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

# Storage & Artifacts

> Configure storage backends and manage artifacts in Laddr - MinIO, S3, local storage, and best practices

Laddr supports multiple storage backends for storing large data artifacts, files, and agent outputs.

***

## Storage Backends

### MinIO (S3-Compatible)

MinIO is an S3-compatible object storage service, ideal for development and production.

```bash theme={null}
# .env
STORAGE_BACKEND=minio
MINIO_ENDPOINT=localhost:9000
MINIO_ACCESS_KEY=minioadmin
MINIO_SECRET_KEY=minioadmin
MINIO_SECURE=false  # Set to true for HTTPS

```

### AWS S3

Use AWS S3 for production deployments:

```bash theme={null}
# .env
STORAGE_BACKEND=s3
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1
S3_BUCKET=laddr-artifacts

```

### Local File System

For development and testing:

```bash theme={null}
# .env
STORAGE_BACKEND=local
STORAGE_PATH=./storage

```

***

## Artifact Storage

### Storing Artifacts

Use the artifact storage system tool to store large data:

```python theme={null}
from laddr import Agent

# Artifacts are automatically stored when using system tools
result = await agent.delegate_task(
    agent_name="processor",
    task_description="Process large dataset",
    task="process",
    task_data={"data": large_dataset}  # Automatically stored if large
)

```

### Custom Artifact Storage

Override artifact storage for custom behavior:

```python theme={null}
from laddr import override_system_tool, ArtifactStorageTool

@override_system_tool("system_store_artifact")
async def custom_storage(
    data: dict,
    artifact_type: str,
    metadata: dict = None,
    bucket: str = None,
    _artifact_storage=None,
    **kwargs
):
    """Custom artifact storage with compression."""
    storage_tool = ArtifactStorageTool(
        storage_backend=_artifact_storage,
        default_bucket=bucket or "artifacts"
    )
    
    # Add custom logic (compression, encryption, etc.)
    return await storage_tool.store_artifact(
        data=data,
        artifact_type=artifact_type,
        metadata=metadata,
        bucket=bucket
    )

```

***

## Configuration

### Bucket Configuration

Configure default buckets:

```bash theme={null}
# .env
STORAGE_DEFAULT_BUCKET=artifacts
STORAGE_ARTIFACTS_BUCKET=laddr-artifacts
STORAGE_LOGS_BUCKET=laddr-logs

```

### Storage Limits

Set storage limits and retention:

```bash theme={null}
# .env
STORAGE_MAX_SIZE=1073741824  # 1GB in bytes
STORAGE_RETENTION_DAYS=30

```

***

## Best Practices

### 1. Use Appropriate Backends

* **Development**: Local file system or MinIO
* **Production**: AWS S3 or MinIO cluster
* **Testing**: In-memory or local file system

### 2. Organize by Bucket

Use different buckets for different artifact types:

```python theme={null}
# Store different types in different buckets
await store_artifact(data, artifact_type="result", bucket="results")
await store_artifact(data, artifact_type="log", bucket="logs")
await store_artifact(data, artifact_type="cache", bucket="cache")

```

### 3. Set Retention Policies

Configure automatic cleanup:

```bash theme={null}
# .env
STORAGE_RETENTION_DAYS=30  # Delete artifacts older than 30 days
STORAGE_CLEANUP_INTERVAL=3600  # Run cleanup every hour

```

***

## Next Steps

* [System Tools](/guides/system-tools/custom-system-tools) - Customize artifact storage
* [Scaling & Operations](/guides/scaling-and-ops) - Production deployment
* [Agent Configuration](/guides/agents/agent-config) - Configure agents