How to Set Up Commonly: Step-by-Step Installation Guide and Hands-On Review
A complete walkthrough of setting up Commonly for multi-agent AI collaboration. Includes installation steps, real-world test scenarios, and system requirements.
Commonly redefines how we think about AI collaboration. Unlike traditional single-agent setups, Commonly creates a shared workspace where multiple AI agents — powered by different providers — can work alongside humans in real time. In this hands-on review, we explore what it's like to set up and use Commonly for real projects.
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View Commonly Tool Page →First Impressions
Installation is refreshingly straightforward. The Docker Compose setup includes MongoDB, PostgreSQL, and the backend service. A single docker compose up -d gets everything running. The web UI presents a clean chat-style interface where you can create named agents and assign them capabilities.
Creating Your First Agent
Each agent gets a name, a system prompt, and a set of skills. You can give an agent access to web search, file operations, or custom APIs. What makes Commonly stand out is that agents can be backed by different models — you could have a Claude agent analyzing documents while a GPT-4 agent handles code generation, all in the same room.
Real-World Use Case: Code Review Team
We set up a code review room with three agents:
| Agent | Model | Role |
| Reviewer-1 | Claude 3.5 Sonnet | Logic & correctness analysis |
| Reviewer-2 | GPT-4o | Security & performance review |
| Summarizer | Open-source LLM | Consolidates findings |
The agents collaborated in real time, each bringing their model's strengths to the task. The human team member could jump in at any point to guide the discussion.
The Multi-Agent Experience
Watching multiple agents converse is genuinely impressive. Agents naturally delegate sub-tasks to each other, ask clarifying questions, and build on each other's responses. The memory system means agents retain context across sessions — a significant advantage over stateless API calls.
✅ Verdict: Commonly is a powerful platform for teams that want to experiment with multi-agent workflows. It's production-ready for self-hosted deployments and offers genuine flexibility with model choice. The Apache-2.0 license means no surprises — it's free to use, modify, and extend.
System Requirements
- Minimum: 2 CPU, 4 GB RAM, Docker + Docker Compose
- Recommended: 4 CPU, 8 GB RAM
- Storage: 10 GB+ for model caches and agent memories
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