raise-cli 3.0.0a4

RaiSE CLI - Reliable AI Software Engineering governance framework

RaiSE CLI

Reliable AI Software Engineering — a lean methodology and deterministic CLI toolkit that turns AI coding assistants into disciplined collaborators.

What is RaiSE?

RaiSE provides governance, memory, and structured workflows for AI-assisted software engineering. You decide what to build. Rai (your AI partner) executes with accumulated memory and calibrated judgment. RaiSE provides the discipline that makes the collaboration reliable.

The result: AI that learns from your project, follows your rules, and compounds knowledge across sessions instead of starting fresh every time.

Install

pip install raise-cli

Optional integrations:

# Jira + Confluence adapters
pip install "raise-cli[jira,confluence]"

# All extras
pip install "raise-cli[jira,confluence,csharp,observability]"

Requires Python 3.12 or 3.13.

Quick start

# Initialize a project
rai init

# Start a session (loads context, memory, proposes work)
rai session start --project . --context

# Build the knowledge graph
rai graph build

Then work through skills in your AI assistant (Claude Code, Cursor, Windsurf):

/rai-session-start       → Load context, propose focused work
/rai-story-start         → Scope a piece of work
/rai-story-plan          → Decompose into tasks
/rai-story-implement     → TDD: test, code, verify, commit
/rai-story-review        → Extract learnings
/rai-story-close         → Merge and clean up
/rai-session-close       → Capture session outcomes

Key features

Structured workflows

44 skills covering the full SDLC — epic, story, bugfix, discovery, session management, documentation, and release.

Knowledge graph

Project context, patterns, and cross-session memory — queryable and persistent.

rai graph query "testing patterns" --types pattern --limit 5

Backlog management

Built-in adapters for Jira and filesystem-backed backlogs.

rai backlog search "project = MYPROJECT AND status = 'In Progress'"
rai backlog create "Fix pagination bug" -p MYPROJECT -t Bug
rai backlog transition MYPROJECT-123 done

Documentation publishing

Publish to Confluence, local filesystem, or both (dual-write).

rai docs publish adr --title "ADR-045: Auth Architecture"

Quality gates

Automated checks before commits and releases.

rai gate check --all        # Run all gates
rai release check           # 10-point release checklist

Session continuity

Memory that compounds across sessions — patterns, calibration, coaching corrections.

rai session start --context  # Loads everything from prior sessions
rai pattern add "Always validate frontmatter before writing"

Multi-language discovery

Scan codebases and build knowledge graphs from: Python, TypeScript, JavaScript, C#, PHP, Dart, Svelte.

rai discover scan src/ --language python --output summary

CLI commands

72 subcommands across 17 groups:

Group	Commands
init	Initialize project governance
session	start, close, context, journal, doctor
graph	build, query, context
pattern	add, query
backlog	create, get, search, transition, update, link, comment
docs	publish, get, search
skill	list, validate, check-name, scaffold
discover	scan
adapter	list, check, validate, status
mcp	list, health, tools, call, install, scaffold
gate	list, check
doctor	diagnostics with --fix auto-remediation
release	check, publish

Documentation

• Getting Started — Install and run your first session
• Guides — Your First Story, Configuring Integrations, Bugfix Lifecycle
• CLI Reference — Every command, flag, and option
• Concepts — Memory, Skills, Governance, Knowledge Graph

License

Apache 2.0 — see LICENSE.