Install Jentic One Beta
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Semantria 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%2Flexalytics.com%2Flexalytics" | shStep 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%2Flexalytics.com%2Flexalytics" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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.
What an agent can do with Semantria API.
Queue text documents for sentiment, entity, and theme analysis via POST /documents and retrieve processed output from GET /documents
Submit document collections for batch analysis through POST /collections and read aggregate results at GET /collections
Manage analysis configurations including language and feature toggles using /configs and /configs/{configId}
Define a programmable taxonomy and query topics for classification through /taxonomy and /topics
GET STARTED
Maintain user entities and blacklist terms to tune extraction precision via /entities and /blacklist
Operate session-scoped credentials by creating, listing, and deleting Semantria sessions on /auth/sessions
Inspect account-level usage statistics, available features, and account details through /statistics, /features, and /details
Patterns agents use Semantria API for, with concrete tasks.
★ Voice-of-customer sentiment analysis
A CX team wants to score thousands of survey responses and reviews for sentiment, themes, and named entities. The integration creates a Semantria session via POST /auth/sessions, configures language and features at POST /configs, queues responses with POST /documents, and polls GET /documents for processed output. Results land in a warehouse as structured rows ready for dashboards rather than free text.
Queue 500 review documents via POST /documents with a configId for English sentiment analysis, then poll GET /documents until each is processed and write sentiment scores to the warehouse.
Domain-tuned classification with custom taxonomy
A media-monitoring vendor needs to classify articles into a bespoke industry taxonomy. They define nodes through POST /taxonomy, register query topics with POST /topics, and submit articles to /collections. Semantria returns each article tagged against the custom taxonomy, allowing the team to build domain-specific dashboards without training a model from scratch.
POST /taxonomy with three new nodes (Renewables, Grid, Policy), then POST /collections containing 50 energy articles tagged for analysis against that taxonomy.
Entity extraction tuning
An analytics team finds that Semantria over-tags certain terms or misses internal product names. They tune precision by adding domain-specific user entities via POST /entities and suppressing noise through POST /blacklist. After re-running representative documents, extracted entities better reflect the customer's domain without retraining.
POST /entities with the company's product code list, then POST /blacklist with 10 noise terms, requeue 100 documents, and verify the new entity coverage.
AI agent text analytics via Jentic
An AI research agent needs sentiment and entity scores for a batch of news mentions. Through Jentic the agent searches for 'analyse text sentiment' or 'extract named entities', loads the matching Semantria operation, and executes with the session token held in your Jentic One instance. The agent gets structured output back without dealing with Semantria's session-based auth dance.
Use Jentic to search 'analyse a batch of text for sentiment', queue documents via POST /documents, and return the processed sentiment polarity per document once GET /documents shows them as processed.
29 endpoints — jentic publishes the only available openapi specification for semantria api, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
/auth/sessions
Create a Semantria session token.
/documents
Queue documents for analysis.
/documents
Retrieve processed documents.
/collections
Queue a collection for batch analysis.
/configs
Create an analysis configuration.
/taxonomy
Add taxonomy nodes for classification.
/entities
Add user-defined entities.
/auth/sessions
Create a Semantria session token.
/documents
Queue documents for analysis.
/documents
Retrieve processed documents.
/collections
Queue a collection for batch analysis.
/configs
Create an analysis configuration.
/taxonomy
Add taxonomy nodes for classification.
/entities
Add user-defined entities.
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Semantria API by hand means first calling POST /auth/sessions for a token, setting it in the Authorization header, tracking configIds for each analysis, and calling api5.semantria.com yourself. Through Jentic you install once, import the Semantria API from the API Directory, store the credential once, and your agent calls it.
Permission scoping
Semantria queues text against collection endpoints like /documents, /collections, and /configs rather than a resource id in the path, so scope the agent to the operations it needs, such as submitting documents or reading results. You leave out configuration or taxonomy writes unless you add them, so it only calls the ones you allow.
Credential isolation
Your Semantria session 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 'analyse text for sentiment' or 'classify articles against a taxonomy', and Jentic returns the matching Semantria operation with its input schema, including the configId requirement, so the agent calls the right endpoint without browsing the reference docs.
Alternatives and complements available in the Jentic catalogue.
Specific to using Semantria API through Jentic.
Why is there no official OpenAPI spec for Semantria API?
Lexalytics does not publish a maintained OpenAPI specification for Semantria. Jentic generates and maintains this spec so that AI agents and developers can call Semantria 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 Semantria API use?
Semantria uses an API token in the 'Authorization' header, obtained from the Create Session endpoint at POST /auth/sessions. The session token scopes calls to your Semantria account. Through Jentic the token lives in your Jentic One instance and is injected on each request.
Can I run sentiment and entity analysis on the same document with Semantria?
Yes. Configure features (sentiment, entities, themes, taxonomy) via POST /configs, then queue the document with POST /documents referencing that configId. The processed result returned from GET /documents includes all enabled feature outputs in one payload.
What are the rate limits for the Semantria API?
Lexalytics applies per-account throughput and document quotas tied to your Semantria plan; exact numbers are not in the public spec. Track usage with GET /statistics, paginate /documents and /collections retrievals, and respect any 429 responses with backoff.
How do I queue documents for analysis through Jentic?
Install with 'pip install jentic', search for 'analyse text for sentiment', and Jentic returns POST /documents. Load the schema, supply the document id, text, and configId, and execute. Then poll GET /documents to retrieve the processed sentiment, entities, and themes.
Can I tune extraction with my own entities and blacklist terms?
Yes. POST /entities registers user entities (e.g., your product names) so they are reliably extracted, and POST /blacklist suppresses noise terms. Both apply at the configuration level and improve precision on domain-specific text without retraining.
Can I limit what my agent is allowed to do with the Semantria API?
Yes. Because you run Jentic One yourself, your own rules decide which Semantria operations and credentials the agent can use. Semantria works against collection endpoints like /documents, /collections, and /configs rather than a resource id in the path, so you can scope the agent to just the operations it needs, such as submitting documents with POST /documents or reading processed results from GET /documents. Configuration and taxonomy writes like POST /configs and POST /taxonomy stay off limits unless you explicitly allow them, and the session token is injected at execution time without entering the agent's prompt.
Know of an official OpenAPI document? Contribute it →
For Agents
Run sentiment, entity, theme, and taxonomy analysis on text with Lexalytics Semantria - queue documents and collections, manage configurations, and pull processed results.
Use for: I need to run sentiment analysis on a batch of customer reviews, Queue a document for entity extraction in Semantria, Create a Semantria session for a long-running analysis job, Add custom taxonomy nodes for classifying support tickets
Not supported: Does not generate text, translate languages, or transcribe audio - use for sentiment, entity, theme, and taxonomy analysis on existing text only.
Jentic publishes the only available OpenAPI specification for Semantria API, keeping it validated and agent-ready. Semantria is the SaaS text analytics service from Lexalytics that runs sentiment analysis, entity extraction, theme detection, taxonomy classification, and topic queries against unstructured text. The 29-endpoint API exposes session-based authentication, document and collection queues for batched analysis, configurable blacklists and user entities, and a programmable taxonomy, so teams can deliver tuned NLP pipelines for voice-of-customer, social listening, and content classification work.