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swarms
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removed mentions of linear

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pull/1245/head
Hugh 2 days ago
parent cdb1f05bb7
commit 7012784c91
4 changed files with 0 additions and 168 deletions
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50
docs/llm.txt
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@ -24130,32 +24130,6 @@ flowchart LR
- Maintains strict ordering of task processing
### Linear Swarm
```python
def linear_swarm(agents: AgentListType, tasks: List[str], return_full_history: bool = True)
```
**Information Flow:**
```mermaid
flowchart LR
Input[Task Input] --> A1
subgraph Sequential Processing
A1((Agent 1)) --> A2((Agent 2))
A2 --> A3((Agent 3))
A3 --> A4((Agent 4))
A4 --> A5((Agent 5))
end
A5 --> Output[Final Result]
```
**Best Used When:**
- Tasks need sequential, pipeline-style processing
- Each agent performs a specific transformation step
- Order of processing is critical
### Star Swarm
```python
def star_swarm(agents: AgentListType, tasks: List[str], return_full_history: bool = True)
@ -24389,7 +24363,6 @@ flowchart TD
## Common Use Cases
1. **Data Processing Pipelines**
- Linear Swarm
- Circular Swarm
2. **Distributed Computing**
@ -24528,29 +24501,6 @@ def run_healthcare_grid_swarm():
print("\nGrid swarm processing completed")
print(result)
def run_finance_linear_swarm():
"""Loan approval process using linear swarm"""
print_separator()
print("FINANCE - LOAN APPROVAL PROCESS (Linear Swarm)")
agents = create_finance_agents()[:3]
tasks = [
"Review loan application and credit history",
"Assess risk factors and compliance requirements",
"Generate final loan recommendation"
]
print("\nTasks:")
for i, task in enumerate(tasks, 1):
print(f"{i}. {task}")
result = linear_swarm(agents, tasks)
print("\nResults:")
for log in result['history']:
print(f"\n{log['agent_name']}:")
print(f"Task: {log['task']}")
print(f"Response: {log['response']}")
def run_healthcare_star_swarm():
"""Complex medical case management using star swarm"""
print_separator()

50
docs/swarms/examples/unique_swarms.md
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@ -61,32 +61,6 @@ flowchart LR
- Maintains strict ordering of task processing
### Linear Swarm
```python
def linear_swarm(agents: AgentListType, tasks: List[str], return_full_history: bool = True)
```
**Information Flow:**
```mermaid
flowchart LR
Input[Task Input] --> A1
subgraph Sequential Processing
A1((Agent 1)) --> A2((Agent 2))
A2 --> A3((Agent 3))
A3 --> A4((Agent 4))
A4 --> A5((Agent 5))
end
A5 --> Output[Final Result]
```
**Best Used When:**
- Tasks need sequential, pipeline-style processing
- Each agent performs a specific transformation step
- Order of processing is critical
### Star Swarm
```python
def star_swarm(agents: AgentListType, tasks: List[str], return_full_history: bool = True)
@ -320,7 +294,6 @@ flowchart TD
## Common Use Cases
1. **Data Processing Pipelines**
- Linear Swarm
- Circular Swarm
2. **Distributed Computing**
@ -459,29 +432,6 @@ def run_healthcare_grid_swarm():
print("\nGrid swarm processing completed")
print(result)
def run_finance_linear_swarm():
"""Loan approval process using linear swarm"""
print_separator()
print("FINANCE - LOAN APPROVAL PROCESS (Linear Swarm)")
agents = create_finance_agents()[:3]
tasks = [
"Review loan application and credit history",
"Assess risk factors and compliance requirements",
"Generate final loan recommendation"
]
print("\nTasks:")
for i, task in enumerate(tasks, 1):
print(f"{i}. {task}")
result = linear_swarm(agents, tasks)
print("\nResults:")
for log in result['history']:
print(f"\n{log['agent_name']}:")
print(f"Task: {log['task']}")
print(f"Response: {log['response']}")
def run_healthcare_star_swarm():
"""Complex medical case management using star swarm"""
print_separator()

24
examples/multi_agent/utils/unique_swarms_examples.py
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@ -121,30 +121,6 @@ def run_healthcare_grid_swarm():
print(result)
def run_finance_linear_swarm():
"""Loan approval process using linear swarm"""
print_separator()
print("FINANCE - LOAN APPROVAL PROCESS (Linear Swarm)")
agents = create_finance_agents()[:3]
tasks = [
"Review loan application and credit history",
"Assess risk factors and compliance requirements",
"Generate final loan recommendation",
]
print("\nTasks:")
for i, task in enumerate(tasks, 1):
print(f"{i}. {task}")
result = linear_swarm(agents, tasks)
print("\nResults:")
for log in result["history"]:
print(f"\n{log['agent_name']}:")
print(f"Task: {log['task']}")
print(f"Response: {log['response']}")
def run_healthcare_star_swarm():
"""Complex medical case management using star swarm"""
print_separator()

44
swarms/structs/swarming_architectures.py
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@ -106,50 +106,6 @@ def grid_swarm(
return history_output_formatter(conversation, output_type)
# Linear Swarm: Agents process tasks in a sequential linear manner
def linear_swarm(
agents: AgentListType,
tasks: List[str],
output_type: OutputType = "dict",
) -> Union[Dict[str, Any], List[str]]:
"""
Implements a linear swarm where agents process tasks in a sequential manner.
Args:
agents (AgentListType): A list of Agent objects to participate in the swarm.
tasks (List[str]): A list of tasks to be processed by the agents.
output_type (OutputType, optional): The format of the output. Defaults to "dict".
Returns:
Union[Dict[str, Any], List[str]]: The formatted output of the swarm's processing.
If output_type is "dict", returns a dictionary containing the conversation history.
If output_type is "list", returns a list of responses.
Raises:
ValueError: If agents or tasks lists are empty.
"""
if not agents or not tasks:
raise ValueError("Agents and tasks lists cannot be empty.")
conversation = Conversation()
for agent in agents:
if tasks:
task = tasks.pop(0)
conversation.add(
role="User",
content=task,
)
response = agent.run(conversation.get_str())
conversation.add(
role=agent.agent_name,
content=response,
)
return history_output_formatter(conversation, output_type)
# Star Swarm: A central agent first processes all tasks, followed by others
def star_swarm(
agents: AgentListType,

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