KI-Agenten
coze-studio
A visual all-in-one platform for building, debugging, and deploying AI agents.
★ 21,7 Tsd. SterneApache-2.0
Anleitung ansehen →coze-loop ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von coze-dev (coze-dev/coze-loop auf GitHub). Es hat 5.765 Sterne auf GitHub und 801 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 8. Oktober 2026 auf GitHub aktualisiert.
A full-lifecycle platform for developing, debugging, evaluating and monitoring AI agents.
Identify what you need to improve — prompt quality, evaluation coverage, or visibility into failed runs.
Clone the repo and prepare the Docker Compose deployment under release/deployment.
Edit model_config.yaml with your model provider's API key and endpoint/model ID.
Bring the services up and open the web interface to develop prompts, run evaluations and inspect traces.
Write, debug, optimize and version prompts in a visual playground with real-time interactive testing and side-by-side comparison.
Automated multi-dimensional evaluation of prompts and agent outputs — accuracy, conciseness, compliance and more.
Records the full execution process from user input to AI output, including prompt parsing, model invocation and tool execution, capturing intermediate results.
Development, debugging, evaluation and monitoring live together instead of in separate tools.
Runs via Docker Compose for a quick local deployment or via a published Helm chart on Kubernetes.
LLM integration is provided by the CloudWeGo Eino framework team, with a Go backend.
Clone Coze Loop and enter the project directory.
git clone https://github.com/coze-dev/coze-loop.git
cd coze-loopEdit the Docker Compose model config with your provider's API key and model/endpoint ID.
# Edit release/deployment/docker-compose/conf/model_config.yaml — set api_key and modelBring Coze Loop up with the provided make target and open the UI.
make compose-up
# Then open http://localhost:8082Coze Loop is maintained by coze-dev, the team behind Coze, with the Eino framework providing LLM integration.
The open-source version is Apache-2.0 licensed and free to self-host; Coze also offers hosted products separately.
Yes — you configure a model provider (the examples use Volcengine Ark / BytePlus ModelArk) with your own API key in model_config.yaml.
Those focus mainly on tracing and evaluation; Coze Loop covers the full lifecycle including a prompt development playground and version management, in a self-hostable Go platform.
Clone the repo, set your model in model_config.yaml, run make compose-up, and open http://localhost:8082.
A visual all-in-one platform for building, debugging, and deploying AI agents.
A framework for evaluating and improving AI agents.
Cross-platform desktop panel for managing OpenClaw and Hermes AI agents.
Management console for installing and monitoring AI agents and pipelines.
SimWorld: an Unreal Engine 5 simulator for evaluating LLM/VLM agents in physical and social worlds.
Build, run, and manage full agent platforms — not just single agents.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls coze-loop nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die coze-loop bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
A visual playground for agentic workflows: Iterate over your agents 10x faster
Enterprise-grade AI agent framework with multi-provider LLM management, secure knowledge bases and high-precision RAG, visual workflow orchestration, and mul…
An MCP integration platform that lets AI agents use tools reliably at any scale.
Vercel's open framework for building agents that run anywhere.
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI …
An open-source platform for AI-native application development — assistants, tools, RAG and models in one API.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.