hermes-llmwiki
"Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase." — Andrej Karpathy
A Karpathy-native, zero-infrastructure memory provider for Hermes Agent. Turns your local Markdown vault into a compounding knowledge system — no Docker, no cloud, no vector DB.
Why This Exists
Every serious Hermes user hits the same wall: the agent forgets everything between sessions. Existing memory providers either:
- Lock you into a SaaS (Honcho, Mem0, Supermemory)
- Require Docker + Qdrant + Redis (Memory OS)
- Are read-only (hermes-homie-memory)
- Don't follow a structured curation model (Open Second Brain)
hermes-llmwiki is different:
| hermes-llmwiki | Others | |
|---|---|---|
| Karpathy 3-layer architecture | ✅ Native | ❌ |
| AI-First note format | ✅ Optimized for LLM retrieval | ❌ Human-first |
| Zero dependencies | ✅ Python + ripgrep + optional SQLite | ❌ Docker/SaaS |
| Schema configurable | ✅ SCHEMA.md drives your vault |
❌ Fixed structure |
| Write + Read | ✅ Bidirectional | ⚠️ Some read-only |
Karpathy 3-Layer Architecture
┌─────────────────────────────────────────┐
│ Layer 3: Compiled Wiki (Query) │
│ entities/ concepts/ comparisons/ │
│ projects/ queries/ │
│ ↑ LLM curation (nightly) │
├─────────────────────────────────────────┤
│ Layer 2: Chronicle (Daily Notes) │
│ chronicle/daily/YYYY-MM-DD.md │
│ ↑ auto-capture from sync_turn │
├─────────────────────────────────────────┤
│ Layer 1: Raw (Session Exports) │
│ raw/session-{id}.md │
│ ↑ on_session_end / on_pre_compress│
└─────────────────────────────────────────┘
- Layer 1 (Raw): Session dumps, temporary, disposable
- Layer 2 (Chronicle): Daily timeline, append-only, human-readable
- Layer 3 (Compiled): Atomic notes with wikilinks, cross-references, frontmatter — the "source of truth"
Install
# Option 1: pip
pip install hermes-llmwiki
# Option 2: clone to Hermes plugins directory
git clone https://github.com/chancelu/hermes-llmwiki.git ~/.hermes/plugins/hermes-llmwiki
Then activate in ~/.hermes/config.yaml:
memory:
provider: llmwiki
llmwiki:
vault_path: ~/Documents/selfwiki # or your Obsidian vault
Run setup wizard:
hermes memory setup
CLI Commands
hermes llmwiki curate # Manually trigger: daily notes → compiled wiki
hermes llmwiki stats # Vault statistics
hermes llmwiki search "prompt injection" # Search compiled layer
hermes llmwiki health # Dead links, orphans, stale notes
hermes llmwiki export --format json # Export wiki to JSON
AI-First Note Format
Every compiled note follows Karpathy's AI-First Vault Principle:
---
type: concept
name: "Prompt Injection"
date: 2026-07-29
tags: [security, llm]
ai-first: true
confidence: high
sources: ["https://..."]
---
## For future Claude
Prompt injection is an attack where malicious input overrides the LLM's
system instructions. This page is the canonical reference.
## Key claims
- Direct injection: user input contains hidden instructions
- Indirect injection: data from external source carries payload
- Source: OWASP LLM Top 10, 2026 edition
## Related
[[LLM Security]] · [[Jailbreak]] · [[System Prompt Hardening]]
Configuration
Full config reference in CONFIG.md.
License
MIT
Release files for hermes-llmwiki 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| hermes_llmwiki-0.1.1.tar.gz | 24.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hermes_llmwiki-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 47.6 kB
Release files / hermes_llmwiki-0.1.1.tar.gz
| Download URL | hermes_llmwiki-0.1.1.tar.gz |
|---|---|
| Size | 24.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.10.11
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Release files / hermes_llmwiki-0.1.1-py3-none-any.whl
| Download URL | hermes_llmwiki-0.1.1-py3-none-any.whl |
|---|---|
| Size | 22.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.10.11
|