#6363rd of 70 in Agent frameworks and SDKs

upsonic

Python framework for building autonomous and tool-calling AI agents

Stars
8k
License
MIT
Last commit
Jun 2026
Last release
May 2026
Language
Python
Stars, last 29 weeks+218
1 Apr 2026: 7,744 stars8 Apr 2026: 7,750 stars15 Apr 2026: 7,759 stars22 Apr 2026: 7,772 stars29 Apr 2026: 7,778 stars6 May 2026: 7,785 stars13 May 2026: 7,797 stars20 May 2026: 7,805 stars27 May 2026: 7,816 stars3 Jun 2026: 7,827 stars10 Jun 2026: 7,836 stars17 Jun 2026: 7,848 stars24 Jun 2026: 7,860 stars1 Jul 2026: 7,866 stars8 Jul 2026: 7,869 stars15 Jul 2026: 7,876 stars22 Jul 2026: 7,885 stars29 Jul 2026: 7,894 stars5 Aug 2026: 7,901 stars12 Aug 2026: 7,908 stars19 Aug 2026: 7,916 stars26 Aug 2026: 7,923 stars2 Sept 2026: 7,925 stars9 Sept 2026: 7,929 stars16 Sept 2026: 7,935 stars23 Sept 2026: 7,939 stars30 Sept 2026: 7,947 stars7 Oct 2026: 7,951 stars14 Oct 2026: 7,958 stars21 Oct 2026: 7,962 stars

7,744 on 1 Apr 20267,962 on 21 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Upsonic is a Python library with two agent types: an AutonomousAgent that works inside a restricted workspace directory (file and shell operations confined, path traversal and dangerous commands blocked), and a traditional Agent with custom tools via a @tool decorator and MCP support. It also ships a layered OCR pipeline supporting EasyOCR, RapidOCR, Tesseract, PaddleOCR and DeepSeek OCR.

Who it is for: Python developers building agents that use tools, MCP and file or shell access

Strengths

  • Autonomous agent confines file and shell operations to a configured workspace
  • Plain Python tools via @tool decorator, plus MCP tool integration
  • Built-in OCR with six engines, including local ones like Tesseract and EasyOCR
  • Community-contributed prebuilt agents bundle a skill, system prompt and first message

Weaknesses

  • README examples only show Anthropic model strings; other provider support is not listed
  • Isolated cloud execution needs an external sandbox provider (E2B)
  • No hardware requirements or deployment guidance in the README
  • Workspace restriction is described briefly; no detail on how dangerous commands are detected

What it needs

  • Needs E2B (optional sandbox), LLM provider API (e.g. Anthropic)
  • Models: anthropic/claude-sonnet-4-5, DeepSeek OCR via Ollama

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