Architor: Open-Source Tool for Phase-Gated Architecture Workflows with Claude Code
Architor is an open-source tool that transforms Claude Code into a phase-gated architecture assistant for structured engineering workflows. Instead of jumping directly from requirements to coding, it guides users through a systematic system design process.
Key Features
The tool structures the workflow into four distinct architecture phases:
- Requirement evaluation
- Architecture decisions (patterns, components, cross-cutting concerns)
- Component design (one by one, without losing track)
- Architecture validation and documentation
One critical feature is persistent architecture memory. Architor maintains decision tracking, phase progress, and design artifacts within an .arch workspace. This addresses the common problem in AI-driven workflows where conversations lose context and design reasoning disappears over time.
Each step in the process requires explicit acceptance from the user, and the reasoning behind decisions is systematically logged. This creates traceable, reviewable architecture decisions suitable for enterprise-grade development workflows.
The tool is available on GitHub at https://github.com/AhmedHabiba/architor and is designed for developers using Claude Code for engineering workflows who need more structured approaches to system architecture.
📖 Read the full source: r/ClaudeAI
👀 See Also
Running Two Claude Code Agents on the Same Repo with Git Worktrees
A Reddit user details how to run multiple Claude Code agents in parallel on the same codebase using git worktrees, avoiding file conflicts and enabling independent agent sessions.
PromptForest: Local-First Prompt Injection Detection with Uncertainty
PromptForest is a lightweight, local-first library that detects prompt injections and jailbreaks, classifying them while evaluating certainty, all without increasing average request latency.
Ephemeral OpenClaw setups with network sandboxing and auto-teardown
A setup that boots OpenClaw inside an ephemeral VM with a network egress allowlist, injects API keys into RAM-backed storage, and includes a 2-hour auto-teardown. All LLM calls are recorded to SQLite for replay.
APEX Testing Benchmark Results: Qwen 3.5 Performance on Real Coding Tasks
APEX Testing benchmark results show Qwen 3.5 models' performance on 70 real GitHub coding tasks, with the 397B version dropping to 1194 ELO on master-level tasks while GLM-4.7 quantized leads local models at 1572 ELO.