Install Jentic One Beta

Connect the DeepSearch API to your agent

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

Look up a person by name, phone, email, or username

Build a sourced dossier of social accounts, contact details, work history, and web mentions

Ask follow-up questions about a researched person

Run reverse-image lookups to find where an image appears online

Search companies and build a company profile, and decode a vehicle by VIN

GET STARTED

Start building with DeepSearch API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use DeepSearch API for, with concrete tasks.

★ Public-Footprint Dossier

A due-diligence agent researches an individual from a single identifier. It looks the person up by email or username, builds a sourced dossier of social accounts, contact details, work history, and web mentions, and asks follow-up questions to fill gaps, keeping to public information only.

Call POST /api/v1/search to find the person, then POST /api/v1/dossier to build their sourced profile

Reverse-Image Investigation

An agent investigating a profile photo runs a reverse-image lookup to find where the image appears online and which visually similar images exist. Paired with a person lookup, this corroborates or challenges an identity claim from public sources.

Call POST /api/v1/reverse-image with the image, then POST /api/v1/search to corroborate the identity

Batched Research as Async Jobs

To research many subjects at once, an agent submits up to 10 operations in a single batch and tracks them as async jobs. It lists jobs, fetches a job by id, replays its events, and cancels an account-owned job that is no longer needed, so large research runs proceed without blocking.

Call POST /api/v1/batch with up to 10 operations, then GET /api/v1/jobs/{id} to fetch each job's result

AI Agent Integration via Jentic

An AI agent reaches the DeepSearch API through Jentic without wiring the bearer key itself. It searches by intent, receives the matching operation and its input schema, and executes with the credential injected at run time, chaining a lookup into a dossier and follow-up questions in one research flow.

Search Jentic for 'look up a person by email', load the POST /api/v1/search schema, and execute it with Jentic-managed credentials

Key Endpoints

18 endpoints — the deepsearch api researches a person's public online footprint for ai agents and applications.

METHOD

PATH

DESCRIPTION

POST

/api/v1/search

Find people by name, phone, email, or username

POST

/api/v1/dossier

Build a full public-footprint profile for a person

POST

/api/v1/chat

Ask follow-up questions about a person

POST

/api/v1/reverse-image

Find where an image appears online

POST

/api/v1/entity/search

Find companies by name

POST

/api/v1/vin

Decode a vehicle by VIN

POST

/api/v1/batch

Run up to 10 operations in one request

GET

/api/v1/jobs

List async jobs, newest first

POST

/api/v1/search

Find people by name, phone, email, or username

POST

/api/v1/dossier

Build a full public-footprint profile for a person

POST

/api/v1/chat

Ask follow-up questions about a person

POST

/api/v1/reverse-image

Find where an image appears online

POST

/api/v1/entity/search

Find companies by name

POST

/api/v1/vin

Decode a vehicle by VIN

POST

/api/v1/batch

Run up to 10 operations in one request

GET

/api/v1/jobs

List async jobs, newest first

Why Jentic?

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

Setup

Wiring DeepSearch by hand means sending its bearer API key on research endpoints, leaving the public discovery and sandbox endpoints unauthenticated, and polling async jobs yourself. Through Jentic you install once, import DeepSearch from the API Directory, store the key once, and your agent calls it.

Permission scoping

A rule can confine your agent to the research operations it needs, such as person search and dossier building, because job cancellation and company research are separate operations you choose to include. Those stay out of reach unless you grant them.

Credential isolation

Your DeepSearch 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 'look up a person by email' or 'build a dossier', and Jentic returns the matching DeepSearch operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using DeepSearch API through Jentic.

What authentication does the DeepSearch API use?

The research endpoints authenticate with a bearer API key created in the developer portal, per the OpenAPI spec, while discovery, health, and sandbox endpoints need no credential. The sandbox exposes a public test key for trialling the flow. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at execution time.

What identifiers can I look someone up by with the DeepSearch API?

The search endpoint accepts a name, phone, email, or username and returns matching people from public sources. From a match an agent can build a sourced dossier and ask follow-up questions, all limited to public information.

Can I run many research lookups at once with the DeepSearch API?

Yes. The batch endpoint runs up to 10 operations in one request, and results can be tracked as async jobs that an agent lists, fetches by id, and cancels. Cached results and idempotent replays are not re-billed, so repeated reads are efficient.

How do I look up a person through Jentic with the DeepSearch API?

Search Jentic for 'look up a person by email', and it returns the DeepSearch search operation with its input schema. Load the schema and execute POST /api/v1/search with the credential injected at run time. To run it on your own infrastructure, install Jentic One from its GitHub repo.

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

Yes. Write a rule that allows only the research operations the agent needs, such as person search and dossier building, while leaving out job cancellation or company research unless you add them. Each call runs through your own instance and is logged for review.

Deepsearch App DeepSearch API

AI ReadinessFoundational (C)56/100

See full scorecard

Official vendor OpenAPI document · agent-readyData EnrichmentContact Enrichmentbearer18 EndpointsREST

For Agents

Look up people by name, phone, email, or username, build sourced dossiers, run reverse-image and company lookups, and ask follow-up questions. Research endpoints use a bearer API key; several discovery endpoints need no credential.

Use for: Look up a person by their email address, Build a sourced dossier for a named individual, Find where a photo appears online with a reverse-image search, Search for a company by name and build its profile

Not supported: Does not handle private or non-public records, background-check adjudication, or identity verification decisions. Use for researching a person's public online footprint only.

The DeepSearch API researches a person's public online footprint for AI agents and applications. It looks someone up by name, phone, email, or username, builds a sourced dossier of social accounts, contact details, work history, relatives, and web mentions, and answers follow-up questions about them. It also searches companies, builds company profiles, decodes a vehicle by VIN, and runs reverse-image lookups, with async jobs and a public sandbox for testing. The research endpoints are authenticated with a bearer API key, while discovery, health, and sandbox endpoints need no credential.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 4

95%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

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

35%

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: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

80%

Example Validity

Percentage of examples that conform to their schemas.

62%

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: FSignals: 4

19%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

56%

OperationId Quality

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

53%

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: B-Signals: 1

60%

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: ASignals: 1

88%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the DeepSearch API to your agent

Capabilities

Start building with DeepSearch API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Deepsearch App DeepSearch API

Jentic AI Readiness Score

DeepSearch API

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