crewAI/docs/en/concepts/checkpointing.mdx

---
title: Checkpointing
description: Automatically save execution state so crews, flows, and agents can resume after failures.
icon: floppy-disk
mode: "wide"
---

<Warning>
Checkpointing is in early release. APIs may change in future versions.
</Warning>

## Overview

Checkpointing automatically saves execution state during a run. If a crew, flow, or agent fails mid-execution, you can restore from the last checkpoint and resume without re-running completed work.

## Quick Start

```python
from crewai import Crew, CheckpointConfig

crew = Crew(
    agents=[...],
    tasks=[...],
    checkpoint=True,  # uses defaults: ./.checkpoints, on task_completed
)
result = crew.kickoff()
```

Checkpoint files are written to `./.checkpoints/` after each completed task.

## Configuration

Use `CheckpointConfig` for full control:

```python
from crewai import Crew, CheckpointConfig

crew = Crew(
    agents=[...],
    tasks=[...],
    checkpoint=CheckpointConfig(
        directory="./my_checkpoints",
        on_events=["task_completed", "crew_kickoff_completed"],
        max_checkpoints=5,
    ),
)
```

### CheckpointConfig Fields

| Field | Type | Default | Description |
|:------|:-----|:--------|:------------|
| `directory` | `str` | `"./.checkpoints"` | Filesystem path for checkpoint files |
| `on_events` | `list[str]` | `["task_completed"]` | Event types that trigger a checkpoint |
| `provider` | `BaseProvider` | `JsonProvider()` | Storage backend |
| `max_checkpoints` | `int \| None` | `None` | Max files to keep; oldest pruned first |

### Inheritance and Opt-Out

The `checkpoint` field on Crew, Flow, and Agent accepts `CheckpointConfig`, `True`, `False`, or `None`:

| Value | Behavior |
|:------|:---------|
| `None` (default) | Inherit from parent. An agent inherits its crew's config. |
| `True` | Enable with defaults. |
| `False` | Explicit opt-out. Stops inheritance from parent. |
| `CheckpointConfig(...)` | Custom configuration. |

```python
crew = Crew(
    agents=[
        Agent(role="Researcher", ...),                  # inherits crew's checkpoint
        Agent(role="Writer", ..., checkpoint=False),     # opted out, no checkpoints
    ],
    tasks=[...],
    checkpoint=True,
)
```

## Resuming from a Checkpoint

```python
# Restore and resume
crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
result = crew.kickoff()  # picks up from last completed task
```

The restored crew skips already-completed tasks and resumes from the first incomplete one.

## Works on Crew, Flow, and Agent

### Crew

```python
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task, review_task],
    checkpoint=CheckpointConfig(directory="./crew_cp"),
)
```

Default trigger: `task_completed` (one checkpoint per finished task).

### Flow

```python
from crewai.flow.flow import Flow, start, listen
from crewai import CheckpointConfig

class MyFlow(Flow):
    @start()
    def step_one(self):
        return "data"

    @listen(step_one)
    def step_two(self, data):
        return process(data)

flow = MyFlow(
    checkpoint=CheckpointConfig(
        directory="./flow_cp",
        on_events=["method_execution_finished"],
    ),
)
result = flow.kickoff()

# Resume
flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
result = flow.kickoff()
```

### Agent

```python
agent = Agent(
    role="Researcher",
    goal="Research topics",
    backstory="Expert researcher",
    checkpoint=CheckpointConfig(
        directory="./agent_cp",
        on_events=["lite_agent_execution_completed"],
    ),
)
result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
```

## Storage Providers

CrewAI ships with two checkpoint storage providers.

### JsonProvider (default)

Writes each checkpoint as a separate JSON file. Simple, human-readable, easy to inspect.

```python
from crewai import Crew, CheckpointConfig
from crewai.state import JsonProvider

crew = Crew(
    agents=[...],
    tasks=[...],
    checkpoint=CheckpointConfig(
        directory="./my_checkpoints",
        provider=JsonProvider(),       # this is the default
        max_checkpoints=5,             # prunes oldest files
    ),
)
```

Files are named `<timestamp>_<uuid>.json` inside the directory.

### SqliteProvider

Stores all checkpoints in a single SQLite database file. Better for high-frequency checkpointing and avoids many small files.

```python
from crewai import Crew, CheckpointConfig
from crewai.state import SqliteProvider

crew = Crew(
    agents=[...],
    tasks=[...],
    checkpoint=CheckpointConfig(
        directory="./.checkpoints.db",
        provider=SqliteProvider(max_checkpoints=50),
    ),
)
```

`SqliteProvider` accepts its own `max_checkpoints` parameter that prunes old rows via SQL. WAL journal mode is enabled for concurrent read access.

<Note>
When using `SqliteProvider`, the `directory` field is the database file path, not a directory. The `max_checkpoints` on `CheckpointConfig` controls filesystem pruning (for `JsonProvider`), while `SqliteProvider.max_checkpoints` controls row pruning in the database.
</Note>

## Event Types

The `on_events` field accepts any combination of event type strings. Common choices:

| Use Case | Events |
|:---------|:-------|
| After each task (Crew) | `["task_completed"]` |
| After each flow method | `["method_execution_finished"]` |
| After agent execution | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
| On crew completion only | `["crew_kickoff_completed"]` |
| After every LLM call | `["llm_call_completed"]` |
| On everything | `["*"]` |

<Warning>
Using `["*"]` or high-frequency events like `llm_call_completed` will write many checkpoint files and may impact performance. Use `max_checkpoints` to limit disk usage.
</Warning>

## Manual Checkpointing

For full control, register your own event handler and call `state.checkpoint()` directly:

```python
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.llm_events import LLMCallCompletedEvent

# Sync handler
@crewai_event_bus.on(LLMCallCompletedEvent)
def on_llm_done(source, event, state):
    path = state.checkpoint("./my_checkpoints")
    print(f"Saved checkpoint: {path}")

# Async handler
@crewai_event_bus.on(LLMCallCompletedEvent)
async def on_llm_done_async(source, event, state):
    path = await state.acheckpoint("./my_checkpoints")
    print(f"Saved checkpoint: {path}")
```

The `state` argument is the `RuntimeState` passed automatically by the event bus when your handler accepts 3 parameters. You can register handlers on any event type listed in the [Event Listeners](/en/concepts/event-listener) documentation.

Checkpointing is best-effort: if a checkpoint write fails, the error is logged but execution continues uninterrupted.