Building a Second Brain for AI: Obsidian Integration Guide

You’ve spent years building your second brain in Obsidian. Thousands of notes. Curated knowledge. Insights, decisions, and connections.

Your AI assistant can’t see any of it.

Every time you ask ChatGPT or Claude a question, you have to manually paste context from your notes. Or worse — you don’t, and the AI hallucinates answers because it’s missing your knowledge.

This is backwards.

Your second brain should be your AI’s primary knowledge source. Not a separate system you copy-paste from.

In this guide, I’ll show you how to:

• Connect Obsidian to AI assistants (multiple methods)
• Let AI search and retrieve from your vault
• Keep your knowledge synced and searchable
• Maintain privacy (local-first options)

Let’s fix this.

Why Obsidian + AI is a Perfect Match

Obsidian is built on plain text Markdown files. This is ideal for AI integration because:

1. No vendor lock-in — AI can read Markdown natively
2. Git-friendly — version control your knowledge
3. Link-aware — Obsidian’s [[wikilinks]] create a knowledge graph
4. Local-first — your data stays on your machine (privacy)
5. API-accessible — Obsidian has a Local REST API plugin

Your Obsidian vault is already structured for AI consumption.

Three Methods to Connect Obsidian to AI

Method 1: Manual Context Injection (Basic)

How it works:
Copy-paste relevant notes into your AI conversation.

Pros:

• Simple, no setup
• Works with any AI (ChatGPT, Claude, etc.)
• Full control over what AI sees

Cons:

• Manual, tedious
• No search (you have to know which note to paste)
• Doesn’t scale beyond a few notes

Use case: Quick one-off queries where you know exactly which note is relevant.

Method 2: Obsidian REST API + AI Agent (Recommended)

How it works:
Install the Obsidian Local REST API plugin. Your AI agent queries your vault programmatically.

Setup (10 minutes):

1. Install Plugin:

   • In Obsidian: Settings → Community Plugins → Browse
   • Search “Local REST API” → Install + Enable

2. Configure API:

   • Settings → Local REST API
   • Note the API port (default: 27123)
   • Optional: Set API key for security

3. Test API:

   curl http://localhost:27123/vault/
   

   You should see a list of your notes.

4. Connect to AI Agent (e.g., OpenClaw):

import requests

OBSIDIAN_API = "http://localhost:27123"
API_KEY = "your-api-key-here"

def search_obsidian(query):
    response = requests.post(
        f"{OBSIDIAN_API}/search/simple",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"query": query}
    )
    return response.json()

def get_note(filepath):
    response = requests.get(
        f"{OBSIDIAN_API}/vault/{filepath}",
        headers={"Authorization": f"Bearer {API_KEY}"}
    )
    return response.text

# Example: AI searches your vault
results = search_obsidian("insurance policies")
if results:
    note_content = get_note(results[0]["path"])
    # Feed note_content to AI as context

Pros:

• Programmatic access (no manual copy-paste)
• Works with any AI agent framework
• Local (no cloud sync required)

Cons:

• Requires running Obsidian with API enabled
• Basic search (keyword matching, not semantic)

Use case: Autonomous AI agents that need real-time access to your knowledge base.

Method 3: Vector DB + Semantic Search (Advanced)

How it works:
Index your Obsidian vault in a vector database (Qdrant, Pinecone). AI performs semantic search to find relevant notes.

Why better than keyword search?

• Finds conceptually similar notes, not just keyword matches
• “How do I invest in stocks?” → finds notes about ETFs, mutual funds, SIPs (related concepts)

Setup (30 minutes):

1. Install Qdrant (local vector DB):

   docker run -p 6333:6333 qdrant/qdrant
   

2. Index Your Vault:

import os
import openai
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams

client = QdrantClient("localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="obsidian_vault",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
)

# Index all notes
vault_path = "/Users/you/ObsidianVault"
for root, dirs, files in os.walk(vault_path):
    for file in files:
        if file.endswith(".md"):
            filepath = os.path.join(root, file)
            with open(filepath, 'r') as f:
                content = f.read()
            
            # Generate embedding
            embedding = openai.Embedding.create(
                model="text-embedding-ada-002",
                input=content
            )["data"][0]["embedding"]
            
            # Store in vector DB
            client.upsert(
                collection_name="obsidian_vault",
                points=[{
                    "id": filepath,
                    "vector": embedding,
                    "payload": {"filepath": filepath, "content": content[:500]}
                }]
            )

3. Search from AI:

def semantic_search(query, limit=5):
    query_embedding = openai.Embedding.create(
        model="text-embedding-ada-002",
        input=query
    )["data"][0]["embedding"]
    
    results = client.search(
        collection_name="obsidian_vault",
        query_vector=query_embedding,
        limit=limit
    )
    
    return [r.payload for r in results]

# AI retrieves relevant notes
notes = semantic_search("What are my investment preferences?")
for note in notes:
    print(note["filepath"], note["content"][:200])

Pros:

• Semantic search (finds conceptually related notes)
• Doesn’t require Obsidian to be running
• Works offline (local vector DB)

Cons:

• Requires re-indexing when notes change
• Setup complexity (vector DB, embeddings)

Use case: Power users with large vaults (1000+ notes) who need advanced search.

My Setup (Real Example)

I run OpenClaw agents daily. Here’s how I integrated Obsidian:

Vault structure:

ObsidianVault/
├─ Projects/
│   ├─ MyDeepBrain.md
│   ├─ InvestEdges.md
├─ Decisions/
│   ├─ 2026-01-15-choosing-hugo-over-jekyll.md
│   ├─ 2026-02-10-insurance-renewal-strategy.md
├─ Preferences/
│   ├─ coding-style.md
│   ├─ writing-tone.md
└─ Archive/
    └─ old-projects/

Integration:

• Method: Obsidian REST API + Qdrant
• Workflow:
  1. I write notes in Obsidian (normal PKM workflow)
  2. Nightly script indexes new/changed notes into Qdrant
  3. OpenClaw agents search Qdrant when they need context
  4. Top 3 relevant notes get loaded into AI context

Example query:

Me: “What decisions did I make about database choice for MyDeepBrain?”

OpenClaw:

1. Searches Qdrant: "MyDeepBrain database"
2. Finds: Decisions/2026-01-20-mydeepbrain-tech-stack.md
3. Loads note into context
4. Responds: “You chose SQLite for self-hosted tier and Postgres for cloud tier, based on your Jan 20 decision log.”

Result: AI has full access to my knowledge base without me manually pasting notes.

Advanced: Bi-Directional Sync (AI Writes Back to Obsidian)

Right now, we’ve only done read (AI reads your notes). What about write (AI updates your notes)?

Use Case: AI as Note-Taking Assistant

Scenario:
You tell your AI: “Remember this decision: I’m switching to Obsidian for PKM.”

Instead of storing it in MEMORY.md, the AI creates a new Obsidian note:

def create_obsidian_note(title, content):
    response = requests.post(
        f"{OBSIDIAN_API}/vault/",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "path": f"Decisions/{datetime.date.today()}-{title}.md",
            "content": content
        }
    )
    return response.json()

# AI creates note
create_obsidian_note(
    title="switching-to-obsidian",
    content="""
# Switching to Obsidian for PKM

**Date:** 2026-02-18  
**Decision:** Migrating from Notion to Obsidian

**Rationale:**
- Markdown-first (portable)
- Local files (privacy)
- Better graph view
- AI-friendly (plain text)
"""
)

Now when you open Obsidian, the note is there — synced from your AI interaction.

This is the endgame: Your AI and your second brain are the same system.

Privacy Considerations (Keep Your Knowledge Local)

If you’re using cloud AI (ChatGPT, Claude), your notes get sent to OpenAI/Anthropic servers.

Options for privacy:

Option 1: Self-Hosted AI (llama.cpp, Ollama)

Run AI models locally. Your notes never leave your machine.

Setup:

# Install Ollama
curl https://ollama.ai/install.sh | sh

# Run local Llama model
ollama run llama2:70b

# Query with Obsidian context
curl http://localhost:11434/api/generate -d '{
  "model": "llama2:70b",
  "prompt": "Based on this note: [paste Obsidian content], answer..."
}'

Pros: Complete privacy
Cons: Weaker models than GPT-4/Claude

Option 2: Obsidian Sync + End-to-End Encryption

Use Obsidian Sync with E2E encryption. Notes sync across devices but are never readable by Obsidian Inc.

Option 3: MyDeepBrain (Self-Hosted Memory Layer)

We’re building a self-hosted option where:

• Your notes stay on your server
• AI queries your memory API (local or VPN-accessible)
• No cloud sync required

Join the waitlist to get early access.

Common Mistakes

Mistake 1: Indexing Your Entire Vault

If you index 10,000 notes, semantic search returns too many irrelevant results.

Fix: Index only high-value notes (Decisions, Projects, Preferences). Exclude daily logs, drafts, random thoughts.

Mistake 2: No Re-Indexing

You update a note in Obsidian, but the vector DB still has the old version.

Fix: Run nightly re-indexing for changed files:

# Check file modification time
if os.path.getmtime(filepath) > last_index_time:
    re_index(filepath)

Mistake 3: Overloading AI Context

You find 20 relevant notes and paste all 50,000 tokens into context.

Fix: Retrieve top 3-5 notes max. Summarize if needed.

Tools & Plugins

Obsidian Plugins:

• Local REST API — Programmatic vault access
• Dataview — Query notes with SQL-like syntax (useful for AI to extract structured data)
• Smart Connections — Built-in semantic search (alternative to external vector DB)

AI Frameworks:

• LangChain — Has Obsidian loader (ObsidianLoader)
• LlamaIndex — Indexes Obsidian vaults for RAG

Vector DBs:

• Qdrant — Self-hosted, open source
• Chroma — Lightweight, embeddable
• Pinecone — Cloud-hosted (if you don’t mind cloud sync)

Key Takeaways

1. Obsidian is AI-ready (Markdown, local, API-accessible)
2. Three methods: Manual, REST API, Vector DB (choose based on scale)
3. Semantic search beats keyword search for large vaults
4. Bi-directional sync = AI reads AND writes to your vault
5. Privacy matters — use local AI or E2E encryption
6. Re-index nightly — keep vector DB fresh

Your second brain and your AI should be one system, not two.

Want seamless Obsidian + AI integration? MyDeepBrain syncs with Obsidian vaults automatically. Join the waitlist.