🚀 Open Source: Multi-Agent AI Orchestrator

[ParthivPandya]

Discussion Type

Product Feedback

Discussion Content

built a proof-of-concept showing how multiple AI agents can collaborate to automate software development workflows.

The system converts plain English requirements into production-ready code through a pipeline of autonomous agents.

Current agent roles include:
• Requirement Analyst
• Architecture Planner
• Developer Agent
• Code Reviewer
• Testing Agent
• Deployment Agent

Tech stack: Next.js, TypeScript, Groq, Vercel AI SDK.

GitHub repo:
https://github.com/ParthivPandya/multi-agent-orchestrator

Curious to hear thoughts from others working on agentic AI and multi-agent systems.

[Anghel01]

This is a really interesting proof-of-concept! The pipeline approach with specialized agents for each stage of development makes a lot of sense — it mirrors how real dev teams are structured.
A few thoughts:
The Code Reviewer and Testing Agent combination is probably the most valuable part of this. Having a dedicated review step before testing catches architectural issues early rather than at the test stage.
One challenge I'd be curious about: how do the agents handle context handoff between steps? In multi-agent systems, information loss between agents is often where things break down, especially when the Architecture Planner's decisions need to be respected by the Developer Agent later in the pipeline.
Also, using Groq for inference is a smart choice for speed — latency in agentic pipelines adds up fast when you have 6+ sequential steps.
Would love to see how this scales with more complex requirements. Great work sharing this openly!

[ParthivPandya]

Hi @Anghel01 , According to your suggetion i have implemented agents to handle context handoff between steps. Please check and let me know if you have more suggetions.

[Nyrok]

The multi-agent pipeline approach is solid. One thing that compounds well with this kind of orchestration: the quality of the input requirements fed into the Requirement Analyst agent. Plain English works, but adding explicit structure (role, objective, constraints, examples) tends to reduce misinterpretation downstream across the full pipeline.

I've been building flompt (https://flompt.dev) for exactly this, a visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. Pairs well with agent pipelines. Open-source: github.com/Nyrok/flompt