Self Hosted LLM Gateway and Feature Rich Chat UI with RBAC

Description and use

**Brief Summary (2‑3 sentences)**
The Self‑Hosted LLM Gateway with a feature‑rich chat UI and RBAC layer provides a ready‑to‑run backend for routing queries to any hosted LLM and a polished front‑end for interactive chat, complete with role‑based access control. It lets teams prototype AI‑driven features, build Retrieval‑Augmented Generation (RAG) pipelines or agent workflows, and evaluate model tooling without assembling a custom stack from scratch. Because the project is actively maintained (last update 2026‑07‑06) but has limited integration metadata, a quick security and dependency audit is recommended before production use.

**Value**

• **Accelerated prototyping** – The gateway abstracts away LLM provider details (OpenAI, Anthropic, local models, etc.) while the UI gives non‑technical stakeholders a usable chat interface out of the box.
• **Access control** – Built‑in RBAC lets you expose different model capabilities or data sources to specific user groups, a feature often missing in DIY setups.
• **Extensible workflow base** – With webhook hooks and a modular routing layer, you can layer RAG, tool‑calling agents, or custom preprocessing without rewriting the core server.

**Practical Adoption Path**

1. **Clone & review** – Fork the repo, inspect the license, read the README, and run the provided Docker Compose file locally.
2. **Configure back‑ends** – Add API keys or endpoint URLs for the LLMs you plan to use (e.g., OpenAI, a local Ollama instance).
3. **Define RBAC policies** – Edit the rbac.yaml (or equivalent) to map roles to allowed models, prompts, or data sources.
4. **Integrate with your product** – Embed the UI via an iframe or use the gateway’s REST/WebSocket API from your own front‑end.
5. **Test & audit** – Run security scans, verify dependency versions, and exercise the RBAC rules with test accounts.
6. **Deploy** – Promote the Docker containers to your staging environment, then to production once the audit passes.

**Production Readiness**

• **Maturity**: Medium. The codebase is recent and functional for internal prototypes, but the ecosystem signals (e.g., extensive docs, CI status, issue triage) are sparse.
• **Risks**: Limited public integration documentation, unknown long‑term maintenance cadence, and potential licensing ambiguities.
• **Mitigations**: Conduct a dependency audit, lock versions via a requirements.txt/package-lock.json, monitor the upstream repo for updates, and consider contributing missing docs or tests to improve confidence.

Overall, the project is a solid foundation for internal AI tooling or early‑stage products, provided you perform the usual due‑diligence checks before scaling to a production environment.

Use in a workflow

Self Hosted LLM Gateway and Feature Rich Chat UI with RBAC helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.