What breaks without openclaw knowledge graph use cases
No blueprint for link intelligence. Untested graph architectures. Hours reinventing patterns.
→
Link-aware automation blueprints × 175-star vetted collection ÷ 2-hour implementation ÷ no architecture guessing = knowledge graph workflows.
Security check — openclaw knowledge graph use cases
Privacy score: 7/10 — accesses connected platform APIs only.
Lock it: review OAuth scopes before install, confirm OpenClaw ≥1.2; Neo4j ≥5 recommended; Node.js ≥18 compatibility.
Quick start — openclaw knowledge graph use cases in Varies per use case — typically 2–4 hours
Setup time: Varies per use case — typically 2–4 hours
!
You need:
- OpenClaw core
- graph database (Neo4j recommended)
- embedding API
Install the package:
# Browse use cases — per-case dependencies listed in each doc
1
Browse the repository README
2
Select a use case aligned with your knowledge management goal
3
Follow the architecture diagram
4
Install required skills per the use case
5
Configure graph database connection
6
Run the ingestion and test queries
Compatibility & status
Works with: OpenClaw ≥1.2; Neo4j ≥5 recommended; Node.js ≥18
advanced
Last updated: Sep 2025
★ 175 on GitHub
MIT
Official docs →
View on GitHub →
FAQ — openclaw knowledge graph use cases
What is 'link intelligence' in this context?
Structured extraction of named entities and their relationships from text, stored as graph nodes and edges.
Does it require Neo4j specifically?
Neo4j is recommended. Memgraph and ArangoDB are noted as alternatives.
Is Moltbook related to the old Moltbot project?
Only in spirit — EvoLinkAI is a separate organisation with its own focus.