9
Built The Same LLM Proxy Over and Over so I'm Open-Sourcing It
r/SideProject
7/21/2025
Content Summary
The author repeatedly built mini backends for LLM features in apps to keep API keys secure. They open-sourced an LLM proxy that handles secrets, auth, limits, and logs, allowing users to call OpenAI from the frontend without backend code. The project is in TypeScript/Node.js with JWT auth and rate limiting, and they are actively adding more features.
Opinion Analysis
The mainstream opinion is supportive of the author's decision to open-source the LLM proxy, recognizing the need for secure and easy integration of LLM features in apps. There are no conflicting or controversial opinions in the comments. The discussion focuses on the potential future features and integrations of the proxy.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Airbolt | https://github.com/Airbolt-AI/airbolt | LLM Proxy | TypeScript/Node.js, JWT auth, rate limiting, handles secrets/auth/limits/logs |
USER NEEDS
Pain Points:
- Repeatedly writing mini backends for LLM features in apps
- Keeping API keys out of client code
Problems to Solve:
- Securely handling API calls for LLM features
- Adding LLM features without backend code
Potential Solutions:
- Open-sourcing an LLM proxy that manages secrets/auth/limits/logs
- Using a small SDK to call OpenAI from the frontend
GROWTH FACTORS
Effective Strategies:
- Open-sourcing the project to attract contributors and users
Marketing & Acquisition:
- Leveraging GitHub and Reddit for community building and user acquisition
Monetization & Product:
- Focusing on limited features initially and actively adding more
- Considering integration with multiple providers, streaming, and existing auth systems
User Engagement:
- Engaging the developer community by providing a useful tool that simplifies LLM integration