Google Knowledge Graph Search API
Browse all Google APIs34
AI ReadinessNon-Ready (F)34/100
Official vendor OpenAPI document · agent-readyData EnrichmentCompany DataapiKey1 EndpointsREST
For Agents
Search the Google Knowledge Graph for entities and return Schema.org JSON-LD results with names, types, descriptions, and images. Useful for entity enrichment and autocomplete.
Use for: Search the Google Knowledge Graph for 'Marie Curie', Find all Knowledge Graph entities matching 'Apple' filtered to Organization, Look up an entity by its kg machine id, I want to enrich a contact record with a Knowledge Graph identifier
Not supported: Does not write to the Knowledge Graph, return web search results, or expose entity relationships beyond the @type field - use only for read-only entity lookup.
The Knowledge Graph Search API exposes a single endpoint that searches Google's Knowledge Graph for entities - people, places, organizations, films, and other notable things - and returns Schema.org-formatted results with names, descriptions, types, image URLs, and detailed descriptions. It is intended for autocomplete experiences, entity disambiguation, and enrichment of internal records with canonical Google identifiers. The API is read-only and works with a simple API key.
Install Jentic One Beta
Connect the Knowledge Graph Search API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Knowledge Graph Search 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.
1
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%2Fgoogleapis.com%2Fkgsearch" | sh2
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%2Fgoogleapis.com%2Fkgsearch" | 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.
Capabilities
What an agent can do with Knowledge Graph Search API.
Search the Knowledge Graph by free text query and return ranked entity matches
Filter entity results by Schema.org type such as Person, Organization, or Movie
Restrict results to a specific language for localized names and descriptions
Limit results to a maximum number of entities per query
Look up an entity directly by its Knowledge Graph machine ID
Receive Schema.org JSON-LD output with image URLs and detailed descriptions
Use Cases
Patterns agents use Knowledge Graph Search API for, with concrete tasks.
★ Entity enrichment for CRM records
Marketing and sales operations teams enrich company and contact records with stable Knowledge Graph IDs so multiple internal systems can refer to the same canonical entity. A single GET /v1/entities:search call with a company name and types=Organization returns ranked candidates with descriptions and image URLs the agent can use to confirm a match.
Search the Knowledge Graph for query 'Stripe' with types=Organization and limit=3, then return the top result's @id, name, and description.
Autocomplete for entity pickers
Apps that let users tag content with people, films, or places call entities:search as the user types. Each request returns ranked entity matches with image URLs and short descriptions, which is enough to render a rich dropdown without maintaining an internal entity database.
On each keystroke, call entities:search with the partial query and limit=5, then render the resulting names and image URLs in the dropdown.
Disambiguating ambiguous names
When a user types a query like 'Mercury' the same string can refer to a planet, a chemical element, a band, or a brand. The Knowledge Graph Search API returns ranked candidates with type and description so an agent can surface a disambiguation list rather than guessing which Mercury the user meant.
Call entities:search?query=Mercury&limit=5 and present each result's @type and description so the user can pick.
Agent-driven knowledge enrichment via Jentic
An agent answering questions about real-world entities can call the Knowledge Graph Search API through Jentic without managing API keys. Jentic surfaces the single search operation in natural language and returns the schema, so the agent can cite Google's canonical entity description when grounding its answer.
When asked 'who is Hedy Lamarr', call google_kgsearch_query with query='Hedy Lamarr' and return the top result's detailedDescription.articleBody.
Key Endpoints
1 endpoints — the knowledge graph search api exposes a single endpoint that searches google's knowledge graph for entities - people, places, organizations, films, and other notable things - and returns schema.
METHOD
PATH
DESCRIPTION
GET
/v1/entities:search
Search the Knowledge Graph for entities by query, types, language, and limit
GET
/v1/entities:search
Search the Knowledge Graph for entities by query, types, language, and limit
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
K
Knowledge Graph Search API
- Non-Ready (F)34/100
100
Foundational Compliance56
Developer Experience & Jentic Compatibility12
AI-Readiness & Agent Experience94
Agent Usability100
Security50
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
100
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
99%
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).
100%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
56
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: CSignals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
25%
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.
12
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
49%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
0%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
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.
100
Security
Trust, risk posture, and security compliance.
Grade: A+Signals: 1
100%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
50
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: C-Signals: 1
50%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Knowledge Graph Search API by hand means provisioning a Google Cloud API key, appending it as the key query parameter on every call, and shaping its entity-search query yourself. Through Jentic you install once, import the Knowledge Graph Search API from the API Directory, store the API key once, and your agent calls it.
Permission scoping
This API exposes a single read-only entity-search operation and passes its terms as query parameters rather than a resource in the URL path, so scope the agent to that operation. Because the only endpoint reads entity data, the agent can look up entities but can never write to the Knowledge Graph.
Credential isolation
Your Google Cloud API key is stored once, encrypted, by your own Jentic One instance and injected at execution time as the key query parameter. The raw key value never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'search the google knowledge graph' or 'look up an entity by name', and Jentic returns the entities:search operation with its query, types, and limit parameters so the agent calls it correctly without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Knowledge Graph Search API through Jentic.
What authentication does the Knowledge Graph Search API use?
A simple API key passed as the key query parameter. The spec lists no OAuth scheme, so the only credential is a Google Cloud API key with the Knowledge Graph Search API enabled. Through Jentic the API key sits in the encrypted vault and is injected at execution time.
What types can I filter Knowledge Graph results by?
The types parameter accepts any Schema.org type, with Person, Organization, Place, Movie, Book, and Event being the most common. Pass multiple types to broaden the search, e.g. types=Person&types=Organization.
What are the rate limits for the Knowledge Graph Search API?
Google's default quota is 100,000 queries per day per project with a per-second burst limit set per API key. Quotas can be raised in the Cloud console for high-volume autocomplete use cases.
How do I look up an entity through Jentic?
Run jentic search 'search the google knowledge graph' to find GET /v1/entities:search, jentic load to fetch its parameters, then jentic execute with query, types, languages, and limit. Jentic handles the API key transparently.
Can I retrieve an entity by its Knowledge Graph ID?
Yes. Pass the machine id as the ids query parameter on /v1/entities:search to fetch a specific entity directly without a free-text query.
Is the Knowledge Graph Search API free?
There is no per-call charge - usage is bounded only by the daily quota of 100,000 queries per project. Higher tiers require a quota increase request through Google Cloud.
Can I limit what my agent is allowed to do with the Knowledge Graph Search API?
Yes. This API exposes a single read-only operation, the GET /v1/entities:search entity lookup, and because you run Jentic One yourself your own rules decide which operations and credentials your agent may use. You can scope the agent to just that search operation, so it can look up entities by query, types, language, or machine id but can never write to the Knowledge Graph. Your Google Cloud API key is stored once by your own instance and injected at execution time, so the agent calls the endpoint without ever seeing the raw key.
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