Persistence

Save and restore conversation state for multi-session workflows.

A ready-to-run example is available here!

How to use Persistence

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

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 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.

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

...

<conversation-id-2>

...

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.

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.

Next Steps

• Pause and Resume - Control execution flow
• Async Operations - Non-blocking operations