Skillget

Upsonic

mcp
Guvenlik Denetimi
Gecti
Health Gecti
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 1 days ago
  • Community trust — 7826 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This tool is an AI agent framework designed for building production-ready applications, particularly focused on the financial and banking sectors. It allows developers to orchestrate multi-agent workflows, integrate with various AI models, and automate tasks like document analysis and data gathering.

Security Assessment
The overall risk is rated as Low. The automated code scan checked 12 files and found no dangerous patterns, hardcoded secrets, or requests for dangerous permissions. Because this is an AI agent framework designed to connect with external AI providers (like OpenAI and Anthropic) and perform automated tasks, it inherently makes external network requests and could process sensitive financial data depending on how you configure it. You should still exercise standard caution regarding data privacy before passing proprietary or customer information to your chosen AI provider's endpoints.

Quality Assessment
This project demonstrates excellent health and high community trust. It is very actively maintained, with its most recent code push occurring just 1 day ago. It has an impressive 7827 GitHub stars, indicating strong adoption and peer review from the developer community. Additionally, it uses the highly permissive and standard MIT license, making it suitable for both personal and commercial use.

Verdict
Safe to use.
SUMMARY

Agent Framework For Fintech and Banks

README.md

Upsonic

Production-Ready AI Agent Framework with Safety First

PyPI version
License
Python Version
GitHub stars
GitHub issues
Documentation
Discord

Documentation • Quickstart • Examples • Discord

Overview

Upsonic is an open-source AI agent framework for building production-ready agents. It supports multiple AI providers (OpenAI, Anthropic, Azure, Bedrock) and includes built-in safety policies, OCR, memory, multi-agent coordination, and MCP tool integration.

What Can You Build?

  • Document Analysis: Extract and process text from images and PDFs
  • Customer Service Automation: Agents with memory and session context
  • Financial Analysis: Agents that analyze data, generate reports, and provide insights
  • Compliance Monitoring: Enforce safety policies across all agent interactions
  • Research & Data Gathering: Automate research workflows with multi-agent collaboration
  • Multi-Agent Workflows: Orchestrate tasks across specialized agent teams

Quick Start

Installation

uv pip install upsonic
# pip install upsonic

Basic 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)

Agent with Tools

from upsonic import Agent, Task
from upsonic.tools.common_tools import YFinanceTools

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

task = Task(
    description="Give me a summary about tesla stock with tesla car models",
    tools=[YFinanceTools()]
)

agent.print_do(task)

Agent with Memory

from upsonic import Agent, Task
from upsonic.storage import Memory, InMemoryStorage

memory = Memory(
    storage=InMemoryStorage(),
    session_id="session_001",
    full_session_memory=True
)

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

task1 = Task(description="My name is John")
agent.print_do(task1)

task2 = Task(description="What is my name?")
agent.print_do(task2)  # Agent remembers: "Your name is John"

Ready for more? Check out the Quickstart Guide for additional examples including Knowledge Base and Team workflows.

Key Features

  • Autonomous Agent: An agent that can read, write, and execute code inside a sandboxed workspace, no tool setup required
  • Safety Engine: Policy-based content filtering applied to user inputs, agent outputs, and tool interactions
  • OCR Support: Unified interface for multiple OCR engines with PDF and image support
  • Memory Management: Session memory and long-term storage with multiple backend options
  • Multi-Agent Teams: Sequential and parallel agent coordination
  • Tool Integration: MCP tools, custom tools, and human-in-the-loop workflows
  • Production Ready: Monitoring, metrics, and enterprise deployment support

Core Capabilities

Autonomous Agent

AutonomousAgent extends Agent with built-in filesystem and shell tools, automatic session memory, and workspace sandboxing. Useful for coding assistants, DevOps automation, and any task that needs direct file or terminal access.

from upsonic import AutonomousAgent, Task

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

task = Task("Read the main.py file and add error handling to every function")
agent.print_do(task)

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

Safety Engine

The Safety Engine applies policies at three points: user inputs, agent outputs, and tool interactions. Policies can block, anonymize, replace, or raise exceptions on matched content.

from upsonic import Agent, Task
from upsonic.safety_engine.policies.pii_policies import PIIAnonymizePolicy

agent = Agent(
    model="anthropic/claude-sonnet-4-5",
    user_policy=PIIAnonymizePolicy,  # anonymizes PII before sending to the LLM
)

task = Task(
    description="My email is [email protected] and phone is 555-1234. What are my email and phone?"
)

# PII is anonymized before reaching the LLM, then de-anonymized in the response
result = agent.do(task)
print(result)  # "Your email is [email protected] and phone is 555-1234"

Pre-built policies cover PII, adult content, profanity, financial data, and more. Custom policies are also supported.

Learn more: Safety Engine Documentation

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

Upsonic AgentOS

AgentOS is an optional deployment platform for running agents in production. It provides a Kubernetes-based runtime, metrics dashboard, and self-hosted deployment.

  • Kubernetes-based FastAPI Runtime: Deploy agents as isolated, scalable microservices
  • Metrics Dashboard: Track LLM costs, token usage, and performance per transaction
  • Self-Hosted: Full control over your data and infrastructure
  • One-Click Deployment: Automated deployment pipelines
AgentOS Dashboard

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.

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 guidelines and code of conduct before submitting pull requests.

Learn more at upsonic.ai

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