HrFlow.ai API

Name: HrFlow.ai API
Brand: HrFlow.ai API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentHR recruitingRecruiting AtsapiKey28 EndpointsREST

For Agents

Index, parse, tag, and match candidate profiles and jobs with AI inference endpoints on the HrFlow.ai HR platform.

Install Jentic One Beta

Connect the HrFlow.ai API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the HrFlow.ai 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 HrFlow.ai API.

Parse resumes from PDF, DOCX, or raw text into structured candidate profiles

Index profiles and jobs into a HrFlow source or board for retrieval

Generate text embeddings for semantic candidate-job matching

GET STARTED

Start building with HrFlow.ai API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI specification for HrFlow.ai API, keeping it validated and agent-ready. The HrFlow.ai API is an AI-powered HR data platform — index profiles and jobs, parse resumes from text or files, tag and vectorize content, geocode locations, run OCR, and rate profile-job fit. It exposes 28 endpoints spanning storage (sources, boards) and inference (parsing, tagging, embedding, rating). Useful for applicant scoring, automated resume ingestion, and AI agents that augment a human-driven ATS.

Use Cases

Patterns agents use HrFlow.ai API for, with concrete tasks.

★ Automated Resume Intake

Ingest resumes from email or career portals automatically. The agent calls POST /profile/parsing/file with the uploaded resume, then POST /profile/indexing to persist the structured profile in a HrFlow source. Replaces manual recruiter data entry and feeds downstream matching.

Parse the uploaded resume.pdf via POST /profile/parsing/file and index the resulting profile into source_key 'inbound-careers'.

Candidate-to-Job Matching

Score every active candidate against open jobs to surface a shortlist. The agent calls POST /rating with the profile and job references and returns ranked matches. For semantic discovery without explicit ratings, use POST /vectorizing/text to generate embeddings and run nearest-neighbour search against indexed profiles.

Call POST /rating with profile_key=p_123 and job_key=j_456 and return the resulting fit score and explanation.

Job Description Enrichment

Enrich job descriptions with structured tags before posting. The agent calls POST /tagging/text to extract skills, seniority, and function labels, and POST /imaging/text to generate a job illustration. Improves search discoverability on career sites.

Tag a job description via POST /tagging/text and attach the returned skill list to the indexed job record.

Resume OCR Pipeline

Process scanned PDF resumes by running them through OCR before parsing. The agent calls POST /ocr/text to extract raw text from images, then POST /parsing/text on the result, then POST /profile/indexing to persist the candidate. Captures candidates from old scanned formats that PDF parsers fail on.

Run OCR on a scanned resume image, parse the resulting text into a profile, and index the profile into source_key 'scanned-archive'.

AI Recruiting Agent via Jentic

Expose HrFlow.ai's parsing, tagging, embedding, and matching endpoints to an AI recruiter agent through Jentic. The agent searches by intent, Jentic returns the right inference endpoint, and execution happens with the API key kept in the MAXsystem vault.

Search Jentic for 'parse resume', load the POST /profile/parsing/file schema, and execute against the candidate's uploaded PDF.

Key Endpoints

28 endpoints — jentic publishes the only available openapi specification for hrflow.

METHOD

PATH

DESCRIPTION

POST

/profile/parsing/file

Parse a resume PDF or DOCX into a profile

POST

/profile/indexing

Index a candidate profile into a source

POST

/job/indexing

Index a job into a board

POST

/tagging/text

Tag text with HR taxonomy labels

POST

/vectorizing/text

Generate text embeddings

POST

/rating

Score a profile against a job

POST

/ocr/text

Extract text from a scanned image

POST

/profile/parsing/file

Parse a resume PDF or DOCX into a profile

POST

/profile/indexing

Index a candidate profile into a source

POST

/job/indexing

Index a job into a board

POST

/tagging/text

Tag text with HR taxonomy labels

POST

/vectorizing/text

Generate text embeddings

Why Jentic?

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

Credential isolation

HrFlow.ai requires both X-API-Key and X-USER-EMAIL headers. Jentic stores both encrypted in the MAXsystem vault and injects them on every request — agents never see either value.

Intent-based discovery

Across 28 storage and inference endpoints, agents use Jentic intent search (e.g., 'parse resume PDF') to land on the right operation without browsing the docs.

Time to first call

Direct integration: 1-2 days across dual-header auth, multipart uploads, and inference response handling. Through Jentic: under an hour for the first parse-and-index flow.

FAQs

Specific to using HrFlow.ai API through Jentic.

Why is there no official OpenAPI spec for HrFlow.ai API?

HrFlow.ai does not publish a maintained public OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call HrFlow.ai 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 HrFlow.ai API use?

The API uses two header credentials: an API key (X-API-Key) and a user email (X-USER-EMAIL). Both are required on every call. Jentic stores them encrypted in the MAXsystem vault and injects both headers at execution time.

Can I parse a resume PDF with the HrFlow.ai API?

Yes. POST /profile/parsing/file accepts a multipart upload of a resume in PDF or DOCX and returns a structured candidate profile. For raw text input, use POST /parsing/text instead. Combine with POST /profile/indexing to persist the result.

What are the rate limits for the HrFlow.ai API?

HrFlow.ai applies per-account rate limits that scale with plan tier. Inference endpoints (parsing, OCR, vectorizing) are heavier than indexing and storage calls — back off on 429 responses and batch where the endpoint supports it.

How do I score a candidate against a job with the HrFlow.ai API through Jentic?

Run pip install jentic, search for 'rate profile against job', load the POST /rating schema, and execute with the profile and job keys. Jentic returns the structured rating response with the API key handled in the vault.

Can I generate embeddings for semantic candidate search with the HrFlow.ai API?

Yes. POST /vectorizing/text returns an embedding vector for any text input. Pair it with profiles indexed via POST /profile/indexing and run nearest-neighbour search externally to surface semantically similar candidates.

HrFlow.ai API

Connect the HrFlow.ai API to your agent

Capabilities

Start building with HrFlow.ai API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs