Selection** in the **"Optimizations"** modal, then you can select each LLM model for the 3 main reasoning steps of Cod3x' workflow. **Claude Haiku 4.5** as the Primary and Simple models and **Sonnet 4.5** as the Main Analysis model is a good balance between cost and good results. For optimal results use **Sonnet 4.5** on all three spots.\ Our internal testing shows that Sonnet offers the best balance between **analytical precision**, **execution stability**, and **cost efficiency**.\ However, users can freely switch to alternative models depending on preference, cost targets, or desired response style.
Our research has shown that **Haiku 4.5** + **Kimi** is a very cost effective solution with solid results as well. *** **Model Settings** * **Temperature** – Controls output creativity.\ Lower values produce focused, deterministic responses; higher values encourage more exploratory ideas. * **Top P** – Adjusts the probability range of word sampling, influencing output diversity and tone. * **Frequency Penalty** – Reduces repetition across responses, ensuring less echoing or redundancy. * **Presence Penalty** – Encourages the model to explore new topics and avoid looping around previous ideas. These parameters allow advanced users to shape model behavior for **consistent execution**, **custom style**, or **maximized reasoning depth** — depending on the task. > ⚙️ *Tip:* If you’re unsure where to start, use the default settings. Cod3x automatically optimizes these values for balanced performance. {% hint style="info" %} These settings won't be live during the beta version. {% endhint %} *** ### Feature Availability Many personality and reasoning features are still being finalized.\ Except for **Trading Approach**, which is fully live, some options (like deep adaptive learning or full social engagement) may remain partially functional during beta. --- # 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.cod3x.org/terminal-overview/settings.md?ask=&goal= ``` `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. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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.