benchling-integration

Benchling Integration

Overview

Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, RNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via the Python SDK and REST API.

Version note: Examples target benchling-sdk 1.25.0 (latest stable on PyPI). Docs: benchling.com/sdk-docs. Platform guide: docs.benchling.com.

When to Use This Skill

This skill should be used when:

Core Capabilities

1. Authentication & Setup

Python SDK installation:

uv pip install "benchling-sdk==1.25.0"

Preview builds (alpha; not for production):

uv pip install "benchling-sdk" --prerelease allow

Environment variables (scoped reads only):

Read only the named keys you need — never dump or iterate over the full environment:

import os

tenant_url = os.environ.get("BENCHLING_TENANT_URL")  # e.g. https://your-tenant.benchling.com
api_key = os.environ.get("BENCHLING_API_KEY")

if not tenant_url or not api_key:
    raise ValueError("Set BENCHLING_TENANT_URL and BENCHLING_API_KEY")

Obtain an API key from Profile Settings in Benchling. For OAuth apps, use the Developer Console and store BENCHLING_CLIENT_ID / BENCHLING_CLIENT_SECRET separately.

Authentication methods:

API key (scripts and personal automation):

from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.api_key_auth import ApiKeyAuth

benchling = Benchling(
    url=tenant_url,
    auth_method=ApiKeyAuth(api_key),
)

OAuth client credentials (multi-user apps and production integrations):

from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.client_credentials_oauth2 import ClientCredentialsOAuth2

benchling = Benchling(
    url=tenant_url,
    auth_method=ClientCredentialsOAuth2(
        client_id=os.environ["BENCHLING_CLIENT_ID"],
        client_secret=os.environ["BENCHLING_CLIENT_SECRET"],
    ),
)

Key points:

For detailed authentication information including OIDC and security best practices, refer to references/authentication.md.

2. Registry & Entity Management

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:

Entity types: dna_sequences, rna_sequences, aa_sequences, custom_entities, mixtures

For comprehensive SDK reference and advanced patterns, refer to references/sdk_reference.md.

3. Inventory Management

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:

4. Notebook & Documentation

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:

5. Workflows & Automation

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. The SDK default max_wait_seconds for polling is 600 seconds (since SDK 1.11.0):

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,  # override for long-running serverless handlers
)

Key Workflow Operations:

6. Events & Integration

Subscribe to Benchling changes via AWS EventBridge (customer-owned bus) or Webhooks (recommended for new Benchling Apps). EventBridge delivers hydrated v2 API objects; webhooks use thinner payloads.

Common EventBridge detail-type values:

Minimal EventBridge rule (filter request creation by schema name):

{
  "detail-type": ["v2.request.created"],
  "detail": {
    "schema": {
      "name": ["Validated Request"]
    }
  }
}

Lambda handler skeleton:

def handler(event, context):
    detail_type = event["detail-type"]
    detail = event["detail"]

    if detail.get("deprecated"):
        # Alert — migrate before Benchling removes this event type
        pass

    if detail.get("excludedProperties"):
        # Payload exceeded 256 KB; re-fetch via detail["request"]["apiURL"]
        pass

    if detail_type == "v2.request.created":
        request_id = (detail.get("request") or {}).get("id")
        # Re-fetch authoritative state — events can be late or out of order
        # request = benchling.requests.get_by_id(request_id)
        return {"request_id": request_id}

    return {"status": "ignored", "detail_type": detail_type}

Setup flow:

Recovery: EventBridge deliveries are not replayed. Use the List Events API for events up to ~2 weeks old after outages.

For payload schema, CloudFormation templates, SDK list/recovery examples, and validation steps, see references/eventbridge.md.

7. Data Warehouse & Analytics

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:

Best Practices

Error Handling

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=tenant_url,
    auth_method=ApiKeyAuth(api_key),
    retry_strategy=RetryStrategy(max_retries=3),
)

Pagination Efficiency

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()

Schema Fields Helper

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"
})

Forward Compatibility

The SDK handles unknown enum values and types gracefully:

Security Considerations

Resources

references/

Detailed reference documentation for in-depth information:

Load these references as needed for specific integration requirements.

Common Use Cases

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)

Additional Resources