generated: '2026-07-21'
method: searched
source: https://docs.artemis.turintech.ai/features/artemis-cli
name: Artemis CLI
binary: artemis
summary: >-
  Terminal-based access to the TurinTech Artemis code and AI optimization
  platform. Imports Git repositories, generates and validates AI-optimized
  code versions, benchmarks combinations to find optimal solutions, and opens
  optimization pull requests.
install:
  methods:
  - type: installer-script
    note: >-
      Automated installer script published in the Artemis quickstart; supports
      environment selection and on-premise base URLs. The installer download is
      protected by a published basic-auth credential documented in the
      quickstart (redacted here).
    env_flag_examples:
    - "--env prod"
    - "--env stg"
    - "--env dev"
    - "--base-url <on-prem-url>"
    installer_url: https://files.artemis.turintech.ai/artemis-cli/prod/artemis-cli-installer.sh
  - type: binary-download
    platforms:
    - linux
    - macos-intel
    - macos-arm
    - windows
command_groups:
- name: project
  purpose: Import and manage Git repositories
- name: key
  purpose: Manage Git authentication tokens (GitHub, GitLab, Bitbucket, Azure DevOps)
- name: target
  purpose: Select code constructs for optimization
- name: target version
  purpose: Generate, validate, score, and deploy AI-optimized code
- name: validation
  purpose: Execute compile, test, and benchmark checks
- name: optimisation
  purpose: Benchmark version combinations to find optimal solutions
- name: task
  purpose: Create single coding work items for AI agents
- name: plan
  purpose: Break complex features into structured task sequences
- name: chat
  purpose: Interactive AI conversations about codebases
- name: scan
  purpose: Security audits and code quality analysis
- name: model
  purpose: View available LLM models
- name: tui
  purpose: Launch interactive terminal dashboard
- name: config
  purpose: Manage CLI configuration
- name: status
  purpose: Display connectivity and configuration
version_generation_methods:
- method: llm
  description: Direct language model calls, ~30 seconds, lowest cost
- method: agent
  description: AI reasoning with context awareness, 1-5 minutes, medium cost
- method: ga
  description: Genetic algorithm with benchmark validation, 10-60 minutes, highest cost; requires a runner
global_flags:
- "--debug"
- "--config <path>"
- "--output-format"
- "-h/--help"
config:
  env_precedence: env vars > .env file > defaults
  required:
  - ARTEMIS_API_KEY
  endpoints_autoset_by_installer:
  - USER_MGMT_URL
  - AGENT_MGMT_URL
  - FALCON_URL
  - QUEST_URL
  - VISION_URL
  optional:
  - ARTEMIS_DEBUG
  - ARTEMIS_OUTPUT_FORMAT   # text | json | yaml
  - ARTEMIS_TIMEOUT
git_providers:
- github
- gitlab
- bitbucket
- azure-devops
example_flows:
  setup: |
    artemis status
    artemis config set ARTEMIS_API_KEY <your-api-key>
    artemis key add --name "github-work" --provider github --token <PAT>
  optimize: |
    artemis project import --git-url <repo> --key-id <key-id>
    artemis target add --project <id> --files <paths>
    artemis target version create --project <id> --target <id> --method llm
    artemis validation run --project <id> --worker <runner> --types compile,test
    artemis optimisation create --project <id> --all-enabled --runner <runner>
    artemis optimisation pr --id <opt-id> --best --title "Performance improvements"