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