Quick Start

1. Prerequisites
2. Install
3. Build a first project
4. Existing codebase (brownfield)?
5. Next

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Prerequisites

Node.js (LTS). Everything else aiflow can install for you.

Install

git clone https://github.com/Cyber93de/aiflow.git
cd aiflow

Windows (PowerShell):

./install.ps1            # creates the aiflow shim + adds bin to the user PATH

Linux / macOS (bash):

bash install.sh          # symlinks 'aiflow' onto your PATH

Per-OS details and demo GIFs: Installation. On every OS the installer asks once whether to also install git, Subversion (svn), and Ollama — so a later aiflow init only has to ask which Ollama models you want.

aiflow doctor               # what's present / missing (+ per-project summary)
aiflow install-deps --all   # install the rest of the toolchain (optional)

Packaged builds: github.com/Cyber93de/aiflow/releases.

Build a first project

mkdir my-app && cd my-app
aiflow init                 # interactive Q&A → writes .aiflow/config.json → renders everything
aiflow init --no-token-saving   # same, but with caveman + rtk off (full, unfiltered output)

aiflow init asks (Enter = the sensible default; token-saving + intensive graph memory are on):

1. caveman / rtk — token-saving output + CLI filtering.
2. graphify (structural graph) and cocoindex-code (semantic RAG).
3. task-master, filesystem MCP, context7 MCP.
4. Memory — persistent memory, graph learning, and intensity (default aggressive).
5. Claude access — apikey (ANTHROPIC_API_KEY) or oauth (claude setup-token).
6. Version control — git / svn / none.
7. Remote host — github | github-enterprise | gitlab | gitlab-self | bitbucket | forgejo | gitea | custom | none, plus which host MCP to wire. Token-based.
8. Sync rule — ask to push + Dolt-sync on each issue close; auto-pull at session start.
9. Ollama — set it up? which models? (qwen3-coder recommended).
10. Shared team preferences — code style, etc.
11. Project aim / architecture / OS / IDE, and the git branching model (if VCS = git).

Don’t skip the project aim — it’s the cheapest quality lever. The aim tunes Claude to your project: every agent reads it before planning or coding. Tell it to aiflow during init (question 11) or later via aiflow change-settings — or write it manually into .claude/memory/project-aim.md and CLAUDE.md §1. A good aim is 2–4 plain sentences: what the product does, for whom, the target architecture, and the quality bar. Example: “Order-management REST API for our internal shops. Hexagonal architecture on PostgreSQL. Correctness and auditability beat raw speed; every endpoint ships fully tested.”

Then fill secrets and start:

# edit .env → your git-host token + (ANTHROPIC_API_KEY or CLAUDE_CODE_OAUTH_TOKEN)
aiflow shell                # loads .env, launches Claude Code with all MCPs wired

Inside the session:

/beads:ready                          # what's ready to work
bd create "Add health endpoint" -t task --claim   # create + claim a task
/implement                            # implementer builds it (code + tests, Google style)
/review-ac                            # reviewer gates it against acceptance criteria

Existing codebase (brownfield)?

aiflow init detects it and offers aiflow onboard, which learns the code into .claude/memory/, CLAUDE.md, and arc42 docs so the agent starts informed — and proposes a project aim from the understanding it built. The proposal is not silently adopted: the onboarder asks you to confirm or correct it (headless runs mark it PROPOSED — please confirm in project-aim.md). Follow up with aiflow modernize-check for a modernisation report the architect can turn into beads:

Build the code indexes any time with aiflow index (graph + RAG).

Next

• Features · Memory · Configuration · Commands