Merge pull request #1326 from hughiwnl/autosavelogic2

autosave logic for sequential and concurrent

Author: kyegomez

examples/multi_agent/concurrent_examples/README.md

@@ -4,6 +4,7 @@ This directory contains examples demonstrating concurrent execution patterns for
 
 ## Examples
 
+- [concurrent_workflow_autosave_example.py](concurrent_workflow_autosave_example.py) - **Autosave feature example** - Shows how to automatically save conversation history
 - [asi.py](asi.py) - ASI (Artificial Super Intelligence) example
 - [concurrent_example_dashboard.py](concurrent_example_dashboard.py) - Dashboard for concurrent workflows
 - [concurrent_example.py](concurrent_example.py) - Basic concurrent execution

examples/multi_agent/concurrent_examples/concurrent_workflow_autosave_example.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+"""
+ConcurrentWorkflow Autosave Example
+
+This example demonstrates how to use the autosave feature to automatically
+save conversation history after workflow execution.
+
+Usage:
+    python concurrent_workflow_autosave_example.py
+"""
+
+from swarms import Agent, ConcurrentWorkflow
+
+
+def main():
+    """Example: Using ConcurrentWorkflow with autosave enabled."""
+    
+    # Create worker agents
+    writer = Agent(
+        agent_name="Writer",
+        agent_description="Content writer",
+        model_name="gpt-4o-mini",
+        max_loops=1,
+        verbose=False,
+    )
+    
+    editor = Agent(
+        agent_name="Editor",
+        agent_description="Content editor",
+        model_name="gpt-4o-mini",
+        max_loops=1,
+        verbose=False,
+    )
+    
+    # Create workflow with autosave enabled
+    # No need to set WORKSPACE_DIR - it defaults to 'agent_workspace' automatically!
+    workflow = ConcurrentWorkflow(
+        name="content-workflow",
+        description="Content creation workflow",
+        agents=[writer, editor],
+        autosave=True,  # Enable autosave - conversation will be saved automatically
+    )
+    
+    print(f"Workspace directory: {workflow.swarm_workspace_dir}")
+    
+    # Run a task
+    result = workflow.run("Write a short paragraph about artificial intelligence.")
+    
+    # Conversation history is automatically saved to:
+    # {workspace_dir}/swarms/ConcurrentWorkflow/content-workflow-{timestamp}/conversation_history.json
+    print(f"\n✅ Task completed! Conversation saved to:")
+    print(f"   {workflow.swarm_workspace_dir}/conversation_history.json")
+
+
+if __name__ == "__main__":
+    # No setup needed - autosave works out of the box!
+    # WORKSPACE_DIR defaults to 'agent_workspace' if not set
+    main()

examples/multi_agent/sequential_workflow/README.md

@@ -4,6 +4,7 @@ This directory contains examples demonstrating sequential workflow patterns for
 
 ## Examples
 
+- [sequential_workflow_autosave_example.py](sequential_workflow_autosave_example.py) - **Autosave feature example** - Shows how to automatically save conversation history
 - [concurrent_workflow.py](concurrent_workflow.py) - Concurrent workflow patterns
 - [sequential_wofkflow.py](sequential_wofkflow.py) - Sequential workflow (typo in filename)
 - [sequential_worflow_test.py](sequential_worflow_test.py) - Sequential workflow testing

examples/multi_agent/sequential_workflow/sequential_workflow_autosave_example.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+"""
+SequentialWorkflow Autosave Example
+
+This example demonstrates how to use the autosave feature to automatically
+save conversation history after workflow execution.
+
+Usage:
+    python sequential_workflow_autosave_example.py
+"""
+
+from swarms import Agent, SequentialWorkflow
+
+
+def main():
+    """Example: Using SequentialWorkflow with autosave enabled."""
+    
+    # Create worker agents
+    writer = Agent(
+        agent_name="Writer",
+        agent_description="Content writer",
+        model_name="gpt-4o-mini",
+        max_loops=1,
+        verbose=False,
+    )
+    
+    editor = Agent(
+        agent_name="Editor",
+        agent_description="Content editor",
+        model_name="gpt-4o-mini",
+        max_loops=1,
+        verbose=False,
+    )
+    
+    # Create workflow with autosave enabled
+    # No need to set WORKSPACE_DIR - it defaults to 'agent_workspace' automatically!
+    workflow = SequentialWorkflow(
+        name="content-workflow",
+        description="Content creation workflow",
+        agents=[writer, editor],
+        max_loops=1,
+        autosave=True,  # Enable autosave - conversation will be saved automatically
+    )
+    
+    print(f"Workspace directory: {workflow.swarm_workspace_dir}")
+    
+    # Run a task
+    result = workflow.run("Write a short paragraph about artificial intelligence.")
+    
+    # Conversation history is automatically saved to:
+    # {workspace_dir}/swarms/SequentialWorkflow/content-workflow-{timestamp}/conversation_history.json
+    print(f"\n✅ Task completed! Conversation saved to:")
+    print(f"   {workflow.swarm_workspace_dir}/conversation_history.json")
+
+
+if __name__ == "__main__":
+    # No setup needed - autosave works out of the box!
+    # WORKSPACE_DIR defaults to 'agent_workspace' if not set
+    main()

swarms/structs/concurrent_workflow.py

@@ -1,7 +1,10 @@
 import concurrent.futures
+import json
+import os
 import time
 from typing import Callable, List, Optional, Union
 
+from loguru import logger as loguru_logger
 from swarms.structs.agent import Agent
 from swarms.structs.conversation import Conversation
 from swarms.structs.swarm_id import swarm_id
@@ -11,6 +14,8 @@
     history_output_formatter,
 )
 from swarms.utils.loguru_logger import initialize_logger
+from swarms.utils.swarm_autosave import get_swarm_workspace_dir
+from swarms.utils.workspace_utils import get_workspace_dir
 
 logger = initialize_logger(log_folder="concurrent_workflow")
 
@@ -76,6 +81,8 @@ def __init__(
         max_loops: int = 1,
         auto_generate_prompts: bool = False,
         show_dashboard: bool = False,
+        autosave: bool = True,
+        verbose: bool = False,
     ):
         self.id = id if id is not None else swarm_id()
         self.name = name
@@ -86,6 +93,9 @@ def __init__(
         self.auto_generate_prompts = auto_generate_prompts
         self.output_type = output_type
         self.show_dashboard = show_dashboard
+        self.autosave = autosave
+        self.verbose = verbose
+        self.swarm_workspace_dir = None
         self.metadata_output_path = (
             f"concurrent_workflow_name_{name}_id_{self.id}.json"
         )
@@ -107,6 +117,10 @@ def __init__(
         if self.show_dashboard is True:
             self.agents = self.fix_agents()
 
+        # Setup autosave workspace if enabled
+        if self.autosave:
+            self._setup_autosave()
+
     def fix_agents(self):
         """
         Configure agents for dashboard mode.
@@ -554,7 +568,27 @@ def run(
                 result = self._run(
                     task, img, imgs, streaming_callback
                 )
+
+            # Save conversation history after successful execution
+            if self.autosave and self.swarm_workspace_dir:
+                try:
+                    self._save_conversation_history()
+                except Exception as e:
+                    logger.warning(
+                        f"Failed to save conversation history: {e}"
+                    )
+
             return result
+        except Exception as e:
+            # Save conversation history on error
+            if self.autosave and self.swarm_workspace_dir:
+                try:
+                    self._save_conversation_history()
+                except Exception as save_error:
+                    logger.warning(
+                        f"Failed to save conversation history on error: {save_error}"
+                    )
+            raise
         finally:
             # Always cleanup resources
             self.cleanup()
@@ -599,3 +633,84 @@ def batch_run(
                 )
             )
         return results
+
+    def _setup_autosave(self):
+        """
+        Setup workspace directory for saving conversation history.
+
+        Creates the workspace directory structure if autosave is enabled.
+        Only conversation history will be saved to this directory.
+        """
+        try:
+            # Set default workspace directory if not set
+            if not os.getenv("WORKSPACE_DIR"):
+                default_workspace = os.path.join(os.getcwd(), "agent_workspace")
+                os.environ["WORKSPACE_DIR"] = default_workspace
+                # Clear the cache so get_workspace_dir() picks up the new value
+                get_workspace_dir.cache_clear()
+                if self.verbose:
+                    loguru_logger.info(
+                        f"WORKSPACE_DIR not set, using default: {default_workspace}"
+                    )
+
+            class_name = self.__class__.__name__
+            swarm_name = self.name or "concurrent-workflow"
+            self.swarm_workspace_dir = get_swarm_workspace_dir(
+                class_name, swarm_name, use_timestamp=True
+            )
+
+            if self.swarm_workspace_dir:
+                if self.verbose:
+                    loguru_logger.info(
+                        f"Autosave enabled. Conversation history will be saved to: {self.swarm_workspace_dir}"
+                    )
+        except Exception as e:
+            loguru_logger.warning(
+                f"Failed to setup autosave for ConcurrentWorkflow: {e}"
+            )
+            # Don't raise - autosave failures shouldn't break initialization
+            self.swarm_workspace_dir = None
+
+    def _save_conversation_history(self):
+        """
+        Save conversation history as a separate JSON file to the workspace directory.
+
+        Saves the conversation history to:
+        workspace_dir/swarms/ConcurrentWorkflow/{workflow-name}-{id}/conversation_history.json
+        """
+        if not self.swarm_workspace_dir:
+            return
+
+        try:
+            # Get conversation history
+            conversation_data = []
+            if hasattr(self, "conversation") and self.conversation:
+                if hasattr(self.conversation, "conversation_history"):
+                    conversation_data = self.conversation.conversation_history
+                elif hasattr(self.conversation, "to_dict"):
+                    conversation_data = self.conversation.to_dict()
+                else:
+                    conversation_data = []
+
+                # Create conversation history file path
+                conversation_path = os.path.join(
+                    self.swarm_workspace_dir, "conversation_history.json"
+                )
+
+                # Save conversation history as JSON
+                with open(conversation_path, "w", encoding="utf-8") as f:
+                    json.dump(
+                        conversation_data,
+                        f,
+                        indent=2,
+                        default=str,
+                    )
+
+                if self.verbose:
+                    loguru_logger.debug(
+                        f"Saved conversation history to {conversation_path}"
+                    )
+        except Exception as e:
+            loguru_logger.warning(
+                f"Failed to save conversation history: {e}"
+            )