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 Proxy Lite on a task with:

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

Alternatively you can run the local web ui with:

make app

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, or even frequent hobbyist, use; it may be very slow when under even moderate load.

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

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

The tool arguments are very important for parsing the tool calls from the model appropriately.

Important: To serve the model locally, install vLLM and transformers with uv sync --all-extras. Qwen-2.5-VL support is not yet available in the latest release of transformers so installation from source is required.

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.")
)

The Runner sets the solver and environment off in a loop, like in a traditional reinforcement learning setup.

When it comes to prompting Proxy Lite, the model expects a message history of the form:

message_history = [
    {
        "role": "system", 
        "content": "You are Proxy Lite...", # Full system prompt in src/proxy_lite/agents/proxy_lite_agent.py
    }, # System prompt
    {
        "role": "user", 
        "content": "Book a table for 2 at an Italian restaurant in Kings Cross tonight at 7pm.",
    }, # Set the task
    {
        "role": "user", 
        "content": [
            {"type": "image_url", "image_url": {base64_encoded_screenshot} },
            {"type": "text", "text": "URL: https://www.google.com/ \n- [0] <a>About</a> \n- [1] <a>Store</a>...."}
        ] # This is the observation from the environment
    },
]

This would then build up the message history, alternating between the assistant (action) and the user (observation), although for new calls, all the last observations other than the current one are discarded.

The chat template will format this automatically, but also expects the appropriate Tools to be passed in so that the model is aware of the available actions. You can do this with transformers:

from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor

from proxy_lite.tools import ReturnValueTool, BrowserTool
from proxy_lite.serializer import OpenAICompatableSerializer

processor = AutoProcessor.from_pretrained("convergence-ai/proxy-lite")
tools = OpenAICompatableSerializer().serialize_tools([ReturnValueTool(), BrowserTool(session=None)])

templated_messages = processor.apply_chat_template(
    message_history, tokenize=False, add_generation_prompt=True, tools=tools
)

image_inputs, video_inputs = process_vision_info(message_history)

batch = processor(
    text=[templated_messages],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)

Or you can send to the endpoint directly, which will handle the formatting:

from openai import OpenAI

client = OpenAI(api_base="http://convergence-ai-demo-api.hf.space/v1")

response = client.chat.completions.create(
    model="convergence-ai/proxy-lite",
    messages=message_history,
    tools=tools,
    tool_choice="auto",
)

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.

Note: We use playwright_stealth to lower the chance of detection by anti-bot services, but this isn't foolproof and Proxy Lite may still get blocked with captchas or other anti-bot measures, especially when using the headless flag. We recommend using network proxies to avoid this issue.

Limitations

This model has not currently been designed to act as a full assistant that can interact with the user, and is instead designed to as a tool that will go out and autonomously complete the task set. As such, it will struggle with tasks that require credentials or user interaction such as actually purchasing items if you don't give all the required details in the prompt.

Future Work

• Pixel level control over the mouse movements.
• Full computer sandbox.
• Multi agent support.

Citation

@article{proxy-lite,
  title={Proxy Lite - A Mini, Open-weights, Autonomous Assistant},
  author={Convergence AI},
  url={https://github.com/convergence-ai/proxy-lite},
  year={2025}
}