fix: robust agent serialization/deserialization and restoration of non-serializable properties (tokenizer, long_term_memory, logger_handler, agent_output, executor). Closes #640
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"""
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Example: Fully Save and Load an Agent (Issue #640)
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This example demonstrates how to save and load an Agent instance such that all non-serializable properties
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(tokenizer, long_term_memory, logger_handler, agent_output, executor) are restored after loading.
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This is a user-facing, production-grade demonstration for swarms.
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"""
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from swarms.structs.agent import Agent
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import os
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# Helper to safely print type or None for agent properties
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def print_agent_properties(agent, label):
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print(f"\n--- {label} ---")
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for prop in ["tokenizer", "long_term_memory", "logger_handler", "agent_output", "executor"]:
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value = getattr(agent, prop, None)
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print(f"{prop}: {type(value)}")
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# --- Setup: Create and configure an agent ---
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agent = Agent(
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agent_name="test",
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user_name="test_user",
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system_prompt="This is a test agent",
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max_loops=1,
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context_length=200000,
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autosave=True,
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verbose=True,
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artifacts_on=True,
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artifacts_output_path="test",
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artifacts_file_extension=".txt",
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)
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# Optionally, interact with the agent to populate state
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agent.run(task="hello")
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# Print non-serializable properties BEFORE saving
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print_agent_properties(agent, "BEFORE SAVE")
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# Save the agent state
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save_path = os.path.join(agent.workspace_dir, "test_state.json")
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agent.save(save_path)
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# Delete the agent instance to simulate a fresh load
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del agent
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# --- Load: Restore the agent from file ---
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agent2 = Agent(agent_name="test") # Minimal init, will be overwritten by load
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agent2.load(save_path)
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# Print non-serializable properties AFTER loading
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print_agent_properties(agent2, "AFTER LOAD")
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# Confirm agent2 can still run tasks and autosave
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result = agent2.run(task="What is 2+2?")
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print("\nAgent2 run result:", result)
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# Clean up test file
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try:
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os.remove(save_path)
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except Exception:
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pass
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"""
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Non-Serializable Properties Handler for Agent
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This module provides helper functions to save and restore non-serializable properties
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(tokenizer, long_term_memory, logger_handler, agent_output, executor) for the Agent class.
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Usage:
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from swarms.structs.agent_non_serializable import restore_non_serializable_properties
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restore_non_serializable_properties(agent)
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"""
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from transformers import AutoTokenizer
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from concurrent.futures import ThreadPoolExecutor
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import logging
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# Dummy/placeholder for long_term_memory and agent_output restoration
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class DummyLongTermMemory:
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def __init__(self):
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self.memory = []
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def query(self, *args, **kwargs):
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# Return an empty list or a default value to avoid errors
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return []
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def save(self, path):
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# Optionally implement a no-op save for compatibility
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pass
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class DummyAgentOutput:
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def __init__(self):
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self.output = None
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def restore_non_serializable_properties(agent):
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"""
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Restore non-serializable properties for the Agent instance after loading.
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This should be called after loading agent state from disk.
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"""
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# Restore tokenizer if model_name is available
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if getattr(agent, "model_name", None):
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try:
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agent.tokenizer = AutoTokenizer.from_pretrained(agent.model_name)
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except Exception:
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agent.tokenizer = None
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else:
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agent.tokenizer = None
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# Restore long_term_memory (dummy for demo, replace with real backend as needed)
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if getattr(agent, "long_term_memory", None) is None or not hasattr(agent.long_term_memory, "query"):
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agent.long_term_memory = DummyLongTermMemory()
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# Restore logger_handler
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try:
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agent.logger_handler = logging.StreamHandler()
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except Exception:
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agent.logger_handler = None
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# Restore agent_output (dummy for demo, replace with real backend as needed)
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agent.agent_output = DummyAgentOutput()
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# Restore executor
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try:
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agent.executor = ThreadPoolExecutor()
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except Exception:
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agent.executor = None
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return agent
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