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.
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.
# .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:
# .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:
# .env
STORAGE_BACKEND=local
STORAGE_PATH=./storage
Artifact Storage
Storing Artifacts
Use the artifact storage system tool to store large data:
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:
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:
# .env
STORAGE_DEFAULT_BUCKET=artifacts
STORAGE_ARTIFACTS_BUCKET=laddr-artifacts
STORAGE_LOGS_BUCKET=laddr-logs
Storage Limits
Set storage limits and retention:
# .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:
# 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:
# .env
STORAGE_RETENTION_DAYS=30 # Delete artifacts older than 30 days
STORAGE_CLEANUP_INTERVAL=3600 # Run cleanup every hour
Next Steps
