Removed linear swarm

docs/llm.txt

@@ -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()
 

docs/swarms/examples/unique_swarms.md

@@ -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()
 

examples/multi_agent/utils/unique_swarms_examples.py

@@ -8,7 +8,6 @@
     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()
 

swarms/structs/__init__.py

@@ -90,7 +90,6 @@
     geometric_swarm,
     grid_swarm,
     harmonic_swarm,
-    linear_swarm,
     log_swarm,
     mesh_swarm,
     one_to_one,
@@ -128,7 +127,6 @@
     "geometric_swarm",
     "grid_swarm",
     "harmonic_swarm",
-    "linear_swarm",
     "log_swarm",
     "mesh_swarm",
     "one_to_one",

swarms/structs/swarming_architectures.py

@@ -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,

tests/structs/test_swarm_architectures.py

@@ -8,7 +8,6 @@
     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)