Mixture of Agents

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-5.4",
    max_loops=1,
)

market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="Analyze market trends, competition, and positioning.",
    model_name="gpt-5.4",
    max_loops=1,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="Analyze risks, threats, and mitigation strategies.",
    model_name="gpt-5.4",
    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-5.4",
    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")

Each layer:

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

agents

List[Agent]

required

List of expert agents to run in parallel

aggregator_agent

Agent

required

Agent that synthesizes expert outputs

layers

int

default:"3"

Number of processing layers (iterations)

max_loops

int

default:"1"

Maximum loops per layer

output_type

OutputType

default:"final"

Output format (final, all, list, dict)

aggregator_system_prompt

str

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."

Validations:

At least one expert agent required
Aggregator system prompt must be provided
Layers must be specified

Performance Considerations

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)

Concurrent Workflow - Parallel without synthesis
Hierarchical Swarm - Director-worker pattern
Heavy Swarm - Multi-phase analysis
Agent Rearrange - Custom flow patterns

Getting Started

Core Concepts

Agent Development

Reasoning Agents

Multi-Agent Architectures

Deployment & Operations

Integrations

CLI Reference

Changelog

Mixture of Agents

When to Use

Key Features

Basic Example

Multi-Layer Processing

Key Parameters

Methods

run()

run_batched()

run_concurrently()

Use Cases

Investment Analysis

Medical Diagnosis

Research Synthesis

Custom Aggregator Prompt

Automatic Aggregator Setup

Architecture Flow

Multi-Layer Flow

Output Types

Best Practices

Reliability Checks

Performance Considerations

Concurrent Execution

Conversation Context

​When to Use

​Key Features

​Basic Example

​Multi-Layer Processing

​Key Parameters

​Methods

​run()

​run_batched()

​run_concurrently()

​Use Cases

​Investment Analysis

​Medical Diagnosis

​Research Synthesis

​Custom Aggregator Prompt

​Automatic Aggregator Setup

​Architecture Flow

​Multi-Layer Flow

​Output Types

​Best Practices

​Reliability Checks

​Performance Considerations

​Concurrent Execution

​Conversation Context

​Related Architectures

When to Use

Key Features

Basic Example

Multi-Layer Processing

Key Parameters

Methods

run()

run_batched()

run_concurrently()

Use Cases

Investment Analysis

Medical Diagnosis

Research Synthesis

Custom Aggregator Prompt

Automatic Aggregator Setup

Architecture Flow

Multi-Layer Flow

Output Types

Best Practices

Reliability Checks

Performance Considerations

Concurrent Execution

Conversation Context

Related Architectures