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[DOCS][FIX][AutoSwarmBuilder]

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pull/1100/head
Kye Gomez 1 week ago
parent daf261cc73
commit 98b3b2cc65
1 changed files with 29 additions and 101 deletions
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docs/swarms/structs/auto_swarm_builder.md
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@ -30,8 +30,8 @@ The AutoSwarmBuilder is designed to:
| `interactive` | bool | False | Whether to enable interactive mode |
| `max_tokens` | int | 8000 | Maximum tokens for the LLM responses |
| `execution_type` | str | "return-agents" | Type of execution to perform (see Execution Types) |
| `return_dictionary` | bool | True | Whether to return dictionary format for agent specs |
| `system_prompt` | str | BOSS_SYSTEM_PROMPT | System prompt for the boss agent |
| `additional_llm_args` | dict | {} | Additional arguments to pass to the LLM |
## Execution Types
@ -39,12 +39,10 @@ The `execution_type` parameter controls how the AutoSwarmBuilder operates:
| Execution Type | Description |
|----------------------------------|-----------------------------------------------------------|
| **"return-agents"** | Creates and returns a list of Agent objects (default) |
| **"return-agents"** | Creates and returns agent specifications as a dictionary (default) |
| **"execute-swarm-router"** | Executes the swarm router with the created agents |
| **"return-swarm-router-config"** | Returns the swarm router configuration as a dictionary |
| **"return-agent-configurations"**| Returns agent configurations as a dictionary |
| **"return-agent-specs"** | Returns agent specifications as a tuple (agents, count) |
| **"return-agent-dictionary"** | Returns agent configurations as a dictionary format |
| **"return-agents-objects"** | Returns agent objects created from specifications |
## Core Methods
@ -84,26 +82,6 @@ Creates specialized agents for a given task using the boss agent system.
- `Exception`: If there's an error during agent creation
### build_agent(agent_name: str, agent_description: str, agent_system_prompt: str)
Builds a single agent with specified parameters and enhanced error handling.
**Parameters:**
| Parameter | Type | Description |
|-----------------------|-------|--------------------------------|
| `agent_name` | str | Name of the agent |
| `agent_description` | str | Description of the agent |
| `agent_system_prompt` | str | System prompt for the agent |
**Returns:**
- `Agent`: The constructed agent
**Raises:**
- `Exception`: If there's an error during agent construction
### create_router_config(task: str)
Creates a swarm router configuration for a given task.
@ -162,43 +140,6 @@ Returns the available execution types.
- `List[str]`: List of available execution types
### _create_agent_specs(task: str)
Create agent specifications for a given task.
**Parameters:**
- `task` (str): The task to create agents for
**Returns:**
- `Tuple[List[Agent], int]`: List of created agents and count
### _create_agent_dictionary(task: str)
Create agent dictionary for a given task.
**Parameters:**
- `task` (str): The task to create agents for
**Returns:**
- `dict`: Dictionary containing agent configurations
### _create_agents_from_specs(task: str, return_dict: bool = False)
Create agents from specifications using the boss agent system.
**Parameters:**
- `task` (str): The task to create agents for
- `return_dict` (bool): Whether to return dictionary format
**Returns:**
- `List[Agent]` or `dict`: Created agents or dictionary
### create_agents_from_specs(agents_dictionary: Any)
Create agents from agent specifications.
@ -211,40 +152,29 @@ Create agents from agent specifications.
- `List[Agent]`: List of created agents
### build_agent_from_spec(agent_name: str, agent_description: str, agent_system_prompt: str, max_loops: int = 1, model_name: str = "gpt-4.1", dynamic_temperature_enabled: bool = True, auto_generate_prompt: bool = False, role: str = "worker", max_tokens: int = 8192, temperature: float = 0.5)
### dict_to_agent(output: dict)
Build a single agent from agent specification with comprehensive configuration options.
Converts a dictionary output to a list of Agent objects.
**Parameters:**
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `agent_name` | str | - | Name of the agent |
| `agent_description` | str | - | Description of the agent's purpose |
| `agent_system_prompt` | str | - | The system prompt for the agent |
| `max_loops` | int | 1 | Maximum number of loops |
| `model_name` | str | "gpt-4.1" | Model name to use |
| `dynamic_temperature_enabled` | bool | True | Whether to enable dynamic temperature |
| `auto_generate_prompt` | bool | False | Whether to auto-generate prompts |
| `role` | str | "worker" | Role of the agent |
| `max_tokens` | int | 8192 | Maximum tokens |
| `temperature` | float | 0.5 | Temperature setting |
- `output` (dict): Dictionary containing agent configurations
**Returns:**
- `Agent`: The constructed agent instance
- `List[Agent]`: List of constructed agents
### dict_to_agent(output: dict)
### _execute_task(task: str)
Converts a dictionary output to a list of Agent objects.
Execute a task by creating agents and initializing the swarm router.
**Parameters:**
- `output` (dict): Dictionary containing agent configurations
- `task` (str): The task to execute
**Returns:**
- `List[Agent]`: List of constructed agents
- `Any`: The result of the swarm router execution
### build_llm_agent(config: BaseModel)
@ -285,6 +215,7 @@ Configuration for an individual agent specification with comprehensive options.
| `temperature` | float | Parameter controlling randomness of agent output (lower = more deterministic) |
| `role` | str | Designated role within the swarm influencing behavior and interactions |
| `max_loops` | int | Maximum number of times the agent can repeat its task for iterative processing |
| `goal` | str | The primary objective or desired outcome the agent is tasked with achieving |
### Agents
@ -302,7 +233,7 @@ Configuration model for individual agents in a swarm.
| Field | Type | Description |
|-----------------|---------|-----------------------------------------------------------------------------------------------|
| `name` | str | Unique identifier for the agent |
| `agent_name` | str | Unique identifier for the agent |
| `description` | str | Comprehensive description of the agent's purpose and capabilities |
| `system_prompt` | str | Detailed system prompt defining agent behavior |
| `goal` | str | Primary objective the agent is tasked with achieving |
@ -465,7 +396,7 @@ from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
swarm = AutoSwarmBuilder(
name="Marketing Swarm",
description="A swarm for marketing strategy development",
execution_type="return-agent-configurations"
execution_type="return-agents"
)
# Get agent configurations without executing
@ -475,7 +406,7 @@ agent_configs = swarm.run(
print("Generated agents:")
for agent in agent_configs["agents"]:
print(f"- {agent['name']}: {agent['description']}")
print(f"- {agent['agent_name']}: {agent['description']}")
```
### Example 4: Getting Swarm Router Configuration
@ -549,24 +480,24 @@ result = swarm.run(
)
```
### Example 7: Getting Agent Specifications
### Example 7: Getting Agent Objects
```python
from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
# Initialize to return agent specifications
# Initialize to return agent objects
swarm = AutoSwarmBuilder(
name="Specification Swarm",
description="A swarm for generating agent specifications",
execution_type="return-agent-specs"
execution_type="return-agents-objects"
)
# Get agent specifications with count
agents, count = swarm.run(
# Get agent objects
agents = swarm.run(
"Create a team of agents for analyzing customer feedback and generating actionable insights"
)
print(f"Created {count} agents:")
print(f"Created {len(agents)} agents:")
for agent in agents:
print(f"- {agent.agent_name}: {agent.description}")
```
@ -580,7 +511,7 @@ from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
swarm = AutoSwarmBuilder(
name="Dictionary Swarm",
description="A swarm for generating agent dictionaries",
execution_type="return-agent-dictionary"
execution_type="return-agents"
)
# Get agent configurations as dictionary
@ -635,16 +566,14 @@ swarm = AutoSwarmBuilder(
description="A highly configured swarm with advanced settings",
model_name="gpt-4.1",
max_tokens=16000,
temperature=0.3,
return_dictionary=True,
additional_llm_args={"temperature": 0.3},
verbose=True,
interactive=False
)
# Create agents with detailed specifications
agent_specs = swarm._create_agents_from_specs(
"Develop a comprehensive cybersecurity strategy for a mid-size company",
return_dict=True
agent_specs = swarm.run(
"Develop a comprehensive cybersecurity strategy for a mid-size company"
)
# Build agents from specifications
@ -672,14 +601,13 @@ for agent in agents:
- Set appropriate `max_loops` based on task complexity (typically 1)
- Use `verbose=True` during development for debugging
- Choose the right `execution_type` for your use case:
- Use `"return-agents"` for direct agent interaction and execution
- Use `"return-agent-configurations"` for inspecting agent setups
- Use `"return-agents"` for getting agent specifications as dictionary (default)
- Use `"execute-swarm-router"` for executing the swarm router with created agents
- Use `"return-swarm-router-config"` for analyzing swarm architecture
- Use `"return-agent-specs"` for getting agent specifications with counts
- Use `"return-agent-dictionary"` for dictionary-format configurations
- Use `"return-agents-objects"` for getting agent objects created from specifications
- Set `max_tokens` appropriately based on expected response length
- Use `interactive=True` for real-time collaboration scenarios
- Set `return_dictionary=True` for easier data manipulation
- Use `additional_llm_args` for passing custom parameters to the LLM
!!! note "Model Selection"
- Choose appropriate `model_name` based on task requirements

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