Google Firebase ML API
Browse all Google APIs✓ Official Vendor SpecAI/MLMl Inferenceoauth23 EndpointsREST
For Agents
Track and clean up long-running Firebase ML model management operations so an agent can supervise custom model upload pipelines.
Use for: List Firebase ML operations under a project, Cancel a stuck Firebase ML model upload operation, Check whether a Firebase ML operation has finished, Find all completed Firebase ML operations to prune
Not supported: Does not train models, perform on-device inference, or upload model artifacts directly via this v1 spec — use for managing Firebase ML long-running operations only.
The Firebase ML API provides programmatic access to long-running operations spawned by Firebase ML model management workflows, such as uploading and publishing custom machine learning models for use by Firebase mobile apps. The v1 surface exposes operations to list, cancel, and delete those operations. Use it to monitor model upload pipelines and manage cleanup of operation history during model publishing automation.
Install Jentic One Beta
Connect the Firebase ML API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Firebase ML 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 Firebase ML API.
List long-running Firebase ML operations under a project
Cancel a Firebase ML custom model upload or publish operation
Delete a completed Firebase ML operation resource
Monitor state of asynchronous Firebase ML model jobs from a pipeline
Reconcile Firebase ML operation history during model release reviews
Use Cases
Patterns agents use Firebase ML API for, with concrete tasks.
★ Custom Model Upload Monitoring
When a Firebase ML pipeline uploads a TensorFlow Lite model to Firebase for distribution to mobile clients, the upload runs as a long-running operation. The Firebase ML v1 API lets release tooling list and inspect those operations to confirm a model has finished processing before promoting it to production via the Firebase ML SDK.
Call GET /v1/{+name} on a Firebase ML operation resource and verify done=true with no error before announcing the new model version.
Cancelling a Stuck Model Operation
If a Firebase ML model upload or publish operation hangs because of an invalid model artifact or expired credential, POST /v1/{+name}:cancel on the operation resource cancels the job. This frees the resource so the pipeline can retry the upload with a corrected artifact.
Call POST /v1/{+name}:cancel on the in-flight Firebase ML operation and confirm via GET /v1/{+name} that done=true with cancellation reported in the status.
Firebase ML Operation Hygiene
Old Firebase ML operation resources accumulate over time as models are repeatedly uploaded and updated. DELETE /v1/{+name} removes a completed operation record so monitoring views remain focused on current activity. Run as a low-frequency maintenance task alongside model release reviews.
Call DELETE /v1/{+name} on a Firebase ML operation that has been done for over 60 days to keep operation listings focused on recent activity.
Agent-Driven Custom Model Release via Jentic
An AI agent connected through Jentic can supervise the upload-and-publish lifecycle of a Firebase ML custom model on behalf of an ML engineer, polling operations to confirm successful processing and cancelling failed jobs. Jentic exposes the 3 Firebase ML v1 endpoints, the agent strings them into a watch-cancel-cleanup workflow, and OAuth 2.0 credentials stay inside the Jentic vault.
Through Jentic, search for monitor firebase ml operations, load the operations.list endpoint, and execute it under projects/PROJECT to flag any operations whose done is still false after 30 minutes.
Key Endpoints
3 endpoints — the firebase ml api provides programmatic access to long-running operations spawned by firebase ml model management workflows, such as uploading and publishing custom machine learning models for use by firebase mobile apps.
METHOD
PATH
DESCRIPTION
GET
/v1/{+name}
List Firebase ML operations under a project
POST
/v1/{+name}:cancel
Cancel a Firebase ML long-running operation
DELETE
/v1/{+name}
Delete a Firebase ML operation resource
GET
/v1/{+name}
List Firebase ML operations under a project
POST
/v1/{+name}:cancel
Cancel a Firebase ML long-running operation
DELETE
/v1/{+name}
Delete a Firebase ML operation resource
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Google OAuth 2.0 service-account credentials are stored in the Jentic vault. Agents receive a scoped access token per call, so the underlying private key never enters the agent's prompt or tool-call payloads.
Intent-based discovery
Agents search Jentic with intents like monitor firebase ml operations and Jentic returns the operations.list, operations.cancel, and operations.delete endpoints along with their input schemas.
Time to first call
Direct integration with the Firebase ML operations API: 1-2 days for OAuth and pipeline polling. Through Jentic: under 30 minutes.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Firebase ML API through Jentic.
What authentication does the Firebase ML API use?
The API uses Google OAuth 2.0 with the firebase or cloud-platform scope. Through Jentic the OAuth credentials are stored in the encrypted vault and a scoped access token is supplied per request without exposing the underlying service-account key.
Can I run model inference with the v1 Firebase ML API endpoints exposed here?
No. The v1 surface in this spec covers long-running operation management only. Inference on Firebase ML custom models happens on-device through the Firebase ML SDKs after a model has been uploaded and published, not through this REST API.
What are the rate limits for the Firebase ML API?
Google enforces standard googleapis.com per-project quotas for the Firebase ML management endpoints. Operation listing and cancel are low-cost calls; pipelines that poll frequently should use exponential backoff to stay within per-minute quotas.
How do I monitor a Firebase ML model upload operation through Jentic?
Run pip install jentic, search for monitor firebase ml operations, load the operations.list endpoint, and execute it under projects/PROJECT to find operations whose done is false. Sign up at https://app.jentic.com/sign-up.
Does the Firebase ML API support cancelling a stuck custom model upload?
Yes. POST /v1/{+name}:cancel on the operation resource cancels the in-flight upload or publish job and reports a cancelled status when complete, letting release pipelines retry with a fixed model artifact.
Is the Firebase ML API free?
Firebase ML custom model hosting is included with Firebase under standard limits, and the operations endpoints themselves do not incur a separate per-call charge. Model storage and download bandwidth fall under standard Firebase project usage.
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