Merge pull request #1 from ascender1729/codex/remove-unwanted-files-from-pr

Remove unwanted files
4 days ago · bb3d5dbcca
parent 063b0c1546 22735cd5bb
commit bb3d5dbcca
2 changed files with 0 additions and 183 deletions
--- a/examples/agent_save_load_full.py
+++ b/examples/agent_save_load_full.py
@ -1,121 +0,0 @@
 """
 Example: Fully Save and Load an Agent (with Conversation History)
 This demonstrates how to:
  1. Auto-save conversation messages to JSON
  2. Save the full Agent state
  3. Load both the Agent state and the conversation back into a fresh Agent
 """
 import os
 from swarms.structs.agent import Agent
 # Helper to safely print type or None for agent properties
 def print_agent_properties(agent, label):
    print(f"\n--- {label} ---")
    for prop in ["tokenizer", "long_term_memory", "logger_handler", "agent_output", "executor"]:
        value = getattr(agent, prop, None)
        print(f"{prop}: {type(value)}")
 # Helper to extract the conversation history list
 def get_conversation_history(agent):
    conv = getattr(agent, "conversation", None) or getattr(agent, "short_memory", None)
    return getattr(conv, "conversation_history", None)
 # Robust helper to reload conversation from JSON into the correct attribute
 def reload_conversation_from_json(agent, filepath):
    conv = getattr(agent, "conversation", None) or getattr(agent, "short_memory", None)
    if conv and hasattr(conv, "load_from_json"):
        conv.load_from_json(filepath)
 # --- 1. Setup: Create and configure an agent with auto-save conversation ---
 agent = Agent(
    agent_name="test",
    user_name="test_user",
    system_prompt="This is a test agent",
    max_loops=1,
    context_length=200000,
    autosave=True,
    verbose=True,
    artifacts_on=True,
    artifacts_output_path="test",
    artifacts_file_extension=".txt",
    conversation_kwargs={
        "auto_save": True,
        "save_as_json_bool": True,
        "save_filepath": "test_conversation_history.json"
    }
 )
 # --- 2. Interact to populate conversation ---
 agent.run(task="hello")
 agent.run(task="What is your purpose?")
 agent.run(task="Tell me a joke.")
 agent.run(task="Summarize our conversation so far.")
 # --- 3. Inspect before saving ---
 print_agent_properties(agent, "BEFORE SAVE")
 print("\nConversation history BEFORE SAVE:", get_conversation_history(agent))
 # --- 4. Save the agent state (conversation JSON was auto-saved under workspace) ---
 state_path = os.path.join(agent.workspace_dir, "test_state.json")
 agent.save(state_path)
 # --- 5. Ensure the conversation JSON file is saved and print its path and contents ---
 json_path = os.path.join(agent.workspace_dir, "test_conversation_history.json")
 if hasattr(agent, "short_memory") and hasattr(agent.short_memory, "save_as_json"):
    agent.short_memory.save_as_json(json_path)
 if os.path.exists(json_path):
    print(f"\n[CHECK] Conversation JSON file found: {json_path}")
    with open(json_path, "r") as f:
        json_data = f.read()
        print("[CHECK] JSON file contents:\n", json_data)
 else:
    print(f"[WARN] Conversation JSON file not found: {json_path}")
 # --- 6. Simulate fresh environment ---
 del agent
 # --- 7. Load: Restore the agent configuration ---
 agent2 = Agent(agent_name="test")
 agent2.load(state_path)
 # --- 8. Load: Restore the conversation history from the workspace directory into a new Conversation object ---
 from swarms.structs.conversation import Conversation
 conversation_loaded = Conversation()
 if os.path.exists(json_path):
    conversation_loaded.load_from_json(json_path)
    print("\n[CHECK] Loaded conversation from JSON into new Conversation object:")
    print(conversation_loaded.conversation_history)
 else:
    print(f"[WARN] Conversation JSON file not found for loading: {json_path}")
 # --- 9. Assign loaded conversation to agent2 and check ---
 agent2.short_memory = conversation_loaded
 print("\n[CHECK] Agent2 conversation history after assigning loaded conversation:", get_conversation_history(agent2))
 # --- 10. Inspect after loading ---
 print_agent_properties(agent2, "AFTER LOAD")
 print("\nConversation history AFTER LOAD:", get_conversation_history(agent2))
 # --- 11. Confirm the agent can continue ---
 result = agent2.run(task="What is 2+2?")
 print("\nAgent2 run result:", result)
 # --- 12. Cleanup test files ---
 print(f"\n[INFO] Test complete. Conversation JSON and agent state files are available for inspection:")
 print(f"  Conversation JSON: {json_path}")
 print(f"  Agent state: {state_path}")
 print("You can open and inspect these files to verify the agent's memory persistence.")
 # Do NOT delete files automatically
 # for path in (state_path, json_path):
 #     try:
 #         os.remove(path)
 #     except OSError:
 #         pass
 # --- 13. Test if agent2 remembers the previous conversation ---
 print("\n[TEST] Checking if agent2 remembers the previous conversation after reload...")
 probe = agent2.run(task="What did I ask you to do earlier?")
 print("\nAgent2 memory probe result:", probe)
--- a/swarms/structs/agent_non_serializable.py
+++ b/swarms/structs/agent_non_serializable.py
@ -1,62 +0,0 @@
 """
 Non-Serializable Properties Handler for Agent
 This module provides helper functions to save and restore non-serializable properties
 (tokenizer, long_term_memory, logger_handler, agent_output, executor) for the Agent class.
 Usage:
    from swarms.structs.agent_non_serializable import restore_non_serializable_properties
    restore_non_serializable_properties(agent)
 """
 from concurrent.futures import ThreadPoolExecutor
 import logging
 # Dummy/placeholder for long_term_memory and agent_output restoration
 class DummyLongTermMemory:
    def __init__(self):
        self.memory = []
    def query(self, *args, **kwargs):
        # Return an empty list or a default value to avoid errors
        return []
    def save(self, path):
        # Optionally implement a no-op save for compatibility
        pass
 class DummyAgentOutput:
    def __init__(self):
        self.output = None
 def restore_non_serializable_properties(agent):
    """
    Restore non-serializable properties for the Agent instance after loading.
    This should be called after loading agent state from disk.
    """
    # Restore tokenizer using LiteLLM if available
    agent.tokenizer = None
    try:
        from swarms.utils.litellm_tokenizer import count_tokens
        agent.tokenizer = count_tokens  # Assign the function as a tokenizer interface
    except Exception:
        agent.tokenizer = None
    # Restore long_term_memory (dummy for demo, replace with real backend as needed)
    if getattr(agent, "long_term_memory", None) is None or not hasattr(agent.long_term_memory, "query"):
        agent.long_term_memory = DummyLongTermMemory()
    # Restore logger_handler
    try:
        agent.logger_handler = logging.StreamHandler()
    except Exception:
        agent.logger_handler = None
    # Restore agent_output (dummy for demo, replace with real backend as needed)
    agent.agent_output = DummyAgentOutput()
    # Restore executor
    try:
        agent.executor = ThreadPoolExecutor()
    except Exception:
        agent.executor = None
    return agent