Introduction

PIXRA 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 PIXRA?

PIXRA 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("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

... View full README on GitHub