Agentset API

Name: Agentset API API
Brand: Agentset API
Availability: InStock

★ Only Publicly Available SpecAI/MLEmbeddings Searchbearer11 EndpointsREST

For Agents

Build retrieval-augmented generation pipelines: create namespaces, ingest documents, and run semantic search with reranking — without managing a vector store.

Quickstart

Get started with Agentset API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"search a knowledge base with semantic retrieval"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Agentset API API.

Provision an isolated namespace per knowledge base or per tenant via POST /v1/namespace

Submit ingest jobs that chunk, embed, and index source documents into a namespace via POST /v1/namespace/{namespaceId}/ingest

Run semantic search with metadata filters and built-in reranking via POST /v1/namespace/{namespaceId}/search

List or delete documents within a namespace to keep retrieval results fresh

GET STARTED

Start building with Agentset API API

Explore with Jentic

View OpenAPI Spec

Use for: I need to set up a new knowledge base for my support documentation, Ingest a batch of PDFs into an existing namespace and wait for indexing, Search the engineering wiki for the top 5 chunks about deployment, List the documents currently indexed in a namespace

Not supported: Does not handle direct LLM completions, conversational state, document OCR, or web crawling — use for namespace, document ingestion, and semantic search only.

Jentic publishes the only available OpenAPI specification for Agentset API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Agentset API, keeping it validated and agent-ready. Agentset is an agentic RAG-as-a-service platform that exposes namespace, document, ingest job, and search endpoints behind 11 routes. Agents create a namespace, ingest source documents, then run semantic search with optional filtering and reranking. The API removes the need to operate a vector database, embeddings pipeline, or reranker on your own infrastructure.

Use Cases

Patterns agents use Agentset API API for, with concrete tasks.

★ Customer Support Knowledge Base

Create a namespace per product line, ingest help articles and PDF manuals, then query the namespace from a support chatbot to retrieve grounding chunks. Agentset handles chunking, embeddings, and reranking — the agent only needs to call /v1/namespace/{id}/search with the user query. Suited to teams that want RAG without operating a vector database.

Create a namespace 'support-v1', ingest 200 help articles, then search with query='how do I reset my password' and top_k=5

Internal Document Search for Agents

Index company policies, runbooks, and meeting notes in dedicated namespaces so a Jentic agent can pull grounded answers during longer plans. The agent calls /v1/namespace/{id}/search and feeds the returned chunks into its prompt before responding. This replaces hand-built retrieval pipelines on top of Pinecone or Weaviate.

Search the 'eng-runbooks' namespace for chunks about 'database failover' and feed the top 3 into the model context

Per-Tenant RAG in a SaaS Product

Map each end customer to its own Agentset namespace so a multi-tenant SaaS app can offer search and Q&A over the customer's uploaded documents. The agent provisions a namespace on signup, ingests files as the customer uploads them, and scopes searches by namespace id. This is a faster path than running per-tenant vector indexes in-house.

On user signup, POST /v1/namespace with name=user_id; on each upload, POST an ingest job; on each query, search the user's namespace

Agent Memory and Tool Result Indexing

Use a Jentic-connected agent to write its own intermediate findings and tool results into Agentset, then search that namespace later in long-running plans to recover earlier context. The agent treats Agentset as an external memory store with semantic recall, removing the need to keep all prior steps in the model's context window.

After each tool call, ingest the result as a new document in the 'agent-memory' namespace; before the next step, search the namespace for related prior results

Key Endpoints

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

METHOD

PATH

DESCRIPTION

POST

/v1/namespace

Create a namespace

POST

/v1/namespace/{namespaceId}/ingest

Submit an ingest job

POST

/v1/namespace/{namespaceId}/search

Run semantic search inside a namespace

GET

/v1/namespace/{namespaceId}/document

List indexed documents

DELETE

/v1/namespace/{namespaceId}/document/{documentId}

Remove a document from the index

POST

/v1/namespace

Create a namespace

POST

/v1/namespace/{namespaceId}/ingest

Submit an ingest job

POST

/v1/namespace/{namespaceId}/search

Run semantic search inside a namespace

GET

/v1/namespace/{namespaceId}/document

List indexed documents

DELETE

/v1/namespace/{namespaceId}/document/{documentId}

Remove a document from the index

Why though Jentic?

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

Credential isolation

Agentset bearer tokens are stored in the Jentic vault and injected as the Authorization header at execution. The agent receives a scoped execution result, never the raw token.

Intent-based discovery

Agents search by intent (e.g. 'search a knowledge base' or 'ingest documents') and Jentic returns the matching Agentset endpoint with its input schema, including namespace path parameters.

Time to first call

Direct Agentset integration: half a day for namespace bootstrap, ingest polling, and search wiring. Through Jentic: under 30 minutes — search, load, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Pinecone API

Managed vector database; you bring the embeddings model and chunking

Choose Pinecone when you need fine control over embedding models and chunking strategy, or when you already have an embeddings pipeline.

Complementary

OpenAI API

Language models that consume the chunks Agentset returns

Pair OpenAI's chat completions with Agentset retrieval — Agentset returns grounding chunks, OpenAI generates the final answer.

Complementary

Cohere API

Embeddings, rerank, and generation models for custom RAG stacks

Use Cohere when you want to swap in your own embedding or reranker rather than relying on Agentset's defaults.

FAQs

Specific to using Agentset API API through Jentic.

Why is there no official OpenAPI spec for Agentset API?

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

Agentset uses HTTP bearer token authentication — pass your token in the Authorization header as 'Bearer <token>'. Through Jentic the token is stored in the credential vault and injected at execution time, so the raw value never enters the agent's context.

Can I run RAG without operating my own vector database with the Agentset API?

Yes. Agentset handles chunking, embeddings, vector storage, and reranking. The agent only calls POST /v1/namespace to create a knowledge base, POST /v1/namespace/{id}/ingest to add documents, and POST /v1/namespace/{id}/search to retrieve relevant chunks.

What are the rate limits for the Agentset API?

Limits depend on the Agentset plan and apply to ingest job throughput and search QPS rather than a single fixed cap. Watch for 429 responses and retry with exponential backoff; long ingest jobs are processed asynchronously and tracked via /v1/namespace/{id}/ingest.

How do I run a semantic search through Jentic?

Use the Jentic search 'search a knowledge base'. Jentic returns POST /v1/namespace/{namespaceId}/search; load its schema, supply the namespace id and your query string, and execute. The response contains ranked chunks with source document references.

Does Agentset support metadata filtering on search?

Yes. The /v1/namespace/{id}/search endpoint accepts filter and rerank options in the request body so the agent can constrain results by document type, source, or other metadata attached at ingest time.