Create a fully functional AI-powered software development team (Developer, QA Engineer, DevOps Engineer) with automated optimization, multi-agent orchestration, and production-ready deployment.
git --version)Set PYTHONUTF8=1 environment variable:
set PYTHONUTF8=1
Choose your preferred installation method
This guide uses DSPy for GEPA optimization and orchestration.
1. Install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh
2. Install SuperOptiX with DSPy:
uv pip install -U superoptix[frameworks-dspy]
3. Verify installation:
super --help
After cd swe, you can use either Super CLI or traditional commands
Talk in Natural Language
Precise Commands
💡 Pro Tip: Start with Super CLI to learn, then switch to traditional CLI for automation and scripting
Follow these 10 steps to build your AI development team (using traditional CLI commands)
Create your software engineering project structure.
super init swe cd swe
Created structure: agents/, evals/, guardrails/, knowledge/, memory/, optimizers/, orchestras/, pipelines/, protocols/, servers/, teams/, tools/
Download a pre-built developer agent from the marketplace.
super agent pull developer
Downloaded: agents/developer_playbook.yaml with complete SuperSpec DSL configuration
Generate executable Python code from the SuperSpec DSL.
super agent compile developer
What happens: Your agent specification is compiled into executable code with built-in evaluation scenarios, ready to run in pipelines/developer_pipeline.py
Test against RSpec-style BDD scenarios to establish baseline performance.
super agent evaluate developer
💡 Don't worry about initial scores!
Agents start with generic prompts. Optimization in Step 5 will significantly improve performance!
GEPA automatically improves agent instructions through evolutionary optimization!
super agent optimize developer --auto medium
⚡ What happens during optimization:
Load optimized agent and measure improvement.
super agent evaluate developer --load-optimized
🏆 Significant performance improvements!
GEPA optimization dramatically increases agent accuracy and reliability
Test your optimized agent on a real-world task.
super agent run developer --goal "Implement a user registration API endpoint with email validation"
Pull QA Engineer and DevOps Engineer to complete the team.
# Pull QA engineer super agent pull qa_engineer # Pull DevOps engineer super agent pull devops_engineer # Compile both agents super agent compile qa_engineer super agent compile devops_engineer
💡 Pro Tip: You can optimize these agents too!
For this quick start, we'll use them as-is for the orchestra. But feel free to run optimize + evaluate on them for even better results!
✅ You now have a complete 3-agent team ready for orchestration!
The Kubernetes moment! Orchestrate your agents like a container platform.
1. Create the Orchestra:
super orchestra create sdlc
Auto-detects your agents and creates orchestras/sdlc_orchestra.yaml
2. List Available Orchestras:
super orchestra list
3. Run the Orchestra:
super orchestra run sdlc --goal "Build a complete web application for a task management system with user authentication, CRUD operations, and real-time notifications. Include comprehensive testing and deployment configuration."
🎭 What Just Happened (The Magic!):
Developer analyzed the goal and provided implementation reasoning
DevOps Engineer created CI/CD pipeline based on developer's output
QA Engineer created comprehensive test plan based on both outputs
The orchestra automatically:
📋 Generated Artifacts:
🎉 The Kubernetes Moment!
SuperOptiX just orchestrated your agents like a container orchestration platform:
Inspect agent traces and monitor performance.
View traces:
super observe traces developer
Launch dashboard:
super observe dashboard
SuperOptiX comes with DSPy and GEPA by default, giving you the most powerful optimization out of the box.
But you're not locked in! Use the exact same workflow with any of 6+ frameworks:
Stanford research framework with powerful optimization capabilities
Best for:
Quick start:
pip install -U superoptix[frameworks-dspy]
super agent pull sentiment_analyzer
Simple agents with OpenAI's powerful models
Best for:
Quick start:
pip install -U superoptix[frameworks-openai]
super agent pull assistant_openai
Multi-agent collaboration
Best for:
Install:
pip install -U superoptix[frameworks-crewai]
Pull agent:
super agent pull researcher_crew
⚠️ Cannot be installed with DSPy (json-repair conflict)
Gemini 2.0 native
Best for:
Install:
pip install -U superoptix[frameworks-google]
Pull agent:
super agent pull assistant_adk
Setup API key:
export GOOGLE_API_KEY=your-google-api-key
Enterprise Azure
Best for:
Install:
pip install -U superoptix[frameworks-microsoft]
Pull agent:
super agent pull assistant_microsoft
Complex planning
Best for:
Install:
pip install -U superoptix[frameworks-deepagents]
Pull agent:
super agent pull research_agent_deepagents
Same command works on ALL 6 frameworks! This is the revolutionary power of SuperOptiX.
# Works on DSPy, OpenAI SDK, CrewAI, Google ADK, Microsoft, DeepAgents! super agent optimize <agent_name> --auto medium # Examples: super agent optimize sentiment_analyzer --auto medium # DSPy super agent optimize assistant_openai --auto medium # OpenAI super agent optimize researcher_crew --auto medium # CrewAI super agent optimize assistant_adk --auto medium # Google ADK super agent optimize assistant_microsoft --auto medium # Microsoft super agent optimize research_agent_deepagents --auto medium # DeepAgents
Quick iteration, prototyping (2-3 iterations)
Most use cases (5 iterations)
Critical production agents (10+ iterations)
✅ Framework-Agnostic Optimization
No framework-specific tricks. No vendor lock-in. Just pure, portable optimization across your entire agentic stack.
# 1. Initialize super init openai_project cd openai_project # 2. Pull OpenAI agent super agent pull assistant_openai # 3. Compile super agent compile assistant_openai # 4. Baseline evaluation super agent evaluate assistant_openai # Result: Check initial performance # 5. Optimize with GEPA super agent optimize assistant_openai --auto medium # GEPA optimizes agent # 6. Re-evaluate super agent evaluate assistant_openai --load-optimized # Result: 100% pass rate! # 7. Run super agent run assistant_openai
You've Explored the Future of Agent Development!
You just experienced a framework-agnostic agent platform with optimization at its core
Developer, QA, DevOps working together
Automatic performance improvements
Production-grade validation
DSPy, OpenAI, CrewAI, Google, Microsoft, DeepAgents
Production-ready artifacts generated
🔓 Zero Lock-In
Switch frameworks anytime. Your agents, optimizations, and tests stay portable.
⚡ Optimization-First
GEPA works universally across all frameworks. Same command, consistent improvements.
🎯 Production-Ready
Built-in evaluation, orchestration, and monitoring from day one.
First platform that works universally across all major agent frameworks
GEPA optimizer built-in from the start, not an afterthought
Orchestra features for seamless agent coordination