Affinda API

Name: Affinda API
Brand: Affinda API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferencebearer140 EndpointsREST

For Agents

Parse resumes, invoices, and other documents, manage extracted data, and run resume and job-description search across 140 Affinda v3 endpoints.

Install Jentic One Beta

Connect the Affinda API to your agent

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

Upload documents to Affinda and retrieve structured extraction results

Manage workspaces, document types, and per-type JSON schemas

Run resume search with filters, embeddings, and match scoring

Search job descriptions and match them against indexed resumes

GET STARTED

Start building with Affinda API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI specification for Affinda API, keeping it validated and agent-ready. Affinda is a document-AI platform that extracts structured data from resumes, invoices, receipts, IDs, and other document types and supports recruiter-facing resume search, job-description search, and skill-and-occupation taxonomies. Version 3 of the API exposes 140 endpoints covering document upload and parsing, workspaces and document types, validation results, mapping data sources, tags and annotations, webhook subscriptions, and dedicated resume and job-description search engines. Authentication uses a bearer token issued from the Affinda dashboard.

Use Cases

Patterns agents use Affinda API for, with concrete tasks.

★ Resume Parsing for Recruiting Workflows

Replace manual resume entry with structured extraction. POST /v3/documents uploads a resume and Affinda returns parsed fields - name, contact, work history, education, skills - using an extraction schema you control via /v3/document_types. Combined with the resume_search endpoints, recruiters can index parsed resumes and rank them against requirements without leaving their ATS.

Call POST /v3/documents with a resume file and document_type=resume and return the parsed name, email, and most recent role

Resume and Job-Description Search

Build a recruiter-facing search engine on top of parsed documents. POST /v3/resume_search ranks candidates against a query, /v3/resume_search/match scores a specific resume against a job, /v3/job_description_search runs the inverse, and /resume_search/suggestion_skill and /suggestion_job_title surface taxonomy-aware hints. Useful for ATS vendors and talent platforms that want production-grade matching without training their own embeddings.

Call POST /v3/resume_search with skills=['python','aws'] and location='Berlin' and return the top 20 ranked candidates with match scores

Invoice and Receipt Extraction

Extract structured line items, totals, taxes, and vendor data from invoices and receipts using the same /v3/documents endpoint with the appropriate document_type. The /v3/validation_results endpoints let an operator confirm or correct extracted fields, and /v3/document_types/{identifier}/json_schema returns the schema being applied so finance systems can map fields one-for-one.

Call POST /v3/documents with an invoice PDF, then call GET /v3/validation_results?document={id} to retrieve the extracted line items

AI Agent Document Pipeline

Give an AI agent a single document-AI tool that handles parsing, search, and tagging. Through Jentic, the agent searches by intent for the right Affinda v3 operation, loads the schema, and executes with the bearer token isolated in the vault. The agent can chain extraction, validation, and downstream CRM updates without bespoke integration code.

Search Jentic for 'parse a resume document', load the /v3/documents schema, and execute with the file payload and document_type=resume

Key Endpoints

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

METHOD

PATH

DESCRIPTION

POST

/v3/documents

Upload a document for parsing

GET

/v3/documents/{identifier}

Retrieve a parsed document

POST

/v3/resume_search

Search the resume index against a query

GET

/v3/resume_search/match

Score a resume against a job description

POST

/v3/job_description_search

Search job descriptions against indexed resumes

GET

/v3/document_types/{identifier}/json_schema

Return the JSON schema applied for a document type

POST

/v3/resthook_subscriptions

Create a webhook subscription for document events

POST

/v3/documents

Upload a document for parsing

GET

/v3/documents/{identifier}

Retrieve a parsed document

POST

/v3/resume_search

Search the resume index against a query

GET

/v3/resume_search/match

Score a resume against a job description

POST

/v3/job_description_search

Search job descriptions against indexed resumes

Why Jentic?

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

Credential isolation

Your Affinda bearer token 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 'parse a resume document', and Jentic returns the matching v3 operation with its multipart or JSON schema so the agent picks the document endpoint without browsing the reference docs.

Time to first call

FAQs

Specific to using Affinda API through Jentic.

Why is there no official OpenAPI spec for the Affinda API?

Affinda publishes interactive docs but does not distribute a downloadable OpenAPI 3 specification. Jentic generates and maintains this spec so that AI agents and developers can call Affinda API via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.

What authentication does the Affinda API use?

Affinda uses an HTTP bearer token; pass Authorization: Bearer <token> on every request with a token issued from the Affinda dashboard. Through Jentic the token is stored in the vault and injected at execution time.

Which regions does the Affinda API serve?

Affinda runs three regional instances - AUS/Global at https://api.affinda.com, US at https://api.us1.affinda.com, and EU at https://api.eu1.affinda.com. Use the URL that matches the region your account was provisioned in to keep documents inside that data boundary.

Can I parse resumes and invoices with the same Affinda API?

Yes. POST /v3/documents handles every document type - the document_type parameter selects the extraction schema, and /v3/document_types lists the schemas available to your account. The same endpoint returns resumes, invoices, receipts, IDs, or any custom type.

What are the rate limits for the Affinda API?

Affinda applies per-tier credit and concurrency limits but does not declare specific numbers in the spec. Check your plan in the Affinda dashboard and watch for HTTP 429 responses; Jentic surfaces them so agents can back off.

How do I run a resume search through Jentic?

Run pip install jentic, search Jentic with 'search resumes by skills and location', load the /v3/resume_search schema, and execute with the criteria in the request body. Jentic injects the bearer token at execution time.

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

Yes. Because you run Jentic One yourself, your own rules decide which Affinda operations and credentials the agent can use, and you pick exactly which endpoints are in the allowed set. Since Affinda puts the document identifier in the URL path at /v3/documents/{identifier}, a rule can pin the agent to reading a specific parsed document, while creation and resume or job-description search take their input in the request body. Operations you leave out stay unavailable, so the agent cannot create /v3/resthook_subscriptions webhooks unless you add that operation to its allowed set.

Affinda API

Connect the Affinda API to your agent

Capabilities

Start building with Affinda API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs