Extract entity-relationship triples from unstructured text and build queryable in-memory knowledge graphs.
kg-extract is an LLM-powered knowledge graph extraction library for TypeScript and JavaScript. It takes unstructured text, prompts a language model to identify entities and relationships, parses the results into structured triples, resolves duplicate entities, and assembles everything into a queryable in-memory knowledge graph.
The library is LLM-agnostic. You provide a function that calls any language model (OpenAI, Anthropic, Google, Cohere, Ollama, or any local model), and kg-extract handles prompt construction, output parsing, entity resolution, and graph assembly. Zero runtime dependencies.
npm install kg-extractimport { extract, buildGraph, KnowledgeGraph } from 'kg-extract';
// Provide any LLM function: (prompt: string) => Promise<string>
const llm = async (prompt: string): Promise<string> => {
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: prompt }],
});
return response.choices[0].message.content ?? '';
};
// Extract entities and triples from a single passage
const result = await extract(
'Alice Johnson founded Acme Corp in New York in 2019.',
{ llm }
);
console.log(result.entities);
// [{ name: 'Alice Johnson', type: 'Person', aliases: [] },
// { name: 'Acme Corp', type: 'Organization', aliases: [] },
// { name: 'New York', type: 'Location', aliases: [] }]
console.log(result.triples);
// [{ subject: 'Alice Johnson', predicate: 'founded', object: 'Acme Corp', confidence: 0.95 },
// { subject: 'Acme Corp', predicate: 'located_in', object: 'New York', confidence: 0.9 }]
// Build a full knowledge graph from multiple texts
const kg = await buildGraph(
[
'Alice Johnson founded Acme Corp in New York.',
'Bob Smith joined Acme Corp as CTO in 2020.',
],
{ llm }
);
// Query the graph
const aliceRelations = kg.query('Alice Johnson');
const worksAtTriples = kg.query(undefined, 'works_at');
const path = kg.findPath('Alice Johnson', 'Bob Smith');- LLM-agnostic extraction -- Bring any language model. Supports simple prompt functions and message-based function signatures.
- Two-pass extraction pipeline -- Entities are extracted first, then relationships are extracted with entity context, producing higher-quality triples.
- Automatic passage splitting -- Long texts are split into sentence-boundary passages to stay within token limits.
- Entity resolution -- Duplicate entities are merged across passages using case-insensitive matching, alias-based merging, and substring containment.
- Queryable knowledge graph -- Pattern-based triple queries, BFS path finding, neighbor traversal, and graph statistics.
- Configurable entity types -- Built-in types (Person, Organization, Location, Date, Event, Concept, Product) with support for custom types.
- Confidence filtering -- Triples carry confidence scores; filter out low-confidence extractions with
minConfidence. - Serialization -- Full JSON round-trip via
toJSON()andKnowledgeGraph.fromJSON(). - Graph merging -- Combine multiple knowledge graphs into one.
- Reusable extractors -- Create pre-configured extractor instances with
createExtractor()for repeated use. - Zero runtime dependencies -- Pure TypeScript implementation with no mandatory dependencies.
Extracts entities and relationship triples from a single text passage.
function extract(text: string, options: ExtractOptions): Promise<ExtractionResult>Parameters:
| Parameter | Type | Description |
|---|---|---|
text |
string |
The input text to extract entities and relationships from. |
options |
ExtractOptions |
Extraction configuration (see Configuration). |
Returns: Promise<ExtractionResult>
Processes one or more texts, extracts entities and triples, resolves duplicates, and assembles a KnowledgeGraph.
function buildGraph(
texts: string | string[],
options: BuildGraphOptions
): Promise<KnowledgeGraph>Parameters:
| Parameter | Type | Description |
|---|---|---|
texts |
string | string[] |
A single text or array of texts to process. |
options |
BuildGraphOptions |
Extraction and graph-building configuration (see Configuration). |
Returns: Promise<KnowledgeGraph>
Entity resolution is enabled by default. Disable it with { resolution: { enabled: false } }.
Creates a pre-configured extractor instance for repeated use, avoiding repeated option setup.
function createExtractor(config: ExtractorConfig): ExtractorParameters:
| Parameter | Type | Description |
|---|---|---|
config |
ExtractorConfig |
Default configuration. llm is required; all other fields are optional. |
Returns: Extractor -- an object with extract() and buildGraph() methods that use the provided defaults, with per-call overrides supported.
const extractor = createExtractor({ llm, minConfidence: 0.5 });
// Uses default config
const result = await extractor.extract('Some text.');
// Override minConfidence for this call
const result2 = await extractor.extract('Other text.', { minConfidence: 0.8 });
// Build a graph with the same defaults
const kg = await extractor.buildGraph(['Text one.', 'Text two.']);An in-memory directed labeled multigraph where nodes are entities and edges are relationship triples.
Adds an entity node to the graph. If an entity with the same name (case-insensitive) already exists, the call is a no-op.
addEntity(entity: Entity): voidAdds a relationship triple to the graph. Automatically creates entity nodes for the subject and object if they do not already exist (with type 'unknown').
addTriple(triple: Triple): voidRetrieves an entity by name. Lookup is case-insensitive.
getEntity(name: string): Entity | undefinedPattern-based triple query. Pass undefined for any parameter to treat it as a wildcard. Matching is case-insensitive for subject and object, and exact for predicate.
query(subject?: string, predicate?: string, object?: string): Triple[]// All triples where Alice is the subject
kg.query('Alice');
// All "works_at" relationships
kg.query(undefined, 'works_at');
// All triples pointing to Acme Corp
kg.query(undefined, undefined, 'Acme Corp');
// Specific triple pattern
kg.query('Alice', 'works_at', 'Acme Corp');Returns all triples connected to an entity, optionally filtered by direction.
getRelationships(
name: string,
direction?: 'outgoing' | 'incoming' | 'both'
): Triple[]| Direction | Description |
|---|---|
'outgoing' |
Triples where the entity is the subject. |
'incoming' |
Triples where the entity is the object. |
'both' |
All connected triples (default). |
Returns all entities within depth hops of the given entity using breadth-first traversal. The source entity is excluded from the result.
getNeighbors(name: string, depth?: number): Entity[]Default depth is 1.
Finds the shortest directed path between two entities using BFS. Returns the sequence of triples forming the path, or null if no path exists. Returns an empty array if from and to are the same entity.
findPath(
from: string,
to: string,
options?: { maxDepth?: number }
): Triple[] | nullDefault maxDepth is 5.
Returns all entities of a given type. Type matching is case-insensitive.
getEntitiesByType(type: string): Entity[]Returns aggregate statistics about the graph.
stats(): GraphStatsThe returned GraphStats object contains:
| Field | Type | Description |
|---|---|---|
nodeCount |
number |
Total number of entities. |
edgeCount |
number |
Total number of triples. |
avgDegree |
number |
Average degree across all nodes. |
maxDegree |
number |
Maximum degree of any node. |
mostConnected |
string |
Name of the entity with the highest degree. |
entityTypes |
Record<string, number> |
Count of entities per type. |
predicates |
Record<string, number> |
Count of triples per predicate. |
Merges another KnowledgeGraph into this one. All entities and triples from the other graph are added.
merge(other: KnowledgeGraph): voidSerializes the graph to a plain JSON object suitable for storage or transmission.
toJSON(): GraphJSONStatic method. Reconstructs a KnowledgeGraph from a previously serialized GraphJSON object.
static fromJSON(json: GraphJSON): KnowledgeGraph// Serialize
const json = kg.toJSON();
const serialized = JSON.stringify(json);
// Deserialize
const restored = KnowledgeGraph.fromJSON(JSON.parse(serialized));Returns an iterable iterator over all entities in the graph.
*entities(): Iterable<Entity>Returns an iterable iterator over all triples in the graph.
*triples(): Iterable<Triple>| Option | Type | Default | Description |
|---|---|---|---|
llm |
LLM |
required | The LLM function to call for extraction. |
entityTypes |
string[] |
undefined |
Custom entity types to look for. When provided, replaces the default set in the prompt. |
maxPassageTokens |
number |
1500 |
Maximum estimated tokens per passage when splitting long text (approx 4 chars per token). |
minConfidence |
number |
0.3 |
Minimum confidence threshold. Triples below this are discarded. |
structuredOutput |
boolean |
false |
When true, use strict structured output mode. |
Extends ExtractOptions with:
| Option | Type | Default | Description |
|---|---|---|---|
resolution.enabled |
boolean |
true |
Enable or disable entity resolution. |
Same as Partial<ExtractOptions> with llm required. Used with createExtractor().
kg-extract accepts two LLM function signatures:
Simple prompt function:
type LLMFunction = (prompt: string) => Promise<string>Message-based function:
type LLMMessageFunction = (messages: LLMMessage[]) => Promise<string>
interface LLMMessage {
role: 'system' | 'user' | 'assistant';
content: string;
}The library detects which signature you provide by checking the function's .length property. Functions with arity 0 or 1 are treated as simple prompt functions; all others are treated as message-based.
Since kg-extract delegates to a caller-provided LLM function, errors can originate from the LLM call itself. Wrap extraction calls in try/catch to handle LLM failures:
try {
const result = await extract('Some text.', { llm });
} catch (error) {
// LLM call failures propagate as-is
console.error('Extraction failed:', error);
}If the LLM returns output that cannot be parsed into entities or triples, the corresponding arrays in the result will be empty rather than throwing. Check result.warnings for diagnostic information.
When no entities are extracted from a passage, the relationship extraction step is skipped entirely for that passage, saving an LLM call.
Restrict or extend the types of entities the LLM looks for:
const result = await extract(
'The golden retriever was bred in Scotland.',
{
llm,
entityTypes: ['Animal', 'Breed', 'Country'],
}
);When entityTypes is provided, only those types are included in the extraction prompt.
For applications that repeatedly extract from many texts with the same configuration:
const extractor = createExtractor({
llm,
minConfidence: 0.5,
entityTypes: ['Person', 'Organization', 'Product'],
});
// Extract from many documents
for (const doc of documents) {
const result = await extractor.extract(doc.text);
// process result...
}
// Or build a single graph from all documents
const kg = await extractor.buildGraph(documents.map(d => d.text));Per-call overrides take precedence over the extractor defaults:
// Override minConfidence just for this call
const result = await extractor.extract(doc.text, { minConfidence: 0.9 });Explore entity neighborhoods and find connections:
const kg = await buildGraph(texts, { llm });
// Get all entities within 2 hops of "Albert Einstein"
const nearby = kg.getNeighbors('Albert Einstein', 2);
// Find shortest directed path between two entities
const path = kg.findPath('Albert Einstein', 'Nobel Prize');
if (path) {
for (const triple of path) {
console.log(`${triple.subject} --[${triple.predicate}]--> ${triple.object}`);
}
}
// Get outgoing relationships only
const outgoing = kg.getRelationships('Albert Einstein', 'outgoing');Combine knowledge graphs built from different sources:
const kg1 = await buildGraph(articlesFromSourceA, { llm });
const kg2 = await buildGraph(articlesFromSourceB, { llm });
kg1.merge(kg2);
console.log(kg1.stats());
// { nodeCount: 42, edgeCount: 87, ... }Save and restore graphs:
import { KnowledgeGraph } from 'kg-extract';
import { writeFileSync, readFileSync } from 'node:fs';
// Save to disk
const json = kg.toJSON();
writeFileSync('knowledge-graph.json', JSON.stringify(json, null, 2));
// Load from disk
const data = JSON.parse(readFileSync('knowledge-graph.json', 'utf-8'));
const restored = KnowledgeGraph.fromJSON(data);
// Verify
console.log(restored.stats().nodeCount);If you want raw extraction results without any entity merging:
const kg = await buildGraph(texts, {
llm,
resolution: { enabled: false },
});If your LLM client uses a message array interface:
import type { LLMMessage } from 'kg-extract';
const messageLLM = async (messages: LLMMessage[]): Promise<string> => {
const response = await anthropic.messages.create({
model: 'claude-sonnet-4-20250514',
messages: messages.map(m => ({ role: m.role, content: m.content })),
max_tokens: 4096,
});
return response.content[0].text;
};
const result = await extract('Some text.', { llm: messageLLM });kg-extract is written in strict TypeScript and ships type declarations alongside the compiled JavaScript. All public types are exported from the package entry point:
import type {
Entity,
EntityType,
Triple,
ExtractionResult,
LLM,
LLMFunction,
LLMMessage,
LLMMessageFunction,
ExtractOptions,
BuildGraphOptions,
ExtractorConfig,
Extractor,
GraphStats,
GraphJSON,
} from 'kg-extract';MIT