Install Jentic One Beta

Connect the APIPie to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the APIPie, 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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fapipie.ai%2Fapipie" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fapipie.ai%2Fapipie" | 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 APIPie API.

Run chat completions against hosted language models

Generate embeddings and query vector collections for semantic search

Generate images and convert text to speech

Transcribe audio to text

Search and scrape the web for current information

GET STARTED

Start building with APIPie API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use APIPie API for, with concrete tasks.

★ One gateway for an agent's model calls

An AI agent connected through Jentic reaches chat, embedding, image, and audio models through APIPie's single interface. It runs a completion, embeds supporting text, and generates an image from one consistent gateway. Because the agent discovers operations by intent, it uses many model capabilities without a developer integrating each provider separately.

Run a chat completion for a prompt, generate embeddings for the supporting text, and return both results

RAG with built-in vectors and parsing

APIPie covers the retrieval side of a RAG pipeline: it parses documents with Apache Tika, generates embeddings, and stores and queries vector collections. An agent can ingest a document, embed its chunks, and query the collection for the passages most relevant to a question, assembling retrieval without stitching a separate vector store and parser together.

Parse a document, create a vector collection, upsert embeddings for its chunks, and query the collection for a question

Grounding answers with live web and data search

The search, scrape, and data endpoints let an agent ground its answers in current information: it searches the web, scrapes a page, or queries job and real-estate datasets, then reasons over the structured result. This keeps an assistant's responses tied to live sources rather than only its training data.

Search the web for a query, scrape the top result, and return the extracted content for the agent to summarize

Key Endpoints

33 endpoints — the apipie api is a single gateway for the model and data operations an agentic workflow needs.

METHOD

PATH

DESCRIPTION

POST

/v1/chat/completions

Run a chat completion

POST

/v1/embeddings

Generate embeddings

POST

/v1/images/generations

Generate an image

POST

/v1/audio/transcriptions

Transcribe audio to text

POST

/v1/search

Search the web

POST

/vectors/query

Query a vector collection

GET

/v1/models

Fetch available models

POST

/v1/chat/completions

Run a chat completion

POST

/v1/embeddings

Generate embeddings

POST

/v1/images/generations

Generate an image

POST

/v1/audio/transcriptions

Transcribe audio to text

POST

/v1/search

Search the web

POST

/vectors/query

Query a vector collection

GET

/v1/models

Fetch available models

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring the APIPie API by hand means handling its header-based API key or bearer auth and learning the request shape for each model and data operation. Through Jentic you install once, import the API from the Directory, store the key once, and your agent calls it.

Permission scoping

APIPie carries its inputs in the request body, so rules bound which operations your agent may call: you can allow chat and embedding operations while leaving vector-collection deletion and state changes out unless you add them. You choose the operations it may use.

Credential isolation

Your APIPie API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'run a chat completion' or 'query a vector collection', and Jentic returns the matching APIPie operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using APIPie API through Jentic.

What authentication does the APIPie API use?

The APIPie API authenticates with an API key sent in the Authorization or x-api-key header, or a bearer token, as declared in its OpenAPI spec. Through Jentic the credential is stored once by your own self-hosted instance and injected at call time, so it never appears in the agent's prompt or logs.

Can I run chat completions and embeddings with the APIPie API?

Yes. You call chat completions against hosted models, generate embeddings, and query vector collections for semantic search through the same gateway. It also generates images, converts text to speech, and transcribes audio.

Is there an APIPie API MCP server?

You do not need an MCP server to give your agent the APIPie API. Jentic connects it directly from the API Directory: import the API, store your API key once, and your agent calls models and vector operations on demand without loading another server's tool definitions into its context.

Can I limit what my agent is allowed to do with the APIPie API?

Yes. Write a rule that allows only the chat and embedding operations, so the agent can reason and retrieve but cannot delete vector collections or change state unless you add those, and every call it makes is logged for review. APIPie carries its inputs in the request body, so rules bound which operations the agent may call.

What are the rate limits for the APIPie API?

The OpenAPI spec does not state numeric rate limits for the APIPie API, which is usage-based per the provider. Check the vendor documentation at https://apipie.ai for current limits before running large batch jobs.

How do I run a chat completion with the APIPie API through Jentic?

Search Jentic for an intent such as 'run a chat completion', and it returns the matching operation with its input schema so your agent passes the model and messages correctly. To run it on your own infrastructure, install Jentic One from its GitHub repo.

APIPie

AI ReadinessFoundational (C)57/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLLanguage ModelsapiKey, bearer33 EndpointsREST

For Agents

Call chat, embedding, image, and audio models through one gateway, manage vector collections for semantic search, and run web search, document parsing, and data lookups.

Use for: I need to run a chat completion with a hosted model, Generate embeddings for a batch of documents, Query a vector collection for similar records, Search the web for current information on a topic

Not supported: Does not train or host your own custom models or manage fine-tuning jobs. Use for calling hosted models, search, document parsing, and vector operations only.

The APIPie API is a single gateway for the model and data operations an agentic workflow needs. It runs chat completions, generates embeddings and images, converts text to speech, and transcribes audio, and it manages vector collections for semantic search. Beyond models it searches and scrapes the web, parses documents with Apache Tika, anonymizes sensitive text, and queries job and real-estate datasets, so an agent reaches many capabilities through one consistent interface.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: D-Signals: 4

66%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: A-Signals: 4

38%

Example Density

How richly the API is illustrated with examples.

92%

Example Validity

Percentage of examples that conform to their schemas.

56%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: DSignals: 4

54%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

67%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

55%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C+Signals: 1

58%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: B-Signals: 1

62%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the APIPie to your agent

Capabilities

Start building with APIPie API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

APIPie

Jentic AI Readiness Score

Introduction

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness