A powerful Multi-Agent AI Framework for intelligent automation and orchestration
Moonlight transforms how you build AI applications by providing a comprehensive framework that orchestrates multiple AI agents, automates complex workflows, and integrates with thousands of tools seamlessly.
🏗️ Multiple Execution Modes
🔌 Massive Tool Ecosystem
⚡ Production Ready
# Install
uv pip install moonlight-ai
# Install Playwright (NEEDED)
playwright install
Note: Install NPX and UVX on your machine to get the most out of MCP Compatibility.
import os
from moonlight_ai import Agent, Hive, MoonlightProvider
from dotenv import load_dotenv
load_dotenv()
# Create a content creation provider
provider = MoonlightProvider(
provider_name="deepseek",
api_key=os.getenv("DEEPSEEK_API_KEY"),
)
# Create a specialized content agent
content_agent = Agent(
name="content_creator",
instruction="""You are an expert content creator specializing in technical blog posts.
Create engaging, well-structured content with clear explanations and practical examples.""",
provider=provider,
model_name="deepseek-chat"
)
# Generate a technical blog post
response = Hive(content_agent).run("""
Write a 500-word blog post about the benefits of microservices architecture,
including real-world examples and potential challenges.
""")
print(response)
# Schema for financial analysis
financial_schema = {
"type": "object",
"properties": {
"risk_level": {
"type": "string",
"enum": ["low", "medium", "high"],
"description": "Investment risk assessment"
},
"recommendation": {
"type": "string",
"enum": ["buy", "hold", "sell"],
"description": "Trading recommendation"
},
"confidence": {
"type": "number",
"minimum": 0,
"maximum": 1,
"description": "Confidence score (0-1)"
},
"key_factors": {
"type": "array",
"items": {"type": "string"},
"description": "Key factors influencing the decision"
}
},
"required": ["risk_level", "recommendation", "confidence", "key_factors"]
}
# Create financial analysis agent
analyst_agent = Agent(
name="financial_analyst",
instruction="""You are a senior financial analyst. Analyze stocks and provide
structured recommendations based on market data, company fundamentals, and trends.""",
provider=provider,
model_name="deepseek-chat",
json_mode=True,
json_schema=financial_schema
)
# Analyze a stock
analysis = Hive(analyst_agent).run("""
Analyze Tesla (TSLA) stock based on recent performance, EV market trends,
and competition. Consider Q4 2024 earnings and 2025 outlook.
""")
print(analysis)
# Returns structured JSON with risk_level, recommendation, confidence, and key_factors
from moonlight_ai import MCPConfig, MCPRegistry
# Create MCP configurations for time and weather
time_config = MCPConfig(
name="time",
command="uvx",
args=["mcp-server-time", "--local-timezone=Asia/Kolkata"],
description="Provides current time in IST timezone",
tags=["time", "utility", "timezone"]
)
weather_config = MCPConfig(
name="weather",
command="uvx",
args=["mcp-server-weather", "--api-key=${WEATHER_API_KEY}"],
description="Real-time weather data and forecasts",
tags=["weather", "forecast", "api"]
)
# Create a personal assistant with multiple MCP servers
assistant_agent = Agent(
name="personal_assistant",
instruction="""You are a helpful personal assistant with access to time and weather data.
Provide contextual responses that combine multiple data sources for better insights.""",
provider=provider,
model_name="deepseek-chat",
mcp_servers=[time_config, weather_config]
)
# Get contextual information
response = Hive(assistant_agent).run("""
What's the current time in Mumbai? Also check the weather and let me know
if it's a good time for an outdoor meeting this afternoon.
""")
print(response.assistant_message)
registry = MCPRegistry()
# Register a server
reistry.register(time_config)
# load a config
registry.get_by_name("time") # or search by tags based on the sdk
from moonlight_ai import Orchestra, Agent
# Create specialized research agents
market_researcher_agent = Agent(
name="market_researcher",
instruction="""You are a market research specialist. Focus on industry trends,
market size, growth rates, and competitive landscape analysis.""",
provider=provider,
model_name="deepseek-chat",
)
competitor_analyst_agent = Agent(
name="competitor_analyst",
instruction="""You are a competitive intelligence expert. Analyze competitor
strategies, pricing, product features, and market positioning.""",
provider=provider,
model_name="deepseek-chat",
)
financial_analyst_agent = Agent(
name="financial_analyst",
instruction="""You are a financial analyst specializing in startup and tech company
valuations, funding rounds, and financial performance metrics.""",
provider=provider,
model_name="deepseek-chat",
)
# Set up the research orchestra
research_team = {
"market_researcher_agent": market_researcher_agent,
"competitor_analyst_agent": competitor_analyst_agent,
"financial_analyst_agent": financial_analyst_agent
}
orchestrator = Orchestra(
orchestra_model="deepseek-chat",
provider=provider
)
orchestrator.add_agents(research_team)
orchestrator.add_task("""
Conduct a comprehensive analysis of the AI code assistant market in 2025.
I need insights on:
1. Market size and growth projections
2. Key competitors and their positioning
3. Recent funding rounds and valuations
4. Emerging trends and opportunities
Provide a structured report with actionable insights for a startup entering this space.
""")
# Execute the research
results = orchestrator.activate()
print(results)
from moonlight_ai import DeepResearcher
# Create a deep researcher for emerging technologies
tech_researcher = DeepResearcher(
model="deepseek-chat",
provider=provider,
research_task="""
Analyze the current state and future prospects of quantum computing in 2025.
Focus on: commercial applications, major players, recent breakthroughs,
investment trends, and timeline for mainstream adoption.
""",
max_depth=3, # Deep recursive research
breadth=4, # Multiple angles per level
links_per_query=8, # Comprehensive source coverage
max_content_length=2000 # Detailed content analysis
)
# Execute comprehensive research
print("🔍 Starting deep research on quantum computing...")
results = tech_researcher.run()
# Get the detailed research report
print("📄 Research Report:")
print("=" * 80)
print(results)
from moonlight_ai import WorkflowGenerator, Workflow, Agent
# Create specialized trading agents
market_scanner = Agent(
name="market_scanner",
instruction="""You are a market scanning specialist. Monitor stock prices,
volume, and technical indicators. Identify potential trading opportunities.""",
provider=provider,
model_name="deepseek-chat"
)
risk_manager = Agent(
name="risk_manager",
instruction="""You are a risk management expert. Evaluate trading opportunities
for risk/reward ratios, position sizing, and portfolio impact.""",
provider=provider,
model_name="deepseek-chat"
)
trade_executor = Agent(
name="trade_executor",
instruction="""You are a trade execution specialist. Execute trades based on
approved signals while managing order timing and execution quality.""",
provider=provider,
model_name="deepseek-chat"
)
available_agents = {
"market_scanner": market_scanner,
"risk_manager": risk_manager,
"trade_executor": trade_executor
}
# Generate automated trading workflow
wfg = WorkflowGenerator(
available_agents=available_agents,
model_name="deepseek-chat",
provider=provider,
)
# Create trading workflow
trading_prompt = """
Create a trading workflow that:
1. Scans for stocks breaking above 20-day moving average with high volume
2. Analyzes risk/reward ratio and position sizing
3. Executes trades only if risk/reward > 2:1
4. Sets stop-loss at 2% and take-profit at 4%
5. Runs every 15 minutes during market hours
"""
print('🤖 Generating automated trading workflow...')
# Generate and execute the workflow
# Step 1: Generate first level workflow
print('\n1. Generating first level workflow...')
first_level_result = wfg.generate_first_level(trading_prompt)
print(f'First level result: {first_level_result}')
# Step 2: Expand first workflow (simulate critique and requirements)
# You should check the first level, then answer the requirements and then expand the workflow based on the critique.
print('\n2. Expanding first workflow...')
# Simulating a critique for the first level workflow
critique = "The workflow needs to be more specific about time checking and should include proper error handling."
# Assuming we have some answered requirements based on the critique
answered_requirements = [
"Q. xxx; A. Check current minute value",
"Q. xxx; A. Determine if minute is even or odd",
"Q. xxx; A. Print appropriate message"
]
previous_level_details = first_level_result
expanded_workflow = wfg.expand_first_workflow(
critique=critique,
answered_requirements=answered_requirements,
previous_level_details=previous_level_details
)
print(f'Expanded workflow: {expanded_workflow}')
# Step 3: Generate final workflow
print('\n3. Generating final workflow...')
final_workflow = wfg.generate_final_workflow(expanded_workflow)
# Final Workflow can be a json or generated.
print(f'Final generated workflow: {final_workflow}')
print('\n' + '=' * 60)
# Create and run the trading workflow
trading_workflow = Workflow(
workflow=final_workflow,
available_agents=available_agents,
provider=provider,
model_name="deepseek-chat"
)
print('📈 Starting automated trading workflow...')
trading_workflow.run()
Perfect for focused tasks requiring deep expertise:
Best For: Content creation, data analysis, specialized consulting
Ideal for complex tasks requiring diverse expertise:
Best For: Research projects, business analysis, multi-step processes
Specialized for comprehensive research workflows:
Best For: Market research, academic investigation, competitive analysis
Advanced automation for complex business processes:
Best For: Business process automation, monitoring systems, trading bots
Moonlight includes validated configurations for thousands of MCP servers across categories:
from moonlight_ai import MCPRegistry, MCPConfig
# Create and register configurations
registry = MCPRegistry()
# Database configuration
db_config = MCPConfig(
name="postgres",
command="uvx",
args=["mcp-server-postgres", "--connection-string=${DB_URL}"],
description="PostgreSQL database integration",
tags=["database", "sql", "postgres"]
)
registry.register(db_config)
# Retrieve and use saved configurations
saved_config = registry.get_by_name("postgres")
database_agent = Agent(
name="db_analyst",
instruction="You are a database analyst with PostgreSQL access.",
provider=provider,
model_name="deepseek-chat",
mcp_servers=[saved_config]
)
Moonlight supports all major AI providers with unified interfaces:
# Switch between providers seamlessly
openai_provider = MoonlightProvider(
provider_name="openai",
api_key=openai_key
)
deepseek_provider = MoonlightProvider(
"deepseek",
api_key=deepseek_key
)
groq_provider = MoonlightProvider(
"groq",
api_key=groq_key
)
custom_provider = MooonlightProvider(
provider_url="https://my_custom_server/v1",
api_key=custom_key
)
# Same agent, different backends
agent_gpt4 = Agent(
"analyst",
instruction="...",
provider=openai_provider,
model_name="gpt-4"
)
agent_deepseek = Agent(
"analyst",
instruction="...",
provider=deepseek_provider,
model_name="deepseek-chat"
)
Made with 🌙 by the Moonlight Team
FAQs
🌙 Advanced Multi-Agent AI Orchestration Framework
We found that moonlight-ai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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