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knowledge-graph Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.
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Zipをダウンロード ダウンロード中... name knowledge-graph description Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents. version 1.0.0 model sonnet invoked_by both user_invocable true tools ["Bash","Read","Write","Edit","Glob","Grep"] best_practices ["Always use entity IDs (not names) as stable references","Store observations with timestamps for temporal reasoning","Normalize entity types across all insertions","Use relation types that are directional (e.g., \"manages\" not \"related_to\")","Prune stale observations periodically to keep graph queryable"] error_handling graceful streaming not_applicable verified true lastVerifiedAt "2026-03-15T00:00:00.000Z" source builtin trust_score 100 provenance_sha 999cb8a134ef1c94
Knowledge Graph
Overview
Build and query persistent knowledge graphs of entities, relationships, and observations across agent sessions. Enables agents to accumulate structured knowledge over time — tracking what they've learned about projects, people, codebases, and domains.
When to Invoke
Skill({ skill: 'knowledge-graph' }) when:
Agent needs to remember structured facts across sessions
Building a model of a codebase, project, or domain from observations
Tracking relationships between people, systems, or concepts
Implementing long-term memory that survives context resets
Option 1: MCP Memory Server (Recommended for Claude Agents)
The official MCP memory server provides a persistent knowledge graph accessible via tool calls.
Setup
npx -y @modelcontextprotocol/server-memory
Claude Desktop / settings.json config:
{
"mcpServers" : {
"memory" : {
"command" : "npx" ,
"args" : [ "-y" , "@modelcontextprotocol/server-memory" ] ,
"env" : {
"MEMORY_FILE_PATH" : ".claude/context/memory/knowledge-graph.json"
}
}
}
}
MCP Memory Server Tools
Add one or more entities with type and observations
create_relationsAdd directed relations between entities
add_observationsAppend new observations to existing entities
delete_entitiesRemove entities (and their relations)
delete_observationsRemove specific observations
delete_relationsRemove specific relations
read_graphReturn the full graph
search_nodesSearch entities by query string
open_nodesRetrieve specific entities by name
Usage Pattern
mcp__memory__create_entities ({
entities : [
{
name : 'authentication-service' ,
entityType : 'service' ,
observations : [
'Handles JWT authentication and refresh token rotation' ,
'Located at src/auth/' ,
'Uses Redis for token storage' ,
'Rate-limited to 100 req/min per IP' ,
],
},
{
name : 'Alice Chen' ,
entityType : 'person' ,
observations : ['Senior engineer, owns the auth service' , 'On-call for auth incidents' ],
},
],
});
mcp__memory__create_relations ({
relations : [
{
from : 'Alice Chen' ,
to : 'authentication-service' ,
relationType : 'owns' ,
},
],
});
mcp__memory__add_observations ({
observations : [
{
entityName : 'authentication-service' ,
contents : ['Migrated to Argon2 password hashing in March 2026' ],
},
],
});
mcp__memory__search_nodes ({ query : 'authentication' });
mcp__memory__open_nodes ({ names : ['authentication-service' , 'Alice Chen' ] });
Session Startup Pattern (MANDATORY) At the start of every agent session that uses knowledge graphs:
const existing = await mcp__memory__read_graph ({});
const relevant = await mcp__memory__search_nodes ({ query : '<current task domain>' });
await mcp__memory__add_observations ({
observations : [
{
entityName : 'my-project' ,
contents : [`Session ${new Date ().toISOString()} : Discovered X, fixed Y` ],
},
],
});
Option 2: Local JSON Knowledge Graph For agents without MCP memory server access, use a local JSON file:
import json
import uuid
from datetime import datetime, timezone
from pathlib import Path
GRAPH_PATH = Path(".claude/context/memory/knowledge-graph.json" )
def load_graph () -> dict :
if GRAPH_PATH.exists():
return json.loads(GRAPH_PATH.read_text())
return {"entities" : {}, "relations" : []}
def save_graph (graph: dict ):
GRAPH_PATH.parent.mkdir(parents=True , exist_ok=True )
GRAPH_PATH.write_text(json.dumps(graph, indent=2 ))
def add_entity (graph: dict , name: str , entity_type: str , observations: list [str ] ) -> str :
"""Add or update an entity. Returns entity ID."""
for eid, entity in graph["entities" ].items():
if entity["name" ] == name:
entity["observations" ].extend(observations)
entity["updated_at" ] = datetime.now(timezone.utc).isoformat()
return eid
eid = str (uuid.uuid4())[:8 ]
graph["entities" ][eid] = {
"id" : eid,
"name" : name,
"type" : entity_type,
"observations" : observations,
"created_at" : datetime.now(timezone.utc).isoformat(),
"updated_at" : datetime.now(timezone.utc).isoformat(),
}
return eid
def add_relation (graph: dict , from_name: str , to_name: str , relation_type: str ):
"""Add a directed relation between two entities by name."""
graph["relations" ].append({
"from" : from_name,
"to" : to_name,
"type" : relation_type,
"created_at" : datetime.now(timezone.utc).isoformat(),
})
def search_entities (graph: dict , query: str ) -> list [dict ]:
"""Search entities by name, type, or observation content."""
query_lower = query.lower()
results = []
for entity in graph["entities" ].values():
if (query_lower in entity["name" ].lower()
or query_lower in entity["type" ].lower()
or any (query_lower in obs.lower() for obs in entity["observations" ])):
results.append(entity)
return results
def get_entity_relations (graph: dict , entity_name: str ) -> dict :
"""Get all relations for an entity (outgoing and incoming)."""
outgoing = [r for r in graph["relations" ] if r["from" ] == entity_name]
incoming = [r for r in graph["relations" ] if r["to" ] == entity_name]
return {"outgoing" : outgoing, "incoming" : incoming}
graph = load_graph()
auth_id = add_entity(graph, "authentication-service" , "service" , [
"Handles JWT authentication" ,
"Located at src/auth/" ,
"Uses Redis for token storage" ,
])
add_entity(graph, "Alice Chen" , "person" , [
"Senior engineer, owns the auth service" ,
])
add_relation(graph, "Alice Chen" , "authentication-service" , "owns" )
save_graph(graph)
results = search_entities(graph, "auth" )
relations = get_entity_relations(graph, "Alice Chen" )
Entity Schema interface Entity {
id : string ;
name : string ;
type : EntityType ;
observations : string [];
created_at : string ;
updated_at : string ;
}
interface Relation {
from : string ;
to : string ;
type : string ;
created_at : string ;
}
type EntityType =
| 'person'
| 'service'
| 'codebase'
| 'file'
| 'concept'
| 'organization'
| 'tool'
| 'decision'
| 'issue'
| 'feature' ;
Common Relation Types Relation Direction Example ownsperson → service Alice owns auth-service depends_onservice → service api-gateway depends_on auth-service callsservice → service checkout calls payment-processor managesperson → person CTO manages engineering team implementsservice → concept auth-service implements JWT documented_infeature → file login documented_in README fixescommit → issue fix/abc123 fixes issue-456 blocksissue → issue JIRA-100 blocks JIRA-101
Codebase Knowledge Graph Pattern Build a knowledge graph of a codebase as you explore it:
async function recordCodebaseDiscovery (findings: Discovery[] ) {
const entities = findings.map (f =>name : f.componentName ,
entityType : f.type ,
observations : f.observations ,
}));
await mcp__memory__create_entities ({ entities });
const relations = findings.flatMap (f =>dependencies .map (dep =>from : f.componentName ,
to : dep,
relationType : "depends_on" ,
}))
);
if (relations.length > 0 ) {
await mcp__memory__create_relations ({ relations });
}
}
async function loadCodebaseContext (projectName: string ) {
const graph = await mcp__memory__search_nodes ({ query : projectName });
if (graph.entities .length > 0 ) {
console .log (`Loaded ${graph.entities.length} known entities for ${projectName} ` );
return graph;
}
return null ;
}
Graph Storage Location
MCP server graph: MEMORY_FILE_PATH env var → default: in-memory (not persisted)
Local JSON graph: .claude/context/memory/knowledge-graph.json
Project-specific graphs: .claude/context/memory/kg-<project-name>.json
Anti-Patterns
Never use mutable names as entity IDs — names change, IDs must not
Never store observations without timestamps — temporal context is critical
Never use symmetric relation types like "related_to" — always directional
Never let graphs grow unbounded — prune entities older than 90 days with no recent observations
Never query the full graph for every lookup — use search_nodes with specific queries
Related Skills
memory-search — Semantic search over agent memorycontext-compressor — Compress large knowledge graphs when approaching token limitsmcp-builder — Build custom MCP servers for specialized knowledge graph backends
Memory Protocol (MANDATORY) Before starting any task:
mcp__memory__search_nodes({ query: "<task domain>" })
mcp__memory__add_observations ({
observations : [
{
entityName : '<project-or-domain>' ,
contents : ['<what was learned this session>' ],
},
],
});
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
