Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
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Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via Python SDK and REST API.
This skill should be used when:
Python SDK Installation:
# Stable release
uv pip install benchling-sdk
# or with Poetry
poetry add benchling-sdk
Authentication Methods:
API Key Authentication (recommended for scripts):
from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.api_key_auth import ApiKeyAuth
benchling = Benchling(
url="https://your-tenant.benchling.com",
auth_method=ApiKeyAuth("your_api_key")
)
OAuth Client Credentials (for apps):
from benchling_sdk.auth.client_credentials_oauth2 import ClientCredentialsOAuth2
auth_method = ClientCredentialsOAuth2(
client_id="your_client_id",
client_secret="your_client_secret"
)
benchling = Benchling(
url="https://your-tenant.benchling.com",
auth_method=auth_method
)
Key Points:
For detailed authentication information including OIDC and security best practices, refer to references/authentication.md.
Registry entities include DNA sequences, RNA sequences, AA sequences, custom entities, and mixtures. The SDK provides typed classes for creating and managing these entities.
Creating DNA Sequences:
from benchling_sdk.models import DnaSequenceCreate
sequence = benchling.dna_sequences.create(
DnaSequenceCreate(
name="My Plasmid",
bases="ATCGATCG",
is_circular=True,
folder_id="fld_abc123",
schema_id="ts_abc123", # optional
fields=benchling.models.fields({"gene_name": "GFP"})
)
)
Registry Registration:
To register an entity directly upon creation:
sequence = benchling.dna_sequences.create(
DnaSequenceCreate(
name="My Plasmid",
bases="ATCGATCG",
is_circular=True,
folder_id="fld_abc123",
entity_registry_id="src_abc123", # Registry to register in
naming_strategy="NEW_IDS" # or "IDS_FROM_NAMES"
)
)
Important: Use either entity_registry_id OR naming_strategy, never both.
Updating Entities:
from benchling_sdk.models import DnaSequenceUpdate
updated = benchling.dna_sequences.update(
sequence_id="seq_abc123",
dna_sequence=DnaSequenceUpdate(
name="Updated Plasmid Name",
fields=benchling.models.fields({"gene_name": "mCherry"})
)
)
Unspecified fields remain unchanged, allowing partial updates.
Listing and Pagination:
# List all DNA sequences (returns a generator)
sequences = benchling.dna_sequences.list()
for page in sequences:
for seq in page:
print(f"{seq.name} ({seq.id})")
# Check total count
total = sequences.estimated_count()
Key Operations:
benchling.<entity_type>.create()benchling.<entity_type>.get(id) or .list()benchling.<entity_type>.update(id, update_object)benchling.<entity_type>.archive(id)Entity types: dna_sequences, rna_sequences, aa_sequences, custom_entities, mixtures
For comprehensive SDK reference and advanced patterns, refer to references/sdk_reference.md.
Manage physical samples, containers, boxes, and locations within the Benchling inventory system.
Creating Containers:
from benchling_sdk.models import ContainerCreate
container = benchling.containers.create(
ContainerCreate(
name="Sample Tube 001",
schema_id="cont_schema_abc123",
parent_storage_id="box_abc123", # optional
fields=benchling.models.fields({"concentration": "100 ng/μL"})
)
)
Managing Boxes:
from benchling_sdk.models import BoxCreate
box = benchling.boxes.create(
BoxCreate(
name="Freezer Box A1",
schema_id="box_schema_abc123",
parent_storage_id="loc_abc123"
)
)
Transferring Items:
# Transfer a container to a new location
transfer = benchling.containers.transfer(
container_id="cont_abc123",
destination_id="box_xyz789"
)
Key Inventory Operations:
Interact with electronic lab notebook (ELN) entries, protocols, and templates.
Creating Notebook Entries:
from benchling_sdk.models import EntryCreate
entry = benchling.entries.create(
EntryCreate(
name="Experiment 2025-10-20",
folder_id="fld_abc123",
schema_id="entry_schema_abc123",
fields=benchling.models.fields({"objective": "Test gene expression"})
)
)
Linking Entities to Entries:
# Add references to entities in an entry
entry_link = benchling.entry_links.create(
entry_id="entry_abc123",
entity_id="seq_xyz789"
)
Key Notebook Operations:
Automate laboratory processes using Benchling's workflow system.
Creating Workflow Tasks:
from benchling_sdk.models import WorkflowTaskCreate
task = benchling.workflow_tasks.create(
WorkflowTaskCreate(
name="PCR Amplification",
workflow_id="wf_abc123",
assignee_id="user_abc123",
fields=benchling.models.fields({"template": "seq_abc123"})
)
)
Updating Task Status:
from benchling_sdk.models import WorkflowTaskUpdate
updated_task = benchling.workflow_tasks.update(
task_id="task_abc123",
workflow_task=WorkflowTaskUpdate(
status_id="status_complete_abc123"
)
)
Asynchronous Operations:
Some operations are asynchronous and return tasks:
# Wait for task completion
from benchling_sdk.helpers.tasks import wait_for_task
result = wait_for_task(
benchling,
task_id="task_abc123",
interval_wait_seconds=2,
max_wait_seconds=300
)
Key Workflow Operations:
Subscribe to Benchling events for real-time integrations using AWS EventBridge.
Event Types:
Integration Pattern:
Use Cases:
Refer to Benchling's event documentation for event schemas and configuration.
Query historical Benchling data using SQL through the Data Warehouse.
Access Method: The Benchling Data Warehouse provides SQL access to Benchling data for analytics and reporting. Connect using standard SQL clients with provided credentials.
Common Queries:
Integration with Analysis Tools:
The SDK automatically retries failed requests:
# Automatic retry for 429, 502, 503, 504 status codes
# Up to 5 retries with exponential backoff
# Customize retry behavior if needed
from benchling_sdk.retry import RetryStrategy
benchling = Benchling(
url="https://your-tenant.benchling.com",
auth_method=ApiKeyAuth("your_api_key"),
retry_strategy=RetryStrategy(max_retries=3)
)
Use generators for memory-efficient pagination:
# Generator-based iteration
for page in benchling.dna_sequences.list():
for sequence in page:
process(sequence)
# Check estimated count without loading all pages
total = benchling.dna_sequences.list().estimated_count()
Use the fields() helper for custom schema fields:
# Convert dict to Fields object
custom_fields = benchling.models.fields({
"concentration": "100 ng/μL",
"date_prepared": "2025-10-20",
"notes": "High quality prep"
})
The SDK handles unknown enum values and types gracefully:
UnknownTypeDetailed reference documentation for in-depth information:
Load these references as needed for specific integration requirements.
This skill currently includes example scripts that can be removed or replaced with custom automation scripts for your specific Benchling workflows.
1. Bulk Entity Import:
# Import multiple sequences from FASTA file
from Bio import SeqIO
for record in SeqIO.parse("sequences.fasta", "fasta"):
benchling.dna_sequences.create(
DnaSequenceCreate(
name=record.id,
bases=str(record.seq),
is_circular=False,
folder_id="fld_abc123"
)
)
2. Inventory Audit:
# List all containers in a specific location
containers = benchling.containers.list(
parent_storage_id="box_abc123"
)
for page in containers:
for container in page:
print(f"{container.name}: {container.barcode}")
3. Workflow Automation:
# Update all pending tasks for a workflow
tasks = benchling.workflow_tasks.list(
workflow_id="wf_abc123",
status="pending"
)
for page in tasks:
for task in page:
# Perform automated checks
if auto_validate(task):
benchling.workflow_tasks.update(
task_id=task.id,
workflow_task=WorkflowTaskUpdate(
status_id="status_complete"
)
)
4. Data Export:
# Export all sequences with specific properties
sequences = benchling.dna_sequences.list()
export_data = []
for page in sequences:
for seq in page:
if seq.schema_id == "target_schema_id":
export_data.append({
"id": seq.id,
"name": seq.name,
"bases": seq.bases,
"length": len(seq.bases)
})
# Save to CSV or database
import csv
with open("sequences.csv", "w") as f:
writer = csv.DictWriter(f, fieldnames=export_data[0].keys())
writer.writeheader()
writer.writerows(export_data)
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