---
title: "coze-loop"
type: "tool"
slug: "coze-dev-coze-loop"
canonical_url: "https://www.graphcanon.com/tools/coze-dev-coze-loop"
github_url: "https://github.com/coze-dev/coze-loop"
homepage_url: null
stars: 5590
forks: 772
primary_language: "Go"
license: "Apache-2.0"
categories: ["evaluation-observability", "ai-agents"]
tags: ["evaluation", "agent-evaluation", "eino", "ai", "agentops", "coze", "agent", "agent-observability"]
updated_at: "2026-07-07T18:39:30.599462+00:00"
---
# coze-loop
> Next-generation AI Agent Optimization Platform
Cozeloop provides full lifecycle management capabilities for AI agent development including development, debugging, evaluation, and monitoring.
## Facts
- Repository: https://github.com/coze-dev/coze-loop
- Stars: 5,590 · Forks: 772 · Open issues: 65 · Watchers: 34
- Primary language: Go
- License: Apache-2.0
- Last pushed: 2026-07-07T11:20:25+00:00
## Categories
- [Evaluation & Observability](/categories/evaluation-observability.md)
- [AI Agents](/categories/ai-agents.md)
## Tags
evaluation, agent-evaluation, eino, ai, agentops, coze, agent, agent-observability
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## README (excerpt)
```text
## What is Coze Loop
[Coze Loop](https://www.coze.cn/loop) is a developer-oriented, platform-level solution focused on the development and operation of AI agents. It addresses various challenges faced during the AI agent development process, providing full lifecycle management capabilities from development, debugging, evaluation, to monitoring.
Based on the commercial version, Coze Loop introduces an open-source edition that offers developers free access to core foundational feature modules. By sharing its core technology framework in an open-source model, developers can customize and extend according to business needs, facilitating community co-construction, sharing, and exchange, helping developers participate in AI agent exploration and practice with zero barriers.
## What can Coze Loop do?
Coze Loop helps developers develop and operate AI Agent more efficiently by providing full lifecycle management capabilities. Whether it is prompt engineering, AI Agent evaluation, or monitoring and optimization after deployment, Coze Loop provides powerful tools and intelligent support, greatly simplifying the development process of AI Agents and enhancing their operational performance and stability.
* **Prompt development**: The Prompt development module of Coze Loop provides developers with end-to-end support for writing, debugging, optimizing, and version management. Through a visual Playground, it enables real-time interactive testing of prompts, allowing developers to intuitively compare the output of different LLMs.
* **Evaluation**: The Coze Loop evaluation module provides developers with systematic evaluation capabilities, enabling automated multi-dimensional testing of prompts and Coze agents' output, such as accuracy, conciseness, compliance, and more.
* **Observability**: Coze Loop provides developers with observability for the entire execution process, fully recording every stage from user input to AI output, including key stages such as prompt parsing, model invocation, and tool execution, and automatically capturing intermediate results and exceptions.
## Feature list
| **Feature** | **Functional points** |
| --- | --- |
| Prompt debugging | *Playground debugging and comparison
* Prompt version management |
| Evaluation | *Manage evaluation sets
Management evaluator
* Manage experiments |
| Observation | SDK trace reporting
* Trace data observation |
| Model | Support integration with OpenAI, Volcengine Ark, and other models |
## Quick Start
>
> Refer to [Quick Start](https://github.com/coze-dev/coze-loop/wiki/2.-Quickstart) to learn in detail how to install and deploy the latest version of Coze Loop.
### Deployment method 1: Docker deployment (Docker Compose)
>
> Please install and start Docker Engine before you start.
Procedure:
1. Clone the source code.
Run the following command to obtain the latest version of the Coze Loop source code.
```Bash
# Clone the code
git clone https://github.com/coze-dev/coze-loop.git
# Enter the coze-loop directory
cd coze-loop
```
2. Configure a model.
1. Enter the `coze-loop` directory.
2. Edit the file `release/deployment/docker-compose/conf/model_config.yaml`.
3. Modify the api_key and model fields. Take Volcengine Ark as an example:
* api_key: Volcengine Ark API Key. Users in China can refer to the [Volcengine Ark documentation](https://www.volcengine.com/docs/82379/1541594), while users ou
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/coze-dev-coze-loop`](/api/graphcanon/tools/coze-dev-coze-loop)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_
