Google Cloud Life Sciences API
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For Agents
Run container-based genomics and bioinformatics pipelines on Google Cloud and track or cancel the resulting long-running operations.
Use for: Run a genomics analysis pipeline on Google Cloud, List all running Cloud Life Sciences operations in europe-west2, Get the status of a specific pipeline operation, Cancel a long-running pipeline that is no longer needed
Not supported: Does not store genomic data, train ML models, or expose Compute Engine VM management — use only to run, list, get, and cancel container-based pipeline operations.
The Cloud Life Sciences API runs container-based pipelines on Google Cloud, primarily for genomics, biology, and other life sciences batch workloads. It exposes a pipelines:run operation that schedules a Pipeline definition onto a Compute Engine VM and a set of operations endpoints to list, get, and cancel the resulting long-running operations. Locations can be discovered via the locations:list endpoint to choose a regional endpoint for compliance.
Install Jentic One Beta
Connect the Cloud Life Sciences API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Cloud Life Sciences API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Cloud Life Sciences API.
Run a containerized pipeline by submitting a Pipeline definition to pipelines:run
List long-running operations to track pipeline progress in a region
Get the status, metadata, and result of a single pipeline operation
Cancel a running pipeline operation
List supported regional locations to choose a compliant endpoint
Use Cases
Patterns agents use Cloud Life Sciences API for, with concrete tasks.
★ Run a genomics variant-calling pipeline
Bioinformatics teams package GATK or DeepVariant tools into a container and submit them via POST /v2beta/{+parent}/pipelines:run. Cloud Life Sciences allocates a Compute Engine VM with the requested machine type and disks, runs the pipeline, and returns a long-running operation that the team can poll for status.
Submit a Pipeline with one Action running gcr.io/myproj/deepvariant:latest and read the returned operation name.
Operation polling and status reporting
Long-running pipelines complete asynchronously, so a workflow orchestrator must poll GET /v2beta/{+name} until done becomes true. The metadata block reports event timelines and any errors so an agent can report progress to the data scientist or trigger downstream steps.
Poll GET /v2beta/{+name} every 30 seconds until done=true, then return metadata.events for human review.
Cancel a runaway pipeline
When a pipeline takes longer than expected or was launched against the wrong inputs, POST /v2beta/{+name}:cancel halts the underlying VM and stops billing. An agent watching cost or run time can call cancel and clean up associated GCS scratch space without manual intervention.
Call POST /v2beta/{+name}:cancel for an operation older than 12 hours and confirm the response is empty.
Region-aware pipeline submission
Some workloads must run in specific regions for residency or pricing reasons. GET /v2beta/{+name}/locations enumerates supported regions so an agent can pick, for example, europe-west2 to keep patient-derived data in the EU before submitting the pipeline.
List locations under projects/{p} and pick europe-west2 as the parent for the next pipelines:run call.
Agent-orchestrated genomics workflow via Jentic
An analysis agent receiving a sample manifest can build the right Pipeline definition, submit it through Jentic, and poll the operation until completion. Jentic surfaces all five Life Sciences operations so the agent does not need a Google Cloud client library to run a multi-step workflow.
Run google_lifesciences_run_pipeline with a Pipeline JSON, then poll until done=true and return metadata.events.
Key Endpoints
5 endpoints — the cloud life sciences api runs container-based pipelines on google cloud, primarily for genomics, biology, and other life sciences batch workloads.
METHOD
PATH
DESCRIPTION
POST
/v2beta/{+parent}/pipelines:run
Run a containerized pipeline
GET
/v2beta/{+name}
Get a long-running operation by resource name
GET
/v2beta/{+name}/operations
List operations within a parent location
POST
/v2beta/{+name}:cancel
Cancel a long-running pipeline operation
GET
/v2beta/{+name}/locations
List supported regional locations
POST
/v2beta/{+parent}/pipelines:run
Run a containerized pipeline
GET
/v2beta/{+name}
Get a long-running operation by resource name
GET
/v2beta/{+name}/operations
List operations within a parent location
POST
/v2beta/{+name}:cancel
Cancel a long-running pipeline operation
GET
/v2beta/{+name}/locations
List supported regional locations
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Service account JSON keys with Life Sciences IAM roles are stored encrypted in the Jentic vault. Agents receive a short-lived OAuth 2.0 access token scoped to cloud-platform at call time and never see the raw JSON key.
Intent-based discovery
Agents search Jentic with intents like 'run a genomics pipeline' or 'cancel a long running operation' and Jentic returns the matching v2beta endpoint with the parent and name path templates already documented.
Time to first call
Direct integration: 1-2 days to set up the service account, design the Pipeline JSON, and implement long-running operation polling. Through Jentic: under 1 hour — search, load, execute, poll.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Cloud Life Sciences API through Jentic.
What authentication does the Cloud Life Sciences API use?
OAuth 2.0 with the https://www.googleapis.com/auth/cloud-platform scope, typically using a Google Cloud service account that has lifesciences.workflows.run and operations.cancel IAM permissions. Through Jentic the service account JSON is encrypted in the vault and the agent receives only a short-lived access token.
Is Cloud Life Sciences being deprecated?
Google announced sunset of Cloud Life Sciences with end of support on July 8, 2025. Existing v2beta workflows continue to run for now, and Google recommends migrating new workloads to Batch or GKE. This API is still useful for legacy genomics pipelines and as a reference.
What are the rate limits for the Cloud Life Sciences API?
The default per-project quota is 1,000 pipelines:run requests per day and 60 operations.list requests per minute. Compute Engine quotas (vCPUs, persistent disk) bound how many pipelines can run concurrently.
How do I run a pipeline through Jentic?
Run jentic search 'run a containerized pipeline on google cloud' to find POST /v2beta/{+parent}/pipelines:run, jentic load for the Pipeline schema, then jentic execute with a body containing actions, resources, and timeout. Jentic returns the long-running operation name for polling.
How do I check if a pipeline has finished?
Poll GET /v2beta/{+name} with the operation name returned by pipelines:run. The response includes a done boolean — when true, either response or error is set, and the metadata.events array contains the timeline of pipeline steps.
What does Cloud Life Sciences cost?
The API itself has no per-call charge. You pay for the underlying Compute Engine VMs (vCPU, memory, GPUs) and persistent disks the pipeline uses, billed at standard Compute Engine rates.
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