ACO System: Open-Source Multi-Agent Pipeline from GitHub Issue to Merged PR
ACO System is an open-source multi-agent framework that runs the entire software development pipeline autonomously — from a GitHub Issue to a merged PR — using six specialized AI agents. Built by Aniket Karne, it's designed to eliminate context loss and handoff overhead typical in human-only development workflows.
Unlike LangChain, AutoGen, or CrewAI, ACO's agents do not communicate with each other directly. Instead, each agent reads from and writes to a shared database. Complexity lives in the schema, not in inter-agent logic. The pipeline runs with SQLite in development and Postgres in production, and ships with a live Kanban dashboard and streaming event feed for real-time agent observation.
Pipeline Stages
- PM agent writes the user story from a given idea.
- Planner agent breaks the story into tasks with estimates.
- Architect agent (hard gate) validates feasibility without using an LLM — it scans for hardcoded secrets, missing acceptance criteria, and invalid tech stack configuration. If the story fails, it never reaches a developer.
- Developer agent creates a branch and opens a PR.
- QA agent reviews the code and runs tests.
- Human gives final sign-off before merge.
The Architect gate is deterministic, not probabilistic. It enforces a hard set of rules: no hardcoded secrets, acceptance criteria must be complete, and tech stack must be consistent. This prevents hallucinated or unworkable PRs from ever being generated.
Why It's Different
Agents run independently via a shared database, not through agent-to-agent messages. This design keeps each agent simple and the overall system predictable. The framework is built in Python with a Next.js frontend for the dashboard.
Getting Started
The project is open source on GitHub. Clone the repo, run with SQLite locally, and connect to Postgres for production. The Kanban dashboard and event stream are live by default.
Who It's For
Dev teams building agentic CI/CD pipelines, researchers exploring multi-agent architectures, and anyone tired of context loss between handoffs.
📖 Read the full source: r/openclaw
👀 See Also
Claude File History: VS Code Extension for Tracking Claude Code Sessions
A VS Code extension called Claude File History tracks every Claude Code session that touched your files, allowing you to find past conversations, preview what was discussed, and resume conversations with a double-click.
Open-source pipeline turns Claude Code workflow into reusable skills
A developer who used Claude Code daily for 9 months has open-sourced a pipeline that structures feature development with checkpoints like functional documentation, technical documentation, complexity estimation, and security checks. The pipeline includes /new-feature and /bug-fix entry points that guide implementation.
Werld: Open-Ended Artificial Life Simulation with Evolving Neural Networks
Werld is a real-time artificial life simulation where agents with NEAT neural networks evolve their own neural architecture, sensory processing, and behaviors without hardcoded rules or reward functions. The simulation starts with 30 agents on a Watts-Strogatz small-world graph with 64 sensory channels, 7 continuous motor functions, and 29 heritable genome traits.
OpenCortex: A Self-Improving Memory System for OpenClaw
OpenCortex replaces OpenClaw's flat MEMORY.md file with structured memory files organized by projects, contacts, workflows, preferences, runbooks, tools, and infrastructure. It includes nightly distillation with principle enforcement audits and weekly synthesis with pattern detection and auto-runbook creation.