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Persistent local knowledge for AI coding agents with structured records, search, and DOT context.
Persistent local knowledge for AI coding agents with structured records, search, and DOT context.
Kilonova MCP is a local knowledge base server with straightforward file I/O and no network calls. Authentication and credential handling are absent—the server relies entirely on environment-based KB root configuration and filesystem permissions. Code quality is solid with proper input sanitization and error handling. Permissions are appropriate for the stated purpose: local file read/write operations only, with no external network access or dangerous operations. Supply chain analysis found 5 known vulnerabilities in dependencies (0 critical, 5 high severity). Package verification found 1 issue.
6 files analyzed · 11 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.
Set these up before or after installing:
Environment variable: KILONOVA_KB_ROOT
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-milnaos-kilonova-mcp": {
"env": {
"KILONOVA_KB_ROOT": "your-kilonova-kb-root-here"
},
"args": [
"kilonova-mcp"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Persistent knowledge base tools for Claude Code.
Claude remembers your projects, decisions, and patterns across sessions — stored on your machine, no cloud required.
Built by AIM Studio · Free · MIT License
Every Claude Code session starts cold. You re-explain your project structure, re-describe decisions you made last week, re-state what's in flight. Context burns fast.
Kilonova gives Claude a persistent knowledge base on your local machine. At session start, Claude loads your DOT (Document of Truth) — a compressed, structured reference doc with your project state, active tasks, decisions, and patterns. During the session, Claude writes new discoveries back to the KB. Next session, it's all there.
Session 1: Claude learns your architecture → kb_write saves the decision
Session 2: dot_load → Claude already knows. No recap needed.
pip install kilonova-mcp
Or from source:
git clone https://github.com/MilnaOS/kilonova-mcp
cd kilonova-mcp
pip install -e .
Add to ~/.claude/settings.json:
{
"mcpServers": {
"kilonova": {
"command": "python",
"args": ["-m", "kilonova_mcp"],
"env": {
"KILONOVA_KB_ROOT": "/path/to/your/kb"
}
}
}
}
Copy CLAUDE.md.template to ~/.claude/CLAUDE.md (or append to your existing one).
1. Create your first KB topic:
In Claude Code, just start writing:
mcp__kilonova__kb_write(
topic="claude_context",
entity_type="project_state",
name="my-project",
data={
"name": "my-project",
"status": "active",
"location": "/path/to/project",
"summary": "What this project is",
"next_action": "What to do next"
}
)
2. Load it next session:
mcp__kilonova__dot_load(topic="claude_context")
3. Search it:
mcp__kilonova__kb_search(topic="claude_context", query="authentication decision")
A DOT is a plain text file with three sections:
---SYMBOLS---
[PR]=My Project (/path/to/project)
[DB]=Database (PostgreSQL on localhost:5432)
---TOC---
1:Projects|1.1:My_Project
2:Active_Tasks
3:Decisions
---CARDS---
## [1] PROJECTS
### [1.1] My Project
STATUS: active
NEXT: wire up the auth flow
Symbols compress repeated references. The TOC lets Claude fetch only the section it needs. Cards hold the actual content.
See example_dot/ for a starter template.
claude_contextCopy schemas/claude_context/ into your KB directory under <kb_root>/claude_context/schemas/:
| Entity type | Use for |
|---|---|
project_state | Current status, location, next action per project |
decision | Architectural choices with rationale |
pattern | Code conventions, gotchas, file locations |
active_task | In-flight work across sessions |
session_note | End-of-session summaries |
| Tool | Description |
|---|---|
dot_load(topic) | Load full DOT document into context |
kb_search(topic, query) | Search records by natural language query |
kb_write(topic, entity_type, name, data) | Write/update a record (merges with existing) |
kb_load(topic, entity_type, name) | Load one specific record |
kb_topics() | List all KB topics and record counts |
kb_schema(topic, entity_type?) | Show field schema for an entity type |
corpus_status(topic) | Show KB size and record counts |
kb_backup(dry?) | Mirror KB to OneDrive |
Kilonova doesn't care what you store. Define your own schemas:
// kb/my_topic/schemas/component.json
{
"name": {"type": "string", "description": "Component name"},
"file": {"type": "string", "description": "Path to file"},
"purpose": {"type": "string", "description": "What it does"},
"dependencies": {"type": "array", "description": "What it depends on"}
}
Then write records to it and search them naturally.
Kilonova MCP is the free, standalone KB layer extracted from Milna OS — a full BYOK multi-model AI terminal. If you want the whole thing (parallel model legs, distillation engine, web ingestion, local+cloud hybrid inference), check out Milna OS.
MIT License · © AIM Studio
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