Report Abuse

Basic Information

Coze Loop is a developer-oriented, platform-level open-source solution for building, operating, debugging, evaluating and monitoring AI agents. It provides full-lifecycle management capabilities including prompt engineering, interactive playgrounds for prompt testing, version management, automated evaluation and experiment management, and observability that traces execution from user input through prompt parsing, model calls and tool execution. The open-source edition exposes core foundational modules so teams can deploy locally or in Kubernetes, integrate different LLMs via the Eino framework, and extend or customize components. The repository includes deployment artifacts, examples, SDKs in multiple languages, and documentation for architecture, model configuration, development and troubleshooting. It targets developers and platform teams who need standardized tooling and infrastructure to accelerate agent development and maintain operational stability.

Links

Categorization

App Details

Features
Key features include a prompt development module with a visual Playground for real-time debugging and comparison, prompt version management, and interactive testing. The evaluation subsystem supports managed evaluation datasets, automated multi-dimensional evaluators, experiment tracking and comprehensive statistics. Observability capabilities capture end-to-end traces via an SDK, record intermediate results and exceptions, and provide trace reporting and query. Model integration supports OpenAI, Volcengine Ark and other models through the Eino framework. Deployment tooling includes Docker Compose recipes and a Helm chart for Kubernetes, example projects, and make targets for common operations. The project also provides developer guides, architecture documentation, and community contribution guidelines under an Apache 2.0 license.
Use Cases
Coze Loop reduces friction in agent development by consolidating prompt authoring, testing, evaluation and monitoring into a single platform. Developers can iterate prompts in a Playground, compare outputs across models, and manage prompt versions to reproduce and refine behavior. Automated evaluators and experiment tracking accelerate validation and benchmarking of agents. Trace reporting improves observability and simplifies debugging of failures or tool interactions by recording each stage of execution. Prebuilt deployment recipes for Docker and Helm lower infrastructure setup time, while SDKs and examples enable integration into existing applications. The open-source edition permits customization and extension, encourages community contributions, and provides an on-premise option for teams that require control over models and telemetry.

Please fill the required fields*