Coze Studio
A visual, self-hosted environment for building, debugging, publishing, and integrating agents and workflows.
- Source repo
- coze-dev/coze-studio
- Stars
- ★ 22k
- Last updated
- 3mo ago
- License
- Apache-2.0
- Primary language
- TypeScript
- FA score
- 45/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A team that has configured OpenAI, Volcengine, or another supported model service and wants to build an agent in a visual environment can add the model through model management first.
- Not a fit if
- Users wanting zero-config plugins; official plugins need third-party auth keys
- Teams needing commercial-only features such as tone customization
- Source review
- 45/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
Coze Studio is an all-in-one AI agent development platform for creating, debugging, and deploying agents, applications, and workflows. It includes model services plus development resources for prompts, RAG, plugins, knowledge bases, databases, and workflows; agents can be configured with workflows and knowledge bases. Its backend is written in Golang, its frontend uses React and TypeScript, and the architecture is described as microservices following domain-driven design principles. The model service manages a model list and explicitly names integrations such as OpenAI and Volcengine. Community Edition API and Chat SDK interfaces cover conversations and workflows, as well as embedding agents or apps into another application.
After deployment, a user registers in the web UI, adds a model at /admin/#model-management, then creates and publishes agents, apps, or workflows. An agent can be configured with resources such as workflows and knowledge bases, while workflows use a visual canvas with drag-and-drop nodes to structure data flow and task processing. The platform also creates and manages plugins, knowledge bases, databases, and prompts, and authenticates the documented API and Chat SDK with a Personal Access Token. It starts from the repository directory with make web; the “Container coze-server Started” message indicates success, after which the UI is available at http://localhost:8888/.
- A team that has configured OpenAI, Volcengine, or another supported model service and wants to build an agent in a visual environment can add the model through model management first.
- An application developer who needs to compose executable business logic can build and publish a workflow by dragging nodes onto the workflow canvas.
- A product team building an end-user assistant can configure an agent with workflows, knowledge bases, and plugins as resources.
- An engineering team embedding a published agent or app into an existing local business system can use the Personal Access Token-authenticated API and Chat SDK.
- A developer prototyping a self-hosted low-code AI product can start the service locally with Docker and Docker Compose, then register through the web UI.
How do you install or deploy this agent?
Prerequisites: at least 2 CPU cores and 4 GB of memory; Docker and Docker Compose must be installed and the Docker service running. On macOS or Linux: git clone https://github.com/coze-dev/coze-studio.git && cd coze-studio && make web. On Windows, clone the repository, enter coze-studio, run cp ./docker/.env.example ./docker/.env, then run docker compose -f ./docker/docker-compose.yml up. Initial startup can pull and build images; “Container coze-server Started” indicates the service started. Then register an account at http://localhost:8888/sign.
How do you use this agent?
After registration, add a model at http://localhost:8888/admin/#model-management; the supplied documentation says model service configuration is required before building agents, workflows, and apps. Visit http://localhost:8888/ to create an agent, app, or workflow, and configure an agent with resources such as workflows and knowledge bases. Official plugins require plugin configuration and authentication keys for third-party services. The API and Chat SDK use a Personal Access Token; the supplied material does not provide a copyable API request example or token-creation procedure.
What are this agent's strengths and limitations?
- Combines agents, apps, workflows, plugins, knowledge bases, databases, and prompts in one visual development environment.
- Provides a drag-and-drop workflow canvas for structuring data flow and task processing.
- Offers both Community Edition API and Chat SDK paths for conversations, workflows, and embedding agents or apps in another application.
- Its stated Golang backend, React + TypeScript frontend, and DDD-oriented microservices architecture give technical teams a concrete basis for evaluating customization.
- Deployment requires Docker and Docker Compose, and the first startup pulls and builds images.
- A model service must be configured before use; official plugins also require third-party authentication keys.
- Public-network deployment carries explicitly documented risks around account registration, Python execution environments in workflow code nodes, listener configuration, SSRF, and some API horizontal privilege escalations.
- The open-source and commercial editions differ; tone customization is explicitly identified as commercial-only.
How does this agent compare with similar options?
The supplied material distinguishes the open-source Community Edition from commercial Coze: tone customization is explicitly limited to the commercial version, but no full feature-by-feature comparison is provided.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Coze Studio This agent | 45 · Major gaps | Self-hosted serviceFree + model costs | ★ 22k | 3mo ago | TypeScript | OpenAI API |
| elizaOS | 88 · Good | CLIFree + model costs | ★ 20k | 1d ago | TypeScript | — |
| Deuz SDK | 69 · Some gaps | Library / SDKFree + model costs | ★ 508 | 20d ago | TypeScript | OpenAI API · Claude API |
| Giselle | 43 · Major gaps | Self-hosted serviceFreemium | ★ 559 | 3d ago | TypeScript | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: Apache-2.0 license, security reporting channel, but no specific mechanisms for least privilege, user confirmation, or data flow transparency. Deductions: no explicit permission controls, user confirmation flows, or data flow documentation.
Evidence shows: CI pipeline with tests and lint, but no specific failure message examples. Deductions: dependency availability not detailed, failure messages not documented.
Evidence shows: README identifies target users (developers) and use cases (AI agent development), provides environment requirements (2 cores, 4GB, Docker). Deductions: trigger precision not specified, environment fit only mentions Docker.
Evidence shows: README well-structured, installation steps, FAQ link, license, contribution guidelines. Deductions: naming stability not specified, versioning/changelog not provided.
Evidence shows: Visual development tools lower barrier, templates and frameworks provided. Deductions: cost-benefit not quantified, but overall value evident.
Evidence shows: Feature list and architecture description, but no specific test results or independent verification. Deductions: claims lack traceability, facts and inferences not clearly separated.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity not verified, proceed with caution.
- Public deployment has security risks, assess and mitigate.
- Dependency security not detailed, recommend checking for vulnerabilities.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v0.5.1 V0.5.1
Release notes excerpt
Image Version > cozedev/coze-studio-server:0.5.1 > cozedev/coze-studio-web:0.5.1 What's Changed * feat: if .venv python not found ues system python by @fanlv in https://github.com/coze-dev/coze-studio/pull/2404 * feat: update readme for model managerment by @fanlv in https://github.com/coze-dev/coze-studio/pull/2405 * feat: add conversation retrieve api & return section id field in chat… by @junwen-lee in https://github.com/coze-dev/coze-studio/pull/2408 * feat: optimize embedding configuration to support omitting model dims by @fanlv in https://github.com/coze-dev/coze-studio/pull/2412 …View on GitHub ↗ -
Release notes excerpt
Image Version > cozedev/coze-studio-server:0.5.0 > cozedev/coze-studio-web:0.5.0 New Features * Support integration with official Coze.cn plugins. * Add configuration management dashboard( via http://127.0.0.1:8888/admin ). What's Changed * feat(singleagent): openapi v3/chat additional message support assista… by @junwen-lee in https://github.com/coze-dev/coze-studio/pull/2067 * refactor(infra): move parse_address to es package by @fanlv in https://github.com/coze-dev/coze-studio/pull/2163 …View on GitHub ↗ -
Release notes excerpt
Image Version > cozedev/coze-studio-server:0.3.16 > cozedev/coze-studio-web:0.0.3 New Features * Chatflow Support: We've support the chatflow feature in workflow, agent, and app scenarios. * ChatSDK Upgrade: Upgraded the usage method of ChatSDK and the function of conversation management * Infra: Elasticsearch supports multi-node setup * Infra: milvus support to use token as auth * Infra: Oceanbase Vector Sql Prevent SQL injection And Complementary helm deployment Fixes * Plugin: refresh oauth access token …View on GitHub ↗