Knowledge Graph for Agents

Add a knowledge graph layer to an AI agent for relationship reasoning and multi-hop recall. Use when agents need to answer "who works with whom", "what's connected to X", or any relationship-based queries that flat search can't handle. Triggers on "knowledge graph", "Neo4j for agents", "entity extraction", "relationship search", "graph memory", "connected entities".

You are an expert in knowledge graphs for AI agent systems. Help the user add a graph layer that captures entities and relationships from their data, enabling multi-hop reasoning that vector and keyword search can't do.

Why a Knowledge Graph?

Vector search finds similar documents. BM25 finds matching keywords. Neither answers:

• "Who works with Alice at Acme Corp?"
• "What services are running on the production server?"
• "Which projects depend on PostgreSQL?"

These require relationship traversal — following connections between entities. That's what a knowledge graph does.

Architecture

text

Ingest → Entity Extraction → Graph Storage → Query
                                    ↓
                            Spreading Activation
                            (2-hop traversal)

Entity Extraction (at ingest time)

Extract entities from every chunk of text you index:

python

def extract_entities(text):
    """Simple heuristic entity extraction — no LLM needed."""
    entities = []
    # Title-case words (proper nouns)
    for word in text.split():
        if word[0].isupper() and len(word) > 2:
            entities.append({"name": word, "label": "Entity"})
    # Email addresses → Person
    for email in re.findall(r'[\w.+-]+@[\w.-]+\.\w+', text):
        entities.append({"name": email.split("@")[0].title(), "label": "Person"})
    return entities

For production, use spaCy NER or an LLM-based extractor for higher quality.

Graph Storage

SQLite graph (simple, zero dependencies):

sql

CREATE TABLE nodes (
    id INTEGER PRIMARY KEY,
    name TEXT UNIQUE,
    label TEXT,
    properties_json TEXT DEFAULT '{}'
);

CREATE TABLE edges (
    source_id INTEGER REFERENCES nodes(id),
    target_id INTEGER REFERENCES nodes(id),
    rel_type TEXT DEFAULT 'RELATED_TO',
    weight REAL DEFAULT 1.0
);

Neo4j (production, scales better):

cypher

CREATE (n:Entity {name: "Alice", label: "Person"})
CREATE (m:Entity {name: "Acme Corp", label: "Organisation"})
CREATE (n)-[:WORKS_AT]->(m)

Co-occurrence Edges

When two named entities appear in the same chunk, create a CO_OCCURS edge:

python

NAMED_LABELS = {"Person", "Place", "Organisation", "Event", "Product"}

for i, e1 in enumerate(chunk_entities):
    for e2 in chunk_entities[i+1:]:
        if e1["label"] in NAMED_LABELS and e2["label"] in NAMED_LABELS:
            graph.add_edge(e1["name"], e2["name"], "CO_OCCURS")

This is what gives the graph traversal value — connecting entities that appear together in context.

Querying: Spreading Activation (2-hop)

Don't just match entities — traverse their connections:

sql

-- Find entities connected to the query entity within 2 hops
WITH start_nodes AS (
    SELECT id, name FROM nodes WHERE name LIKE '%Alice%'
),
hop1 AS (
    SELECT CASE WHEN e.source_id = s.id THEN e.target_id ELSE e.source_id END as mid_id
    FROM start_nodes s JOIN edges e ON (e.source_id = s.id OR e.target_id = s.id)
    WHERE e.weight >= 0.5
),
hop2 AS (
    SELECT CASE WHEN e.source_id = h.mid_id THEN e.target_id ELSE e.source_id END as end_id,
           h.mid_id
    FROM hop1 h JOIN edges e ON (e.source_id = h.mid_id OR e.target_id = h.mid_id)
)
SELECT DISTINCT n.name, n.label FROM hop2 JOIN nodes n ON n.id = hop2.end_id;

Hebbian Strengthening

Edges between co-accessed entities get stronger over time:

python

def hebbian_strengthen(accessed_entities):
    """Strengthen edges between entities accessed in the same query."""
    for i, e1 in enumerate(accessed_entities):
        for e2 in accessed_entities[i+1:]:
            graph.update_edge_weight(e1, e2, delta=0.1)

Integration with Hybrid Search

The graph layer works alongside BM25 and vector search:

1. Extract entities from query — "What services does Alice use?" → entities: [Alice, services]
2. Query graph — find Alice node, traverse USES relationships
3. Boost matching chunks — chunks mentioning graph-discovered entities get a score boost
4. Fuse with other layers — graph results merge into the unified ranking

Common Patterns

Resolving Ambiguity

python

# Merge duplicate entities
graph.merge("Alice", "Alice Smith")  # Same person, different references

Temporal Edges

python

# Add timestamps to relationships
graph.add_edge("Alice", "Project Alpha", "WORKS_ON", 
               properties={"since": "2024-01-15"})

Entity Types for Agents

Label	Examples	Use Case
Person	team members, contacts	Who questions
Organisation	companies, teams	Affiliation queries
Project	initiatives, repos	What's connected
System	services, tools	Infrastructure queries
Place	offices, cities	Location queries

Pitfalls

1. Edge explosion — don't create edges between ALL entities, only named ones (Person, Place, Org). Topic words create too many low-value edges.
2. No graph layer — you'll hit a ceiling where flat retrieval can't answer relationship questions.
3. Over-reliance on LLM extraction — heuristic extraction (capitalisation + patterns) is 80% as good at 0% of the cost.
4. Forgetting to prune — graphs grow. Schedule periodic cleanup of orphan nodes and weak edges.

Getting Started

1. Pick a backend: SQLite (simple) or Neo4j (production)
2. Add entity extraction to your ingest pipeline
3. Create co-occurrence edges between named entities per chunk
4. Add 2-hop spreading activation to your search
5. Fuse graph results with your existing BM25/vector search
6. Set up a simple evaluation (test queries → expected results)