I
io.github.cklam12345/wisdomgraph
Neo4j-native DIKW memory for Claude Code — knowledge that compounds across sessions
ActiveNot tested
★ 1GitHub
28DNo CVEsactive
Quick Install
{
"mcpServers": {
"io-github-cklam12345-wisdomgraph": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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Should you use this server?
Neo4j-native DIKW memory for Claude Code — knowledge that compounds across sessions
✓
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
✓
Is it maintained?
Last commit 3 days ago. 1 stars.
i
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
README
wisdomGraph
graphify gives you a snapshot. wisdomGraph gives you memory that compounds.
Type /wisdom in Claude Code or OpenClaw. Feed it your codebases, notes, papers, conversations — every run merges into a living Neo4j graph. The graph doesn't reset. It accumulates. Facts become patterns. Patterns become insights. Insights become wisdom.
/wisdom . # absorb this project into the wisdom graph
/wisdom ask "what patterns repeat across all my projects?"
/wisdom reflect # promote insights → wisdom, close the feedback loop
The step function over graphify
graphify is excellent at what it does: turn a folder into a knowledge graph snapshot. One run, one graph.json, one GRAPH_REPORT.md. Read it. Next session, start over.
wisdomGraph does something fundamentally different.
| graphify | wisdomGraph | |
|---|---|---|
| Storage | graph.json file (per-project) | Neo4j (persistent, all projects) |
| Node types | flat (code entities, concepts) | typed DIKW: Knowledge / Experience / Insight / Wisdom |
| Runs | snapshot, overwrites | MERGE — each run grows the graph |
| Query | read GRAPH_REPORT.md | live Cypher traversal at inference time |
| Memory | resets each session | accumulates across sessions, projects, months |
| Reasoning | community detection (topology) | graph path traversal + DIKW hierarchy |
| Feedback loop | none | Wisdom → Knowledge (neuroplasticity) |
| Database | none required | Neo4j Aura (free) or DozerDB Docker |
The difference is not incremental. It's architectural. graphify compresses a codebase into a readable report. wisdomGraph builds an artificial epistemology — one that remembers, connects, and grows.
The DIKW pyramid, operationalized
Human experts don't store flat facts. They organize experience into layers:
Wisdom ← actionable principles derived from patterns
↑
Insight ← patterns detected across multiple experiences
↑
Experience ← events, decisions, outcomes with context
↑
Knowledge ← verified facts, documented behaviors, extracted structure
Every node in the wisdomGraph carries a tier label. The graph topology is the cognitive architecture. When you ask a question, Cypher traverses upward through the tiers — not keyword-matching flat text, but reasoning across lived experience.
The feedback loop is critical: when a Wisdom node is queried and found useful, it reinforces connected Knowledge nodes. The graph learns what matters.
Install
Requires: Python 3.10+ and one of: Claude Code, OpenClaw
And one of: Neo4j Aura Free (cloud, no install) or DozerDB (local Docker, APOC included)
pip install 'wisdomgraph[mcp]' && wisdom install
Option A — Neo4j Aura (zero infra, recommended for individuals)
- Create a free account at neo4j.com/cloud/aura
- Create a free AuraDB instance — copy the connection URI and password
- Run:
wisdom connect bolt+s://xxxxxxxx.databases.neo4j.io --user neo4j --password <your-password>
Free tier: 200,000 nodes. Enough for years of accumulated wisdom.
Option B — DozerDB local Docker (full control, APOC included)
wisdom docker up # pulls graphstack/dozerdb:5.26.3.0 and starts it
wisdom connect bolt://localhost:7687 --user neo4j -
... [View full README on GitHub](https://github.com/cklam12345/wisdomGraph#readme)
Test This Server
No automated test available for this server. Check the GitHub README for setup instructions.
Help improve this page
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Add informationScore Breakdown
Security
3/30No known vulnerabilities.
○
Known CVEs — No package registry to scan
○
Tool safety — No tools to analyze
○
Tool poisoning — No tools to analyze
○
Injection vectors — No tools to analyze
✓
Tool stability — Tool definitions stable
○
Dependency health — No package to analyze
✗
License — No license specified
○
Authentication — No authentication required
○
Repository — active repo
CVEs checked daily via OSV.dev. Score algorithm is open source.
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Frequently Asked Questions
- Is io.github.cklam12345/wisdomgraph safe to use?
- io.github.cklam12345/wisdomgraph has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
- How do I install io.github.cklam12345/wisdomgraph?
- io.github.cklam12345/wisdomgraph can be installed by cloning its GitHub repository and following the setup instructions in the README.
- What AI clients work with io.github.cklam12345/wisdomgraph?
- io.github.cklam12345/wisdomgraph is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.
- Is io.github.cklam12345/wisdomgraph actively maintained?
- io.github.cklam12345/wisdomgraph is actively maintained — last commit was 3 days ago. It has 1 GitHub stars.
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