README.md

A mini, open-weights version of our Proxy assistant.

---

Installation

Clone the repository:

git clone https://github.com/convergence-ai/proxy-lite.git

Set-up the environment with:

make proxy

Or do it manually:

pip install uv
uv venv --python 3.11 --python-preference managed
uv sync
uv pip install -e .
playwright install

Usage

proxy --help

You can directly run the proxy with:

proxy "Book a table for 2 at an Italian restaurant in Kings Cross tonight at 7pm."

Proxy-Lite Endpoint

By default, Proxy-Lite will point to an endpoint set up on HuggingFace spaces. This is a demo endpoint and is not suitable for production use; it may be very slow when under heavy load.

We recommend hosting your own endpoint with vLLM, you can use the following command:

vllm serve --model convergence-ai/proxy-lite-7b \
    --trust-remote-code \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --port 8008 \

You can set the api_base to point to your local endpoint when calling Proxy-Lite:

proxy --api-base http://localhost:8008/v1 "Book a table...

or by setting the environment variable:

export PROXY_LITE_API_BASE=http://localhost:8008/v1

Scaffolding Proxy-Lite in Python

We use the RunnerConfig to control the setup of the task. The library is designed to be modular and extendable, you can easily swap the environment, solver, or agent.

Example:

import asyncio
from proxy_lite import Runner, RunnerConfig

config = RunnerConfig.from_dict(
    {
        "environment": {
            "name": "webbrowser",
            "homepage": "https://www.google.com",
            "headless": True, # Don't show the browser
        },
        "solver": {
            "name": "simple",
            "agent": {
                "name": "proxy_lite",
                "client": {
                    "name": "convergence",
                    "model_id": "convergence-ai/proxy-lite",
                    "api_base": "https://convergence-ai-demo-api.hf.space/v1",
                },
            },
        },
        "max_steps": 50,
        "action_timeout": 1800,
        "environment_timeout": 1800,
        "task_timeout": 18000,
        "logger_level": "DEBUG",
    },
)

proxy = Runner(config=config)
result = asyncio.run(
    proxy.run("Book a table for 2 at an Italian restaurant in Kings Cross tonight at 7pm.")
)

Webbrowser Environment

The webbrowser environment is a simple environment that uses the playwright library to navigate the web.

We launch a Chromium browser and navigate to the homepage provided in the RunnerConfig.

Actions in an environment are defined through available tool calls, which in the browser case are set as default in the BrowserTool class. This allows the model to click, type, etc. at relevant mark_id elements on the page. These elements are extracted using JavaScript injected into the page in order to make interaction easier for the models.

If you want to not use this set-of-marks approach, you can set the no_pois_in_image flag to True, and the include_poi_text flag to False in the EnvironmentConfig. This way the model will only see the original image, and not the annotated image with these points-of-interest (POIs). In this case, you would want to update the BrowserTool to interact with pixel coordinates instead of the mark_ids.