Skillget

Upsonic

mcp
Security Audit
Pass
Health Pass
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 7849 GitHub stars
Code Pass
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose

This tool provides a Python framework for building and running autonomous AI agents. It allows developers to define tasks that an AI model can execute, including file operations and shell commands within a restricted workspace environment.

Security Assessment

The tool's core function involves executing AI-driven tasks that inherently require file system and shell access. However, the documentation explicitly states that these operations are sandboxed to a designated workspace directory, with path traversal and dangerous commands blocked. A light code audit of 12 files found no dangerous patterns, hardcoded secrets, or requests for risky permissions. The framework is also designed to integrate with isolated cloud execution environments like E2B for secure operation.

Quality Assessment

The project demonstrates strong health and community trust. It is licensed under the permissive MIT license, ensuring clear open-source usage rights. With nearly 8,000 GitHub stars, it has a high level of community adoption and visibility. The repository is actively maintained, evidenced by a recent push within the last day. The tool is highly versatile and is officially compatible with major AI coding assistants.

Verdict

Safe to use, provided you properly configure the designated workspace and utilize sandbox environments when running autonomous tasks.
SUMMARY

Build autonomous AI agents in Python.

README.md
Upsonic_README

Upsonic

Build Autonomous AI Agents in Python

PyPI version
License
Python Version
GitHub stars
GitHub issues
Documentation
Discord

Documentation • Quickstart • Examples • Discord

Overview

Upsonic is a Python framework for building autonomous agents like OpenClaw and Claude Cowork, as well as more traditional agent systems.

Quick Start

Installation

uv pip install upsonic
# pip install upsonic

IDE Integration

Add Upsonic docs as a source in your coding tools:

Cursor: Settings → Indexing & Docs → Add https://docs.upsonic.ai/llms-full.txt

Also works with VSCode, Windsurf, and similar tools.

Create Autonomous Agent

Build Your Own

from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="anthropic/claude-sonnet-4-5",
    workspace="/path/to/logs"
)

task = Task("Analyze server logs and detect anomaly patterns")

agent.print_do(task)

All file and shell operations are restricted to workspace. Path traversal and dangerous commands are blocked.

Use Our Prebuilt Ones

Prebuilt autonomous agents are ready-to-run agents built by the Upsonic community, each packaging a skill, system prompt, and first message so you can go from install to running in seconds. The collection is open to contributions, bring your agent and open a PR.

Learn more: Prebuilt Autonomous Agents

Next steps: Connect a Sandbox Provider (E2B) for isolated cloud execution environments.

Create Traditional Agent

from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5", name="Stock Analyst Agent")

task = Task(description="Analyze the current market trends")

agent.print_do(task)

Add Custom Tools

from upsonic import Agent, Task
from upsonic.tools import tool

@tool
def sum_tool(a: float, b: float) -> float:
    """
    Add two numbers together.

    Args:
        a: First number
        b: Second number

    Returns:
        The sum of a and b
    """
    return a + b

task = Task(
    description="Calculate 15 + 27",
    tools=[sum_tool]
)

agent = Agent(model="anthropic/claude-sonnet-4-5", name="Calculator Agent")

result = agent.print_do(task)

Next steps: Integrate MCP Tools to connect your agents to thousands of external data sources and services.

OCR and Document Processing

Upsonic provides a unified OCR interface with a layered pipeline: Layer 0 handles document preparation (PDF to image conversion, preprocessing), Layer 1 runs the OCR engine.

uv pip install "upsonic[ocr]"

from upsonic.ocr import OCR
from upsonic.ocr.layer_1.engines import EasyOCREngine

engine = EasyOCREngine(languages=["en"])
ocr = OCR(layer_1_ocr_engine=engine)

text = ocr.get_text("invoice.pdf")
print(text)

Supported engines: EasyOCR, RapidOCR, Tesseract, PaddleOCR, DeepSeek OCR, DeepSeek via Ollama.

Learn more: OCR Documentation

Check Our Videos

Upsonic Demo Video 1 Upsonic Demo Video 2

Documentation and Resources

Community and Support

💬 Join our Discord community! — Ask questions, share what you're building, get help from the team, and connect with other developers using Upsonic.

  • Discord - Chat with the community and get real-time support
  • Issue Tracker - Report bugs and request features
  • Changelog - See what's new in each release

License

Upsonic is released under the MIT License. See LICENCE for details.

Contributing

We welcome contributions from the community! Please read our Contributing Guide and code of conduct before submitting pull requests.

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