diff --git a/swarms/structs/spreadsheet_swarm.py b/swarms/structs/spreadsheet_swarm.py
index da045627..e2b0933d 100644
--- a/swarms/structs/spreadsheet_swarm.py
+++ b/swarms/structs/spreadsheet_swarm.py
@@ -1,6 +1,6 @@
import asyncio
import csv
-from datetime import datetime
+import datetime
import os
import uuid
from typing import Dict, List, Union
@@ -16,8 +16,21 @@ from swarms.utils.loguru_logger import initialize_logger
logger = initialize_logger(log_folder="spreadsheet_swarm")
-# Replace timestamp-based time with a UUID for file naming
-run_id = uuid.uuid4().hex # Unique identifier for each run
+time = datetime.datetime.now().isoformat()
+uuid_hex = uuid.uuid4().hex
+
+# --------------- NEW CHANGE START ---------------
+# Format time variable to be compatible across operating systems
+formatted_time = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+# --------------- NEW CHANGE END ---------------
+
+class AgentConfig(BaseModel):
+ """Configuration for an agent loaded from CSV"""
+
+ agent_name: str
+ description: str
+ system_prompt: str
+ task: str
class AgentOutput(BaseModel):
agent_name: str
@@ -25,16 +38,15 @@ class AgentOutput(BaseModel):
result: str
timestamp: str
-
class SwarmRunMetadata(BaseModel):
run_id: str = Field(
- default_factory=lambda: f"spreadsheet_swarm_run_{run_id}"
+ default_factory=lambda: f"spreadsheet_swarm_run_{uuid_hex}"
)
name: str
description: str
agents: List[str]
start_time: str = Field(
- default_factory=lambda: str(datetime.now().timestamp()), # Numeric timestamp
+ default_factory=lambda: time,
description="The start time of the swarm run.",
)
end_time: str
@@ -45,7 +57,6 @@ class SwarmRunMetadata(BaseModel):
description="The number of agents participating in the swarm.",
)
-
class SpreadSheetSwarm(BaseSwarm):
"""
A swarm that processes tasks concurrently using multiple agents.
@@ -65,7 +76,7 @@ class SpreadSheetSwarm(BaseSwarm):
def __init__(
self,
name: str = "Spreadsheet-Swarm",
- description: str = "A swarm that processes tasks concurrently using multiple agents and saves the metadata to a CSV file.",
+ description: str = "A swarm that that processes tasks concurrently using multiple agents and saves the metadata to a CSV file.",
agents: Union[Agent, List[Agent]] = [],
autosave_on: bool = True,
save_file_path: str = None,
@@ -88,19 +99,22 @@ class SpreadSheetSwarm(BaseSwarm):
self.autosave_on = autosave_on
self.max_loops = max_loops
self.workspace_dir = workspace_dir
+ self.load_path = load_path
+ self.agent_configs: Dict[str, AgentConfig] = {}
- # Create a timestamp without colons or periods
- timestamp = datetime.now().isoformat().replace(":", "_").replace(".", "_")
-
- # Use this timestamp in the CSV filename
- self.save_file_path = f"spreadsheet_swarm_{timestamp}_run_id_{run_id}.csv"
+ # --------------- NEW CHANGE START ---------------
+ # The save_file_path now uses the formatted_time and uuid_hex
+ self.save_file_path = (
+ f"spreadsheet_swarm_run_id_{formatted_time}.csv"
+ )
+ # --------------- NEW CHANGE END ---------------
self.metadata = SwarmRunMetadata(
- run_id=f"spreadsheet_swarm_run_{run_id}",
+ run_id=f"spreadsheet_swarm_run_{formatted_time}",
name=name,
description=description,
agents=[agent.name for agent in agents],
- start_time=str(datetime.now().timestamp()), # Numeric timestamp
+ start_time=time,
end_time="",
tasks_completed=0,
outputs=[],
@@ -166,10 +180,22 @@ class SpreadSheetSwarm(BaseSwarm):
),
docs=[row["docs"]] if "docs" in row else "",
dynamic_temperature_enabled=True,
- max_loops=row["max_loops"] if "max_loops" in row else 1,
- user_name=row["user_name"] if "user_name" in row else "user",
+ max_loops=(
+ row["max_loops"]
+ if "max_loops" in row
+ else 1
+ ),
+ user_name=(
+ row["user_name"]
+ if "user_name" in row
+ else "user"
+ ),
# output_type="str",
- stopping_token=row["stopping_token"] if "stopping_token" in row else None,
+ stopping_token=(
+ row["stopping_token"]
+ if "stopping_token" in row
+ else None
+ ),
)
# Add agent to swarm
@@ -252,8 +278,7 @@ class SpreadSheetSwarm(BaseSwarm):
print(log_agent_data(self.metadata.model_dump()))
return self.metadata.model_dump_json(indent=4)
-
-
+
def run(self, task: str = None, *args, **kwargs):
"""
Run the swarm with the specified task.
@@ -267,30 +292,11 @@ class SpreadSheetSwarm(BaseSwarm):
str: The JSON representation of the swarm metadata.
"""
- logger.info(f"Running the swarm with task: {task}")
- self.metadata.start_time = str(datetime.now().timestamp()) # Numeric timestamp
-
- # Check if we're already in an event loop
- if asyncio.get_event_loop().is_running():
- # If so, create and run tasks directly using `create_task` without `asyncio.run`
- task_future = asyncio.create_task(self._run_tasks(task, *args, **kwargs))
- asyncio.get_event_loop().run_until_complete(task_future)
- else:
- # If no event loop is running, run using `asyncio.run`
- asyncio.run(self._run_tasks(task, *args, **kwargs))
-
- self.metadata.end_time = str(datetime.now().timestamp()) # Numeric timestamp
-
- # Synchronously save metadata
- logger.info("Saving metadata to CSV and JSON...")
- asyncio.run(self._save_metadata())
-
- if self.autosave_on:
- self.data_to_json_file()
-
- print(log_agent_data(self.metadata.model_dump()))
-
- return self.metadata.model_dump_json(indent=4)
+ try:
+ return asyncio.run(self._run(task, *args, **kwargs))
+ except Exception as e:
+ logger.error(f"Error running swarm: {e}")
+ raise e
async def _run_tasks(self, task: str, *args, **kwargs):
"""
@@ -360,7 +366,7 @@ class SpreadSheetSwarm(BaseSwarm):
agent_name=agent_name,
task=task,
result=result,
- timestamp=str(datetime.now().timestamp()), # Numeric timestamp
+ timestamp=time,
)
)
@@ -381,7 +387,7 @@ class SpreadSheetSwarm(BaseSwarm):
create_file_in_folder(
folder_path=f"{self.workspace_dir}/Spreedsheet-Swarm-{self.name}/{self.name}",
- file_name=f"spreedsheet-swarm-{self.metadata.run_id}_metadata.json",
+ file_name=f"spreedsheet-swarm-{uuid_hex}-metadata.json",
content=out,
)
@@ -396,19 +402,38 @@ class SpreadSheetSwarm(BaseSwarm):
"""
Save the swarm metadata to a CSV file.
"""
- logger.info(f"Saving swarm metadata to: {self.save_file_path}")
+ logger.info(
+ f"Saving swarm metadata to: {self.save_file_path}"
+ )
run_id = uuid.uuid4()
# Check if file exists before opening it
file_exists = os.path.exists(self.save_file_path)
- async with aiofiles.open(self.save_file_path, mode="a") as file:
+ async with aiofiles.open(
+ self.save_file_path, mode="a"
+ ) as file:
+ writer = csv.writer(file)
+
# Write header if file doesn't exist
if not file_exists:
- header = "Run ID,Agent Name,Task,Result,Timestamp\n"
- await file.write(header)
+ await writer.writerow(
+ [
+ "Run ID",
+ "Agent Name",
+ "Task",
+ "Result",
+ "Timestamp",
+ ]
+ )
- # Write each output as a new row
for output in self.metadata.outputs:
- row = f"{run_id},{output.agent_name},{output.task},{output.result},{output.timestamp}\n"
- await file.write(row)
\ No newline at end of file
+ await writer.writerow(
+ [
+ str(run_id),
+ output.agent_name,
+ output.task,
+ output.result,
+ output.timestamp,
+ ]
+ )
\ No newline at end of file