JayGoga-Orchestra is a powerful AI agent orchestration framework for intelligent automation. It provides seamless coordination of AI agents for complex workflows and enterprise-grade automation solutions.
- 🎭 Dual Architecture: Choose between Classical (v1) and Modern (v2) orchestration patterns
- 🔮 Intelligent Orchestration: Advanced agent coordination and workflow management
- ⚡ High Performance: Optimized for speed and scalability
- 🛡️ Enterprise Ready: Built for production environments
- 🎨 Flexible Design: Adapt to any AI workflow requirement
- 📚 Rich Documentation: Comprehensive guides and examples
- 🔄 Seamless Integration: Easy integration with existing systems
pip install jaygoga-orchestraPerfect for structured workflows, enterprise environments, and predictable processes.
from jaygoga_orchestra.v1 import Agent, Task, Squad, Process
# Create specialized agents
analyst = Agent(
role="Senior Data Analyst",
goal="Extract meaningful insights from complex datasets",
backstory="You are a seasoned analyst with 10+ years of experience in data science and business intelligence."
)
researcher = Agent(
role="Market Researcher",
goal="Gather comprehensive market intelligence",
backstory="You specialize in market analysis and competitive intelligence gathering."
)
# Define specific tasks
analysis_task = Task(
description="Analyze Q4 sales data and identify key trends, patterns, and anomalies",
agent=analyst,
expected_output="Detailed analysis report with visualizations and recommendations"
)
research_task = Task(
description="Research market conditions and competitor performance in Q4",
agent=researcher,
expected_output="Market intelligence report with competitor analysis"
)
# Create coordinated squad
intelligence_squad = Squad(
agents=[analyst, researcher],
tasks=[analysis_task, research_task],
process=Process.sequential,
verbose=True
)
# Execute the orchestrated workflow
results = intelligence_squad.execute()
print(f"Analysis Complete: {results}")Ideal for adaptive workflows, AI-driven decisions, and dynamic environments.
from jaygoga_orchestra.v2 import Agent, Team, Workflow
# Create intelligent agents with advanced capabilities
data_agent = Agent(
name="JayGoga_DataSage",
description="Advanced AI agent specialized in data analysis with deep learning capabilities",
instructions="You are an expert data scientist with the ability to adapt your analysis approach based on data characteristics",
model="gpt-4",
tools=["python_interpreter", "data_visualization", "statistical_analysis"]
)
insight_agent = Agent(
name="JayGoga_InsightMaster",
description="Strategic insight generator with business acumen",
instructions="Transform data findings into actionable business strategies and recommendations",
model="claude-3-sonnet",
tools=["business_analysis", "report_generation", "strategic_planning"]
)
# Create dynamic team with shared context
intelligence_team = Team(
agents=[data_agent, insight_agent],
name="Strategic Intelligence Unit",
description="Elite team for comprehensive business intelligence",
shared_memory=True,
collaboration_mode="adaptive"
)
# Execute with dynamic adaptation
results = intelligence_team.run(
task="Analyze our Q4 performance data and provide strategic recommendations for Q1",
context={"data_source": "sales_db", "priority": "high", "deadline": "2024-01-15"}
)
print(f"Strategic Analysis: {results.summary}")
print(f"Key Insights: {results.insights}")
print(f"Recommendations: {results.recommendations}")| Feature | Classical v1 | Modern v2 | Best For |
|---|---|---|---|
| Structure | Hierarchical, Role-based | Dynamic, Capability-based | v1: Enterprise, v2: Startups |
| Execution | Sequential/Parallel | Adaptive Intelligence | v1: Predictable, v2: Creative |
| Memory | Task-scoped | Shared Context | v1: Privacy, v2: Collaboration |
| Scalability | Linear | Exponential | v1: Controlled, v2: Rapid growth |
| Learning | Rule-based | AI-driven | v1: Compliance, v2: Innovation |
| Complexity | Structured | Self-organizing | v1: Governance, v2: Agility |
Choose Classical v1 when:
- 🏢 Enterprise environment with strict governance
- 📋 Well-defined, repeatable processes
- 🔒 Compliance and audit requirements
- 👥 Large teams with clear role definitions
- 📊 Predictable workflows and outcomes
Choose Modern v2 when:
- 🚀 Startup or innovation-focused environment
- 🧠 AI-driven decision making required
- 🔄 Dynamic, adaptive workflows needed
- 🌐 Collaborative, context-sharing scenarios
- 🎯 Creative problem-solving and exploration
jaygoga_orchestra/
├── __init__.py # Main orchestration entry point
├── v1/ # Classical Orchestration (Structured)
│ ├── __init__.py # Agent, Task, Squad, Process
│ ├── agent.py # Role-based agents
│ ├── team.py # Structured squads
│ ├── task.py # Defined tasks
│ ├── process.py # Execution processes
│ ├── cli/ # Command-line tools
│ ├── tools/ # Agent tools and utilities
│ └── ...
├── v2/ # Modern Orchestration (Intelligent)
│ ├── __init__.py # Agent, Team, Workflow
│ ├── agent/ # Intelligent agents
│ ├── team/ # Collaborative teams
│ ├── workflow/ # Dynamic workflows
│ ├── memory/ # Shared context
│ ├── reasoning/ # AI reasoning
│ └── ...
└── legacy/ # 🧪 Beta Features (Experimental)
├── README_BETA.md # Beta documentation
└── experimental/ # Cutting-edge features
Our legacy directory contains experimental and beta features that showcase the future of AI orchestration:
# 🚧 Beta Features - Available Soon!
from jaygoga_orchestra.legacy import ExperimentalAgent, AdvancedWorkflow
# Cutting-edge features in development
beta_agent = ExperimentalAgent(
name="Krishna_BetaAgent",
capabilities=["quantum_reasoning", "multi_dimensional_analysis"],
status="beta"
)
# Advanced experimental workflows
experimental_flow = AdvancedWorkflow(
name="Future_Intelligence",
description="Next-generation AI orchestration patterns",
beta_features=["auto_optimization", "self_healing", "predictive_scaling"]
)Beta Features Include:
- 🔮 Quantum Reasoning: Advanced decision-making algorithms
- 🌊 Self-Healing Workflows: Automatic error recovery and optimization
- 🎯 Predictive Scaling: AI-driven resource management
- 🧠 Neural Orchestration: Brain-inspired coordination patterns
- ⚡ Lightning Execution: Ultra-fast processing capabilities
These features are experimental and will be integrated into v3 in future releases.
# Start with Classical v1 for structured workflows
from jaygoga_orchestra.v1 import Agent, Task, Squad, Process
# Upgrade to Modern v2 for intelligent orchestration
from jaygoga_orchestra.v2 import Agent, Team, Workflow
# Mix and match as needed
from jaygoga_orchestra.v1 import Task
from jaygoga_orchestra.v2 import Agent, WorkflowMulti-Agent Research Pipeline (v1):
from jaygoga_orchestra.v1 import Agent, Task, Squad, Process
# Create research squad
researcher = Agent(role="Research Specialist", goal="Gather comprehensive data")
analyst = Agent(role="Data Analyst", goal="Analyze and synthesize findings")
writer = Agent(role="Technical Writer", goal="Create detailed reports")
# Define research pipeline
tasks = [
Task(description="Research AI trends in 2024", agent=researcher),
Task(description="Analyze research findings", agent=analyst),
Task(description="Write comprehensive report", agent=writer)
]
research_squad = Squad(agents=[researcher, analyst, writer], tasks=tasks)
report = research_squad.execute()Intelligent Content Creation (v2):
from jaygoga_orchestra.v2 import Agent, Team, Workflow
# Create intelligent content team
content_team = Team([
Agent(name="ContentStrategist", model="gpt-4"),
Agent(name="CreativeWriter", model="claude-3"),
Agent(name="SEOOptimizer", model="gpt-3.5-turbo")
])
result = content_team.run("Create a viral blog post about AI trends")JayGoga-Orchestra represents the perfect harmony of AI agents working together like a well-conducted orchestra. Each agent plays its part while contributing to a greater symphony of intelligent automation.
Core Principles:
- 🎭 Master Orchestration: Seamlessly coordinates multiple agents
- 🧠 Intelligent Coordination: Smart decision-making and adaptation
- ⚡ High Performance: Efficient execution with elegant simplicity
- 🌟 Universal Compatibility: Works across all domains and use cases
- 🛡️ Enterprise Reliability: Robust error handling and fault tolerance
- 🐛 Issues: GitHub Issues
- 📧 Email: aistudentlearn4@gmail.com
MIT License - see LICENSE file for details.
We welcome contributions from the community! Please read our contributing guidelines before submitting PRs.
"Orchestrating AI agents in perfect harmony for intelligent automation." 🎼