# Artemis Docs > User documentation for deploying and using Artemis, including Discovery tutorials for driving Artemis through the CLI with a coding agent. This file contains links to documentation sections following the llmstxt.org standard. ## Table of Contents - [FAQs](https://docs.artemis.turintech.ai/FAQs): The value-add in Artemis is in the scalability and credibility Artemis adds to the code reworking process. - [Artemis architecture](https://docs.artemis.turintech.ai/architecture/architecture-diagram): This is a high-level overview of the Artemis architecture: - [Embedding models in Artemis](https://docs.artemis.turintech.ai/architecture/embeddings-models): | Model | Description | Cloud Provider | - [LLMs in Artemis](https://docs.artemis.turintech.ai/architecture/llm-usage): Artemis supports a set of built-in LLM providers out of the box. You can also bring your own LLM by configuring a custom provider profile — see [Ad... - [Scoring models in Artemis](https://docs.artemis.turintech.ai/architecture/scoring-models): | Model | Description | Cloud Provider | - [Windows Local Installation Wizard](https://docs.artemis.turintech.ai/artemis-on-ai-pc/local-installation-wizard): > **Note:** This guide is specifically designed for Intel AI machines running Windows OS. - [Overview](https://docs.artemis.turintech.ai/artemis-on-ai-pc/overview): Artemis on AI PC enables you to run Artemis entirely on your local machine using Intel AI hardware. All AI processing, including large language mod... - [Make a Plan](https://docs.artemis.turintech.ai/build/Plan/Make-plan): In the **Build** section, switch the agent to **Plan** mode. Describe what you want to build in your own words, or pick one of the ready-made templ... - [Build and Validate the Plan](https://docs.artemis.turintech.ai/build/Plan/build-and-validate-plan): From the plan dashboard, execute the Agent-assigned tasks: - [Publish the Plan](https://docs.artemis.turintech.ai/build/Plan/git-actions): After reviewing and validating your changes, open a pull request straight from Artemis. - [Plan Overview](https://docs.artemis.turintech.ai/build/Plan/plan-overview): **Plan Mode** in Artemis helps you turn high-level ideas into complete, structured development roadmaps. Instead of working with isolated tasks, a ... - [Chats](https://docs.artemis.turintech.ai/build/chats): **Chat** mode is an interactive, conversational way to work with Artemis. Choose a model, ask anything about your project, and get real-time help —... - [Build Overview](https://docs.artemis.turintech.ai/build/overview): The **Build** section of Artemis helps you improve and extend your project with the help of agents. It has three modes — **Chat**, **Plan**, and **... - [Plans](https://docs.artemis.turintech.ai/build/plans): **Plan Mode** in Artemis enables you to create comprehensive development roadmaps by working interactively with AI agents. Unlike standalone tasks ... - [Code](https://docs.artemis.turintech.ai/build/standalone): Use **Code** mode in the Build section to hand Artemis a self-contained coding task — fix a bug, add tests, harden a security issue — and get back ... - [Deployment options](https://docs.artemis.turintech.ai/deployment/deployment-options): Currently there are five options for you to access Artemis. See details below: - [Artemis security scanning](https://docs.artemis.turintech.ai/deployment/image-scanning-security): Artemis private images are hosted on Dockerhub, available at: https://hub.docker.com/. - [Third-party services and licenses](https://docs.artemis.turintech.ai/deployment/licenses): > Latest update 24/10/2024 - [Windows Local Installation Wizard](https://docs.artemis.turintech.ai/deployment/local-installation-wizard): > **Note:** This guide is specifically designed for Intel AI machines running Windows OS. - [On-premise deployment guide](https://docs.artemis.turintech.ai/deployment/on-prem): Deploy Artemis on your own infrastructure, either as a single Docker/Podman host or on a Kubernetes cluster. This page covers prerequisites and lin... - [Kubernetes cluster](https://docs.artemis.turintech.ai/deployment/on-prem/kubernetes): Artemis combines public Helm charts (mostly Bitnami) with proprietary charts. A cross-platform CLI (Node.js, bundling `kubectl`, `helm`, and `skope... - [Single Machine / VM](https://docs.artemis.turintech.ai/deployment/on-prem/single-machine): Artemis ships as a set of Docker Compose files managed by the `./artemis` CLI (works with both `docker compose` and `podman compose`). - [Pre-deployment questionnaire](https://docs.artemis.turintech.ai/deployment/pre-deployment-qs-on-prem): 1. What is your preferred mode of accessing the platform, out of the following: - [SaaS onboarding questionnaire](https://docs.artemis.turintech.ai/deployment/pre-deployment-qs-saas): 1. How many users will be using Artemis? - [Pre-deployment questionnaire](https://docs.artemis.turintech.ai/deployment/pre-deployment-questions): 1. What is your preferred mode of accessing the platform, out of the following: - [Artemis Custom Runner](https://docs.artemis.turintech.ai/downloads/custom-runner-downloads): The Artemis Custom Runner is now distributed as a single **self-contained binary**, per operating system and architecture — no Python or tools bund... - [Artemis Skills](https://docs.artemis.turintech.ai/features/artemis-agent-skills): Skills are Claude Code plugins that guide you through setting up and configuring Artemis — directly from your terminal. They are powered by the [tu... - [Artemis CLI](https://docs.artemis.turintech.ai/features/artemis-cli): Command-line interface for the Artemis AI code platform. - [Artemis Custom Runner](https://docs.artemis.turintech.ai/features/artemis-custom-runner): A custom runner enables you to validate your code by running builds, tests, and benchmarks on your own machine. The Artemis Runner is a single self... - [Artemis Intelligence](https://docs.artemis.turintech.ai/features/artemis-intelligence): Artemis Intelligence is the optimization engine of the Artemis platform, developed by TurinTech. - [Changesets](https://docs.artemis.turintech.ai/features/changeset): Review, publish, and open pull requests for your changesets — code changes that live entirely in Artemis. Your git remote stays untouched until you... - [Index your codebase](https://docs.artemis.turintech.ai/features/code-indexing): Code indexing in Artemis allows you to create a comprehensive understanding of your codebase beyond just the specific files you've targeted for opt... - [Custom Metrics](https://docs.artemis.turintech.ai/features/custom-metrics): Track domain-specific measurements — throughput, accuracy, error rates, or anything your benchmark produces. Artemis already records runtime, CPU, ... - [MCP Servers](https://docs.artemis.turintech.ai/features/mcp-servers): Configure Model Context Protocol (MCP) servers for your account. Configured servers are available across all agent runs on the Artemis platform — c... - [Agents Memory](https://docs.artemis.turintech.ai/features/memories): Agents Memory keeps a record of every experiment Artemis has run across all your projects. You can ask it questions — what has worked, what hasn't,... - [Settings](https://docs.artemis.turintech.ai/features/settings-build): Configure your project's execution environment and project-level details from the **Settings** tab inside any project. - [Quick Overview](https://docs.artemis.turintech.ai/getting-started/quick-overview): Artemis is built around four modules — Build, Maintain, Discover, and Optimise — that share a single connection to your codebase. Every module read... - [Account Setup & Authentication](https://docs.artemis.turintech.ai/getting-started/signup-login): Create your Artemis account or log in to your existing account to start optimizing your code. This guide covers the complete account setup process,... - [Artemis Documentation](https://docs.artemis.turintech.ai/introduction): Artemis is an AI-powered code intelligence platform that helps engineering teams build, maintain, discover, and optimise their codebases — through ... - [Artemis Standard T&Cs](https://docs.artemis.turintech.ai/legal/artemis-tnc): These Standard Terms (SaaS) are made between Turing Intelligence Technology Limited (Company No. 10318035) whose registered office is at Unit 2G, 2... - [Actions on an Optimized Version](https://docs.artemis.turintech.ai/optimization/code-optimization/actions-on-versions): Learn how to work with your optimized code versions using the available actions in Artemis. - [Creating an Optimization](https://docs.artemis.turintech.ai/optimization/code-optimization/creating-an-optimization): Learn how to create and configure an optimization in Artemis to find the best-performing combination of your code versions. - [Code Optimization Overview](https://docs.artemis.turintech.ai/optimization/code-optimization/overview): Artemis provides a **powerful optimization engine** that helps you discover the most effective combinations of code versions to improve performance... - [Reviewing Optimization Results](https://docs.artemis.turintech.ai/optimization/code-optimization/reviewing-results): Learn how to read an optimization's results in Artemis and pick the best-performing combination of your code versions. - [Agent Targeting](https://docs.artemis.turintech.ai/optimization/code-targeting/agent-targeting): Agent Targeting is the default, agent-driven way to find optimisation targets. Instead of scanning every file yourself, you describe your goals to ... - [Chat with Agent](https://docs.artemis.turintech.ai/optimization/code-targeting/chat-with-agent): Chat with Agent lets you have an open conversation with Artemis about your codebase. Before you commit to any targets — or let the agent change any... - [File-by-file targeting](https://docs.artemis.turintech.ai/optimization/code-targeting/file-by-file): Prefer to hand-pick exactly what Artemis works on? File-by-file targeting lets you browse your repository and turn specific files into optimisation... - [Code Targeting Overview](https://docs.artemis.turintech.ai/optimization/code-targeting/overview): Artemis can work with codebases of any size, from small projects to large enterprise applications. Code targeting lets you extract a focused subset... - [Score your targets](https://docs.artemis.turintech.ai/optimization/code-targeting/scoring-original): After analyzing your codebase and extracting targets, the next step is to **score** them. - [Semantic Search](https://docs.artemis.turintech.ai/optimization/code-targeting/semantic-search): Semantic Search is a method for extracting targets by finding similar patterns and potential duplicates across your repository. - [Code Scoring](https://docs.artemis.turintech.ai/optimization/code-validation/code-evaluation-with-scoring): You can generate **new scores** for different versions of a target. - [Code Validation Overview](https://docs.artemis.turintech.ai/optimization/code-validation/code-validation-overview): After generating alternative code versions for your targets, Artemis provides tools to help you decide which versions are best to integrate into yo... - [Review and Compare](https://docs.artemis.turintech.ai/optimization/code-validation/review-and-compare-new-versions): After Artemis generates optimized versions of your targets, you can **review and compare** them using several built-in tools. - [Validate New Versions](https://docs.artemis.turintech.ai/optimization/code-validation/validation-and-performance-metrics): Validation ensures that generated versions not only **look correct** but also **function as intended**. - [Experiments](https://docs.artemis.turintech.ai/optimization/discover/experiments): An **Experiment** is the main unit of Discovery — a distinct approach the agent tries against your objective, each carrying a **confidence score**.... - [Goals](https://docs.artemis.turintech.ai/optimization/discover/goals): **Goals** are the fitness criteria every version is scored against — they define what "better" means for your run. Each goal has a **direction** (M... - [Graph](https://docs.artemis.turintech.ai/optimization/discover/graph): The **Graph** is the main view of a Discovery run — a visual map that grows from your objective into the experiments the agent tries and the versio... - [Discover](https://docs.artemis.turintech.ai/optimization/discover/overview): **Autonomous multi-file evolution** - [Discovery tutorials](https://docs.artemis.turintech.ai/optimization/discover/tutorials/overview): These tutorials help you and your coding agent learn how to drive Discovery through the Artemis CLI. Rather than documenting commands in isolation,... - [Get started with Particle Life](https://docs.artemis.turintech.ai/optimization/discover/tutorials/particle-life-example): This example gets you familiar with using a coding assistant to drive Artemis. You will use the Artemis skills and CLI to import a ready-made C++ p... - [Make a repository ready](https://docs.artemis.turintech.ai/optimization/discover/tutorials/particle-life-harness-example): This example assumes you have completed [Get started with Particle Life](./particle-life-example.md): skills loaded, CLI authenticated, Git key ava... - [Verifying commands on a runner](https://docs.artemis.turintech.ai/optimization/discover/tutorials/particle-life-runner-commands-example): This example assumes you have completed [Get started with Particle Life](./particle-life-example.md): skills loaded, CLI authenticated, Git key ava... - [Steer a running discovery](https://docs.artemis.turintech.ai/optimization/discover/tutorials/particle-life-steering-example): This example assumes you have completed [Get started with Particle Life](./particle-life-example.md). Your Particle Life project, runner, commands,... - [Versions](https://docs.artemis.turintech.ai/optimization/discover/versions): A **Version** is the actual code diff an experiment produced. Each version is built, tested, and benchmarked on your runner, then **Scored** agains... - [Optimise Overview](https://docs.artemis.turintech.ai/optimization/overview): Artemis provides a comprehensive system for improving your codebase across performance, quality, security, and any other metrics you define. - [Agent-based Generation](https://docs.artemis.turintech.ai/optimization/version-generation/agents): Agent-based generation uses specialized AI agents to analyze, optimize, and generate improved versions of your code. This approach leverages multip... - [Generation with Artemis Intelligence](https://docs.artemis.turintech.ai/optimization/version-generation/artemis-intelligence): Artemis Intelligence is the built-in AI system that provides intelligent code analysis, optimization, and generation capabilities. This system comb... - [Generation Steps](https://docs.artemis.turintech.ai/optimization/version-generation/generation-steps): Before diving into the methods, here’s the general workflow: - [LLM-based Generation](https://docs.artemis.turintech.ai/optimization/version-generation/llms): LLM-based generation leverages Large Language Models to understand, analyze, and generate optimized versions of your code. This approach uses advan... - [Version Generation Overview](https://docs.artemis.turintech.ai/optimization/version-generation/overview): Artemis provides automated tools for creating **alternative implementations** of your optimization targets. - [Git Integration](https://docs.artemis.turintech.ai/project-setup/git-keys): Manage your git integrations from **Platform Settings → Integrations → Git**. - [Import your codebase](https://docs.artemis.turintech.ai/project-setup/import-codebase): This page covers how to bring a code repository into Artemis. - [Project Overview](https://docs.artemis.turintech.ai/project-setup/overview): The **Overview** tab is your project's home page — a single place to see everything happening across Artemis and jump straight into any part of it. - [Benchmarks](https://docs.artemis.turintech.ai/project-setup/settings/benchmarks): Configure how Artemis measures your code's performance from the **Settings → Benchmarks** tab. Benchmark results are what Artemis uses to compare v... - [General](https://docs.artemis.turintech.ai/project-setup/settings/general): Manage your project's general settings from the **Settings → General** tab. This is where you name the project, review its Git connection, and cont... - [Runner and Scripts](https://docs.artemis.turintech.ai/project-setup/settings/runner-and-scripts): Configure the execution environment and the scripts Artemis uses to compile and verify your code from the **Settings → Runner and Scripts** tab. - [Version 1.10.0](https://docs.artemis.turintech.ai/release-notes/v-1.10.0): *Release date: 26 February 2025* - [Version 1.11.0](https://docs.artemis.turintech.ai/release-notes/v-1.11.0): *Release date: 9 May 2025* - [Version 1.4.0](https://docs.artemis.turintech.ai/release-notes/v-1.4.0): Artemis includes a range of LLMs for code analysis and code optimisations tasks. We have added the following to our current list of LLMs: - [Version 1.6.1](https://docs.artemis.turintech.ai/release-notes/v-1.6.1): We've created Artemis Intelligence for genetic code optimisation at snippet-level. For those of you who know our work, we pioneer the use of geneti... - [Version 1.8.3](https://docs.artemis.turintech.ai/release-notes/v-1.8.3): _Release date: 16 December 2024_ - [Version 1.9.2](https://docs.artemis.turintech.ai/release-notes/v-1.9.2): *Release date: 22 January 2025* - [Version 2.0](https://docs.artemis.turintech.ai/release-notes/v-2.0): *Release date: October 2025* - [Version 2.1](https://docs.artemis.turintech.ai/release-notes/v-2.1): *Release date: December 1, 2025* - [Version 2.5.1](https://docs.artemis.turintech.ai/release-notes/v-2.5.1): *Release date: March 26, 2026* - [Version 2.6.0](https://docs.artemis.turintech.ai/release-notes/v-2.6.0): *Release date: April 27, 2026* - [Version 2.6.2](https://docs.artemis.turintech.ai/release-notes/v-2.6.2): *Release date: May 25, 2026* - [Version 3.0.0](https://docs.artemis.turintech.ai/release-notes/v-3.0.0): *Release date: July 22nd, 2026* - [Artemis Sample Repository Guide](https://docs.artemis.turintech.ai/sample-projects/sample-projects-guide): This guide introduces the sample repositories available for testing Artemis features. - [Issues](https://docs.artemis.turintech.ai/scan/issues): **Issues** are the results generated by a Maintain run with one or more enabled rules. - [Maintain Overview](https://docs.artemis.turintech.ai/scan/overview): The **Maintain** feature in Artemis provides automated code auditing and quality analysis. It allows developers to define custom or pre-built rules... - [Rules](https://docs.artemis.turintech.ai/scan/rules): Rules define the checks that guide a Maintain run. Each rule captures a focused quality standard, pattern, or constraint that Artemis can evaluate ... - [Admin Settings](https://docs.artemis.turintech.ai/settings/admin-settings): Admin Settings are available to administrators only and provide controls for managing users, governing model access, setting agent limits, and moni... - [User Settings](https://docs.artemis.turintech.ai/settings/user-settings): User Settings let you configure agent limits, manage contexts, connect integrations, and monitor system health. - [ALE Benchmark Optimization](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/ale-bench): **Goal:** Optimize AI agents for competitive programming using the ALE Benchmark through evolutionary prompt engineering and advanced search strate... - [Building an Anomaly Detection Tool](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/anomaly-detection): Here we will demonstrate how to use the Artemis Planning Agent to build a command-line tool for anomaly detection in tabular data. The starting poi... - [Building a Clustering Tool](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/clustering): Here we will demonstrate how to use the Artemis Planning Agent to build a command-line tool for clustering analysis in tabular data. The starting p... - [Feature Generation with Artemis and evoML](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/feature-engineering): **Goal:** Generate domain-specific features for credit default risk classification using Artemis Intelligence's genetic algorithm - [Livestock Health Monitoring System](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/livestock-app): TThis use case demonstrates how the Artemis Planning Agent transformed a simple idea— - [Productionising a Spam Email Classifier](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/ml-classification-project): This project demonstrates how to use Artemis' Planning agent to expand a basic ML pipeline into an informed, production-grade, deployable machine l... - [Building a Regression Tool](https://docs.artemis.turintech.ai/use-cases/ai-ml-solutions/regression): This guide demonstrates how to leverage the Artemis Planning Agent to create a command-line regression tool for tabular data. Beginning with a basi... - [Building Optimisation Agents with Artemis](https://docs.artemis.turintech.ai/use-cases/code-generation-enhancement/build-optimisation-agent): How can we build an agent to optimise the performance of software with Artemis? Performance optimisation presents a unique challenge in the landsca... - [Evolutionary Dashboard Development](https://docs.artemis.turintech.ai/use-cases/code-generation-enhancement/dashboard_generation): Here we demonstrate how a data scientist or a business analyst can use Artemis Intelligence to rapidly generate dashboard apps and enhance either a... - [Agentic Chat](https://docs.artemis.turintech.ai/use-cases/local-deployment/agentic-chat): Use this guide to explore your codebase using Artemis Agentic Chat, powered by RAG (Retrieval-Augmented Generation) and the on-device Qwen 3 4B mod... - [Code Audit](https://docs.artemis.turintech.ai/use-cases/local-deployment/audit): Follow this workflow to run comprehensive code audits on a locally deployed Artemis instance that uses the on-device Qwen 3 4B model for AI assista... - [Standalone Optimisation](https://docs.artemis.turintech.ai/use-cases/local-deployment/optimisation): Use this guide to run the Standalone Optimisation workflow on a locally deployed instance of Artemis that is powered by an on-device Qwen 3 4B model. - [Agentic Planning (BETA)](https://docs.artemis.turintech.ai/use-cases/local-deployment/planning): Use this workflow to build strategic delivery plans with Artemis Planning Mode. All interactions run on your locally deployed instance using the Qw... - [Migrating a Credit Risk Model from SAS to Python](https://docs.artemis.turintech.ai/use-cases/migration/sas-to-python): :::tip Videos: See the Complete Migration Workflow - [Makefile Optimization](https://docs.artemis.turintech.ai/use-cases/performance-system-optimization/makefile/makefile-optimization): **Project Name:** Wavelet Transform - [Math Odyssey Optimization](https://docs.artemis.turintech.ai/use-cases/performance-system-optimization/math-odyssey-original-cost): **Goal:** Optimize CrewAI agents for 37% cost reduction on mathematical problem-solving through prompt and parameter optimization - [NanoChat Optimization](https://docs.artemis.turintech.ai/use-cases/performance-system-optimization/nanochat): NanoChat is a lightweight, educational LLM framework created by Andrej Karpathy that provides a minimal, end-to-end pipeline for building language ... - [Optimizing Uber's Zap Logging Library](https://docs.artemis.turintech.ai/use-cases/performance-system-optimization/zap-uber-opt): {/* :::tip Video: See the Complete Workflow