Coze Loop
A self-hosted platform for AI agent prompt development, evaluation, and execution tracing.
What does this agent do, and when should you use it?
Coze Loop is a developer-oriented platform for the AI agent lifecycle, spanning prompt development, debugging, evaluation, and monitoring. Its visual Playground supports interactive prompt testing, comparison of outputs from different LLMs, and prompt version management. The evaluation module manages evaluation sets, evaluators, and experiments for automated checks of prompt and Coze agent outputs, including accuracy, conciseness, and compliance. Its observability workflow uses SDK trace reporting and query to capture the path from user input through model calls and tool execution, including intermediate results and exceptions. The open-source edition can be deployed with Docker Compose or a Kubernetes Helm Chart, with model settings supplied in deployment configuration files.
After deployment, Coze Loop accepts prompt-debugging inputs in its Playground, calls configured models, and displays outputs for comparison; it also stores prompt versions. Its evaluation workflow uses evaluation sets, evaluators, and experiments to run automated tests and produce experiment-result statistics. Execution traces can be reported through an SDK, then queried in the platform to inspect user input, prompt parsing, model invocations, tool execution, intermediate results, and exceptions. For Docker Compose, users edit api_key and model in release/deployment/docker-compose/conf/model_config.yaml and run make compose-up; the Helm path uses release/deployment/helm-chart/umbrella/conf/model_config.yaml and make helm-up.
- A prompt engineer wants to interactively test a prompt and compare outputs from different LLMs before release.
- A team responsible for Coze agent quality needs evaluation sets, evaluators, and experiments to check accuracy, conciseness, or compliance.
- A platform or operations engineer needs to investigate model calls, tool execution, exceptions, and intermediate results within an agent request.
- A development team wants to self-host an agent-development and tracing platform in a Docker environment while configuring its own model credentials.
- A team with Kubernetes, Nginx Ingress, Kubectl, and Helm wants to deploy the platform through its Helm Chart.
What are this agent's strengths and limitations?
- Combines a prompt Playground, version management, evaluation sets/evaluators/experiments, and trace observability in one platform.
- Traces cover user input, prompt parsing, model calls, tool execution, intermediate results, and exceptions rather than only final answers.
- Offers two documented self-hosted deployment modes: Docker Compose and Kubernetes Helm.
- Explicitly supports OpenAI, Volcengine Ark, and other models, with multi-LLM support described through the Eino framework.
- Deployment requires configuring a model API key and model Endpoint ID, making a model service an external dependency.
- The Docker path requires Docker Engine; the Kubernetes path additionally requires a cluster, Nginx Ingress, Kubectl, and Helm.
- The documented public-network risks include account registration, listening-address configuration, SSRF, and some API horizontal-privilege-escalation concerns, so operators must assess and mitigate security exposure.
- The README does not provide a complete supported-model list for the open-source edition or describe feature differences across model configurations.
How do you install or deploy this agent?
Install and start Docker Engine. Run:
git clone https://github.com/coze-dev/coze-loop.git
cd coze-loop
Edit release/deployment/docker-compose/conf/model_config.yaml and set api_key and model; the README uses Volcengine Ark as its example. Then run:
make compose-up
The service starts in development mode by default. Open http://localhost:8082 in a browser.
How do you use this agent?
Before first use, configure a model API key and model Endpoint ID in model_config.yaml. After startup, open http://localhost:8082 and use the Playground to debug and compare prompt outputs; organize automated evaluation with evaluation sets, evaluators, and experiments; and query execution paths after integrating SDK trace reporting. For Kubernetes, configure release/deployment/helm-chart/umbrella/conf/model_config.yaml and templates/ingress.yaml, then run make helm-up.
How does this agent compare with similar options?
The README positions this project as the open-source edition of the commercial Coze Loop: it provides core foundational modules at no charge and is intended to be customized and extended for business needs.