SUPEROPTIX AI

Full Stack Agentic AI Optimization Framework

🧪 Evaluation-First ⚡ Optimization-Core 🕸️ Multi-Agent Orchestration

Powered by DSPy. Refined by Superagentic AI.

🎉 Oracle & Genies are FREE to try! 📊 View All Tiers →

🚀 Quick Start 📖 Learn More 📚 Guides 🌐 Website

🔍 What is SuperOptiX?

SuperOptiX is the world's first universal agent optimization framework that brings end-to-end optimization to AI agents across 6 major frameworks. Build, evaluate, and optimize agents from any framework with a unified workflow powered by GEPA (Genetic-Pareto Algorithm).

Unlike frameworks that only build agents, SuperOptiX makes optimization, evaluation, and quality assurance core to the development lifecycle. Whether you're using DSPy, OpenAI SDK, CrewAI, Google ADK, Microsoft, or DeepAgents, SuperOptiX gives you the power to optimize and deploy production-grade agents.

🔬 Multi-Framework Support. 🧬 GEPA Universal Optimizer. 📝 SuperSpec DSL. 📊 Built-in Evaluation.

🚀 Context-First Development

❌ Traditional Approach

Framework-First Development

Write code in specific framework
Locked into that framework ecosystem
Want to try another framework? Rewrite everything!
Framework vendor lock-in
Can't leverage framework innovations

✅ SuperOptiX Approach

Context-First Development

Define agent context in SuperSpec (framework-free)
Generate code for ANY framework on demand
Try DSPy today, CrewAI tomorrow, OpenAI next week
Zero framework lock-in
Leverage best features from each framework

🔮 Future-Proof Architecture

SuperOptiX is designed for extensibility across three critical dimensions:

🔬 Framework Layer

Swap AI frameworks on demand

DSPy, OpenAI, CrewAI, Google ADK, Microsoft, DeepAgents
New framework released? Just add an adapter
No rewrites, no migrations

target_framework: any_framework

🔌 Protocol Layer

Swap communication protocols

MCP (Model Context Protocol)
A2A (Agent-to-Agent) when it arrives
Custom protocols via adapters

protocol: mcp | a2a | custom

🧬 Optimizer Layer

Swap optimization engines

GEPA (Genetic-Pareto)
DSPy Optimizers (BootstrapFewShot, MIPRO)
Future optimizers via plugin system

optimizer: gepa | mipro | custom

Abstraction Without Lock-In

Define your agent context once. Swap frameworks, protocols, and optimizers as the ecosystem evolves. SuperOptiX adapts to innovation without breaking your agents.

🏗️ Full-Stack Agentic Optimization

SuperOptiX is the only framework that optimizes across every layer of the agentic stack with a universal, framework-agnostic approach:

💬 Prompts & Instructions

GEPA evolutionary optimization of system prompts, instructions, and reasoning patterns

📚 RAG & Retrieval

Optimize retrieval queries, chunking strategies, and context selection for maximum relevance

🛠️ Tools & Functions

MCP-based tool optimization for better parameter selection and execution strategies

🧠 Memory & Context

Optimize memory retrieval, context window usage, and long-term storage strategies

🎯 End-to-End Optimization

Traditional frameworks optimize prompts only. SuperOptiX optimizes the entire agentic stack.

Universal GEPA algorithm works across all layers, all frameworks, all protocols.

One optimizer. Every layer. Maximum performance.

🌟 What Makes SuperOptiX Special?

🔬 Multi-Framework Support

One optimization workflow, 6 frameworks:

DSPy - Structured prompting & signatures
OpenAI SDK - Native OpenAI agents
CrewAI - Multi-agent teams
Google ADK - Gemini agents
Microsoft - Enterprise agents
DeepAgents - LangGraph workflows

Learn More →

🧬 GEPA Universal Optimizer

The only optimizer that works across ALL frameworks:

Proven Results: 37.5% → 80% improvements
Framework-Agnostic: One command for all
Auto-Configuration: Light, medium, intensive
RSpec-Style BDD: Optimize against behavior specs

Learn More →

🔌 MCP & RAG Optimization

Advanced context optimization:

MCP Protocol: Model Context Protocol integration
RAG Optimization: Optimize retrieval quality
Memory Optimization: GEPA-based context selection
Vector DBs: ChromaDB, LanceDB, Weaviate, Qdrant, Milvus

Learn More →

📝 SuperSpec DSL

Universal agent specification language:

Framework Freedom: Write once, deploy anywhere
Kubernetes-Style: Declarative YAML specs
RSpec-Style BDD: Built-in behavior scenarios
Full Lifecycle: Build → Eval → Optimize → Deploy

Learn More →

🎯 Quick Install

Stable Release Available!

SuperOptiX is now available as a stable release with support for 6 major AI frameworks!

# Core installation (includes DSPy)
pip install superoptix

# With DSPy (for GEPA optimization)
pip install superoptix[frameworks-dspy]

# With specific framework
pip install superoptix[frameworks-openai]  # OpenAI
pip install superoptix[frameworks-google]  # Google ADK
pip install superoptix[frameworks-crewai]  # CrewAI

# All DSPy-compatible frameworks
pip install superoptix[frameworks]

Requirements: Python 3.11+, Git

📖 Full Installation Guide →

⚡ End-to-End Agent Optimization Features

🧬 GEPA Universal Optimizer

The world's first universal agent optimizer. Optimize agents across DSPy, OpenAI SDK, CrewAI, Google ADK, Microsoft, and DeepAgents with a single command. Proven 37.5% → 80% improvements.

🔬 Multi-Framework Support

6 major frameworks, 1 workflow. Build agents in your preferred framework (DSPy, OpenAI, CrewAI, Google ADK, Microsoft, DeepAgents), keep the same optimization and evaluation workflow.

🔌 MCP & RAG Optimization

Optimize context retrieval and protocols. GEPA-based optimization for RAG systems, MCP protocol integration, and memory context window optimization for better relevance and efficiency.

📝 SuperSpec DSL

Universal agent specification language. Write agent specs once in Kubernetes-style YAML, deploy to any framework. Includes RSpec-style BDD scenarios, tool definitions, and evaluation criteria.

✅ RSpec-Style BDD Testing

Test-driven agent development. Define behavior scenarios like RSpec for Ruby, run automated evaluations, track metrics across optimization iterations. Professional CI/CD integration ready.

💡 Memory Optimization

GEPA-optimized memory selection. Automatically select the most relevant memories to fit within LLM context windows, improving quality while reducing token usage by up to 60%.

📊 Dataset Import & Management

Support for all major formats. Import datasets from CSV, JSON, Parquet, HuggingFace, and more. Automatic conversion to SuperOptiX format for optimization and evaluation.

🧠 Advanced DSPy Integration

Pure DSPy mode + SuperOptiX enhancements. Use DSPy optimizers (SIMBA, MIPROv2, BootstrapFewShot) or GEPA universal optimizer. Custom agentic DSPy modules for multi-agent coordination.

📈 Observability & Monitoring

Track everything. Integrated MLFlow, LangFuse, and Weights & Biases support. Production-ready tracing, debugging, and performance monitoring across all frameworks.

🤖 Model Management

Local and cloud inference. Ollama, MLX (Apple Silicon), HuggingFace, LM Studio for local development. OpenAI, Anthropic, Groq, vLLM, SGLang, TGI for production scale.

📈 Proven Optimization Results

Real results from GEPA optimization across multiple frameworks

Framework	Agent	Baseline	After GEPA	Improvement
🔬 DSPy	Sentiment Analyzer	37.5%	80.0%	+42.5 pts 🏆
👥 CrewAI	Research Crew	75%	100%	+25 pts ⭐
🤖 OpenAI SDK	AI Assistant	100%	100%	Maintained ✅
🔮 Google ADK	Gemini Assistant	Ready for optimization
🏢 Microsoft	Enterprise Agent	Ready for optimization
🌊 DeepAgents	Research Agent	Ready for optimization

🧬 Try GEPA Optimizer 🔬 Framework Comparison

🚀 Universal Optimization Workflow

Same workflow across ALL frameworks - choose your framework, keep the optimization power

# Step 1: Initialize project
super init my_agent_project
cd my_agent_project

# Step 2: Pull demo agent (works for ANY framework!)
super agent pull sentiment_analyzer        # DSPy
super agent pull assistant_openai          # OpenAI SDK
super agent pull researcher_crew           # CrewAI
super agent pull assistant_adk             # Google ADK
super agent pull assistant_microsoft       # Microsoft
super agent pull research_agent_deepagents # DeepAgents

# Step 3: Compile agent
super agent compile <agent_name>

# Step 4: Evaluate baseline performance
super agent evaluate <agent_name>

# Step 5: Optimize with GEPA (same command for ALL frameworks!)
super agent optimize <agent_name> --auto medium

# Step 6: Evaluate optimized version (automatically loads optimized weights)
super agent evaluate <agent_name>

# Step 7: Run in production
super agent run <agent_name>

🎯 One workflow. Six frameworks. Unlimited possibilities.

🧠 SuperOptiX & DSPy

Agentic DSPy - Taking Optimization to the Next Level

SuperOptiX harnesses the full power of DSPy's optimization principles and elevates them to the agentic layer.
We're not just a DSPy wrapper, we're Agentic DSPy.

DSPy is the most powerful optimization framework in the AI space and the only framework that systematically optimizes language model programs. SuperOptiX recognizes this strength and builds upon DSPy's revolutionary optimization-first approach, extending it specifically for agentic AI and multi-agent orchestration.

🎯 Why DSPy is Perfect for Agentic Systems

DSPy's iterative optimization principles align perfectly with Test-Driven Development (TDD) and Behavior-Driven Development (BDD) methodologies (like RSpec for Ruby/Rails). It's as if DSPy was designed specifically for building reliable, testable agentic systems:

DSPy Core Strength	Agentic System Need	SuperOptiX Innovation
Optimization-First	Reliable agent behavior	RSpec-style BDD specifications
Assertions & Evaluations	Agent validation	Multi-tier evaluation framework
Signature Generation	Context engineering	Advanced prompt optimization
Module Composition	Multi-agent coordination	Orchestra-level optimization

🚀 SuperOptiX: The Agentic Evolution of DSPy

SuperOptiX includes sophisticated modules designed specifically for agentic and multi-agent scenarios that extend beyond the standard DSPy offering:

🤝 Multi-Agent Coordination Modules - Advanced orchestration patterns for complex multi-agent scenarios
🔗 Protocol Support Modules - MCP (Model Context Protocol) and A2A (Agent-to-Agent) integration
🧠 Memory-Optimized Modules - Context-aware memory management across agent interactions
🛡️ Guardrail Modules - Safety and compliance checks for production deployment

⚡ Automatic Pipeline Generation from Specifications

SuperOptiX uses DSPy's optimization engine to automatically generate entire agent pipelines from high-level SuperSpec definitions. This includes:

Signature Generation: Automatic DSPy signature creation from agent specifications
Module Composition: Intelligent chaining of DSPy modules for complex workflows
Evaluation Setup: Built-in evaluation metrics and test scenarios
Optimization Loops: Automated optimization with multiple DSPy optimizers

🏗️ Progressive Tier System

💼 Commercial Package with Free Tiers

SuperOptiX is a commercial package, but we offer Oracle and Genie tiers completely free to try! This allows you to experience the power of SuperOptiX before considering our commercial tiers (Protocol, Superagent, and Sovereign) for advanced enterprise features.

Scale from simple to enterprise complexity with our 5-tier architecture inspired by Nick Bostrom's Superintelligence and Sam Altman's AGI stages:

🧙‍♂️ Oracles (Free to Try)

Simple, fast question answering system that involves interaction with LLMs and responding to your queries.

Status: ✅ Available (Free to Try)
Use Case: Basic automation, simple Q&A
Complexity: Low
Cost: 🆓 Free

Key Features: Single-step reasoning, Template-based responses, Built-in optimization

🧞‍♂️ Genies (Free to Try)

Multi-step reasoning agents that involve interaction with LLMs and external systems like knowledge and tools.

Status: ✅ Available (Free to Try)
Use Case: Customer service, content creation
Complexity: Medium
Cost: 🆓 Free

Key Features: Multi-step reasoning with ReAct, Dynamic tool selection, RAG integration

🎭 Protocols (Commercial)

Highly advanced tier with support of industry-evolving protocols like MCP and A2A.

Status: 🔒 Commercial (Contact Us)
Use Case: Business processes, decision making
Complexity: High
Cost: 💼 Commercial

Key Features: MCP & A2A protocols, External API integration, Advanced optimizers

🤖 Superagents (Commercial)

Multi-agent systems with coordination where a lead agent may spawn automated subagents.

Status: 🔒 Commercial (Work in Progress)
Use Case: Complex business workflows
Complexity: Expert
Cost: 💼 Commercial

Key Features: Superagent orchestration, AgentLines governance, Human-in-the-loop

👑 Sovereigns (Commercial)

Autonomous AI systems suitable for large-scale AI operations and enterprise workflows. These are the highest level of AI autonomy with advanced multi-agent orchestration and strategic planning.

Status: 🔒 Commercial (Coming Soon) | Use Case: Large-scale AI operations | Complexity: Enterprise | Cost: 💼 Commercial

Key Features: Advanced multi-agent orchestration, Strategic planning, Automatic agent discovery, Ephemeral agents

📊 View Full Tier Details →

🔧 Key Capabilities

✅

RSpec-Style BDD

Write behavior-driven specifications (like RSpec for Ruby) and validate agent performance automatically.

📈

Performance Optimization

GEPA optimizer delivers proven performance improvements across all frameworks.

🎼

Multi-Agent Orchestration

Build complex multi-agent systems with coordination and communication.

🔧

Production Operations

Deploy with confidence using built-in monitoring, tracing, and observability.

🧠

Memory Systems

Short-term, long-term, and vector memory for intelligent agent interactions.

📚

RAG Integration

Connect to ChromaDB, LanceDB, Weaviate, Qdrant, Milvus, and more.

💻

Model Management

Support for OpenAI, Anthropic, HuggingFace, Ollama, MLX, and local models.

☁️

Local & Cloud Inference

Run locally with Ollama/MLX or scale to cloud with vLLM/SGLang/TGI.

🚀 Ready to Build the Future of AI?

Join the SuperOptiX movement to create intelligent, optimized, and reliable AI agents.

🆓 Start FREE today with Oracle & Genie tiers - no credit card required!

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👑 SuperOptiX: The King of Agent Frameworks - Where optimization meets intelligence.