Reading
A development workflow orchestration system inspired by OpenAI Symphony, integrating GitHub Webhooks and Linear to automatically schedule multi-agent teams for tasks like code review, bug fixing, feature development, etc.
Section 01
orch-agents is a development workflow orchestration system inspired by OpenAI Symphony. By integrating GitHub Webhooks and Linear, it automatically schedules multi-agent teams to handle repetitive tasks such as code review, bug fixing, feature development, and CI failure resolution. Its goal is to reduce engineers' time spent on these tasks so they can focus on more creative work.
Section 02
In software development, repetitive tasks like code review, bug fixing, and CI failure debugging take up a lot of engineers' time. Inspired by OpenAI Symphony and Linear Agent Interaction Guidelines, the orch-agents project aims to automate these workflows through agent orchestration, building a scalable system that monitors events from code repositories and project management tools, then schedules the appropriate AI agent teams to complete tasks.
Section 03
The system uses an event-driven design with the following flow: Webhook arrival → signature verification → input cleaning → event standardization → search for WORKFLOW.md → template matching → create agent workspace → sequential execution → quality review → submit results → clean workspace. It supports GitHub and Linear event sources, which are uniformly mapped to the internal IntakeEvent format.
WORKFLOW.md is the core configuration file, defining agent team templates (e.g., tdd-workflow corresponds to coder + tester) and event routing rules (e.g., opening a GitHub PR triggers github-ops, tagging an Issue with 'bug' triggers tdd-workflow).
Each agent runs in an independent Git worktree to ensure environment isolation, security boundaries, and rollback capability.
Section 04
The project includes over 18 built-in agents covering various development stages: architect (system design), coder (code implementation), tester (test writing), reviewer (code review), security-architect (security audit), etc. Each agent's role and specifications are defined via Markdown files in the .claude/agents/ directory.
Section 05
Connect to Linear via configuration files. When a card moves to an active state (unstarted/started), the corresponding workflow is triggered.
Runtime: Node.js 22+, Language: TypeScript, AI Engine: Claude Code, Tests: Over 730 cases, Deployment: Supports Docker and Cloudflare Tunnel.
Section 06
Agent calls consume Claude API tokens, which may lead to significant costs for large projects or high-frequency events.
AI-generated code may still have logical errors or improper boundary handling; critical code requires final manual review.
The routing rules and conditional judgments in WORKFLOW.md have a certain learning curve.
Section 07
orch-agents demonstrates the great potential of AI agents in software development workflows. Through reasonable orchestration and quality control, it significantly reduces engineers' time spent on repetitive tasks. For teams exploring AI-driven development processes, this is a project worth researching and trying. Its modular design and clear architecture also provide a good reference for building complex agent systems. It is recommended that interested teams deeply understand and practice this system.
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