Benchling Integration Skill

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:

Working with Benchling's Python SDK or REST API
Managing biological sequences (DNA, RNA, proteins) and registry entities
Automating inventory operations (samples, containers, locations, transfers)
Creating or querying electronic lab notebook entries
Building workflow automations or Benchling Apps
Syncing data between Benchling and external systems
Querying the Benchling Data Warehouse for analytics
Setting up event-driven integrations with AWS EventBridge

Core Capabilities

Seven capability areas, each with code, are in references/core_capabilities.md:

Authentication and setup — API key and OAuth app auth; see references/authentication.md.
Registry and entity management — DNA and AA sequences, custom entities, schemas, and registration.
Inventory management — containers, boxes, plates, locations, and transfers.
Notebook and documentation — entries, day-to-day notes, and structured tables.
Workflows and automation — tasks, flowcharts, and assay runs.
Events and integration — EventBridge subscriptions; see references/eventbridge.md.
Data warehouse and analytics — SQL access to the warehouse.

Endpoint and SDK detail is in references/api_endpoints.md and references/sdk_reference.md.

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:

Unknown enum values are preserved
Unrecognized polymorphic types return UnknownType
Allows working with newer API versions

Security Considerations

Never commit API keys or OAuth secrets to version control
Read only named environment variables (BENCHLING_TENANT_URL, BENCHLING_API_KEY, etc.)
Route network calls exclusively to your tenant URL
Rotate keys if compromised; use OAuth for multi-user production apps
Grant minimal necessary permissions for apps in the Developer Console

Resources

references/

Detailed reference documentation for in-depth information:

authentication.md - Comprehensive authentication guide including OIDC, security best practices, and credential management
sdk_reference.md - Detailed Python SDK reference with advanced patterns, examples, and all entity types
api_endpoints.md - REST API endpoint reference for direct HTTP calls without the SDK
eventbridge.md - EventBridge setup, event payload schema, rule examples, Lambda handler, validation, and recovery

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

Official Documentation: https://docs.benchling.com
Python SDK Reference: https://benchling.com/sdk-docs/
API Reference: https://benchling.com/api/reference
Support: [email protected]

Agent Skills: Benchling Integration

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