COMPUTE & SANDBOXES · FOR AI AGENTS
Groq
Run supported models through managed inference APIs and SDKs.
Use Groq for hosted model inference in agent workflows, selecting the model and output limits appropriate to the task.
Your integration checklist
- Capabilities
- What you need from your owner
- Create a Groq account and API key. Choose an available model and configure the organization’s tier, payment method and spend limits before production use.
- Authentication
- Store the API key server-side; official SDKs can read GROQ_API_KEY from the environment.
- Your first task
- Send one representative request with a bounded output budget and validate its result against your task requirements.
- Billing & payment
- A Free tier is available with limits. The Developer tier requires a valid payment method and bills token usage monthly in arrears or at progressive billing thresholds. Model and service tier determine applicable pricing.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Organization-wide request and token limits apply. Some accounts have separate input/output token caps; any reached limit can stop a request. Model capabilities and availability must be checked separately.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Coordinate model calls across agents sharing one organization
Editorial recommendation · Reviewed 2026-10-26
When to choose this service
Choose Groq when a workflow needs hosted inference capacity for a supported model.
Managed model serving and specialized compute are external resources. An agent can write a client but cannot create the underlying serving capacity through reasoning alone.
Suggested workflow
- Choose a model using representative task examples. Define output size and success criteria before increasing request volume.
- Validate responses and proposed actions in application code. Keep the decision to execute a tool separate from generated arguments.
- Allocate a shared request and token budget across workers, with a separate monetary ceiling for the workflow.
Acceptance checks before real work
- Confirm exact model support for the required response format.
- Measure useful accepted outputs, not only completed requests.
- Check organization-wide consumption before attributing a limit to one worker.
If the workflow fails
Identify the exhausted quota, slow the shared queue and retry within a bounded budget. Reconcile already executed actions before asking the model to continue.