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This example is available on GitHub: examples/01_standalone_sdk/10_persistence.py
Save conversation state to disk and restore it later for long-running or multi-session workflows:
examples/01_standalone_sdk/10_persistence.py
import os
import uuid

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from openhands.sdk.tool import Tool, register_tool
from openhands.tools.execute_bash import BashTool
from openhands.tools.file_editor import FileEditorTool


logger = get_logger(__name__)

# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "openhands/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Tools
cwd = os.getcwd()
register_tool("BashTool", BashTool)
register_tool("FileEditorTool", FileEditorTool)
tools = [
    Tool(name="BashTool"),
    Tool(name="FileEditorTool"),
]

# Add MCP Tools
mcp_config = {
    "mcpServers": {
        "fetch": {"command": "uvx", "args": ["mcp-server-fetch"]},
    }
}
# Agent
agent = Agent(llm=llm, tools=tools, mcp_config=mcp_config)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


conversation_id = uuid.uuid4()
persistence_dir = "./.conversations"

conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=cwd,
    persistence_dir=persistence_dir,
    conversation_id=conversation_id,
)
conversation.send_message(
    "Read https://github.com/OpenHands/OpenHands. Then write 3 facts "
    "about the project into FACTS.txt."
)
conversation.run()

conversation.send_message("Great! Now delete that file.")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")

# Conversation persistence
print("Serializing conversation...")

del conversation

# Deserialize the conversation
print("Deserializing conversation...")
conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=cwd,
    persistence_dir=persistence_dir,
    conversation_id=conversation_id,
)

print("Sending message to deserialized conversation...")
conversation.send_message("Hey what did you create? Return an agent finish action")
conversation.run()
Running the Example
export LLM_API_KEY="your-api-key"
cd agent-sdk
uv run python examples/01_standalone_sdk/10_persistence.py

​
Saving State

Create a conversation with a unique ID to enable persistence: 
import uuid

conversation_id = uuid.uuid4()
persistence_dir = "./.conversations"

conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=cwd,
    persistence_dir=persistence_dir,
    conversation_id=conversation_id,
)
conversation.send_message("Start long task")
conversation.run()  # State automatically saved

​
Restoring State

Restore a conversation using the same ID and persistence directory: 
# Later, in a different session
del conversation

# Deserialize the conversation
print("Deserializing conversation...")
conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=cwd,
    persistence_dir=persistence_dir,
    conversation_id=conversation_id,
)

conversation.send_message("Continue task")
conversation.run()  # Continues from saved state

​
What Gets Persisted

The conversation state includes comprehensive information that allows seamless restoration:
  • Message History: Complete event log including user messages, agent responses, and system events
  • Agent Configuration: LLM settings, tools, MCP servers, and agent parameters
  • Execution State: Current agent status (idle, running, paused, etc.), iteration count, and stuck detection settings
  • Tool Outputs: Results from bash commands, file operations, and other tool executions
  • Statistics: LLM usage metrics like token counts and API calls
  • Workspace Context: Working directory and file system state
  • Activated Skills: Skills that have been enabled during the conversation
  • Secrets: Managed credentials and API keys
For the complete implementation details, see the ConversationState class in the source code.

​
Next Steps