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Graph-native memory for AI agents: a knowledge graph built from conversation, via MCP.
Graph-native memory for AI agents: a knowledge graph built from conversation, via MCP.
Kernal is a well-structured knowledge graph MCP server with proper authentication, parameterized SQL queries, and appropriate permission scoping. The codebase demonstrates security best practices for local-first data handling, though some areas like error handling and logging could be more rigorous. Permissions align well with the stated purpose of entity extraction and knowledge graph management. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
5 files analyzed · 9 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-pintomatic-kernal-mcp": {
"args": [
"-y",
"kernal-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Open-source knowledge graph for professionals. Auto-extracts entities and relationships from natural conversation via MCP.
Talk to Claude naturally about your meetings, calls, and interactions. Kernal stores people, organizations, topics, and relationships — building a knowledge graph you own.
Everything you need to run Kernal locally on your own machine:
init, serve, status, exportThis is a fully functional knowledge graph you can run yourself, for free, forever.
For teams and professionals who want more, Andes offers:
The open-source core is the engine. Andes wraps it with infrastructure, UX, and support.
npx kernal-mcp init
This creates a SQLite database at ~/.kernal/kernal.db and prints the config to add to Claude Desktop.
Add to your claude_desktop_config.json:
{
"mcpServers": {
"kernal": {
"command": "npx",
"args": ["-y", "kernal-mcp", "serve"]
}
}
}
Restart Claude Desktop. Then talk naturally:
"I had lunch with Jonas Lindberg from Nordvik Energy today. He's their VP of Digital. We discussed their cloud migration — targeting Q3."
Claude extracts Jonas, Nordvik Energy, the cloud migration topic, and stores them via Kernal's write tools. Then ask:
Kernal uses an LLM-driven extraction pattern:
kernal_remember with the raw textkernal_add_person, kernal_add_org, kernal_add_activity, etc.)The MCP server is a clean data store. The LLM is the brain.
| Tool | Description |
|---|---|
kernal_remember | Store raw text, get extraction instructions and existing entity list for dedup |
kernal_add_person | Create or update a person (auto-deduplicates by fuzzy name match) |
kernal_add_org | Create or update an organization (auto-deduplicates) |
kernal_add_activity | Log an interaction with participant and org linking |
kernal_add_action | Create a follow-up or task, optionally assigned to a person |
kernal_link | Create a relationship between any two entities (person, org, or topic) |
| Tool | Description |
|---|---|
kernal_recall | Search the knowledge base by keyword across all entity types |
kernal_people | List/search contacts — filter by name, org, role |
kernal_orgs | List/search organizations — filter by type, industry |
kernal_activities | Recent interactions — filter by type, person, date |
kernal_actions | Open follow-ups — filter by status, owner, due date |
kernal_context | Full briefing on a person or org — timeline, network, topics |
| Tool | Description |
|---|---|
kernal_correct | Update fields, delete entities, merge duplicates, or reset the database |
From a single paragraph like "Had coffee with Sofia Andersen from Arctura Tech. She's their VP of Sales. We discussed their expansion into APAC. I need to send her the partner proposal by Friday.", Claude will call:
kernal_add_person — Sofia Andersen, VP of Sales, at Arctura Techkernal_add_org — Arctura Techkernal_add_activity — Coffee meeting, today, participants: [Sofia Andersen], orgs: [Arctura Tech]kernal_add_action — "Send partner proposal to Sofia", due Friday, owner: Sofia Andersenkernal_link — Sofia → works_at → Arctura TechEach call is a deliberate, structured decision by the LLM — not a regex guess.
kernal init Create database + print Claude Desktop config
kernal serve Start MCP server (stdio transport)
kernal status Show database stats
kernal export Export database to a file
kernal help Show help
The repo includes a React dashboard (dashboard/) with four views:
Natural language command bar routes queries to views ("Show me my network" → graph).
# Start the cloud API server
KERNAL_API_KEY=your-key KERNAL_DB_PATH=~/.kernal/kernal.db npm run cloud
# Start the dashboard (separate terminal)
cd dashboard && npm run dev
Kernal stores 6 entity types connected by a generic relationship graph:
People ←→ Organizations
↕ ↕
Activities ←→ Topics
↕
Actions ←→ Notes
All entities can link to any other entity via the relationships table, enabling queries like:
crypto.timingSafeEqual)git clone https://github.com/pintomatic/kernal.git
cd kernal
npm install
npm run build
npm test # 50 tests
KERNAL_API_KEY=your-secret KERNAL_DB_PATH=~/.kernal/kernal.db npm run cloud
A Dockerfile is included. Environment variables:
| Variable | Default | Description |
|---|---|---|
KERNAL_DB_PATH | ~/.kernal/kernal.db | SQLite database path |
KERNAL_API_KEY | (required for cloud) | API key for authentication |
KERNAL_CORS_ORIGIN | http://localhost:5174 | Allowed CORS origins (comma-separated) |
KERNAL_RATE_LIMIT | 120 | Max requests per minute per IP |
PORT | 3001 | Server port |
npx tsx scripts/seed-demo.ts
Creates 12 contacts, 18 orgs, 19 activities with 123 relationships — a realistic professional services scenario.
MIT
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