Contextual AI API

Name: Contextual AI API API
Brand: Contextual AI API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLEmbeddings Searchbearer3 EndpointsREST

For Agents

List and create RAG agents on the Contextual AI platform and list datastores that those agents query, authenticated with a bearer token.

Quickstart

Get started with Contextual AI 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:
"create a contextual ai agent"

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

Capabilities

What an agent can do with Contextual AI API API.

List the RAG agents configured in the Contextual AI workspace

Create a new RAG agent definition tied to one or more datastores

List the datastores available to be attached to an agent

Authenticate per request with a bearer API key scoped to the workspace

Inspect agent configuration to understand what each production RAG agent is wired to

GET STARTED

Start building with Contextual AI API API

Explore with Jentic

View OpenAPI Document

Use for: List all Contextual AI agents in the workspace, Create a new Contextual AI RAG agent for a finance-policy use case, Find which datastores are available to back a new agent, Get the configuration of a specific Contextual AI agent

Not supported: Does not handle direct LLM inference, embedding generation, or end-user chat — use for managing Contextual AI RAG agents and listing datastores only.

Jentic publishes the only available OpenAPI document for Contextual AI API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Contextual AI API, keeping it validated and agent-ready. Contextual AI is an enterprise platform for building production-grade Retrieval-Augmented Generation (RAG) agents that combine retrieval against governed datastores with grounded language-model responses. The current public API surface exposes endpoints to list and create agents and to list datastores, with bearer-token authentication scoped to the customer's workspace.

Use Cases

Patterns agents use Contextual AI API API for, with concrete tasks.

★ Provisioning Production RAG Agents

Platform engineering teams use POST /agents to provision new Contextual AI agents from infrastructure-as-code or admin tooling, instead of clicking through the dashboard. Each agent is bound to a datastore, so agents are repeatable and code-reviewed. This standardises how RAG agents are launched across multiple business lines.

POST /agents with name='policy-bot' and a datastore reference for the finance-policy datastore, then verify the agent is listed via GET /agents

Datastore Inventory and Governance

Compliance and data-governance teams use GET /datastores to inventory the datastores backing the workspace's RAG agents and confirm that each agent is connected to a sanctioned source. The list can be cross-referenced against the agents returned by GET /agents to catch agents pointing to deprecated datastores. This avoids RAG agents quietly serving answers from outdated content.

GET /datastores, GET /agents, and produce a report of agents whose configured datastore is no longer in the active datastore list

AI Agent Bootstrap via Jentic

An AI agent helping an internal platform engineer uses Jentic to create new Contextual AI agents on demand. The agent reads the schema, fills in name and datastore, and POSTs to /agents, then returns the new agent id. Through Jentic the bearer token is held in MAXsystem so the agent never sees the raw key.

Search Jentic for 'create a contextual ai agent', execute with name='hr-bot' and the datastore_id for the HR-policies datastore, and return the new agent id

Key Endpoints

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

METHOD

PATH

DESCRIPTION

GET

/agents

List RAG agents in the workspace

POST

/agents

Create a new RAG agent

GET

/datastores

List datastores available in the workspace

GET

/agents

List RAG agents in the workspace

POST

/agents

Create a new RAG agent

GET

/datastores

List datastores available in the workspace

Why though Jentic?

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

Credential isolation

Contextual AI bearer tokens are stored encrypted in the Jentic vault (MAXsystem). Agents receive scoped access at call time and never see the raw token, so a single agent can manage agents across multiple Contextual AI workspaces without leaking credentials.

Intent-based discovery

Agents search Jentic by intent (e.g. 'create a contextual ai agent') and Jentic returns the matching /agents or /datastores operation with its input schema, so the agent calls the right endpoint without browsing Contextual AI's docs.

Time to first call

Direct Contextual AI integration: half a day to wire bearer auth, model the agent payload, and parse the datastores list. Through Jentic: under 30 minutes to search, load schema, and execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Pinecone API

Pinecone is a vector database where developers compose retrieval pipelines themselves rather than buy a managed agent platform.

Choose Pinecone for fine-grained control over embeddings and indexes; choose Contextual AI for a managed RAG agent platform with governance built in.

Complementary

OpenAI API

OpenAI provides the language model that Contextual AI agents can layer over their retrieval step.

Use OpenAI for raw model inference; use Contextual AI when retrieval, governance, and agent definition need to live together in one platform.

Alternative

Context Link API

Context Link offers a single retrieval endpoint over connected sources without the agent-management layer.

Choose Context Link when only the retrieval call is needed; choose Contextual AI when multiple production RAG agents need to be defined and governed.

FAQs

Specific to using Contextual AI API API through Jentic.

Why is there no official OpenAPI spec for Contextual AI API?

Contextual AI publishes API reference docs at docs.contextual.ai but does not host a stable OpenAPI 3 file at a permanent URL. Jentic generates and maintains this spec so that AI agents and developers can call Contextual AI 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 Contextual AI API use?

Each request must include an Authorization: Bearer header with a workspace API key. Through Jentic the bearer token is stored in the MAXsystem vault and applied at call time, so the agent does not handle the raw key.

Can I create a new RAG agent with the Contextual AI API?

Yes. POST /agents creates a new agent record bound to a datastore, and the new agent appears in GET /agents. The agent can then be invoked through Contextual AI's runtime endpoints once provisioning completes.

What are the rate limits for the Contextual AI API?

Contextual AI applies per-workspace rate limits sized to the customer's plan; current limits and usage are visible in the Contextual AI dashboard. Through Jentic, retries with exponential backoff are handled at the SDK level so transient throttling does not surface to agent code.

How do I list datastores with the Contextual AI API through Jentic?

Run `jentic.search('list contextual ai datastores')`, load the operation, and execute. Jentic resolves this to GET /datastores and returns the datastores available in the workspace ready to attach to a new agent.

Does the Contextual AI API expose runtime query endpoints?

The current OpenAPI surface covers agent and datastore management via /agents and /datastores; the runtime query endpoints for invoking an agent are documented separately in Contextual AI's API reference. Use this spec for provisioning and inventory, and contact Contextual AI for the runtime invocation endpoint shape.