Cerebras Inference API

Name: Cerebras Inference API
Brand: Cerebras Inference API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLLanguage Modelsbearer3 EndpointsREST

For Agents

Run fast chat completions on hosted open-weight models such as Llama variants using an OpenAI-compatible request shape, and list which models are currently served.

Install Jentic One Beta

Connect the Cerebras Inference API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Cerebras Inference 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.

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 | sh

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 instance

Jentic 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 Cerebras Inference API.

Generate chat completions with streaming or non-streaming responses against Cerebras-hosted models

List the open-weight models currently available on the Cerebras inference fleet

Retrieve metadata for a specific model including ownership and creation timestamp

GET STARTED

Start building with Cerebras Inference API

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View OpenAPI Document

Use Cases

Patterns agents use Cerebras Inference API for, with concrete tasks.

★ Low-Latency Chat Completion

Generate chat completions where end-to-end latency matters more than maximum context window or tool-use breadth. The /chat/completions endpoint returns assistant messages from Cerebras-hosted open-weight models at significantly higher tokens per second than commodity GPU inference, which is useful for live agents, voice front ends, and interactive coding assistants. The request shape mirrors OpenAI's chat completions, so messages, temperature, and max_tokens carry over unchanged.

Call POST /chat/completions with model llama3.1-8b and a system+user message pair, max_tokens 256, and return the assistant content

Model Discovery

Discover which open-weight models are currently served by Cerebras before sending traffic, since the catalogue changes over time as new model families are added. GET /models returns the full list, and GET /models/{model_id} returns ownership and creation metadata for a single entry. Useful when an agent needs to pick the largest available model that fits a latency budget.

Call GET /models and return the list of model ids whose id field contains 'llama'

Drop-in OpenAI Client Replacement

Swap an existing OpenAI Python or JavaScript client over to Cerebras for chat workloads by changing only the base URL to https://api.cerebras.ai/v1 and the API key. Because the request and response schemas of /chat/completions match OpenAI's, the rest of the application code, including streaming handlers and token counting, can stay the same. This is useful for cost or latency-driven migrations on production traffic.

Configure an OpenAI Python client with base_url https://api.cerebras.ai/v1, send a chat.completions.create call, and confirm the response object has the standard choices[0].message.content shape

AI Agent Inference Routing via Jentic

An agent framework that routes requests across multiple inference backends needs to call Cerebras alongside OpenAI, Groq, and Anthropic. Through Jentic, the agent searches for a 'fast chat completion' operation, loads the Cerebras input schema, and executes the call with a key drawn from the Jentic vault. The agent can switch backends per turn without rewriting auth code or response parsing.

Use Jentic to search 'generate a chat completion', load the Cerebras chat operation, and execute it with the messages array from the agent's current turn

Key Endpoints

3 endpoints — jentic publishes the only available openapi specification for cerebras inference api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/chat/completions

Create a chat completion against a Cerebras-hosted model

GET

/models

List available models

GET

/models/{model_id}

Retrieve a single model's metadata

POST

/chat/completions

Create a chat completion against a Cerebras-hosted model

GET

/models

List available models

GET

/models/{model_id}

Retrieve a single model's metadata

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Cerebras API keys are stored encrypted in the Jentic vault and injected into the Authorization bearer header at execution time. Agents receive scoped access only — the raw key never enters the agent's context window or prompt history.

Intent-based discovery

Agents search Jentic by intent (e.g., 'generate a fast chat completion') and Jentic returns the matching Cerebras operation with its input schema, so the agent calls /chat/completions with a typed messages array rather than hand-built JSON.

Time to first call

Direct Cerebras integration: 1-2 hours including key provisioning, base URL change, and streaming handling. Through Jentic: under 15 minutes — search, load schema, execute.

FAQs

Specific to using Cerebras Inference API through Jentic.

Why is there no official OpenAPI spec for Cerebras Inference API?

Cerebras does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Cerebras Inference API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Cerebras Inference API use?

The API uses bearer token authentication with an API key issued from the Cerebras cloud console, sent as Authorization: Bearer <key>. Through Jentic, the key is held in the Jentic vault and injected at execution time so it never reaches the agent's prompt or context window.

Can I use the Cerebras Inference API as a drop-in replacement for OpenAI?

For chat completions, yes. POST /chat/completions accepts the same messages array, model id, temperature, top_p, max_tokens, and stop fields as OpenAI's chat completions, and returns the same choices[0].message.content shape. Repoint an OpenAI client at base URL https://api.cerebras.ai/v1, pass a Cerebras key, and choose a Cerebras-served model id.

What are the rate limits for the Cerebras Inference API?

Rate limits are enforced per API key as requests per minute and tokens per minute, with the exact ceilings tied to the plan attached to the key. Cerebras returns 429 with a Retry-After header when limits are hit, so agents should respect that header rather than hard-coding a fixed throttle.

How do I generate a chat completion with the Cerebras Inference API through Jentic?

Search Jentic for 'generate a chat completion with cerebras', load the operation backed by POST /chat/completions, and execute it with a messages array and a model id returned by GET /models. Jentic handles the bearer header so the agent only sees a typed input and output.

Which models are available on the Cerebras Inference API?

The catalogue is dynamic and exposed through GET /models. Call that endpoint to get the current list of model ids and pass one of them as the model field on /chat/completions. Use GET /models/{model_id} to read per-model metadata such as ownership and creation timestamp.