Commonly — Open-source workspace for humans and AI agents to collaborate, each with memory, skills, and workstation.
Analyzed by Sai Pavan Gopularam · AI · Agent Orchestration · View on GitHub
- Stars: 1361
- Forks: 188
- Commits last 30 days: 100
- Health: Active (100 commits this month)
- Language: TypeScript
- License: Apache-2.0
What It Is
Imagine a shared project room or a Slack workspace, but specifically designed for a team that includes both humans and AI agents. Commonly is that space. Each AI agent gets its own 'seat' with a name, persistent memory, and specialized skills, just like a human team member, ensuring they don't forget past conversations or tasks.
This project kills the problem of disjointed AI tools or agents that constantly lose context. Instead of treating AI as a series of one-off prompts, Commonly lets you build persistent, specialized AI team members that work alongside humans, maintaining context and collaborating on tasks. Plus, being self-hostable, it removes per-agent fees and vendor lock-in.
License Verdict
Apache 2.0 License — Build and Sell Freely — Commercial Use Approved • Patent Grant • No Copyleft
The Apache 2.0 license permits you to use, modify, and distribute this software for any purpose, including commercial. You can incorporate it into proprietary products without revealing your source code. You must include the original license and any copyright notices.
How to Use It
Set up Commonly locally with Docker Compose. This command clones the repository, generates a local JWT secret, builds the application, and starts all necessary services. Access the web interface via `localhost:3000`.
Prerequisites:
- Docker
- Docker Compose v2 plugin
Estimated setup time: 10 minutes.
git clone https://github.com/Team-Commonly/commonly.git
cd commonly
./install.sh
curl --fail --silent http://localhost:5000/api/healthWhat I'd Build With This
AI Dev Team-in-a-Box for Small Teams (micro-saas)
Offer a specialized, self-hosted Commonly instance pre-configured with the "Built by Agents" team (Theo, Nova, Pixel, Ops, Cody). Target small dev shops or indie hackers who need an intelligent dev assistant but don't want to manage individual agents. They get a full AI dev team that triages, codes, and reviews, all within their own infrastructure.
Effort: 2 Weeks Build Time · Target: Indie Dev Teams, Small Agencies · Pricing: $199/month for self-hosted package + support
Managed Multi-Agent Collaboration Platform (saas)
Host Commonly as a managed service. Provide dedicated "pods" for teams, offering an easy way to onboard and manage various AI agents (e.g., custom agents, pre-built domain-specific agents). Charge based on team size, number of agents, or compute usage for managed agents. This removes the self-hosting burden for businesses.
Effort: 3 Months Build Time · Target: Mid-Market Tech Teams, AI-First Startups · Pricing: Starts at $499/month per team
Secure Internal AI Workflow Orchestration for Enterprises (enterprise)
Provide custom, on-premise or private cloud deployments of Commonly for large enterprises. Focus on integrating with their existing internal systems (e.g., Jira, ServiceNow, internal knowledge bases) and ensuring data privacy. Develop specialized agents for internal processes like IT support, compliance checks, or internal documentation generation.
Effort: 6 Months+ Build Time · Target: Large Enterprises, Government Agencies · Pricing: Custom Enterprise Contracts (e.g., $50k+/year)
Sai Pavan Gopularam's Take
This project nails the 'AI as a team member' vision by giving agents identity, memory, and a shared workspace. I see a huge opportunity here for a managed service that pre-configures specialized agent teams for specific business functions, easily fetching $999/month for a well-tuned marketing or support agent pod. The Apache 2.0 license means you can build that business without looking over your shoulder.
Watch Out For
- Complexity of Deployment: Commonly is a powerful system with many moving parts (MongoDB, PostgreSQL, Frontend, Backend, Agent Gateway, LiteLLM Proxy). Setting up, maintaining, and scaling a production instance requires significant DevOps knowledge.
- Agent Development Learning Curve: While it supports 'any runtime,' building effective agents that leverage all of Commonly's features (memory, skills, task board integration) requires understanding its API and agent runtime protocol.
- Resource Consumption: Running multiple active agents, especially those using large language models, can consume substantial compute and memory resources, leading to higher infrastructure costs if not optimized.
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