Integration

Cerebrium integration

Cerebrium is a serverless ML infrastructure platform for deploying and scaling machine learning models. Build custom inference pipelines, deploy pre-trained models, and run GPU workloads with automatic scaling and pay-per-second pricing. Supports PyTorch, TensorFlow, ONNX, and custom containers. Features include model chaining, streaming responses, async execution with webhooks, and built-in observability. Ideal for LLM applications, computer vision, generative AI, and real-time ML inference.

Connect Cerebrium How integrations work

Ask it in chat

Say what should happen.
The workflow builds itself.

When this happens, do that in Cerebrium.Built from the 29 Cerebrium tools below, on the connection you add once.

Common Cerebrium use cases

capacity planning
debug build failures
cost optimization
add API keys
discover app IDs
video processing
view app secrets
abort slow builds
inventory secrets
inventory storage

What you can do with Cerebrium

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

App, 4 operations

delete_appDelete appDELETEDeletes a deployed Cerebrium app permanently.
get_appGet appGETRetrieves details of a specific Cerebrium app including configuration and metadata.
list_appsList appsGETLists all deployed apps in a Cerebrium project.
modify_appModify appPATCHUpdates Cerebrium app configuration including hardware, scaling, and concurrency settings.

Build, 4 operations

cancel_buildCancel buildDELETECancels an in-progress Cerebrium build.
get_buildGet buildGETRetrieves details of a specific Cerebrium build.
get_build_logsGet build logsGETRetrieves build logs for a specific Cerebrium build.
list_buildsList buildsGETLists all builds for a Cerebrium app.

Run, 4 operations

cancel_runCancel runDELETECancels an in-progress or queued Cerebrium run.
count_queued_runsCount queued runsGETReturns the queue depth (number of queued runs) for a Cerebrium app.
get_runGet runGETRetrieves status and details of a specific Cerebrium run. Returns status and outputs when complete.
list_runsList runsGETLists inference runs for a Cerebrium app. Returns run metadata with status and timing information.

Secret, 4 operations

list_app_secretsList app secretsGETLists all app-level secrets for a specific Cerebrium app.
list_secretsList secretsGETLists all project-level secrets for a Cerebrium project.
update_app_secretsUpdate app secretsPATCHUpdates app-level secrets for a specific Cerebrium app.
update_secretsUpdate secretsPATCHUpdates project-level secrets for a Cerebrium project.

Inference, 3 operations

run_asyncRun asyncPOSTRuns async inference on Cerebrium. Returns a run ID immediately for later result retrieval via webhook.
run_inferenceRun inferencePOSTRuns synchronous inference on a Cerebrium model. Sends inputs and receives predictions in real-time.
run_streamRun streamPOSTRuns inference with streaming response on Cerebrium. Returns results incrementally via Server-Sent Events.

Container, 2 operations

list_app_containersList app containersGETLists all currently active containers for a Cerebrium app.
stop_app_containerStop app containerDELETEStops a specific running container for a Cerebrium app.

Metric, 2 operations

get_app_dashboard_metricsGet app dashboard metricsGETRetrieves dashboard metrics for a Cerebrium app.
get_app_resource_metricsGet app resource metricsGETRetrieves resource utilization metrics (CPU, memory, GPU) for a Cerebrium app.

Project, 2 operations

get_projectGet projectGETRetrieves details of a specific Cerebrium project.
list_projectsList projectsGETLists all Cerebrium projects accessible to the authenticated user.

Volume, 2 operations

list_volumesList volumesGETLists all persistent storage volumes for a Cerebrium project.
resize_volumeResize volumePATCHResizes a persistent storage volume in a Cerebrium project.

Hardware, 1 operation

list_hardwareList hardwareGETLists all available hardware options (GPU types) on the Cerebrium platform.

Log, 1 operation

get_app_logsGet app logsGETRetrieves logs for a Cerebrium app with optional date, container, and run filtering.

Use Cerebrium from Claude Code, Cursor or Codex

Connect Cerebrium in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Cerebrium operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Set up the MCP server step by step

How to connect Cerebrium

Create your workspace
Sign up free. Nothing to install.
Connect Cerebrium once
Add your Cerebrium connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
Describe the job in chat
The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.

Cerebrium integration FAQ

What can you do with the Cerebrium integration?

LiveContext exposes 29 operations on Cerebrium, covering app, build, run and secret. Common uses include capacity planning, debug build failures and cost optimization.