LLM Proxy: Managing SSE Streams and Timeouts

HTTP timeouts are the silent killer of LLM-powered apps, especially when dealing with long-running Server-Sent Events (SSE) streams that keep connections open for minutes. If you've ever had a frontend disconnect because a load balancer or proxy decided the request took too long, this Python-based LLM Proxy is a direct solution.

It acts as a middleware layer that handles stream aggregation and prevents premature timeouts, ensuring the data flow from the LLM to the client remains stable. For anyone building a production-grade AI workflow, this solves the "hanging request" problem without needing to rewrite the entire backend architecture.

Getting Started

Since this is a lightweight Python implementation, deployment is straightforward.

1. Clone the repository and install the dependencies.
2. Configure your target LLM endpoint in the environment settings.
3. Run the proxy server.

# Basic installation and run flow
git clone https://github.com/lordraw77/llmproxy
cd llmproxy
pip install -r requirements.txt
python main.py

Why this is useful for LLM agents

When deploying an LLM agent, the latency between the model generating a response and the client receiving it can be unpredictable. A standard proxy often kills the connection if no bytes are sent for 30-60 seconds. This tool ensures the SSE stream stays alive and aggregates chunks correctly, which is critical for a smooth user experience in chat interfaces.

It's a practical tutorial in how to handle asynchronous streaming in Python. If you're tired of debugging 504 Gateway Timeout errors during long model generations, this is a lean, focused utility that does one thing well.

https://github.com/lordraw77/llmproxy