This release is a pre-release and may not be stable for production use.
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.
🛠️ Getting Started
Prerequisites
- Python 3.10 or higher
- Access to OpenAI or Anthropic API keys (Azure and Bedrock Supported)
Installation
pip install upsonic
Basic Example
Set your OPENAI_API_KEY
export OPENAI_API_KEY=sk-***
Start the agent
from upsonic import Task, Agent
task = Task("Who developed you?")
agent = Agent(name="Coder")
agent.print_do(task)
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
class ReliabilityLayer:
prevent_hallucination = 10
agent = Agent(name="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.
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.
from upsonic import Agent, Task
from pydantic import BaseModel
# Define Fetch MCP configuration
class FetchMCP:
command = "uvx"
args = ["mcp-server-fetch"]
# Create response format for web content
class WebContent(BaseModel):
title: str
content: str
summary: str
word_count: int
# Initialize agent
web_agent = Agent(
name="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
result = web_agent.print_do(task)
print(result.title)
print(result.summary)
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.
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.
import os
os.environ["UPSONIC_TELEMETRY"] = "False"
1 maintainer
Release files for upsonic 0.59.8a1751111016
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Source distribution (sdist)
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Total release size: 252.4 kB
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