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A ready-to-run example is available here!

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How to use Persistence

Save conversation state to disk and restore it later for long-running or multi-session workflows.

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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

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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

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What Gets Persisted

The conversation state includes 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.

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Persistence Directory Structure

When you set a persistence_dir, your conversation will be persisted to a directory structure where each conversation has its own subdirectory. By default, the persistence directory is workspace/conversations/ (unless you specify a custom path). Directory structure:
workspace/conversations
<conversation-id-1>
base_state.json
events
event-00000-<event-id>.json
event-00001-<event-id>.json
...
...
Each conversation directory contains:
  • base_state.json: The core conversation state including agent configuration, execution status, statistics, and metadata
  • events/: A subdirectory containing individual event files, each named with a sequential index and event ID (e.g., event-00000-abc123.json)
The collection of event files in the events/ directory represents the same trajectory data you would find in the trajectory.json file from OpenHands V0, but split into individual files for better performance and granular access.

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Ready-to-run Example

This example is available on GitHub: examples/01_standalone_sdk/10_persistence.py
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
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


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", "anthropic/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()
tools = [
    Tool(name=TerminalTool.name),
    Tool(name=FileEditorTool.name),
]

# 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()

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")
You can run the example code as-is.
The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.
ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

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Reading serialized events

Convert persisted events into LLM-ready messages for reuse or analysis.
This example is available on GitHub: examples/01_standalone_sdk/36_event_json_to_openai_messages.py
examples/01_standalone_sdk/36_event_json_to_openai_messages.py
"""Load persisted events and convert them into LLM-ready messages."""

import json
import os
import uuid
from pathlib import Path

from pydantic import SecretStr


conversation_id = uuid.uuid4()
persistence_root = Path(".conversations")
log_dir = (
    persistence_root / "logs" / "event-json-to-openai-messages" / conversation_id.hex
)

os.environ.setdefault("LOG_JSON", "true")
os.environ.setdefault("LOG_TO_FILE", "true")
os.environ.setdefault("LOG_DIR", str(log_dir))
os.environ.setdefault("LOG_LEVEL", "INFO")

from openhands.sdk import (  # noqa: E402
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    Tool,
)
from openhands.sdk.logger import get_logger, setup_logging  # noqa: E402
from openhands.tools.terminal import TerminalTool  # noqa: E402


setup_logging(log_to_file=True, log_dir=str(log_dir))
logger = get_logger(__name__)

api_key = os.getenv("LLM_API_KEY")
if not api_key:
    raise RuntimeError("LLM_API_KEY environment variable is not set.")

llm = LLM(
    usage_id="agent",
    model=os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929"),
    base_url=os.getenv("LLM_BASE_URL"),
    api_key=SecretStr(api_key),
)

agent = Agent(
    llm=llm,
    tools=[Tool(name=TerminalTool.name)],
)

######
# Create a conversation that persists its events
######

conversation = Conversation(
    agent=agent,
    workspace=os.getcwd(),
    persistence_dir=str(persistence_root),
    conversation_id=conversation_id,
)

conversation.send_message(
    "Use the terminal tool to run `pwd` and write the output to tool_output.txt. "
    "Reply with a short confirmation once done."
)
conversation.run()

conversation.send_message(
    "Without using any tools, summarize in one sentence what you did."
)
conversation.run()

assert conversation.state.persistence_dir is not None
persistence_dir = Path(conversation.state.persistence_dir)
event_dir = persistence_dir / "events"

event_paths = sorted(event_dir.glob("event-*.json"))

if not event_paths:
    raise RuntimeError("No event files found. Was persistence enabled?")

######
# Read from serialized events
######


events = [Event.model_validate_json(path.read_text()) for path in event_paths]

convertible_events = [
    event for event in events if isinstance(event, LLMConvertibleEvent)
]
llm_messages = LLMConvertibleEvent.events_to_messages(convertible_events)

if llm.uses_responses_api():
    logger.info("Formatting messages for the OpenAI Responses API.")
    instructions, input_items = llm.format_messages_for_responses(llm_messages)
    logger.info("Responses instructions:\n%s", instructions)
    logger.info("Responses input:\n%s", json.dumps(input_items, indent=2))
else:
    logger.info("Formatting messages for the OpenAI Chat Completions API.")
    chat_messages = llm.format_messages_for_llm(llm_messages)
    logger.info("Chat Completions messages:\n%s", json.dumps(chat_messages, indent=2))

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")
You can run the example code as-is.
The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.
ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

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Next Steps