
gpt-computer-assistant
Task oriented AI agent framework for digital workers and vertical AI agents
Stars: 5814

GPT Computer Assistant (GCA) is an open-source framework designed to build vertical AI agents that can automate tasks on Windows, macOS, and Ubuntu systems. It leverages the Model Context Protocol (MCP) and its own modules to mimic human-like actions and achieve advanced capabilities. With GCA, users can empower themselves to accomplish more in less time by automating tasks like updating dependencies, analyzing databases, and configuring cloud security settings.
README:
Hi, this is an open source framework to build vertical AI agent. We just support many llms and new technologies like mcp. You can build your own vertical ai agent army in few commands with the stucturized API.
GCA is an AI agent framework designed to make computer use across Windows, macOS, and Ubuntu. GCA enables you to replace repetitive, small-logic-based tasks worker to an AI. There is an really important potential that we believe. Whether you’re a developer, analyst, or IT professional, GCA can empower you to accomplish more in less time.
Imagine this:
- Extract the tech stacks of xxx Company | Sales Development Representer
- Identify Relevant tables for Analysis for xxx | Data Analytics
- Check the logs to find core cause of this incident | Technical Support Engineer
- Making CloudFlare Security Settings | Security Specialist
These examples shows how GCA is realize the concept of Vertical AI Agents solutions that not only replicate human tasks, GCA also in the beyond of human speed at same cases.
GCA is a Python-based project that runs on multiple operating systems, including Windows, macOS, and Ubuntu. It integrates external concepts, like the Model Context Protocol (MCP), along with its own modules, to interact with and control a computer efficiently. The system performs both routine and advanced tasks by mimicking human-like actions and applying computational precision.
GCA can replicate common user actions, such as:
- Clicking: Interact with buttons or other UI elements.
- Reading: Recognize and interpret text on the screen.
- Scrolling: Navigate through documents or web pages.
- Typing: Enter text into forms or other input fields.
Through MCP and GCA’s own modules, it achieves tasks that go beyond standard human interaction, such as:
- Updating dependencies of a project in seconds.
- Analyzing entire database tables to locate specific data almost instantly.
- Automating cloud security configurations with minimal input.
Installation
pip install gpt-computer-assistant
Single Instance:
from gpt_computer_assistant import Cloud, Task, TypeVerifier
# Starting instance
agent = Cloud.agent()
# Run task
star_number = agent.run(
Task(
"Extract the github star number of https://github.com/Upsonic/gpt-computer-assistant",
TypeVerifier("integer")
)
)
print(star_number)
agent.close()
Pulling Image
- If you are using ARM computer like M Chipset macbooks you should use ARM64 at the end.
docker pull upsonic/gca_docker_ubuntu:dev0-AMD64
Starting container
docker run -d -p 5901:5901 -p 7541:7541 upsonic/gca_docker_ubuntu:dev0-AMD64
LLM Settings&Using
from gpt_computer_assistant import docker
# Starting instance
agent = docker.agent("http://localhost:7541/")
# Connecting to OpenAI and Anthropic
agent.client.save_model("gpt-4o")
agent.client.save_openai_api_key("sk-**")
agent.client.save_anthropic_api_key("sk-**")
# Asking and getting result
result = agent.request("Extract the tech stacks of gpt-computer-assitant Company", "i want a list")
print(result)
agent.close()
Installation
pip install 'gpt-computer-assistant[base]'
pip install 'gpt-computer-assistant[api]'
LLM Settings&Using
from gpt_computer_assistant import local
# Starting agent
agent = local.agent()
# Connecting to OpenAI and Anthropic
agent.client.save_model("gpt-4o")
agent.client.save_openai_api_key("sk-**")
agent.client.save_anthropic_api_key("sk-**")
# Asking and getting result
result = agent.request("Extract the tech stacks of gpt-computer-assitant Company", "i want a list")
print(result)
agent.close()
instance.client.add_mcp_server("websearch", "npx", ["-y", "@mzxrai/mcp-webresearch"])
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