sina2266/Gozar — Self-hosted, Docker-first OpenAI-compatible LLM gateway with provider routing, fallbacks, API keys,…

Description and use

**Project Summary:** Gozar is an open-source, self-hosted LLM (Large Language Model) gateway that enables users to create repeatable agent workflows by integrating isolated prompts and tools. This project provides a robust platform for coordinating multi-agent workflows, adding tool-use pipelines, and standardizing agent memory.

**Value Proposition:** The main value of Gozar lies in its ability to turn isolated tools and prompts into efficient, repeatable workflows. This enables users to streamline their processes, improve productivity, and enhance the overall performance of their AI/ML applications.

**Practical Adoption Path:**

1. **Evaluation:** Interested users can easily evaluate Gozar by assessing its exposed implementation signals, such as API/SDK/CLI, language metadata, and focused topics.
2. **Setup:** Gozar is designed to be Docker-first, making it easy to deploy and manage. Users can set up the project using Docker containers, which provides a flexible and scalable environment for testing and development.
3. **Integration:** Once set up, users can integrate Gozar with their existing tools and systems, leveraging its provider routing, fallbacks, API keys, and usage controls to create customized workflows.

**Production Readiness:** Gozar is considered production-ready due to its recent activity, adoption, and ecosystem signals. With

Problem

sina2266/Gozar helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

Self-hosted, Docker-first OpenAI-compatible LLM gateway with provider routing, fallbacks, API keys, and usage controls.

Source