ACO System: Multi-Agent AI Pipeline from GitHub Issue to Merged PR

ACO System is a multi-agent framework that automates the full software development pipeline from GitHub Issue to merged pull request. It uses six specialized AI agents working independently through a shared database — no agent-to-agent communication, no dropped context.
Pipeline Overview
- PM Agent writes the user story from an issue.
- Planner Agent breaks the story into tasks with estimates.
- Architect Agent validates feasibility with a deterministic gate (no LLM, no hallucination risk).
- Developer Agent creates a branch, writes code, and opens a PR.
- QA Agent reviews the PR and runs tests.
- Human gives final sign-off before merge.
Key Design Decisions
Unlike LangChain, AutoGen, or CrewAI, the agents do not talk to each other. They all read and write through a shared database. Each agent runs independently. Complexity stays in the schema, not the logic.
Deterministic Architect Gate
The Architect gate is the feature the author is most proud of: it scans for hardcoded secrets, missing acceptance criteria, and invalid tech stack config. If anything fails, the story never reaches a developer — zero bad PRs.
Tech Stack
- Backend: Python
- Frontend: Next.js
- Database: SQLite in dev, Postgres in prod
- UI: Live Kanban dashboard and streaming event feed to watch agents in real time
Who It's For
Developers building in the agentic tooling space who want a practical, no‑babysitting pipeline that produces real artifacts.
The project is open source on GitHub: github.com/aniketkarne/aco-system
📖 Read the full source: r/openclaw
👀 See Also

Building a Self-Improving Knowledge System with Claude Code and Obsidian
A developer built a 25-tool system that gives Claude Code persistent memory through semantic search, knowledge graphs, and spaced repetition over an Obsidian vault. The system indexes content with bge-m3 embeddings, detects contradictions, auto-prunes stale notes, and generates Obsidian Canvas maps automatically.

Claude Code Routines: Automated Cloud Tasks for AI Development Workflows
Claude Code Routines allow developers to save Claude Code configurations as automated tasks that run on Anthropic-managed cloud infrastructure. Routines support scheduled, API, and GitHub triggers for unattended execution of prompts against repositories.

LLM Circuit Finder: Duplicate 3 layers to boost reasoning without training
A new toolkit finds 'reasoning circuits' in transformer models - contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicating these blocks (layers 12-14 in Devstral-24B) improves logical deduction from 0.22 to 0.76 on BBH benchmarks with no weight changes or training.

Two MCP Tools for Claude Code: Idea Validation and Trading Agent Memory
A developer built two MCP tools for Claude Code: idea-reality-mcp checks GitHub and Hacker News before coding to avoid duplicates, while tradememory-protocol provides memory for AI trading agents to store trades with context and track strategy performance. Both are open source and available on PyPI.