Introduction

Upsonic is a reliability-focused framework designed for real-world applications. It enables trusted agent workflows in your organization through advanced reliability features, including verification layers, triangular architecture, validator agents, and output evaluation systems.

Why Choose Upsonic?

Upsonic is a next-generation framework that makes agents production-ready by solving three critical challenges:

1- Reliability: While other frameworks require expertise and complex coding for reliability features, Upsonic offers easy-to-activate reliability layers without disrupting functionality.

2- Model Context Protocol (MCP): The MCP allows you to leverage tools with various functionalities developed both officially and by third parties without requiring you to build custom tools from scratch.

3- Integrated Browser Use and Computer Use: Directly use and deploy agents that works on non-API systems.

4- Secure Runtime: Isolated environment to run agents

📊 Reliability Layer

LLM output reliability is critical, particularly for numerical operations and action execution. Upsonic addresses this through a multi-layered reliability system, enabling control agents and verification rounds to ensure output accuracy.

Verifier Agent: Validates outputs, tasks, and formats - detecting inconsistencies, numerical errors, and hallucinations

Editor Agent: Works with verifier feedback to revise and refine outputs until they meet quality standards

Rounds: Implements iterative quality improvement through scored verification cycles

Loops: Ensures accuracy through controlled feedback loops at critical reliability checkpoints

Upsonic is a reliability-focused framework. The results in the table were generated with a small dataset. They show success rates in the transformation of JSON keys. No hard-coded changes were made to the frameworks during testing; only the existing features of each framework were activated and run. GPT-4o was used in the tests.

10 transfers were performed for each section. The numbers show the error count. So if it says 7, it means 7 out of 10 were done incorrectly. The table has been created based on initial results. We are expanding the dataset. The tests will become more reliable after creating a larger test set. Reliability benchmark repo

python
class ReliabilityLayer:
  prevent_hallucination = 10

agent = Agent("Coder", reliability_layer=ReliabilityLayer, model="openai/gpt4o")

Key features:

• Production-Ready Scalability: Deploy seamlessly on AWS, GCP, or locally using Docker.
• Task-Centric Design: Focus on practical task execution, with options for:
  • Basic tasks via LLM calls.
  • Advanced tasks with V1 agents.
  • Complex automation using V2 agents with MCP integration.
• MCP Server Support: Utilize multi-client processing for high-performance tasks.
• Tool-Calling Server: Exception-secure tool management with robust server API interactions.
• Computer Use Integration: Execute human-like tasks using Anthropic’s ‘Computer Use’ capabilities.
• Easily adding tools: You can add your custom tools and MCP tools with a single line of code.
  

📙 Documentation

You can access our documentation at docs.upsonic.ai All concepts and examples are available there.

🛠️ Getting Started

Prerequisites

• Python 3.10 or higher
• Access to OpenAI or Anthropic API keys (Azure and Bedrock Supported)

Installation

bash
pip install upsonic

Basic Example

Set your OPENAI_API_KEY

console
export OPENAI_API_KEY=sk-***

Start the agent

python
from upsonic import Task, Agent

task = Task("Who developed you?")

agent = Agent("Coder")

agent.print_do(task)

Tool Integration via MCP

Upsonic officially supports Model Context Protocol (MCP) and custom tools. You can use hundreds of MCP servers at glama or mcprun We also support Python functions inside a class as a tool. You can easily generate your integrations with that.

python
from upsonic import Agent, Task, ObjectResponse

# Define Fetch MCP configuration
class FetchMCP:
    command = "uvx"
    args = ["mcp-server-fetch"]

# Create response format for web content
class WebContent(ObjectResponse):
    title: str
    content: str
    summary: str
    word_count: int

# Initialize agent
web_agent = Agent(
    "Web Content Analyzer",
    model="openai/gpt-4o",  # You can use other models
)

# Create a task to analyze a web page
task = Task(
    description="Fetch and analyze the content from url. Extract the main content, title, and create a brief summary.",
    context=["https://upsonic.ai"],
    tools=[FetchMCP],
    response_format=WebContent
)
    
# Usage
web_agent.print_do(task)
print(result.title)
print(result.summary)

Agent with Multi-Task Example

Distribute tasks effectively across agents with our automated task distribution mechanism. This tool matches tasks based on the relationship between agent and task, ensuring collaborative problem-solving across agents and tasks. The output is essential for deploying an AI agent across apps or as a service. Upsonic uses Pydantic BaseClass to define structured outputs for tasks, allowing developers to specify exact response formats for their AI agent tasks.

python
from upsonic import Agent, Task, MultiAgent, ObjectResponse
from upsonic.tools import Search
from typing import List

# Targeted Company and Our Company
our_company = "https://redis.io/"
targeted_url = "https://upsonic.ai/"


# Response formats
class CompanyResearch(ObjectResponse):
   industry: str
   product_focus: str
   company_values: List[str]
   recent_news: List[str]

class Mail(ObjectResponse):
   subject: str
   content: str


# Creating Agents
researcher = Agent(
   "Company Researcher",
   company_url=our_company
)

strategist = Agent(
   "Outreach Strategist", 
   company_url=our_company
)


# Creating Tasks and connect
company_task = Task(
   "Research company website and analyze key information",

   context=[targeted_url],
   tools=[Search],
   response_format=CompanyResearch
)

position_task = Task(
   "Analyze Senior Developer position context and requirements",
   context=[company_task, targeted_url],
)

message_task = Task(
   "Create personalized outreach message using research",
   context=[company_task, position_task, targeted_url],
   response_format=Mail
)


# Run the Tasks over agents
results = MultiAgent.do(
   [researcher, strategist],
   [company_task, position_task, message_task]
)


# Print the results
print(f"Company Industry: {company_task.response.industry}")
print(f"Company Focus: {company_task.response.product_focus}")
print(f"Company Values: {company_task.response.company_values}")
print(f"Company Recent News: {company_task.response.recent_news}")
print(f"Position Analyze: {position_task.response}")
print(f"Outreach Message Subject: {message_task.response.subject}")
print(f"Outreach Message Content: {message_task.response.content}")

Direct LLM Call

Direct LLM calls offer faster, cheaper solutions for simple tasks. In Upsonic, you can make calls to model providers without any abstraction level and organize structured outputs. You can also use tools with LLM calls.

python
from upsonic import Task, Direct

direct = Direct(model="openai/gpt-4o")

task = Task("Where can I use agents in real life?")

direct.print_do(task)

Cookbook

You can check out many examples showing how to build agents using MCP tools and browser use with Upsonic.

Telemetry

We use anonymous telemetry to collect usage data. We do this to focus our developments on more accurate points. You can disable it by setting the UPSONIC_TELEMETRY environment variable to false.

python
import os
os.environ["UPSONIC_TELEMETRY"] = "False"