---
title: "Upsonic"
type: "tool"
slug: "upsonic-upsonic"
canonical_url: "https://www.graphcanon.com/tools/upsonic-upsonic"
github_url: "https://github.com/Upsonic/Upsonic"
homepage_url: "https://docs.upsonic.ai"
stars: 7903
forks: 737
primary_language: "Python"
license: "MIT"
categories: ["ai-agents"]
tags: ["llms", "autonomous-agent", "openclaw", "claude", "agent"]
updated_at: "2026-07-07T18:36:43.211507+00:00"
---

# Upsonic

> Build Autonomous AI Agents in Python

Upsonic is a Python framework designed for developing autonomous agents, integrating with various models and providing restricted workspace operations for enhanced security.

## Facts

- Repository: https://github.com/Upsonic/Upsonic
- Homepage: https://docs.upsonic.ai
- Stars: 7,903 · Forks: 737 · Open issues: 29 · Watchers: 56
- Primary language: Python
- License: MIT
- Last pushed: 2026-06-18T14:55:50+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)

## Tags

llms, autonomous-agent, openclaw, claude, agent

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## README (excerpt)

```text
<div align="center">

<img width="947" alt="Upsonic_README" src="https://github.com/user-attachments/assets/acb3f413-e4fe-44a6-9aff-40d4e9031188" />

# Upsonic

**Build Autonomous AI Agents in Python**









[Documentation](https://docs.upsonic.ai) • [Quickstart](https://docs.upsonic.ai/get-started/quickstart) • [Examples](https://docs.upsonic.ai/examples) • [Discord](https://discord.gg/pmYDMSQHqY)

</div>

---

## 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

```bash
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

```python
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](https://github.com/Upsonic/Upsonic/tree/master/src/upsonic/prebuilt), bring your agent and open a PR.

Learn more: [Prebuilt Autonomous Agents](https://docs.upsonic.ai/concepts/prebuilt-autonomous-agents/overview)

> **Next steps:** Connect a [Sandbox Provider (E2B)](https://docs.upsonic.ai/concepts/autonomous-agent/overview) for isolated cloud execution environments.

---

## Create Traditional Agent

```python
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

```python
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](https://docs.upsonic.ai/concepts/tools/mcp-tools/overview) 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.

```bash
uv pip install "upsonic[ocr]"
```

```python
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](https://docs.upsonic.ai/concepts/ocr/overview)

---

## Check Our Videos

<table>
  <tr>
    <td align="center">
      <a href="https://www.youtube.com/watch?v=GOYko0KfBtg">
        <img src="https://img.youtube.com/vi/GOYko0KfBtg/maxresdefault.jpg" width="400" alt="Upsonic Demo Video 1"/>
      </a>
    </td>
    <td align="center">
      <a href="https://www.youtube.com/watch?v=ulUEFIolesQ">
        <img src="https://img.youtube.com/vi/ulUEFIolesQ/maxresdefault.jpg" width="400" alt="Upsonic Demo Video 2"/>
      </a>
    </td>
  </tr>
</table>

---

## Do
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/upsonic-upsonic`](/api/graphcanon/tools/upsonic-upsonic)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_
