COMPUTE & SANDBOXES · FOR AI AGENTS
Fireworks AI
Run model inference for agent workflows on managed serving capacity.
Call hosted models through Fireworks APIs or compatible SDKs, including supported tool-calling and structured-output workflows, without operating the model-serving infrastructure yourself.
Your integration checklist
- Capabilities
- What you need from your owner
- Create a Fireworks account and API key, configure billing and choose a supported model and serving mode. Set an application budget and decide which tools the model may propose.
- Authentication
- Use a Fireworks API key from a trusted backend. Compatible clients can target https://api.fireworks.ai/inference/v1; keep the key out of prompts and client-side code.
- Your first task
- Send a small representative request to an explicitly selected model. If using tools, inspect and validate the proposed arguments before executing one harmless test operation.
- Billing & payment
- Serverless inference is metered by model and serving mode, with separate input, cached-input and output token rates. Dedicated deployment and training costs are separate from this serverless workflow.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Model capabilities differ. Adaptive token limits can produce 429 responses and capacity pressure can produce 503 responses. A model’s tool call is a proposed action; your application executes the tool and remains responsible for permissions and output validation.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Separate model inference from permission to execute a tool
Editorial recommendation · Reviewed 2026-10-21
When to choose this service
Choose Fireworks when an agent workflow needs a selected hosted model or additional inference capacity, rather than assuming the current agent can reproduce another model’s behavior locally.
The dependency is available model weights served on managed accelerator capacity. Ordinary application code and the current agent’s reasoning do not supply that external model execution infrastructure.
Suggested workflow
- Select the exact model and serving mode, then bound input size, output tokens and total spend. Define a small tool schema for the initial integration.
- Run representative and malformed-input cases. Validate tool names, arguments and authorization in application code before executing anything with side effects.
- Store the request context and tool result together. Monitor actual token usage and quota responses before increasing concurrency.
Acceptance checks before real work
- Check the chosen model supports the required tool or structured-output behavior.
- Reject unknown tools and invalid arguments even when the response is well-formed JSON.
- Reconcile completed tool actions before retrying a model request.
If the workflow fails
Back off within a bounded retry budget for quota or capacity errors. Preserve completed external actions across retries so a regenerated tool call cannot repeat a payment, message or other mutation.