ara-cli

ara-cli

ara-cli is a powerful, open source command-line tool for managing, structuring and automating software development artifacts in line with Behavior-Driven Development (BDD) and AI-assisted processes. With an intuitive interface and platform-independent implementation in Python, ara-cli enables teams to structure business goals, capabilities, features, user stories, and tasks, and to leverage integrated AI/chat capabilities for requirements engineering, documentation, and process automation.

Features

• Comprehensive Artefact Management:
  Create, edit, rename, delete, and list all core artefacts of the software development lifecycle: businessgoals, vision, capabilities, keyfeatures, features, epics, userstories, examples, and tasks.

• Structured Traceability:
  Organize and link artefacts for full traceability from business goals to implementation tasks. Effortlessly navigate artefact hierarchies and dependencies.

• Integrated AI and Chat:
  Interact with AI language models directly from your terminal. Use chat and prompt commands to assist with documentation, requirements refinement, and artefact management.

• Prompt Templates:
  Fetch, use, and manage reusable prompt templates for consistent and efficient requirements and documentation workflows.

• Artefact Status and User Management:
  Assign and query status and responsible users for artefacts to support project coordination and tracking.

• Automated Quality Assurance:
  Scan artefact trees for inconsistencies and automatically correct issues using integrated LLM-powered autofix functionality.

• Powerful Listing and Search:
  List artefacts and filter by type, tags, content, contributor relationships, file extensions, and more.

• Open Source & Platform Independent:
  Implemented in Python and available on PyPI for easy installation and integration into any workflow.

Use Cases

• Requirements Engineering:
  Capture and structure business requirements and user stories with clear traceability.

• Agile Development:
  Manage and automate backlog refinement, sprint planning, and task tracking.

• AI-Enhanced Productivity:
  Use chat and prompt features to accelerate documentation, code review, and knowledge management.

• Quality Management:
  Ensure artefact consistency and high documentation quality via automated scans and fixes.

Quick Start

Install from PyPI:

pip install ara-cli

Create your first feature artefact:

ara create feature login

List all features:

ara list --include-extension .feature

Chat with the integrated AI:

ara chat

Scan and autofix artefacts:

ara scan
ara autofix

Command Overview

Action	Description
create	Create a classified artefact with data directory
delete	Delete an artefact and its data directory
rename	Rename an artefact and its data directory
list, list-tags	List artefacts, show tags, filter by content, extension, hierarchy etc.
prompt, chat	Use AI-powered chat and prompt templates for artefact management
template	Print artefact templates in the terminal
fetch-templates	Download and manage reusable prompt templates
read	Output artefact contents and their full contribution chain
reconnect	Connect artefacts to parent artefacts
read-status, set-status	Query and assign status to artefacts
read-user, set-user	Query and assign responsible users
classifier-directory	Show directory of artefact classifiers
scan	Scan the ARA tree for incompatible or inconsistent artefacts
autofix	Automatically correct artefact issues with LLM assistance

See ara -h for the complete list of commands and usage examples.

Agent Commands

ara-cli includes powerful agent-based capabilities that can be accessed through the interactive chat. These agents can perform complex, multi-step tasks, such as conducting interviews or automating coding workflows.

To use the agent commands, first start an interactive chat session:

ara prompt chat <artefact_classifier> <artefact_name>

Once inside the chat, you can use the following commands to manage agents:

Command	Shortcut	Description
AGENT_RUN	a	Run an agent by name.
AGENT_STOP	as	Stop the currently running agent.
AGENT_CONTINUE	ac	Continue the agent's operation without new input.
AGENT_STATUS	astat	Show the status of the current agent.
exit		Exit from agent interfacto back to chat.

Example:

ara> a interview_agent

Important: The agent functionality requires the ara-agents package to be installed separately. If you do not have ara-agents installed, please contact the Talsen Team for assistance.

Artefact Structure

ara-cli organizes your project artefacts in a clear directory structure:

./ara/
   ├── businessgoals/
   ├── vision/
   ├── capabilities/
   ├── keyfeatures/
   ├── features/
   ├── epics/
   ├── userstories/
   ├── examples/
   ├── tasks/

Example Workflows

• Create a new feature and link it to a user story:

ara create feature payment contributes-to userstory checkout

• Read an artefact's content and its full parent chain:

ara read task implement_api --branch

• List tasks containing specific content:

ara list --include-extension .task --include-content "API integration"

• Automate prompt-based LLM interaction for a task:

ara prompt send task implement_api
ara prompt extract task implement_api

Requirements

• Python 3.8+
• Platform-independent; tested on Linux, macOS, and Windows

License

This project is open source and freely available under the MIT License.

Links

• PyPI: https://pypi.org/project/ara-cli/
• Source code: [GitHub link or repository URL]
• Documentation: [Link if available]

Contributing

Contributions, issues, and feature requests are welcome! Please open an issue or submit a pull request via GitHub.

ara-cli — Structure your development. Automate with AI. Build better software.