SuperOptiX
The King of Agent Frameworks
Full-Stack Agentic AI Framework. Design and deploy production-grade AI agents with declarative specs, rigorous BDD/TDD evaluation, DSPy-powered optimization, and seamless multi-agent orchestration.
"You Do Context Engineering. SuperOptiX Does Agent Engineering."
🔍 What is SuperOptiX?
SuperOptiX is a full-stack, production-grade Agentic AI framework built for evaluation-first, optimization-core, and orchestration-ready agent systems. It empowers developers and organizations to design, optimize, and deploy multi-agent architectures using declarative specifications, DSPy optimization, and robust testing frameworks.
👤 Who is it for?
SuperOptiX is designed for:
- AI Engineers building agent copilots and autonomous systems
- Tech Teams & Startups deploying LLM-native workflows and agent stacks
- Enterprise AI Architects seeking scalable, composable, multi-agent infrastructure
- R&D Labs experimenting with agent simulations, protocols, and agentic architectures
- Optimization-obsessed devs who want reproducible, test-driven, fine-tuned agents
⏳ Why now?
The age of monolithic copilots is ending.
The next wave of GenAI is agentic, multi-step, multi-agent, memory-rich, and orchestration-first.
Yet today's tools are fragmented, evaluation-poor, and brittle in production.
SuperOptiX solves this with a purpose-built framework:
- Powered by DSPy for optimization
- Built around SuperSpecX DSL for declarative agent design
- Equipped with agent-tiering, memory layers, test harnesses, and model management
✨ Core Features
Everything you need to build, evaluate, and deploy production-grade AI agents
Evaluation-First by Design
Agents are defined and validated using BDD-style specs before orchestration
BDD-Style Spec Runner
Human-readable DSL with iterative optimization
DSPy-Powered Optimization Engine
Declarative optimization with transparent tracing
Agentic DSPy Evolution
Multi-agent coordination and protocol support
Modular Optimization Architecture
Framework-agnostic with user choice
Prompt & Context Optimization
Automatic decomposition and testing
Automated Agent Pipeline Generation
Complete pipeline from spec to deployment
SuperSpec DSL
Domain-specific language for agents
Inbuilt Memory System
Modular memory layers for agents
Built-in Evals
Native evaluation suite for agents
Context Engineering Layer
Structured context frames and templating
Model & Inference Management
Plug-and-play with multiple providers
Marketplace for Prebuilt Agents & Tools
Discover and deploy pre-optimized components
AgentOps Layer
Observability, replay, and debugging
🧠 Core Philosophy
Unlike most frameworks that bolt on evals and monitoring as an afterthought, SuperOptiX makes evaluation, optimization, and guardrails core to the development lifecycle.
Evaluation-First
When agents fail to perform reliably, the root cause is almost always insufficient or poorly structured context, unclear instructions, or missing tools that haven't been properly communicated to the model.
Optimization-Core
DSPy-powered optimization engine automatically improves your agents' performance based on evaluation metrics, making optimization a first-class citizen.
Declarative Specs
SuperSpec provides a Kubernetes-style declarative specification for AI agents. You declare what you want, not how to get it.
🚀 Complete Development Workflow
From local LLM setup to multi-agent orchestration. Build production-ready AI teams that actually deliver results.
Setup LLMs
Configure local language models for privacy and speed
Key Features:
- Install Ollama for easy model management
- Download models for different agent tiers
- Support for MLX, LM Studio, HuggingFace
- Local inference for data privacy
Commands:
Initialize Project
Create your SuperOptiX project structure
Key Features:
- Generate proper directory structure
- Create .super configuration file
- Setup agent and orchestra directories
- Configure workspace management
Commands:
Pull Agents
Download pre-built agents from marketplace
Key Features:
- 100+ production-ready agents
- Industry-specific specializations
- SuperSpec YAML configurations
- One-command installation
Commands:
Compile Agent
Convert YAML playbooks to executable DSPy pipelines
Key Features:
- SuperSpec to DSPy compilation
- BDD scenarios as test cases
- Pipeline generation
- Executable Python code
Commands:
BDD Test Runner
Professional spec execution with quality gates
Key Features:
- Behavior-driven development scenarios
- Multi-criteria evaluation system
- Professional test output
- Baseline performance measurement
Commands:
DSPy Optimization
Automatic prompt optimization using BDD scenarios
Key Features:
- BDD scenarios as training data
- BootstrapFewShot optimization
- Automatic prompt improvement
- Performance enhancement
Commands:
Execute Goals
Run optimized agents to accomplish specific tasks
Key Features:
- Natural language goal specification
- Optimized performance
- Progress tracking and logging
- Result validation and metrics
Commands:
Multi-Agent Orchestra
Coordinate multiple agents for complex workflows
Key Features:
- Multi-agent coordination
- Sequential and parallel execution
- Shared workspace management
- Production-ready deployment
Commands:
Observe and Trace Agents
Monitor agent performance and debug execution
Key Features:
- Real-time agent monitoring
- Execution trace analysis
- Performance metrics dashboard
- Interactive debugging tools
Commands:
CI/CD Integration
Automated testing and deployment pipeline
Key Features:
- Automated quality gates
- BDD/TDD workflow integration
- Multi-platform CI/CD support
- Production deployment automation
Commands:
🎯 The Problem We Solve
The Challenge
Building production-grade AI agents remains a significant challenge. Most frameworks are designed for demos, not deployment, leading to most AI projects failing to reach production.
- Fragile, hardcoded prompts
- No systematic optimization
- Lack of evaluation frameworks
- Poor multi-agent coordination
- Missing production guardrails
- Context engineering neglect
Our Solution
SuperOptiX is the first evaluation-first, behavior-driven framework that combines evaluation, optimization, and orchestration in a single solution.
- Built-in Evaluation (BDD first)
- DSPy-powered optimization
- Orchestration-Ready Pipelines
- Production-Grade Monitoring
- Declarative Agent Specifications
- Context engineering excellence
Ready to Build Your First AI Team?
From installation to deployment - follow our step-by-step guide to create production-ready AI agents in minutes.
Context Engineering
Systematic approach to delivering the right context
Agent Engineering
Next evolution of AI engineering
DSPy Integration
Built on Stanford's optimization framework
Quick Start: Install with uv pip install superoptix --prerelease=allow and run super to verify installation.