Mixture of Agents (MoA)
The MixtureOfAgents architecture runs multiple expert agents in parallel and synthesizes their diverse outputs through an aggregator agent. This collaborative approach achieves superior results through multi-perspective analysis.
When to Use
- Complex problem-solving: Tasks requiring multiple expert perspectives
- Quality through collaboration: Combine diverse viewpoints for better outcomes
- State-of-the-art performance: Achieve highest quality through synthesis
- Expert systems: Leverage specialized knowledge from multiple domains
- Comprehensive analysis: Get well-rounded insights
Key Features
- Parallel execution of expert agents
- Automatic aggregation and synthesis
- Multi-layer processing (optional)
- Team awareness capabilities
- Flexible output formatting
- Conversation history tracking
Basic Example
from swarms import Agent, MixtureOfAgents
# Define expert agents
financial_analyst = Agent(
agent_name="Financial-Analyst",
system_prompt="Analyze financial data, ratios, and performance metrics.",
model_name="gpt-4o-mini",
max_loops=1,
)
market_analyst = Agent(
agent_name="Market-Analyst",
system_prompt="Analyze market trends, competition, and positioning.",
model_name="gpt-4o-mini",
max_loops=1,
)
risk_analyst = Agent(
agent_name="Risk-Analyst",
system_prompt="Analyze risks, threats, and mitigation strategies.",
model_name="gpt-4o-mini",
max_loops=1,
)
# Define aggregator agent
aggregator = Agent(
agent_name="Investment-Advisor",
system_prompt="Synthesize financial, market, and risk analyses into a comprehensive investment recommendation.",
model_name="gpt-4o-mini",
max_loops=1,
)
# Create MoA swarm
moa = MixtureOfAgents(
name="Investment-Analysis-Team",
agents=[financial_analyst, market_analyst, risk_analyst],
aggregator_agent=aggregator,
layers=1, # Single layer of analysis
)
# Execute
recommendation = moa.run("Should we invest in NVIDIA stock?")
print(recommendation)
Multi-Layer Processing
Use multiple layers for iterative refinement:
moa = MixtureOfAgents(
name="Deep-Analysis-Team",
agents=[expert1, expert2, expert3],
aggregator_agent=synthesizer,
layers=3, # Three rounds of analysis and synthesis
max_loops=1,
)
result = moa.run("Complex strategic decision")
- Experts analyze the current context
- Outputs are added to conversation history
- Next layer sees all previous analysis
- Aggregator synthesizes final result
Key Parameters
name
str
default:"MixtureOfAgents"
Name for the MoA instance
List of expert agents to run in parallel
Agent that synthesizes expert outputs
Number of processing layers (iterations)
output_type
OutputType
default:"final"
Output format (final, all, list, dict)
Custom system prompt for aggregator (uses default if not provided)
aggregator_model_name
str
default:"claude-sonnet-4-20250514"
Model for the aggregator agent
Methods
run()
Execute the mixture of agents with a task.
result = moa.run(
task="Analyze investment opportunity",
img=None, # Optional image input
)
run_batched()
Process multiple tasks sequentially.
tasks = [
"Analyze tech sector",
"Analyze healthcare sector",
"Analyze energy sector"
]
results = moa.run_batched(tasks)
run_concurrently()
Process multiple tasks in parallel.
tasks = ["Task 1", "Task 2", "Task 3"]
results = moa.run_concurrently(tasks)
Use Cases
Investment Analysis
# Multiple financial experts with synthesis
value_investor = Agent(agent_name="Value-Investor", ...)
growth_investor = Agent(agent_name="Growth-Investor", ...)
momentum_trader = Agent(agent_name="Momentum-Trader", ...)
aggregator = Agent(agent_name="Portfolio-Manager", ...)
investment_moa = MixtureOfAgents(
agents=[value_investor, growth_investor, momentum_trader],
aggregator_agent=aggregator,
)
advice = investment_moa.run("Evaluate Tesla for our portfolio")
Medical Diagnosis
# Multiple specialists with diagnostic synthesis
cardiologist = Agent(agent_name="Cardiologist", ...)
neurologist = Agent(agent_name="Neurologist", ...)
internist = Agent(agent_name="Internist", ...)
diagnostician = Agent(agent_name="Chief-Diagnostician", ...)
medical_moa = MixtureOfAgents(
agents=[cardiologist, neurologist, internist],
aggregator_agent=diagnostician,
)
diagnosis = medical_moa.run("Patient presents with chest pain and dizziness")
Research Synthesis
# Domain experts with research synthesis
ml_researcher = Agent(agent_name="ML-Researcher", ...)
data_scientist = Agent(agent_name="Data-Scientist", ...)
domain_expert = Agent(agent_name="Domain-Expert", ...)
research_lead = Agent(agent_name="Research-Lead", ...)
research_moa = MixtureOfAgents(
agents=[ml_researcher, data_scientist, domain_expert],
aggregator_agent=research_lead,
layers=2, # Two rounds of analysis
)
insights = research_moa.run("Novel approaches to computer vision")
Custom Aggregator Prompt
from swarms.prompts.ag_prompt import AGGREGATOR_SYSTEM_PROMPT_MAIN
# Use default prompt
moa = MixtureOfAgents(
agents=experts,
aggregator_agent=aggregator,
aggregator_system_prompt=AGGREGATOR_SYSTEM_PROMPT_MAIN,
)
# Or create custom aggregator prompt
custom_prompt = """
You are a senior investment advisor synthesizing expert analyses.
Your role:
1. Review all expert opinions carefully
2. Identify consensus and conflicts
3. Weight opinions by confidence and evidence
4. Provide clear, actionable recommendations
5. Explain your reasoning and risk factors
Always be conservative and highlight uncertainty.
"""
moa = MixtureOfAgents(
agents=experts,
aggregator_agent=aggregator,
aggregator_system_prompt=custom_prompt,
)
Automatic Aggregator Setup
If no aggregator agent is provided, one is created automatically:
moa = MixtureOfAgents(
name="Analysis-Team",
agents=[expert1, expert2, expert3],
# aggregator_agent not provided - will be created automatically
aggregator_model_name="claude-sonnet-4-20250514",
)
# Automatic aggregator created with:
# - agent_name="Aggregator Agent"
# - Default AGGREGATOR_SYSTEM_PROMPT_MAIN
# - Specified model_name
# - temperature=0.5
# - max_loops=1
Architecture Flow
Multi-Layer Flow
With layers=3:
Layer 1:
User Task → Experts → Conversation
Layer 2:
Full Context → Experts → Conversation
(Experts see Layer 1 outputs)
Layer 3:
Full Context → Experts → Conversation
(Experts see Layers 1-2 outputs)
Final:
Full Context → Aggregator → Output
Output Types
# Only final aggregated output
moa_final = MixtureOfAgents(
agents=experts,
aggregator_agent=aggregator,
output_type="final",
)
# All conversation history
moa_all = MixtureOfAgents(
agents=experts,
aggregator_agent=aggregator,
output_type="all",
)
# Structured dictionary
moa_dict = MixtureOfAgents(
agents=experts,
aggregator_agent=aggregator,
output_type="dict",
)
Best Practices
Expert Selection: Choose agents with complementary expertise for maximum benefit
- Diverse Experts: Select agents with different perspectives/specializations
- Clear Prompts: Give each expert a specific focus area
- Quality Aggregator: Use strong model for synthesis (Claude Sonnet, GPT-4)
- Layer Count: Start with 1 layer, add more only if needed
- Aggregator Instructions: Provide clear synthesis guidelines
More experts and layers increase cost and latency - balance quality with efficiency
Reliability Checks
The system validates configuration on initialization:
try:
moa = MixtureOfAgents(
agents=[], # Empty list
aggregator_agent=aggregator,
)
except ValueError as e:
print(e) # "No agents provided."
- At least one expert agent required
- Aggregator system prompt must be provided
- Layers must be specified
Concurrent Execution
Experts run in true parallel using ThreadPoolExecutor:
# All expert agents execute simultaneously
agent_outputs = run_agents_concurrently(
agents=self.agents,
task=task,
img=img,
return_agent_output_dict=True,
)
Conversation Context
Full context flows through layers:
# Each layer sees complete history
full_context = self.conversation.get_str()
step_output = self.step(task=full_context, img=img)