Chatlayer API

Name: Chatlayer API
Brand: Chatlayer API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentCommunicationsChat Messagingbasic5 EndpointsREST

For Agents

Read and update Chatlayer bot reference tables and list customer profiles attached to a bot via 5 endpoints.

Install Jentic One Beta

Connect the Chatlayer API to your agent

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

List the structured-data tables defined for a bot and inspect a single table's schema

Query rows in a Chatlayer table to power dynamic chatbot responses

Perform table actions to insert, update, or remove rows used by conversation flows

GET STARTED

Start building with Chatlayer API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI specification for Chatlayer API, keeping it validated and agent-ready. Chatlayer is a conversational AI platform from Sinch that powers chatbots and virtual assistants for enterprise contact centres. This API exposes structured access to two domains used at runtime by bots: Tables, which store and serve reference data used inside conversation flows, and Customers, which lists customer profiles bound to a bot. It is designed for teams that need to read or update bot-side data without going through the Chatlayer UI.

Use Cases

Patterns agents use Chatlayer API for, with concrete tasks.

★ Sync CRM Data Into Chatbot Tables

Keep a Chatlayer bot's reference tables in sync with a CRM or product catalogue so conversations reflect up-to-date prices, opening hours, or shipping zones. The integration calls GET /api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}/data to read current state and POST .../action to write changes. Sync runs nightly or on CRM webhook, with no need for human staff to log into the Chatlayer console.

List tables for bot id 'support-bot', find the one named 'opening-hours', and update the row for 'Friday' to '09:00-18:00'.

Customer Profile Lookups in Contact Centre

Pull the list of customers known to a Chatlayer bot to power back-office reporting or to check whether an inbound caller is already a registered profile. GET /api/v1/teams/{teamId}/customers supports filtering, so workflows can target a single segment without scanning the entire customer base. This avoids exporting CSVs from the UI and keeps reports current.

List Chatlayer customers for team id 'eu-team' filtered to those with the attribute language='nl' and return the count.

Auditing Bot Reference Data

Periodically read every table on a bot and snapshot its content for compliance and change-tracking. The list-tables and get-table-data endpoints together produce a structured dump that can be diffed against the previous run to detect unauthorised edits in the Chatlayer UI. Useful for regulated industries where bot scripts must be auditable.

For every table on bot id 'finance-bot', fetch all rows and write the result to an audit log keyed by today's date.

AI Agent-Driven Chatbot Operations

Let an AI agent operating through Jentic update Chatlayer tables in response to upstream events instead of writing custom integration code. The agent searches for 'update chatbot table', loads the perform-table-action schema, and executes with the right team, bot, and table identifiers. The basic-auth credentials never enter the agent's context — Jentic injects them at execute time.

Search Jentic for 'perform a Chatlayer table action', load the schema, and add a row to the 'promo-codes' table when a marketing system fires a new-promo event.

Key Endpoints

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

METHOD

PATH

DESCRIPTION

GET

/api/v1/teams/{teamId}/bots/{botId}/tables

List tables for a bot

GET

/api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}

Get a single table

GET

/api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}/data

Read rows from a table

POST

/api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}/action

Insert, update, or remove rows in a table

GET

/api/v1/teams/{teamId}/customers

List customer profiles bound to a bot

GET

/api/v1/teams/{teamId}/bots/{botId}/tables

List tables for a bot

GET

/api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}

Get a single table

GET

/api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}/data

Read rows from a table

POST

/api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}/action

Insert, update, or remove rows in a table

GET

/api/v1/teams/{teamId}/customers

List customer profiles bound to a bot

Why Jentic?

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

Credential isolation

Chatlayer basic-auth tokens are stored encrypted in the Jentic vault. Agents call operations through scoped Jentic tokens — the raw username:password value never enters agent context.

Intent-based discovery

Agents search by intent (e.g., 'list chatbot tables' or 'update chatbot data') and Jentic returns the matching Chatlayer operation with its input schema, so the agent picks the correct teamId, botId, and tableId without reading the docs.

Time to first call

Direct Chatlayer integration: 1-2 days for token provisioning and request signing. Through Jentic: under 30 minutes to list tables and post the first table action.

FAQs

Specific to using Chatlayer API through Jentic.

Why is there no official OpenAPI spec for Chatlayer API?

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

The API uses HTTP basic authentication. The username and password are derived from an access token generated in the Chatlayer console (username:password format). Through Jentic, the token is stored encrypted and injected at request time, so the agent does not handle the credential directly.

Can I update a row in a Chatlayer table with the API?

Yes. POST /api/v1/teams/{teamId}/bots/{botId}/tables/{tableId}/action accepts a payload describing the action to perform (such as inserting or updating rows) on the named table. Read endpoints under the same path return the current rows so you can diff before writing.

What are the rate limits for the Chatlayer API?

The OpenAPI spec does not declare numeric rate limits. Chatlayer applies per-tenant throttling that varies by contract, so agents should handle 429 responses with exponential backoff.

How do I list customers on a bot through Jentic?

Search Jentic for 'list Chatlayer customers', load the schema for GET /api/v1/teams/{teamId}/customers, and execute with the relevant teamId. Filter parameters in the request narrow results to a specific attribute, so a single call can replace a UI export.

Which regions does the Chatlayer API serve?

The spec lists four regional gateways: europe-west1, us-east4, sa-east1, and asia-south1. Pick the region that matches the bot's data residency to keep latency and compliance simple.