GitHub - Upsonic/Upsonic: Build autonomous AI agents in Python.

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

Documentation and Resources

• Documentation - Complete guides and API reference
• Quickstart Guide - Get started in 5 minutes
• Examples - Real-world examples and use cases
• API Reference - Detailed API documentation

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.