The First Agentic AI Framework Purpose-Built for Optimization at Its Core, Evaluation from the Start, and Multi-Agent Intelligence by Design, to Build Production-Grade Agents.
Full-Stack Agentic AI Framework
The First Agentic AI Framework Purpose-Built for Optimization at Its Core, Evaluation from the Start, and Multi-Agent Intelligence by Design.
Get a quick overview of SuperOptiX in just 1 minute
Agents are defined and validated using BDD-style specs before orchestration
Human-readable DSL with iterative optimization
Declarative optimization with transparent tracing
Multi-agent coordination and protocol support
Framework-agnostic with user choice
Automatic decomposition and testing
Complete pipeline from spec to deployment
Domain-specific language for agents
Modular memory layers for agents
Native evaluation suite for agents
Structured context frames and templating
Plug-and-play with multiple providers
Discover and deploy pre-optimized components
Observability, replay, and debugging
SuperOptiX is designed for forward-thinking teams and individuals who are ready to build the next generation of AI systems.
Building agent copilots and autonomous systems
Create sophisticated multi-agent architectures with built-in optimization, testing, and deployment pipelines.
Deploying LLM-native workflows and agent stacks
Accelerate your AI product development with composable, scalable agent infrastructure.
Seeking scalable, composable, multi-agent infrastructure
Build enterprise-grade AI systems with robust governance, security, and observability.
Experimenting with agent simulations and protocols
Push the boundaries of AI research with advanced agent architectures and simulation capabilities.
Wanting reproducible, test-driven, fine-tuned agents
Achieve peak performance with systematic optimization, testing, and continuous improvement.
We're at a pivotal moment in AI evolution. The next wave of GenAI is agentic, multi-step, multi-agent, memory-rich, and orchestration-first.
Single-purpose AI assistants are being replaced by sophisticated multi-agent systems
Multi-step, multi-agent, memory-rich, and orchestration-first architectures
Today's solutions lack proper evaluation, testing, and production readiness
Purpose-built framework with DSPy optimization and SuperSpecX DSL
Full Stack Agentic AI Framework
SuperOptiX is Full-Stack Agentic AI Framework designed for Context and Agent Engineering.
Unlike most frameworks that bolt on evals and monitoring as an afterthought, SuperOptiX makes evaluation, optimization, and guardrails core to the development lifecycle.
Declarative by Design
Optimized by Default
Orchestration-Ready
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.
DSPy-powered optimization engine automatically improves your agents' performance based on evaluation metrics, making optimization a first-class citizen.
SuperSpec provides a Kubernetes-style declarative specification for AI agents. You declare what you want, not how to get it.
SuperOptiX brings proven methodologies from traditional software development to AI agents. Write tests first, then build agents that pass them. Use BDD scenarios as both test cases and training data for optimization.
SuperSpec → DSPy Pipeline
super agent compile developerBDD Test Runner
super agent evaluate developerDSPy BootstrapFewShot
super agent optimize developerProduction Ready
super agent run developerExecute specifications with pytest-quality output and multi-criteria evaluation
Automatic prompt optimization using BDD scenarios as training data
Comprehensive agent validation with semantic similarity and quality metrics
Significant improvements required before deployment
Minor improvements needed for production
Production ready - deploy with confidence
SuperOptiX provides a complete workflow from local LLM setup to multi-agent orchestration. Build production-ready AI teams that actually deliver results.
Declarative agent specifications with YAML
DSPy-powered automatic performance improvement
Coordinate teams of agents for complex tasks
Built-in monitoring, evaluation, and deployment
Configure local language models for privacy and speed
curl -fsSL https://ollama.com/install.sh | shsuper model install llama3.2:1bsuper model install llama3.2:8bsuper model run llama3.2:3b "Hello world"
Create your SuperOptiX project structure
super init swecd swels -la # View project structure
Download pre-built agents from marketplace
super marketplace install agent developersuper agent pull qa_engineersuper agent list --pre-built
Convert YAML playbooks to executable DSPy pipelines
super agent compile developer# Creates DSPy pipeline with BDD tests# Output: developer_pipeline.py
Professional spec execution with quality gates
super agent evaluate developer# Runs BDD scenarios as tests# Multi-criteria scoring system
Automatic prompt optimization using BDD scenarios
super agent optimize developer# Uses BDD scenarios for training# Saves optimized weights
⚠️ Resource-intensive: Requires 16GB+ GPU RAM and may incur cloud costs. Use responsibly.
Run optimized agents to accomplish specific tasks
super agent run developer --goal "Build REST API"# Uses optimized pipeline automaticallysuper agent logs developer
Coordinate multiple agents for complex workflows. Add few agents to your orchestra before running it.
super orchestra create sdlcsuper orchestra run sdlc --goal "Build full-stack app"super orchestra status sdlc
Monitor agent performance and debug execution
super observe enable developersuper observe dashboardsuper observe traces developersuper observe analyze developer --days 7
Automated testing and deployment pipeline
super agent evaluate developer --format jsonsuper agent evaluate developer --format junit# GitHub Actions, GitLab CI, Jenkins# Quality gates: pass_rate ≥ 80%
Watch how to build an AI team in 10 minutes with our CLI workflow
This demo shows the complete workflow from installation to running your first AI agent
From simple agents to complex orchestras - all in one framework
Progressive complexity from simple to enterprise-level agents. Scale your AI systems as your needs grow.
Simple, fast question answering system that involves interaction with LLMs and responding to your queries. No connection to external data.
Multi-step reasoning agents that involve interaction with LLMs and external systems like knowledge and tools using ReAct.
Highly advanced tier with support of industry-evolving protocols like MCP and A2A, covering all features from Oracles and Genies.
Multi-agent systems with coordination where a lead agent called Superagent may spawn automated subagents and work with other superagents.
Autonomous AI systems suitable for large-scale AI operations and enterprise workflows with the highest level of AI autonomy.
Start with Oracles for simple automation, progress to Genies for complex workflows, and advance to enterprise tiers for large-scale operations.
Globally aligned local optimization for compound AI systems. Optimize prompts, hyperparameters, routing, and more, across your favorite stacks.
Single-iteration optimization by prompting for fast improvements
Multi-iteration optimization for deeper prompt refinement
Cooperative optimization across multiple components
OpenAI SDK, CrewAI, AutoGen, and DSPy adapters
pip install "superoptix[optimas,optimas-openai]"super init test_optimas && cd test_optimassuper agent pull optimas_openaisuper agent compile optimas_openai --target optimas-openaisuper agent evaluate optimas_openai --engine optimas --target optimas-openaiSUPEROPTIX_OPRO_NUM_CANDIDATES=3 super agent optimize optimas_openai --engine optimas --target optimas-openai --optimizer oproFully Working
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Comprehensive observability with real-time monitoring, advanced analytics, and seamless integrations with MLFlow and LangFuse.
Live agent execution tracking with instant performance insights
Deep insights into agent behavior and optimization opportunities
Powerful debugging capabilities for troubleshooting agent issues
Comprehensive trace data storage and retrieval system
super observe listsuper observe traces developer_20250714_200501super observe dashboard --auto-opensuper observe analyze developer --days 7Connect with industry-leading observability platforms for enhanced monitoring and analysis.
ML experiment tracking and model management
SuperSpec is our declarative DSL that makes agent building as simple as writing a specification.
Think of it as "Kubernetes for AI agents" - you describe what you want, and SuperOptiX builds the entire pipeline.
Write agent specifications in YAML using our domain-specific language
Define behavior-driven scenarios that serve as both tests and training data
Automatic prompt and context optimization using DSPy framework
Generate complete DSPy pipelines from high-level specifications
Describe what you want, not how to get it. SuperSpec handles the complexity of agent implementation.
Every agent is automatically optimized using BDD scenarios as training data for maximum performance.
Built-in evaluation, monitoring, and quality gates ensure your agents are ready for production deployment.