How to Deploy Coze Studio with Docker: Self-Hosting Tutorial
Step-by-step tutorial to self-host Coze Studio with Docker Compose: requirements, setup, first agent, and troubleshooting.
π Want to deploy Coze Studio yourself?
Docker configs, system requirements, and installation guides β all on one page.
View Coze Studio Tool Page βBefore you start
Coze Studio ships as two containers: the server (backend API) and the web (frontend). Both images are published on Docker Hub and have hundreds of thousands of pulls, so they are production-proven.
System requirements
| Resource | Minimum | Recommended |
|---|---|---|
| CPU | 2 cores | 4 cores |
| RAM | 4 GB | 8 GB |
| Disk | 10 GB free | 20 GB SSD |
Step 1 β Clone the repository
git clone https://github.com/coze-dev/coze-studio.git cd coze-studio
Step 2 β Configure environment
Copy the example environment file and set your database, Redis, and LLM provider credentials. Coze Studio supports OpenAI-compatible endpoints, so you can plug in virtually any model provider:
cp .env.example .env # edit .env β set DB, REDIS, and LLM_API_KEY
Step 3 β Start with Docker Compose
docker compose up -d
This starts the server on port 8080 and the web frontend. Wait for the containers to become healthy, then open http://localhost:8080.
Step 4 β Build your first agent
Once the UI loads, create a new agent, pick a model, and start adding plugins or designing a workflow. Use the built-in debugger to test conversations, then publish your agent to a web app or API endpoint.
Troubleshooting
- Port already in use: change the host port mapping in
docker-compose.yml. - Agents not responding: verify your LLM API key and network access to the model endpoint.
- Slow first load: the web container builds static assets on first start; allow a few minutes.
π Want to deploy Coze Studio yourself?
Docker configs, system requirements, and installation guides β all on one page.
View Coze Studio Tool Page β