Changelog

aiflow follows Keep a Changelog and Semantic Versioning. The authoritative, always-current changelog lives in the repository: CHANGELOG.md.

0.1.1 — quality gates & senior-engineer agents

Highlights:

• implementer as senior engineer — mandatory pre-analysis (architecture, change impact, effort, complexity) before code; architecture-fit check with targeted refactoring; SOLID/DRY/KISS/YAGNI, testable by design (DI, deterministic); proven open-source frameworks and design patterns over self-implementations; no duplication, reusable/generic solutions; PO-level clarification questions with recorded decisions.
• reviewer as architect + quality gate (one agent) — architecture/design/risk review (layers, module boundaries, tech debt, over-/under-engineering, vulnerabilities, concurrency, breaking changes) plus an objective release checklist; verdict PASS or CHANGES REQUIRED; suggestions persisted as [suggestion] beads for the next loop.
• tester as test/QA engineer — negative/edge/boundary/exception/invalid-input tests plus test-quality audit (assertions, determinism, independence); runs adaptively when the pre-analysis flags high risk/complexity.
• Objective metric targets — 0 % new duplication, no new smells, 0 architecture violations, 0 linter/compiler warnings, 0 high/critical security findings (CLAUDE.md §3a table).
• Production readiness & architecture hygiene — production-ready output only (low-maturity tech flagged by reviewer + tester), small classes/KISS with divide & conquer + interfaces, monolith avoidance, state-of-the-art check (SOAP/XML/legacy-MQ requests are questioned as PO decisions), deliberate Redis/SQLite/Elasticsearch consideration.
• New checkers (on demand, outside the loop) — accessibility-checker (aiflow a11y-check, strict WCAG 2.2 AA → [accessibility] beads + E2E-a11y-tool recommendation) and modernization-advisor (aiflow modernize-check, brownfield modernisation concepts as a report for the architect: microservices, REST/cloud-native, git over svn, supported stacks, missing test frameworks).
• Quality gates (CLAUDE.md §3a) — static analysis on every implementation (tool or the agent itself), > 80 % line coverage + all non-static methods tested, unit + BDD end-to-end always, integration/system where sensible, leveled logging required.
• REST versioned + secured (§3b) — /api/v1/… from day one; OAuth2/OIDC, JWT, or managed API keys — Basic Auth is insufficient; every new/changed endpoint ships an IDE-testable .http file (host/port/test credentials from .env).
• Database rules (§3c) — new models: ≥3NF, real FKs, constraints, precise types, junction tables, no needless surrogate keys (R1–R20). Brownfield: existing schemas handled with care (shared consumers, rollback to older app versions) — improvement potential becomes recommendation beads, never side-effect fixes; commissioned changes need consumer check + rollback plan (B1–B8).
• Ralph loop — the implementer decides automatically from its pre-analysis; manual triggers win (/implement <bead> ralph|no-ralph, or a directive written into the issue).
• --no-token-saving — aiflow init / aiflow change-settings flag that switches caveman + rtk off for full, unfiltered output.
• Project aim — onboarder proposes an aim from brownfield code and asks the user to confirm; aim-writing guidance (where + how) added to READMEs, docs, and the CLAUDE.md template.
• Positioning — aiflow = one strong, universal base config with deliberately generic, customisable agents; ~70–80 % less configuration effort than starting Claude blank.
• Terminal GIFs — install, init Q&A, and change-settings demos in the READMEs and docs.
• New docs — AI Basics (plain-language primer) and the example-project walk-through (all defaults + first feature end-to-end); honest token framing (quality rules spend tokens; first-pass-production-ready saves them).

0.1.0 — first public release

Highlights:

• Setup — aiflow init interactive Q&A → .aiflow/config.json → renders the whole project; change-settings, install-deps, doctor; installer offers git/svn/Ollama.
• Version control & remotes — git / svn / none; token-based GitHub, GitHub Enterprise, GitLab, self-managed GitLab, Bitbucket, Forgejo, Gitea, or custom — with the matching host MCP wired.
• Models — Claude API key or OAuth; Ollama local models (qwen3-coder recommended); model routing.
• Memory — graphify structural graph + cocoindex-code semantic RAG + context7 docs + a retrieval routing policy; aiflow index refreshes both indexes.
• Team — shared Dolt issue graph, session-start auto-pull, atomic claiming, pull-before-push.
• Agents & workflow — delivery + audit + brownfield agents, slash skills, the Ralph loop.
• Quality, git & releases — Google style, Conventional Commits, enforcement hooks, branching models, aiflow release.
• Token savings — caveman + rtk on by default; graph/RAG retrieval; aiflow cost.
• Containers & CI/CD — Podman/Docker headless runs; ci.yml, release.yml, pages.yml.
• Docs & project — extensive README (EN/DE), this documentation site, MIT license, no funding prompts.

See the full list in CHANGELOG.md.