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Merge pull request #1243 from hughiwnl/removed_linear_swarm

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Removed linear swarm
pull/1242/head^2
Kye Gomez 2 days ago committed by GitHub
parent cdb1f05bb7 03302fee63
commit edaea9fa0b
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50
docs/llm.txt
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@ -24130,31 +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
@ -24420,7 +24395,6 @@ from swarms.structs.swarming_architectures import (
exponential_swarm,
fibonacci_swarm,
grid_swarm,
linear_swarm,
mesh_swarm,
one_to_three,
prime_swarm,
@ -24528,29 +24502,6 @@ 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()
@ -24684,7 +24635,6 @@ async def run_all_examples():
# Finance examples
run_finance_circular_swarm()
run_finance_linear_swarm()
run_finance_mesh_swarm()
run_mathematical_finance_swarms()

51
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)
@ -351,7 +325,6 @@ from swarms.structs.swarming_architectures import (
exponential_swarm,
fibonacci_swarm,
grid_swarm,
linear_swarm,
mesh_swarm,
one_to_three,
prime_swarm,
@ -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()
@ -615,7 +565,6 @@ async def run_all_examples():
# Finance examples
run_finance_circular_swarm()
run_finance_linear_swarm()
run_finance_mesh_swarm()
run_mathematical_finance_swarms()

26
examples/multi_agent/utils/unique_swarms_examples.py
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@ -8,7 +8,6 @@ from swarms.structs.swarming_architectures import (
exponential_swarm,
fibonacci_swarm,
grid_swarm,
linear_swarm,
mesh_swarm,
one_to_three,
prime_swarm,
@ -121,30 +120,6 @@ 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()
@ -287,7 +262,6 @@ async def run_all_examples():
# Finance examples
run_finance_circular_swarm()
run_finance_linear_swarm()
run_finance_mesh_swarm()
run_mathematical_finance_swarms()

2
swarms/structs/__init__.py
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@ -90,7 +90,6 @@ from swarms.structs.swarming_architectures import (
geometric_swarm,
grid_swarm,
harmonic_swarm,
linear_swarm,
log_swarm,
mesh_swarm,
one_to_one,
@ -128,7 +127,6 @@ __all__ = [
"geometric_swarm",
"grid_swarm",
"harmonic_swarm",
"linear_swarm",
"log_swarm",
"mesh_swarm",
"one_to_one",

43
swarms/structs/swarming_architectures.py
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@ -107,49 +107,6 @@ 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,

16
tests/structs/test_swarm_architectures.py
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@ -8,7 +8,6 @@ from swarms.structs.swarming_architectures import (
geometric_swarm,
grid_swarm,
harmonic_swarm,
linear_swarm,
log_swarm,
mesh_swarm,
one_to_one,
@ -69,21 +68,6 @@ def test_grid_swarm():
assert len(result) > 0
def test_linear_swarm():
"""Test linear swarm sequential processing"""
agents = create_test_agents(3)
tasks = ["Research task", "Write content", "Review output"]
result = linear_swarm(agents, tasks)
assert isinstance(result, list)
assert len(result) > 0
for log in result:
assert "role" in log
assert "content" in log
def test_star_swarm():
"""Test star swarm with central and peripheral agents"""
agents = create_test_agents(4)

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