Upsonic
Upsonic is a reliability-focused AI agent framework with dockerized server-client architecture that enables trusted agent workflows through advanced features like verification layers, triangular architecture, and Model Context Protocol (MCP) integration.
https://github.com/Upsonic/Upsonic?ref=aipure
Product Information
Updated:Mar 9, 2025
What is Upsonic
Upsonic is a next-generation framework designed for real-world AI applications that makes agents production-ready. It provides a comprehensive solution for managing and deploying AI agents with a strong focus on reliability and security. Built with Python, Upsonic offers a task-centric approach where developers can implement everything from basic LLM calls to complex automation using different agent versions, all while maintaining high reliability standards through its multi-layered verification system.
Key Features of Upsonic
Upsonic is a reliability-focused AI agent framework designed for production-ready applications. It provides advanced reliability features including verification layers, triangular architecture, validator agents, and output evaluation systems. The framework stands out for its task-centric design, Model Context Protocol (MCP) integration, secure runtime environment, and ability to work with both API and non-API systems, making it particularly suitable for enterprise-level AI deployments.
Multi-layered Reliability System: Implements verifier agents, editor agents, verification rounds, and feedback loops to ensure accurate and consistent AI outputs, particularly for numerical operations and action execution
Model Context Protocol Integration: Supports integration with various MCP servers and custom tools, allowing developers to leverage existing tools and create new ones with minimal coding
Structured Task Management: Uses Pydantic BaseClass for defining structured outputs and automated task distribution across agents, ensuring organized and efficient workflow management
Secure Runtime Environment: Provides isolated environment for running agents with dockerized server-client architecture, ensuring secure and scalable deployment
Use Cases of Upsonic
Enterprise Data Analysis: Automated analysis of company data with reliable numerical processing and verification systems for accurate business intelligence
Web Content Management: Automated content analysis, summarization, and management using web agents with structured output formats
Business Research Automation: Automated company research and analysis for business development, including competitive analysis and outreach message generation
Multi-Agent Task Processing: Complex task execution across multiple specialized agents for tasks requiring diverse capabilities and coordination
Pros
High reliability with multiple verification layers
Easy integration with existing tools through MCP
Production-ready scalability with Docker support
Strong focus on structured outputs and task organization
Cons
Requires Python 3.10 or higher
Server-client architecture may add latency to development
Limited to specific API providers (OpenAI, Anthropic, Azure, Bedrock)
How to Use Upsonic
Install Prerequisites: Ensure you have Python 3.10 or higher installed and obtain API keys for OpenAI or Anthropic (Azure and Bedrock are also supported)
Set Environment Variables: Export your API key as an environment variable: export OPENAI_API_KEY=sk-***
Basic Usage: Import and use the basic agent functionality:
1. from upsonic import Task, Agent
2. Create a task: task = Task('Your question here')
3. Create an agent: agent = Agent('Coder')
4. Run the task: agent.print_do(task)
Enable Reliability Layer: Add reliability checking:
1. Create reliability config: class ReliabilityLayer: prevent_hallucination = 10
2. Create agent with reliability: agent = Agent('Coder', reliability_layer=ReliabilityLayer)
Use MCP Tools Integration: Integrate Model Context Protocol tools:
1. Define MCP config class with command and args
2. Create response format class inheriting from ObjectResponse
3. Initialize agent with model specification
4. Create task with tools and response format
5. Execute task with agent
Implement Multi-Agent Tasks: Set up multiple agents working together:
1. Import MultiAgent and required components
2. Define response formats using ObjectResponse
3. Create multiple agents with specific roles
4. Create connected tasks with contexts and tools
5. Run tasks using MultiAgent.do()
Make Direct LLM Calls: For simple tasks, use direct LLM calls:
1. from upsonic import Direct
2. Direct.do(your_task)
Configure Telemetry (Optional): Disable telemetry if desired:
1. import os
2. os.environ['UPSONIC_TELEMETRY'] = 'False'
Upsonic FAQs
Upsonic is a reliability-focused AI agent framework designed for real-world applications. It enables trusted agent workflows through advanced reliability features including verification layers, triangular architecture, validator agents, and output evaluation systems.
Upsonic Video
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