AI Frameworks · coze-dev

coze-studio

Coze Studio is an open-source, visual AI agent development platform built in TypeScript and Go that enables teams to create, debug, and deploy AI agents through no-code and low-code workflows. It provides integrated tooling for prompt management, RAG, plugins, and workflow orchestration, with deployment via Docker.

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Source: GitHub — github.com/coze-dev/coze-studio

21.1k

GitHub stars

3.1k

Forks

TypeScript

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	coze-dev/coze-studio
Owner	coze-dev
Primary language	TypeScript
License	Apache-2.0 — OSI-approved
Stars	21.1k
Forks	3.1k
Open issues	523
Latest release	v0.5.1 (2026-02-05)
Last updated	2026-04-20
Source	https://github.com/coze-dev/coze-studio

What coze-studio is

Golang backend with React + TypeScript frontend following microservices and domain-driven design (DDD) principles. Supports model integration (OpenAI, Volcengine), workflow execution, knowledge base management, plugin extensibility, and OpenAPI/Chat SDK for agent integration. Requires Docker/Docker Compose for deployment.

Quickstart

Get the coze-studio source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/coze-dev/coze-studio.git2cd coze-studio3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Rapid internal AI assistant development

Teams needing to build and iterate on AI agents quickly without engineering overhead. Visual workflow design and pre-built templates reduce time-to-deployment for customer support, knowledge retrieval, or task automation agents.

Multi-model AI orchestration

Organizations using multiple LLM providers (OpenAI, Volcengine, others). Centralized model service management and workflow composition enable non-engineers to leverage different models without code changes.

Low-code AI product customization

SaaS or enterprise teams building white-label or customized AI applications. Workflow builder, knowledge base integration, and Chat SDK enable rapid feature development and client-specific deployments.

Implementation considerations

Minimum system requirements: 2 Core CPU, 4 GB RAM. Docker and Docker Compose must be pre-installed and running; first deployment may take time to pull and build images.
Model service configuration is mandatory before agent/workflow creation; requires adding API keys and model endpoints (e.g., OpenAI, Volcengine) via admin panel.
Security: Publicly accessible deployments require threat modeling and mitigation for account registration, Python code node execution, SSRF, and horizontal privilege escalation. See README and FAQ for guidance.
Knowledge base, plugin, database, and prompt resources must be set up separately; integration with external data sources requires custom configuration.
Chat SDK integration supports embedding agents/apps in external applications; OpenAPI authentication uses Personal Access Tokens (no OAuth/SSO documented).

When to avoid it — and what to weigh

Production-critical compliance requirements — README explicitly warns of security risks in public networks (account registration, Python code execution, SSRF, privilege escalation). Comprehensive security hardening assessment required before production use; not recommended for regulated environments without significant defensive infrastructure.
Requires stable, long-term vendor support — Project created 2025-06-26 with latest release v0.5.1 (2026-02-05). Young codebase with 523 open issues suggests evolving API and potential breaking changes. Enterprise support and SLA guarantees are Unknown.
Need for advanced observability and monitoring — No documentation provided on logging, metrics, tracing, or alerting capabilities. Health check and operational insights maturity Unknown; may require custom instrumentation.
Existing tightly coupled vendor ecosystem — Heavy dependency on Coze platform conventions and integrations. If your stack requires deep integration with non-Coze services or proprietary LLM infrastructure, customization complexity is likely high.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license permitting commercial use, modification, and distribution provided copyright and license notices are preserved and changes are documented.

Apache-2.0 is a permissive license allowing commercial use. However, security warnings in the README (public network risks, Python execution, privilege escalation) mean a commercial deployment requires independent security assessment and hardening. Coze provides a commercial/managed version (coze.cn); the open-source edition is Community Edition with feature parity Unknown. Verify licensing of integrated dependencies (Go, React, third-party packages) for your commercial context.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

README explicitly lists security risks requiring assessment before public deployment: unrestricted account registration, Python code execution in workflow nodes, SSRF vulnerabilities, and horizontal privilege escalation in APIs. No mention of authentication hardening, rate limiting, audit logging, or secrets management. Project recommends security risk assessment before public network use; suitable for development/staging in isolated environments only until hardening is verified.

Alternatives to consider

LangChain / LangSmith

Open-source LLM orchestration with broader integration ecosystem and mature observability. Steeper learning curve; less visual drag-and-drop UI for non-engineers.

n8n / Make.com

Visual workflow automation platforms with extensive pre-built integrations and managed hosting options. Less AI-agent-specific; broader IT automation focus.

Hugging Face Transformers / vLLM

Lower-level model serving and fine-tuning frameworks. Requires more engineering; no visual workflow builder or agent abstractions.

Software development agency

Build on coze-studio with DEV.co software developers

Start with a local Docker deployment to test workflows and agent creation. Assess security implications carefully for production use. Contact us to discuss architecture fit, customization, and integration with your tech stack.

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coze-studio FAQ

Can I use Coze Studio in production without modification?

Not recommended without security hardening. README warns of public network risks (account registration, Python execution, SSRF, privilege escalation). Independent threat modeling and infrastructure controls are required.

What models and LLM providers are supported?

OpenAI and Volcengine are documented. Model service integration is pluggable; others require custom configuration. Exact supported list and API compatibility Unknown.

How is agent state and conversation memory managed?

Agents support memory features for user-specific conversation history. Detailed persistence, scaling, and retention policies are not documented; see official Coze docs and API Reference.

Is high availability or clustering supported?

Not documented. Architecture mentions microservices and domain-driven design, but HA, replication, and multi-node deployment patterns are Unknown.

Software development & web development with DEV.co

From first prototype to production, DEV.co delivers software development services around tools like coze-studio. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

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