Hatz AI REST API

Name: Hatz AI REST API API
Brand: Hatz AI REST API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLLanguage ModelsapiKey, bearer3 EndpointsREST

For Agents

Run chat completions with tool calling on Hatz AI models, list available models, and orchestrate multi-step responses through a single OpenAI-compatible endpoint.

Connect the Hatz AI REST API to your agent

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

Install Jentic One Beta (Self-Hosted)

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

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 Hatz AI REST API API.

Generate chat completions on Hatz AI hosted models with system, user, and assistant message turns

Discover the catalogue of available Hatz AI models with the /chat/models endpoint

Orchestrate higher-level reasoning runs via the /openai/responses endpoint

GET STARTED

Start building with Hatz AI REST API API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Hatz AI REST API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Hatz AI REST API, keeping it validated and agent-ready. Hatz AI exposes a chat-completions interface compatible with OpenAI-style request shapes and supports tool calling so agents can register functions and execute multi-turn reasoning. The API also provides a model-listing endpoint and a separate responses endpoint for higher-level orchestrated calls. Authentication is via an X-API-Key header or HTTP bearer token.

Use Cases

Patterns agents use Hatz AI REST API API for, with concrete tasks.

★ OpenAI-Compatible Chat Backend Swap

Teams that already build against OpenAI's chat-completions schema can repoint to Hatz AI by changing the base URL and credential, keeping the same request body shape with messages and tools. Hatz AI's /chat/completions accepts the same role-based message turns and returns the same tool_call structures, so agent loops do not need to be rewritten. This makes Hatz AI a drop-in alternative for evaluating different model backends without refactoring application code.

Send a POST to /chat/completions with a system message, a user message, and a tools array describing one function, then parse the assistant's tool_call output

Tool-Calling Agent Loop

Hatz AI returns structured tool_call outputs when the model decides a registered function should run, allowing agents to execute the call, append the result as a tool message, and re-prompt for the final answer. This pattern works for retrieval, data lookup, and action execution within a single conversation. The agent can repeat the loop until the model returns a normal assistant message with no further tool calls.

Run a chat completion with a tools array, detect a tool_call in the response, execute the tool, append the tool result as a message, and call /chat/completions again to get the final answer

Model Discovery and Selection

Before issuing a chat completion an agent can call /chat/models to discover the active set of supported models on Hatz AI and pick one based on capability. This avoids hardcoding model names in agent code and keeps integrations resilient when Hatz AI rotates its hosted catalogue. The result is a deterministic selection step at the start of any agent run.

Call GET /chat/models, pick the model id whose name matches a configured capability tag, and pass that id into the next /chat/completions request

Agent Integration via Jentic

An AI agent built on Jentic can search for the Hatz AI chat-completions operation by intent and execute it without storing the X-API-Key in agent context. Jentic's MAXsystem holds the credential and returns scoped access to the agent at execution time. This is useful when the same agent must call multiple LLM providers and credential isolation is a requirement.

Use Jentic search for 'send a chat completion to hatz ai', load the operation schema, then execute it with a messages array and the configured model id

Key Endpoints

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

METHOD

PATH

DESCRIPTION

POST

/chat/completions

Generate a chat completion with optional tool calling

GET

/chat/models

List available Hatz AI models

POST

/openai/responses

Run an orchestrated responses-style call

POST

/chat/completions

Generate a chat completion with optional tool calling

GET

/chat/models

List available Hatz AI models

POST

/openai/responses

Run an orchestrated responses-style call

Why though Jentic?

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

Credential isolation

The Hatz AI X-API-Key or bearer token is stored encrypted in the Jentic vault. Agents receive scoped execution tokens at call time and never see the raw credential.

Intent-based discovery

Agents search by intent such as 'send a chat completion to hatz ai' and Jentic returns the matching /chat/completions operation with its full input schema, so the agent can build a valid request without reading the spec.

Time to first call

Direct integration: half a day to set up auth, request shaping, and tool-call handling. Through Jentic: under 30 minutes from search to first executed call.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

OpenAI API

OpenAI's chat-completions and tools API; the schema Hatz AI is compatible with

Choose OpenAI when you need GPT-class models or wider ecosystem support; choose Hatz AI when you want a compatible request shape against a different model backend

Alternative

Anthropic Messages API

Anthropic's Claude messages API for chat and tool use

Choose Anthropic Messages when the agent needs Claude reasoning; choose Hatz AI for an OpenAI-shape interface

Alternative

Groq API

Groq's low-latency inference for hosted open models

Choose Groq when latency is the deciding factor; choose Hatz AI when you want its specific hosted catalogue

Complementary

Hugging Face Inference API

Run open-weights models for tasks Hatz AI does not cover

Use Hugging Face for embeddings, vision, or speech models alongside Hatz AI for chat completions

FAQs

Specific to using Hatz AI REST API API through Jentic.

Why is there no official OpenAPI spec for Hatz AI REST API?

Hatz AI does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Hatz AI REST 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 Hatz AI REST API use?

The API accepts either an X-API-Key header or an HTTP bearer token. When called through Jentic the credential is held in the Jentic vault and never enters agent context, only a scoped execution token is granted at call time.

Can I use tool calling with the Hatz AI REST API?

Yes. POST /chat/completions accepts a tools array and the response contains tool_call entries when the model decides to invoke a function. The agent executes the function, appends the result as a tool message, and re-calls /chat/completions to obtain the final assistant turn.

What are the rate limits for the Hatz AI REST API?

The OpenAPI spec does not declare explicit rate limits. Treat the API as standard HTTPS and respect any 429 responses with exponential backoff; check Hatz AI's account dashboard for current quotas.

How do I run a chat completion on Hatz AI through Jentic?

Install the Jentic SDK with pip install jentic, search for 'send a chat completion to hatz ai', load the /chat/completions operation, and execute it with your messages array and chosen model id. Sign up at https://app.jentic.com/sign-up to get a Jentic agent key.

How do I discover which models are available on Hatz AI?

Call GET /chat/models to retrieve the catalogue of currently supported model ids. Use the returned id as the model field in subsequent /chat/completions calls so the integration is not tied to a hardcoded name.