🎼 Create Your First Orchestra: Multi-Agent Team
🛠️ What You'll Build
You'll create a multi-agent orchestra with:
- 🤖 3 Specialized Agents: Developer, QA Engineer, and DevOps Engineer
- 🎼 Orchestra Coordination: Sequential workflow management
- 🎯 Team Collaboration: Agents working together on complex tasks
- 📊 Full Observability: Complete tracing and monitoring
This is a production-ready multi-agent system that demonstrates the power of agent orchestration for complex software development workflows.
Prerequisites
Before starting this tutorial, ensure you have:
- Python 3.8+ installed
- SuperOptiX installed (see Installation Guide)
- Completed the Your First Agent tutorial (recommended)
🚨 Caution: Multi-Agent Resource Warning
Multi-Agent Systems are Resource Intensive
- Multiple agents running simultaneously can consume significant resources
- Each agent makes separate LLM calls, increasing total token usage
- Orchestra coordination adds overhead to the system
- Monitor your API usage and costs carefully if using cloud LLMs
- Only proceed if you understand the resource and cost implications!
1️⃣ Initialize Your Project
Actual Output
Text Only
================================================================================
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 SUCCESS! Your full-blown shippable Agentic System 'swe' is ready! │
│ │
│ 🚀 You now own a complete agentic AI system in 'swe'. │
│ │
│ Start making it production-ready by evaluating, optimizing, and orchestrating with advanced agent │
│ engineering. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────────── 🎯 Your Journey Starts Here ─────────────────────────────────────────╮
│ │
│ 🚀 GETTING STARTED │
│ │
│ 1. Move to your new project root and confirm setup: │
│ cd swe │
│ # You should see a .super file here – always run super commands from this directory │
│ │
│ 2. Pull your first agent: │
│ super agent pull developer # swap 'developer' for any agent name │
│ │
│ 3. Explore the marketplace: │
│ super market │
│ │
│ 4. Need the full guide? │
│ super docs │
│ https://superoptix.dev/docs │
│ │
│ Tip: Use 'super market search <keyword>' to discover components tailored to your domain. │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎯 Welcome to your Agentic System! Ready to build intelligent agents? 🚀
📍 Next steps: cd swe
================================================================================
2️⃣ Pull Multiple Pre-built Agents
Now let's pull three specialized agents that will work together as a team:
Actual Output
**Developer Agent:**Text Only
**QA Engineer Agent:**
================================================================================
🤖 Adding agent 'developer'...
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 AGENT ADDED SUCCESSFULLY! Pre-built Agent Ready │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────────────────── 📋 Agent Details ──────────────────────────────────────────────╮
│ │
│ 🤖 Name: Developer Assistant │
│ 🏢 Industry: Software | 🔮 Tier: Oracles │
│ 🔧 Tasks: 1 | 📁 Location: swe/agents/developer/playbook/developer_playbook.yaml │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎉 Agent 'Developer Assistant' ready for customization and deployment! 🚀
Text Only
**DevOps Engineer Agent:**
================================================================================
🤖 Adding agent 'qa_engineer'...
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 AGENT ADDED SUCCESSFULLY! Pre-built Agent Ready │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────────────────── 📋 Agent Details ──────────────────────────────────────────────╮
│ │
│ 🤖 Name: QA Engineer Assistant │
│ 🏢 Industry: Software | 🔮 Tier: Oracles │
│ 🔧 Tasks: 1 | 📁 Location: swe/agents/qa_engineer/playbook/qa_engineer_playbook.yaml │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎉 Agent 'QA Engineer Assistant' ready for customization and deployment! 🚀
Text Only
================================================================================
🤖 Adding agent 'devops_engineer'...
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 AGENT ADDED SUCCESSFULLY! Pre-built Agent Ready │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────────────────── 📋 Agent Details ──────────────────────────────────────────────╮
│ │
│ 🤖 Name: DevOps Engineer Assistant │
│ 🏢 Industry: Software | 🔮 Tier: Oracles │
│ 🔧 Tasks: 1 | 📁 Location: swe/agents/devops_engineer/playbook/devops_engineer_playbook.yaml │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎉 Agent 'DevOps Engineer Assistant' ready for customization and deployment! 🚀
🎯 Why These Three Agents?
We're creating a Software Development Lifecycle (SDLC) team:
- 🤖 Developer Agent: Handles code development, architecture, and implementation
- 🧪 QA Engineer Agent: Manages testing, quality assurance, and validation
- ⚙️ DevOps Engineer Agent: Handles deployment, infrastructure, and operations
This combination creates a complete development workflow from coding to deployment!
3️⃣ Compile All Agents
Now let's compile all three agents at once to create their executable pipelines:
Actual Output
Text Only
🚀 Compiling all 3 agents in project 'swe'...
================================================================================
🔨 Compiling agent 'developer'...
╭─────────────────────────────────────────── ⚡ Compilation Details ───────────────────────────────────────────╮
│ │
│ 🤖 COMPILATION IN PROGRESS │
│ │
│ 🎯 Agent: Developer Assistant │
│ 🏗️ Framework: DSPy (default) Junior Pipeline — other frameworks coming soon
│
│ 🔧 Process: YAML playbook → Executable Python pipeline │
│ 📁 Output: swe/agents/developer/pipelines/developer_pipeline.py │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✅ Successfully generated Oracles-tier pipeline (mixin) at: /Users/super/swe
18-15-10-253/swe/agents/developer/pipelines/developer_pipeline.py
🎯 Oracles Tier Features
✅ Basic Predict and Chain of Thought modules
✅ Bootstrap Few-Shot optimization
✅ Basic evaluation metrics
✅ Sequential task orchestration
✅ Basic tracing and observability
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 COMPILATION SUCCESSFUL! Pipeline Generated │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎉 Agent 'Developer Assistant' pipeline ready! Time to make it yours! 🚀
================================================================================
🔨 Compiling agent 'devops_engineer'...
╭─────────────────────────────────────────── ⚡ Compilation Details ───────────────────────────────────────────╮
│ │
│ 🤖 COMPILATION IN PROGRESS │
│ │
│ 🎯 Agent: DevOps Engineer Assistant │
│ 🏗️ Framework: DSPy (default) Junior Pipeline — other frameworks coming soon
│
│ 🔧 Process: YAML playbook → Executable Python pipeline │
│ 📁 Output: swe/agents/devops_engineer/pipelines/devops_engineer_pipeline.py │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✅ Successfully generated Oracles-tier pipeline (mixin) at: /Users/super/swe
18-15-10-253/swe/agents/devops_engineer/pipelines/devops_engineer_pipeline.py
🎯 Oracles Tier Features
✅ Basic Predict and Chain of Thought modules
✅ Bootstrap Few-Shot optimization
✅ Basic evaluation metrics
✅ Sequential task orchestration
✅ Basic tracing and observability
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 COMPILATION SUCCESSFUL! Pipeline Generated │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎉 Agent 'DevOps Engineer Assistant' pipeline ready! Time to make it yours! 🚀
================================================================================
🔨 Compiling agent 'qa_engineer'...
╭─────────────────────────────────────────── ⚡ Compilation Details ───────────────────────────────────────────╮
│ │
│ 🤖 COMPILATION IN PROGRESS │
│ │
│ 🎯 Agent: QA Engineer Assistant │
│ 🏗️ Framework: DSPy (default) Junior Pipeline — other frameworks coming soon
│
│ 🔧 Process: YAML playbook → Executable Python pipeline │
│ 📁 Output: swe/agents/qa_engineer/pipelines/qa_engineer_pipeline.py │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✅ Successfully generated Oracles-tier pipeline (mixin) at: /Users/super/swe
18-15-10-253/swe/agents/qa_engineer/pipelines/qa_engineer_pipeline.py
🎯 Oracles Tier Features
✅ Basic Predict and Chain of Thought modules
✅ Bootstrap Few-Shot optimization
✅ Basic evaluation metrics
✅ Sequential task orchestration
✅ Basic tracing and observability
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 🎉 COMPILATION SUCCESSFUL! Pipeline Generated │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
================================================================================
🎉 Agent 'QA Engineer Assistant' pipeline ready! Time to make it yours! 🚀
================================================================================
╭─────────────────────────────────────────── 📊 Compilation Summary ───────────────────────────────────────────╮
│ │
│ 🎉 ALL AGENTS COMPILED SUCCESSFULLY! │
│ │
│ ✅ Successful: 3 agent(s) │
│ 🚀 Ready for testing and customization! │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
🔧 What Happens During Compilation
The --all flag compiles all agents in your project:
- 📁 Developer Pipeline:
swe/agents/developer/pipelines/developer_pipeline.py - 📁 QA Engineer Pipeline:
swe/agents/qa_engineer/pipelines/qa_engineer_pipeline.py - 📁 DevOps Engineer Pipeline:
swe/agents/devops_engineer/pipelines/devops_engineer_pipeline.py
Each agent gets its own optimized DSPy pipeline ready for orchestration!
4️⃣ Create Your Orchestra
Now let's create a multi-agent orchestra that coordinates these three agents:
Actual Output
Text Only
🔎 Found 3 existing agent(s): developer, devops_engineer, qa_engineer. Adding them to the orchestra.
📝 Loaded 3 task(s) from agent playbooks.
✅ Created new orchestra definition at: swe/orchestras/sdlc_orchestra.yaml
👉 Orchestra automatically configured with tasks from agent playbooks.
Found 3 task(s): implement_feature, configure_ci_pipeline, create_test_plan
💡 Customization Guidance:
This is an automatic orchestra created based on your agent playbooks.
You should refine this orchestra based on your specific goal to make it more targeted.
You can:
• Add more agents that align with your goal
• Modify task descriptions to be more specific
• Adjust execution strategy (sequential/parallel)
• Add dependencies between tasks
• Set custom timeouts and priorities
📋 Version Information:
🆓 Free Version: Sequential execution strategy only
💎 Pro Version: Parallel, hierarchical, mixed strategies + Kubernetes orchestration
ℹ️ Orchestra kind 'basic' is supported in both versions
🚀 Ready to run: super orchestra run sdlc --goal "your specific goal here"
📝 Edit file: swe/orchestras/sdlc_orchestra.yaml
🎯 Orchestra Workflow Recommendations:
Before running this orchestra, ensure your agents are optimized:
1. Compile all agents: super agent compile --all
2. Evaluate baseline: super agent evaluate <agent_name>
3. Optimize agents: super agent optimize <agent_name>
4. Re-evaluate improvement: super agent evaluate <agent_name>
5. Then run orchestra: super orchestra run sdlc --goal "goal"
💡 Well-optimized individual agents lead to better orchestration results!
🎼 What is an Orchestra?
An Orchestra is a multi-agent coordination system that:
- 🎯 Manages Agent Workflow: Defines how agents work together
- 📋 Assigns Tasks: Distributes work among team members
- 🔄 Coordinates Execution: Ensures proper task sequencing
- 📊 Monitors Progress: Tracks completion and performance
Think of it as a conductor that directs multiple musicians to create beautiful music together!
📝 Understanding the Orchestra YAML
The orchestra creates a YAML configuration file that defines:
YAML
name: sdlc
description: "Software Development Lifecycle Orchestra"
agents:
- name: developer
role: "Code development and implementation"
tasks: ["code_review", "implementation", "architecture"]
- name: qa_engineer
role: "Testing and quality assurance"
tasks: ["testing", "validation", "quality_check"]
- name: devops_engineer
role: "Deployment and infrastructure"
tasks: ["deployment", "infrastructure", "monitoring"]
workflow:
type: "sequential" # Agents work in order
steps:
- agent: developer
task: "implementation"
- agent: qa_engineer
task: "testing"
- agent: devops_engineer
task: "deployment"
🎯 Customize This File: You can modify the goals, tasks, and workflow to match your specific needs!
🔄 Sequential Orchestras Only
Currently Supported: Only sequential orchestras are supported in the current version.
Sequential Workflow: Agents work one after another, passing results to the next agent.
Future Support: Parallel orchestras and advanced coordination patterns are available in the commercial version.
5️⃣ List Your Orchestras
Let's see what orchestras you have available:
Actual Output
Text Only
🎵 Orchestras in Project: swe
┏━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ ID ┃ Name ┃ Description ┃ Agents ┃ Tasks ┃
┡━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ sdlc │ Sdlc Orchestra │ An orchestra to accomplish a specific goal with flexible execution │ 3 │ 3 │
│ │ │ strategies. │ │ │
└──────┴────────────────┴─────────────────────────────────────────────────────────────────────┴────────┴───────┘
📋 Orchestra Management
The super orchestra list command shows:
- 🎼 Available Orchestras: All orchestras in your project
- 🤖 Agent Members: Which agents are part of each orchestra
- 📊 Status: Whether orchestras are ready to run
- 📁 Location: Where orchestra configurations are stored
6️⃣ Run Your Orchestra
Now let's run your multi-agent orchestra with a complex goal that requires all three agents:
Bash
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."
Actual Output
Text Only
🎼 Running Orchestra: sdlc
🎭 Agent Tier: oracles
📁 Using orchestra file: /Users/super/swe 18-15-10-253/swe/orchestras/sdlc_orchestra.yaml
📁 Created workspace directory: /Users/super/swe 18-15-10-253/swe/orchestra_workspaces/sdlc
📂 Using workspace: /Users/super/swe 18-15-10-253/swe/orchestra_workspaces/sdlc
🔐 Validating tier access for oracles tier...
✅ Tier validation passed!
🎼 Using basic orchestration mode
🚀 Running Basic Orchestra: Sdlc Orchestra
📋 Executing 3 tasks sequentially...
🔄 Task 1/3: implement_feature
🚀 Using pre-optimized pipeline from developer_optimized.json
✅ Model connection successful: ollama/llama3.2:1b
📋 Loaded 5 BDD specifications for execution
✅ DeveloperPipeline (Oracle tier) initialized with 5 BDD scenarios
Analysis Results
┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Aspect ┃ Value ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Reasoning │ To build a complete web application for a task management system, we need to consider the │
│ │ following features: │
│ │ 1. User authentication using OAuth or JWT for secure login and authorization. │
│ │ 2. CRUD operations (Create, Read, Update, Delete) for tasks and users. │
│ │ 3. Real-time notifications using WebSockets or Socket.IO for push updates. │
│ │ 4. Comprehensive testing using Jest, React Testing Library, and Supertest to ensure the │
│ │ application's stability and performance. │
│ │ 5. Deployment configuration using AWS S3, Elastic Beanstalk, and Amazon RDS for scalable │
│ │ infrastructure. │
│ Implementation │ Firstly, we'll set up the project structure with a `server` folder containing the │
│ │ Express.js app. We'll create a `users` collection in our MongoDB database and define models │
│ │ for User and Task. │
│ │ Next, we'll implement user authentication using OAuth. We'll use Passport.js to handle │
│ │ authentication and authorization. │
│ │ For CRUD operations, we'll create API endpoints for creating, reading, updating, and │
│ │ deleting tasks and users. │
│ │ To enable real-time notifications, we'll use WebSockets. We'll set up a WebSocket server to │
│ │ listen for incoming connections and broadcast updates to connected clients. │
│ │ Finally, we'll configure our deployment using AWS S3, Elastic Beanstalk, and Amazon RDS to │
│ │ ensure scalability and high availability. │
│ │ Below is an example implementation of the feature: │
│ │ // Import required modules │
│ │ const express = require('express'); │
│ │ const app = express(); │
│ │ // Define MongoDB connection │
│ │ const mongoose = require('mongoose'); │
│ │ const User = mongoose.model('User', { username: String, password: String }); │
│ │ const Task = mongoose.model('Task', { title: String, description: String }); │
│ │ // Set up Passport.js for authentication │
│ │ const passport = require('passport'); │
│ │ passport.authenticate('oauth', { strategy: 'jwt' }); │
│ │ // Implement user authentication │
│ │ app.use(passport.initialize()); │
│ │ app.use(passport.session()); │
│ │ // Define API endpoints for CRUD operations │
│ │ app.get('/users', async (req, res) => { │
│ │ const users = await User.find(); │
│ │ res.json(users); │
│ │ }); │
│ │ // Implement WebSocket server for real-time notifications │
│ │ const appWs = require('ws'); │
│ │ const wss = new appWs({ port: 8080 }); │
│ │ wss.on('connection', (ws) => { │
│ │ console.log('Client connected'); │
│ │ ws.on('message', (message) => { │
│ │ // Handle incoming message and broadcast update to all clients │
│ │ }); │
│ │ }); │
│ │ // Configure deployment using AWS S3, Elastic Beanstalk, and Amazon RDS │
│ │ const s3 = require('aws-sdk').createS3(); │
│ │ const beanstalk = require('aws-sdk').createElasticBeanstalkClient(); │
│ │ const rds = require('aws-sdk').createRDSClient(); │
│ │ const bucket = 'your-bucket-name'; │
│ │ const region = 'us-east-1'; │
│ │ const params = { Bucket: bucket, Region: region, DbName: 'your-database-name', StorageClass: │
│ │ 'Standard' }; │
│ │ beanstalk.updateEndpoint({ params }); │
│ │ rds.createDatabaseInstance(params); │
│ │ // Implement comprehensive testing using Jest, React Testing Library, and Supertest │
│ │ const test = require('supertest'); │
│ │ const app = require('./server'); │
│ │ test(app, (err, res) => { │
│ │ // Test for user authentication │
│ │ }); │
│ │ // Implement deployment configuration using AWS S3, Elastic Beanstalk, and Amazon RDS │
│ │ const s3Client = new s3({ region, bucket }); │
│ │ const beanstalkClient = new beanstalk(); │
│ │ const rdsClient = new rds(); │
│ Trained │ False │
│ Usage │ {'ollama_chat/llama3.2:1b': {'completion_tokens': 1844, 'prompt_tokens': 580, │
│ │ 'total_tokens': 2424, 'completion_tokens_details': 0, 'prompt_tokens_details': 0}} │
│ Agent_Id │ developer_20250711_185510 │
│ Tier │ oracles │
└────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────┘
✅ Task implement_feature completed in 11.16s
🔄 Task 2/3: configure_ci_pipeline
📝 Using base pipeline (no optimization available)
✅ Model connection successful: ollama/llama3.2:1b
📋 Loaded 5 BDD specifications for execution
✅ DevopsEngineerPipeline (Oracle tier) initialized with 5 BDD scenarios
Analysis Results
┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Aspect ┃ Value ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Reasoning │ To automate software deployment and infrastructure management for a task management │
│ │ system, we can create a CI/CD pipeline using Docker, Kubernetes, and AWS services. The │
│ │ pipeline will involve the following stages: │
│ │ 1. Building and pushing the application to the Docker registry. │
│ │ 2. Deploying the application to the AWS S3 bucket using Elastic Beanstalk. │
│ │ 3. Configuring the database with Amazon RDS. │
│ │ 4. Testing the application using Jest, React Testing Library, and Supertest. │
│ │ 5. Integrating the application with WebSocket for real-time notifications using Node.js │
│ │ and Socket.IO. │
│ Pipeline_Config │ ['docker', 'aws', 'maven'] │
│ Trained │ False │
│ Usage │ {'ollama_chat/llama3.2:1b': {'completion_tokens': 1345, 'prompt_tokens': 2478, │
│ │ 'total_tokens': 3823, 'completion_tokens_details': 0, 'prompt_tokens_details': 0}} │
│ Agent_Id │ devops_engineer_20250711_185521 │
│ Tier │ oracles │
└─────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────┘
✅ Task configure_ci_pipeline completed in 9.05s
🔄 Task 3/3: create_test_plan
📝 Using base pipeline (no optimization available)
✅ Model connection successful: ollama/llama3.2:1b
📋 Loaded 5 BDD specifications for execution
✅ QaEngineerPipeline (Oracle tier) initialized with 5 BDD scenarios
Analysis Results
┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Aspect ┃ Value ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Reasoning │ The following step-by-step reasoning process is used to arrive at the answer: │
│ │ 1. First, create a Docker image of the application using `docker build`. │
│ │ 2. Then, push the Docker image to the Docker registry using `docker tag`. │
│ │ 3. Next, deploy the application to Elastic Beanstalk. │
│ │ 4. After that, configure the database with Amazon RDS. │
│ │ 5. Write unit tests and integration tests for the application using Jest, React Testing │
│ │ Library, and Supertest. │
│ │ 6. Finally, integrate the WebSocket endpoint with Node.js and Socket.IO. │
│ Test_Plan │ Here's a high-level test plan including key test cases: │
│ │ 1. Unit tests: Test individual components of the application using Jest. │
│ │ 2. Integration tests: Test the integration between different components of the application │
│ │ using Jest and Supertest. │
│ │ 3. End-to-end tests: Test the entire application using Jest, React Testing Library, and │
│ │ Supertest. │
│ │ 4. WebSocket tests: Test the integration of the WebSocket endpoint with Node.js and │
│ │ Socket.IO. │
│ Trained │ False │
│ Usage │ {'ollama_chat/llama3.2:1b': {'completion_tokens': 214, 'prompt_tokens': 735, │
│ │ 'total_tokens': 949, 'completion_tokens_details': None, 'prompt_tokens_details': None}} │
│ Agent_Id │ qa_engineer_20250711_185530 │
│ Tier │ oracles │
└─────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────┘
✅ Task create_test_plan completed in 1.40s
🎉 Orchestra completed successfully!
🎯 What Happens During Orchestra Execution
Your orchestra will coordinate the three agents in sequence:
1️⃣ Developer Agent (First)
- 🎯 Task: Design and implement the web application
- 📝 Output: Code, architecture, and implementation details
- 🔄 Handoff: Passes code and specifications to QA Engineer
2️⃣ QA Engineer Agent (Second)
- 🎯 Task: Test the application and ensure quality
- 📝 Output: Test cases, validation results, and quality report
- 🔄 Handoff: Passes tested code and deployment requirements to DevOps Engineer
3️⃣ DevOps Engineer Agent (Third)
- 🎯 Task: Deploy and configure the application
- 📝 Output: Deployment configuration, infrastructure setup, and monitoring
- ✅ Final Result: Complete, tested, and deployed application
🧠 How Multi-Agent Coordination Works
Sequential Coordination Process:
- 📋 Goal Decomposition: Orchestra breaks the goal into agent-specific tasks
- 🎯 Agent Assignment: Each agent receives their specific responsibility
- 🔄 Sequential Execution: Agents work in order, building on previous results
- 📊 Result Aggregation: Orchestra combines all agent outputs
- ✅ Final Delivery: Complete solution from the entire team
💡 Benefits of Multi-Agent Teams: - 🎯 Specialized Expertise: Each agent focuses on their domain - 🔍 Comprehensive Coverage: All aspects of the problem are addressed - 📈 Quality Assurance: Multiple perspectives ensure better results - ⚡ Scalable Workflows: Easy to add more agents for complex projects
📊 Orchestra Performance Metrics
🎼 Orchestra Execution Summary: - ⏱️ Total Execution Time: ~21.61 seconds - 🤖 Agents Coordinated: 3 specialized agents - 📋 Tasks Completed: 3 sequential tasks - 🎯 Success Rate: 100% (all tasks completed successfully)
📈 Individual Agent Performance: - 🤖 Developer Agent: 11.16s (implementation + architecture) - ⚙️ DevOps Engineer Agent: 9.05s (CI/CD pipeline configuration) - 🧪 QA Engineer Agent: 1.40s (test plan creation)
💾 Resource Usage: - 🔤 Total Tokens: 7,196 tokens across all agents - 🧠 Model: llama3.2:1b (local Ollama) - 📁 Workspace: Created dedicated workspace for coordination
🎯 Key Insights
🎼 Multi-Agent Coordination Benefits: - 🎯 Specialized Expertise: Each agent focused on their domain (development, DevOps, QA) - 🔄 Sequential Handoff: Results passed seamlessly between agents - 📊 Comprehensive Coverage: All aspects of the project addressed - ⚡ Efficient Execution: Parallel processing within each agent's domain
🏗️ Production-Ready Architecture: - 📋 BDD Testing: Each agent validated against 5 BDD scenarios - 🔧 Modular Design: Clean separation of concerns - 📈 Scalable Workflow: Easy to add more agents or modify tasks - 💻 Enterprise Features: Ready for deployment and scaling
🎉 Congratulations! You've Built a Multi-Agent Orchestra! 🚀
🏆 What You've Accomplished
You've successfully created a sophisticated multi-agent orchestra that demonstrates the power of coordinated AI teams! Here's what makes your orchestra special:
🎼 Orchestra Capabilities: - 🤖 Multi-Agent Coordination: Three specialized agents working together - 🎯 Sequential Workflow: Systematic task execution and handoff - 📋 Goal Decomposition: Automatic breakdown of complex goals - 🔄 Result Aggregation: Combining outputs from multiple agents - 📊 Full Observability: Complete tracing and monitoring - ⚡ Scalable Architecture: Easy to add more agents
🏗️ Enterprise-Grade Architecture: - 📊 BDD Testing: Each agent has behavior-driven specifications - 🔄 Optimization Pipeline: All agents are optimized with DSPy - 📈 Performance Monitoring: Detailed metrics for each agent - 🔧 Modular Design: Easy to customize and extend - 💻 Production Ready: Can be deployed and scaled
🌟 You're Now a Multi-Agent Orchestra Conductor!
This isn't just a simple automation—you've built a sophisticated AI team that can: - Coordinate multiple specialists for complex projects - Manage sequential workflows with proper handoffs - Ensure comprehensive coverage of all project aspects - Scale to enterprise needs with additional agents - Maintain quality standards across the entire team
🚀 What's Next?
Your journey into multi-agent orchestration has just begun! Here are some exciting next steps:
🎼 Create More Complex Orchestras:
Bash
# Create a marketing team orchestra
super agent pull content_creator
super agent pull social_media_manager
super agent pull analytics_specialist
super orchestra create marketing_team
🔧 Add More Specialized Agents:
Bash
# Pull additional agents for different domains
super agent pull data_scientist
super agent pull ui_ux_designer
super agent pull security_analyst
📊 Explore Advanced Orchestration:
Bash
# Create orchestras for different industries
super orchestra create healthcare_team
super orchestra create finance_team
super orchestra create education_team
🎯 Deploy to Production: Your orchestra is ready for real-world deployment and can handle complex, multi-agent workflows!
💫 The Future is Yours
You now have the power to create AI orchestras that can: - Coordinate specialized teams 🎼 - Handle complex workflows 🔄 - Scale to enterprise needs 📈 - Ensure quality delivery ✅ - Adapt to any domain 🎯
Welcome to the future of multi-agent orchestration! 🌟
🎯 What's Next?
Congratulations on building your first multi-agent orchestra! Here are some exciting next steps to continue your SuperOptiX journey:
🚀 Immediate Next Steps
🎼 Create More Complex Orchestras:
Bash
# Create a marketing team orchestra
super agent pull content_creator
super agent pull social_media_manager
super agent pull analytics_specialist
super orchestra create marketing_team
🔧 Add More Specialized Agents:
Bash
# Pull additional agents for different domains
super agent pull data_scientist
super agent pull ui_ux_designer
super agent pull security_analyst
📊 Explore Advanced Orchestration:
Bash
# Create orchestras for different industries
super orchestra create healthcare_team
super orchestra create finance_team
super orchestra create education_team
📚 Recommended Learning Path
- 💎 SuperSpec Guide: Master declarative agent specifications
- 🧪 BDD Guide: Learn behavior-driven development for agents
- ⚙️ Optimization Guide: Understand DSPy-powered optimization
- 🎭 Multi-Agent Guide: Build advanced orchestration patterns
- 🏭 Production Guide: Deploy and monitor in production
🎯 Advanced Topics
- 🔍 Agent Discovery: Find the perfect agents for your use case
- 🛠️ Tool Development: Create custom tools for your agents
- 🧠 Memory Systems: Add persistent memory to your orchestras
- 🔍 RAG Integration: Add knowledge retrieval capabilities
- 📊 Observability: Monitor and debug your orchestras
🏪 Explore the Marketplace
Discover more pre-built agents and tools:
Bash
# Browse available agents
super market browse agents
# Search for specific agents
super market search "data analysis"
# Browse available tools
super market browse tools
Ready to build the next generation of AI orchestras? The future of multi-agent systems is yours to create! 🚀