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:

• 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:

1. Authentication and setup — API key and OAuth app auth; see references/authentication.md.
2. Registry and entity management — DNA and AA sequences, custom entities, schemas, and registration.
3. Inventory management — containers, boxes, plates, locations, and transfers.
4. Notebook and documentation — entries, day-to-day notes, and structured tables.
5. Workflows and automation — tasks, flowcharts, and assay runs.
6. Events and integration — EventBridge subscriptions; see references/eventbridge.md.
7. 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]

Source: K-Dense-AI/scientific-agent-skills → skills/benchling-integration/SKILL.md