Coze Studio is a visual AI agent and workflow development platform with no-code/low-code design tools, knowledge base integration, plugin support, and an open-source core engine. It requires external LLM services such as OpenAI or Volcengine for model inference and carries specific security risks if exposed to public networks.
Project overview
The project provides a microservices-based, domain-driven design platform derived from a commercial system, enabling developers to visually build, publish, and manage executable AI workflows.
Project type
RAG
Use cases
Knowledge Q&A
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Developers building and deploying AI agents and automated workflows using a visual canvas, provided the deployment is restricted to protected network environments.
Users needing to integrate domain expertise via knowledge bases and third-party services via plugins into their AI agents.
Key capabilities
Enables developers to quickly create and debug agents using no-code or low-code approaches via visual design tools.
Provides a visual canvas to build, publish, and manage executable workflows by dragging and dropping nodes to structure data flow and task processing.
Manage the model list and integrate services such as OpenAI and Volcengine.
Create and manage knowledge bases to resolve model hallucination and add domain expertise to agents.
Create and manage plugins to extend agent capabilities with third-party services.
Create and publish apps, building business logic through workflows.
Create conversations, initiate chats, and integrate agents or apps into external systems via API and SDK.
Enables agents to generate more accurate responses based on a user's historical conversations during interactions.
Limitations and risks
Security risks exist for public network deployment, including account registration, SSRF, and API privilege escalation.
Requires a configured large language model service, such as OpenAI or Volcengine. Local orchestration of the platform depends on these remote inference endpoints.
Getting started
Clone the repository, run make web and docker compose up. This process requires Docker Compose setup and post-deployment model configuration before use.
Register an account at http://localhost:8888/sign, configure an external large language model service such as OpenAI or Volcengine at http://localhost:8888/admin/#model-management, and visit http://localhost:8888/ to begin.
Alternatives and comparisons
A PHP-native agentic framework providing a workflow architecture for building and orchestrating production-ready AI agents.
Unifies AI agent orchestration and workflow automation into a single platform with centralized management and enterprise-grade security.
A self-hostable, source-available automation platform where AI is the execution model, allowing users to visually build and observe AI workflows.