> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openhands.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Persistence

> Save and restore conversation state for multi-session workflows.

export const path_to_script_1 = "examples/01_standalone_sdk/36_event_json_to_openai_messages.py"

export const path_to_script_0 = "examples/01_standalone_sdk/10_persistence.py"

> A ready-to-run example is available [here](#ready-to-run-example)!

## 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:

```python focus={3-4,10-11} icon="python" wrap theme={null}
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:

```python focus={9-10} icon="python" theme={null}
# 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](/sdk/guides/skill) that have been enabled during the conversation
* **Secrets**: Managed credentials and API keys
* **Agent State**: Custom runtime state stored by agents (see [Agent State](#agent-state) below)

<Tip>
  For the complete implementation details, see the [ConversationState class](https://github.com/OpenHands/software-agent-sdk/blob/main/openhands-sdk/openhands/sdk/conversation/state.py) in the source code.
</Tip>

## 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:**

<Tree>
  <Tree.Folder name="workspace/conversations" defaultOpen>
    <Tree.Folder name="<conversation-id-1>" defaultOpen>
      <Tree.File name="base_state.json" />

      <Tree.Folder name="events" defaultOpen>
        <Tree.File name="event-00000-<event-id>.json" />

        <Tree.File name="event-00001-<event-id>.json" />

        <Tree.File name="..." />
      </Tree.Folder>
    </Tree.Folder>

    <Tree.Folder name="<conversation-id-2>">
      <Tree.File name="base_state.json" />

      <Tree.Folder name="events">
        <Tree.File name="..." />
      </Tree.Folder>
    </Tree.Folder>

    <Tree.Folder name="..." />
  </Tree.Folder>
</Tree>

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

<Note>
  This example is available on GitHub: [examples/01\_standalone\_sdk/10\_persistence.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/01_standalone_sdk/10_persistence.py)
</Note>

```python icon="python" expandable examples/01_standalone_sdk/10_persistence.py theme={null}
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.

<Note>
  The model name should follow the [LiteLLM convention](https://models.litellm.ai/): `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.
</Note>

<CodeGroup>
  <CodeBlock language="bash" filename="Bring-your-own provider key" icon="terminal" wrap>
    {`export LLM_API_KEY="your-api-key"\nexport LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.\ncd software-agent-sdk\nuv run python ${path_to_script_0}`}
  </CodeBlock>

  <CodeBlock language="bash" filename="OpenHands Cloud" icon="terminal" wrap>
    {`# https://app.all-hands.dev/settings/api-keys\nexport LLM_API_KEY="your-openhands-api-key"\nexport LLM_MODEL="openhands/claude-sonnet-4-5-20250929"\ncd software-agent-sdk\nuv run python ${path_to_script_0}`}
  </CodeBlock>
</CodeGroup>

<Tip>
  **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](/sdk/guides/llm-subscriptions) for details.
</Tip>

## Reading serialized events

Convert persisted events into LLM-ready messages for reuse or analysis.

<Note>
  This example is available on GitHub: [examples/01\_standalone\_sdk/36\_event\_json\_to\_openai\_messages.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/01_standalone_sdk/36_event_json_to_openai_messages.py)
</Note>

```python icon="python" expandable examples/01_standalone_sdk/36_event_json_to_openai_messages.py theme={null}
"""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.

<Note>
  The model name should follow the [LiteLLM convention](https://models.litellm.ai/): `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.
</Note>

<CodeGroup>
  <CodeBlock language="bash" filename="Bring-your-own provider key" icon="terminal" wrap>
    {`export LLM_API_KEY="your-api-key"\nexport LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.\ncd software-agent-sdk\nuv run python ${path_to_script_1}`}
  </CodeBlock>

  <CodeBlock language="bash" filename="OpenHands Cloud" icon="terminal" wrap>
    {`# https://app.all-hands.dev/settings/api-keys\nexport LLM_API_KEY="your-openhands-api-key"\nexport LLM_MODEL="openhands/claude-sonnet-4-5-20250929"\ncd software-agent-sdk\nuv run python ${path_to_script_1}`}
  </CodeBlock>
</CodeGroup>

<Tip>
  **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](/sdk/guides/llm-subscriptions) for details.
</Tip>

## How State Persistence Works

The SDK uses an **automatic persistence** system that saves state changes immediately when they occur. This ensures that conversation state is always recoverable, even if the process crashes unexpectedly.

### Auto-Save Mechanism

When you modify any public field on `ConversationState`, the SDK automatically:

1. Detects the field change via a custom `__setattr__` implementation
2. Serializes the entire base state to `base_state.json`
3. Triggers any registered state change callbacks

This happens transparently—you don't need to call any save methods manually.

```python theme={null}
# These changes are automatically persisted:
conversation.state.execution_status = ConversationExecutionStatus.RUNNING
conversation.state.max_iterations = 100
```

### Events vs Base State

The persistence system separates data into two categories:

| Category       | Storage               | Contents                                                                 |
| -------------- | --------------------- | ------------------------------------------------------------------------ |
| **Base State** | `base_state.json`     | Agent configuration, execution status, statistics, secrets, agent\_state |
| **Events**     | `events/event-*.json` | Message history, tool calls, observations, all conversation events       |

Events are appended incrementally (one file per event), while base state is overwritten on each change. This design optimizes for:

* **Fast event appends**: No need to rewrite the entire history
* **Atomic state updates**: Base state is always consistent
* **Efficient restoration**: Events can be loaded lazily

## Next Steps

* **[Pause and Resume](/sdk/guides/convo-pause-and-resume)** - Control execution flow
* **[Async Operations](/sdk/guides/convo-async)** - Non-blocking operations