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# Aggregate Multi-Agent Responses
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The `aggregate` function allows you to run multiple agents concurrently on the same task and then synthesize their responses using an intelligent aggregator agent. This is useful for getting diverse perspectives on a problem and then combining them into a comprehensive analysis.
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## Installation
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You can get started by first installing swarms with the following command, or [click here for more detailed installation instructions](https://docs.swarms.world/en/latest/swarms/install/install/):
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```bash
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pip3 install -U swarms
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```
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## Environment Variables
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```txt
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OPENAI_API_KEY=""
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ANTHROPIC_API_KEY=""
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```
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## Function Parameters
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### `workers: List[Callable]` (Required)
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A list of Agent instances that will work on the task concurrently. Each agent should be a callable object (typically an Agent instance).
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### `task: str` (Required)
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The task or question that all agents will work on simultaneously. This should be a clear, specific prompt that allows for diverse perspectives.
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### `type: HistoryOutputType` (Optional, Default: "all")
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Controls the format of the returned conversation history. Available options:
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| Type | Description |
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|------|-------------|
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| **"all"** | Returns the complete conversation including all agent responses and the final aggregation |
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| **"list"** | Returns the conversation as a list format |
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| **"dict"** or **"dictionary"** | Returns the conversation as a dictionary format |
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| **"string"** or **"str"** | Returns only the final aggregated response as a string |
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| **"final"** or **"last"** | Returns only the final aggregated response |
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| **"json"** | Returns the conversation in JSON format |
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| **"yaml"** | Returns the conversation in YAML format |
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| **"xml"** | Returns the conversation in XML format |
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| **"dict-all-except-first"** | Returns dictionary format excluding the first message |
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| **"str-all-except-first"** | Returns string format excluding the first message |
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| **"basemodel"** | Returns the conversation as a base model object |
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| **"dict-final"** | Returns dictionary format with only the final response |
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### `aggregator_model_name: str` (Optional, Default: "anthropic/claude-3-sonnet-20240229")
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The model to use for the aggregator agent that synthesizes all the individual agent responses. This should be a model capable of understanding and summarizing complex multi-agent conversations.
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## How It Works
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1. **Concurrent Execution**: All agents in the `workers` list run the same task simultaneously
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2. **Response Collection**: Individual agent responses are collected into a conversation
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3. **Intelligent Aggregation**: A specialized aggregator agent analyzes all responses and creates a comprehensive synthesis
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4. **Formatted Output**: The final result is returned in the specified format
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## Code Example
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```python
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from swarms.structs.agent import Agent
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from swarms.structs.ma_blocks import aggregate
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# Create specialized agents for different perspectives
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agents = [
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Agent(
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agent_name="Sector-Financial-Analyst",
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agent_description="Senior financial analyst at BlackRock.",
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system_prompt="You are a financial analyst tasked with optimizing asset allocations for a $50B portfolio. Provide clear, quantitative recommendations for each sector.",
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max_loops=1,
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model_name="gpt-4o-mini",
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max_tokens=3000,
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),
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Agent(
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agent_name="Sector-Risk-Analyst",
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agent_description="Expert risk management analyst.",
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system_prompt="You are a risk analyst responsible for advising on risk allocation within a $50B portfolio. Provide detailed insights on risk exposures for each sector.",
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max_loops=1,
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model_name="gpt-4o-mini",
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max_tokens=3000,
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),
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Agent(
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agent_name="Tech-Sector-Analyst",
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agent_description="Technology sector analyst.",
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system_prompt="You are a tech sector analyst focused on capital and risk allocations. Provide data-backed insights for the tech sector.",
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max_loops=1,
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model_name="gpt-4o-mini",
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max_tokens=3000,
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),
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]
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# Run the aggregate function
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result = aggregate(
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workers=agents,
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task="What is the best sector to invest in?",
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type="all", # Get complete conversation history
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aggregator_model_name="anthropic/claude-3-sonnet-20240229"
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)
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print(result)
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```
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## Code Example
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## Use Cases
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| Use Case | Description |
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|----------|-------------|
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| **Investment Analysis** | Get multiple financial perspectives on investment decisions |
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| **Research Synthesis** | Combine insights from different research agents |
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| **Problem Solving** | Gather diverse approaches to complex problems |
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| **Content Creation** | Generate comprehensive content from multiple specialized agents |
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| **Decision Making** | Get balanced recommendations from different expert perspectives |
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## Error Handling
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The function includes validation for:
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- Required parameters (`task` and `workers`)
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- Proper data types (workers must be a list of callable objects)
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- Agent compatibility
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## Performance Considerations
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- All agents run concurrently, so total execution time is limited by the slowest agent
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- The aggregator agent processes all responses, so consider response length and complexity
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- Memory usage scales with the number of agents and their response sizes
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