PKM

Notion + AI: Your Personal Knowledge Base as AI Memory

February 18, 2026 · 7 min read

Notion is where you live. Your tasks, projects, notes, databases, wikis — everything.

But when you talk to ChatGPT or Claude, they can’t see any of it. You’re stuck copy-pasting project details, task lists, and decisions into every conversation.

What if your AI could just… read your Notion workspace?

  • Ask “What’s on my plate this week?” → AI reads your task database
  • Ask “What did I decide about the tech stack?” → AI finds the decision log
  • Ask “Summarize my meeting notes from last week” → AI scans your meeting pages

This is possible. And easier than you think.

In this guide, I’ll show you:

  • How to connect Notion to AI (API setup)
  • Let AI search and read your databases
  • Auto-sync Notion content to AI memory
  • Real-world examples from my setup

Let’s make Notion your AI’s knowledge source.

Why Notion + AI Works

Notion has a robust API. Unlike some PKM tools, Notion was built for integrations.

Key advantages:

  1. Structured data — Databases, properties, relations (better than plain Markdown)
  2. API-first — Official REST API with good documentation
  3. Rich formatting — Supports tables, embeds, toggles (more than plain text)
  4. Collaboration — Team workspaces mean shared AI context
  5. No local install — Cloud-based (works from anywhere)

Notion is a queryable knowledge graph, not just a note-taking app.

Setting Up Notion API (10 Minutes)

Step 1: Create a Notion Integration

  1. Go to https://www.notion.so/my-integrations
  2. Click "+ New integration"
  3. Name: “MyDeepBrain AI”
  4. Select workspace
  5. Copy the Integration Token (starts with secret_...)

Step 2: Grant Access to Pages

Notion integrations don’t auto-access all pages (security). You must explicitly share pages/databases.

  1. Open a Notion page you want AI to access
  2. Click "…" (top right) → Add connections
  3. Select your integration (“MyDeepBrain AI”)
  4. Repeat for each database/page you want AI to read

Step 3: Test API Access

curl -X POST https://api.notion.com/v1/search \
  -H "Authorization: Bearer YOUR_INTEGRATION_TOKEN" \
  -H "Notion-Version: 2022-06-28" \
  -H "Content-Type: application/json" \
  --data '{}'

You should see a JSON response with accessible pages.

Querying Notion from AI (Python Examples)

Search All Pages

import requests

NOTION_TOKEN = "secret_your_token_here"
NOTION_VERSION = "2022-06-28"

headers = {
    "Authorization": f"Bearer {NOTION_TOKEN}",
    "Notion-Version": NOTION_VERSION,
    "Content-Type": "application/json"
}

def search_notion(query):
    response = requests.post(
        "https://api.notion.com/v1/search",
        headers=headers,
        json={"query": query}
    )
    return response.json()

# Example: Find all pages mentioning "MyDeepBrain"
results = search_notion("MyDeepBrain")
for page in results["results"]:
    print(page["url"], page.get("properties", {}).get("title"))

Read a Specific Page

def get_page_content(page_id):
    # Get page properties
    page = requests.get(
        f"https://api.notion.com/v1/pages/{page_id}",
        headers=headers
    ).json()
    
    # Get page blocks (actual content)
    blocks = requests.get(
        f"https://api.notion.com/v1/blocks/{page_id}/children",
        headers=headers
    ).json()
    
    # Extract text from blocks
    content = []
    for block in blocks["results"]:
        block_type = block["type"]
        if block_type in ["paragraph", "heading_1", "heading_2", "heading_3"]:
            text = "".join([t["plain_text"] for t in block[block_type]["rich_text"]])
            content.append(text)
    
    return "\n".join(content)

# Example: Read a project page
content = get_page_content("PAGE_ID_HERE")
print(content)

Query a Database

def query_database(database_id, filter=None):
    response = requests.post(
        f"https://api.notion.com/v1/databases/{database_id}/query",
        headers=headers,
        json={"filter": filter} if filter else {}
    )
    return response.json()

# Example: Get all tasks assigned to "Pappu"
tasks_db_id = "your-database-id"
tasks = query_database(
    tasks_db_id,
    filter={
        "property": "Assigned to",
        "select": {"equals": "Pappu"}
    }
)

for task in tasks["results"]:
    title = task["properties"]["Name"]["title"][0]["plain_text"]
    status = task["properties"]["Status"]["status"]["name"]
    print(f"{title} — {status}")

Real-World Use Cases

Use Case 1: Task Management Assistant

Setup: Connect your Notion Tasks database to AI.

Queries:

  • “What tasks are due this week?”
  • “What’s my highest priority item?”
  • “Mark ‘Write blog post’ as done”

Implementation:

def get_tasks_this_week():
    tasks = query_database(
        tasks_db_id,
        filter={
            "and": [
                {"property": "Due Date", "date": {"this_week": {}}},
                {"property": "Status", "status": {"does_not_equal": "Done"}}
            ]
        }
    )
    return [(t["properties"]["Name"]["title"][0]["plain_text"], 
             t["properties"]["Due Date"]["date"]["start"]) 
            for t in tasks["results"]]

# AI can now answer: "What's on my plate this week?"

Use Case 2: Decision Log Retrieval

Setup: Create a “Decisions” database in Notion with properties:

  • Title (text)
  • Date (date)
  • Category (select: Tech, Product, Business)
  • Context (rich text)

Queries:

  • “What did I decide about database choice?”
  • “Show me all tech decisions from Q1”

Implementation:

def search_decisions(keyword):
    decisions = query_database(decisions_db_id)
    results = []
    for decision in decisions["results"]:
        title = decision["properties"]["Title"]["title"][0]["plain_text"]
        if keyword.lower() in title.lower():
            date = decision["properties"]["Date"]["date"]["start"]
            results.append({"title": title, "date": date, "page_id": decision["id"]})
    return results

# AI retrieves context before answering questions

Setup: Store meeting notes in Notion with template:

  • Title: “Meeting: [Person] — [Date]”
  • Attendees (multi-select)
  • Action Items (database relation)
  • Notes (rich text)

Queries:

  • “Summarize my meeting with Sarah last week”
  • “What action items came out of the Feb 15 design review?”

Implementation:

def get_meeting_notes(query):
    meetings = search_notion(query)
    for meeting in meetings["results"]:
        page_id = meeting["id"]
        content = get_page_content(page_id)
        return content  # Feed to AI for summarization

My Setup (Real Example from OpenClaw)

I use Notion for:

  • Task tracking (personal and work)
  • Project wiki (documentation, decisions)
  • AMC registry (annual maintenance contracts, renewal dates)

Integration:

OpenClaw agents can:

  1. Read my task database → “What’s my highest priority task?”
  2. Search project wiki → “What’s the deployment process for MyDeepBrain?”
  3. Check AMC renewals → “Which contracts expire this month?”

Code snippet:

# In OpenClaw agent's tool
def notion_query(query):
    # Search Notion workspace
    results = search_notion(query)
    
    # Get top 3 pages
    top_pages = results["results"][:3]
    
    # Extract content
    context = []
    for page in top_pages:
        content = get_page_content(page["id"])
        context.append(content)
    
    # Return to AI as context
    return "\n\n---\n\n".join(context)

Now when I ask OpenClaw, “What tasks need review?”, it:

  1. Queries my Notion Tasks DB
  2. Filters for Status = "Review"
  3. Returns the list

No manual copy-paste.

Advanced: Auto-Sync Notion to AI Memory

Instead of querying on-demand, you can index Notion content into a vector DB for semantic search.

Nightly Sync Script

def sync_notion_to_memory():
    # Get all recently updated pages
    pages = search_notion("")  # Empty query = all pages
    
    for page in pages["results"]:
        page_id = page["id"]
        content = get_page_content(page_id)
        
        # Generate embedding
        embedding = openai.Embedding.create(
            model="text-embedding-ada-002",
            input=content
        )["data"][0]["embedding"]
        
        # Store in vector DB
        qdrant_client.upsert(
            collection_name="notion_memory",
            points=[{
                "id": page_id,
                "vector": embedding,
                "payload": {"content": content, "url": page["url"]}
            }]
        )

# Run nightly via cron
if __name__ == "__main__":
    sync_notion_to_memory()

Now AI can semantically search your Notion workspace:

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

# Example: "Find all notes about insurance policies"
notes = semantic_search_notion("insurance policy renewals")

Bi-Directional: AI Writes Back to Notion

So far, AI only reads Notion. What about writing?

Use Case: AI Creates Tasks

You tell AI: “Remind me to renew HDFC policy on April 10.”

AI creates a Notion task:

def create_notion_task(title, due_date, assigned_to="Pappu"):
    response = requests.post(
        "https://api.notion.com/v1/pages",
        headers=headers,
        json={
            "parent": {"database_id": tasks_db_id},
            "properties": {
                "Name": {"title": [{"text": {"content": title}}]},
                "Due Date": {"date": {"start": due_date}},
                "Assigned to": {"select": {"name": assigned_to}},
                "Status": {"status": {"name": "Not started"}}
            }
        }
    )
    return response.json()

# AI creates task
create_notion_task("Renew HDFC policy", "2026-04-10")

Now when you open Notion, the task is there.

Use Case: AI Logs Decisions

You tell AI: “Remember this decision: We’re using Cloudflare Pages for hosting.”

AI creates a decision log page:

def create_decision_log(title, context, category="Tech"):
    response = requests.post(
        "https://api.notion.com/v1/pages",
        headers=headers,
        json={
            "parent": {"database_id": decisions_db_id},
            "properties": {
                "Title": {"title": [{"text": {"content": title}}]},
                "Date": {"date": {"start": str(datetime.date.today())}},
                "Category": {"select": {"name": category}}
            },
            "children": [
                {
                    "object": "block",
                    "type": "paragraph",
                    "paragraph": {
                        "rich_text": [{"type": "text", "text": {"content": context}}]
                    }
                }
            ]
        }
    )
    return response.json()

# AI creates decision log
create_decision_log(
    title="Hosting platform: Cloudflare Pages",
    context="Chose Cloudflare Pages over Vercel for better edge performance and free tier."
)

Notion vs Obsidian for AI Integration

FeatureNotionObsidian
API✅ Official REST API⚠️ Community plugin (Local REST API)
Cloud sync✅ Built-in⚠️ Optional (Obsidian Sync)
Structured data✅ Databases, relations❌ Plain Markdown
Privacy⚠️ Cloud-hosted✅ Local-first
Collaboration✅ Team workspaces❌ Individual vaults
CostFree tier limitedFree (unlimited)

When to use Notion: Team knowledge, structured databases, cloud-first
When to use Obsidian: Personal PKM, privacy, local-first, graph-based thinking

Common Mistakes

Mistake 1: Not Sharing Pages with Integration

You create an integration but forget to grant access. API returns no results.

Fix: Explicitly share pages/databases via “Add connections.”

Mistake 2: Over-Indexing

You index every Notion page, including drafts and random notes.

Fix: Only index curated pages (Projects, Decisions, Tasks). Use tags or a specific database for “AI-accessible” content.

Mistake 3: No Rate Limiting

Notion API has rate limits (3 requests/second). Bulk queries can fail.

Fix: Add delays between requests:

import time
time.sleep(0.4)  # ~2.5 requests/sec

Tools & Libraries

Python:

  • notion-client — Official Python SDK
  • notion2md — Convert Notion pages to Markdown

JavaScript:

  • @notionhq/client — Official Node.js SDK

AI Frameworks:

  • LangChain — Has NotionDBLoader for indexing databases
  • LlamaIndex — Notion connector for RAG

Key Takeaways

  1. Notion has a robust API — easier to integrate than most PKM tools
  2. Query databases for structured data (tasks, decisions, projects)
  3. Semantic search via vector DB for large workspaces
  4. Bi-directional sync — AI reads AND writes to Notion
  5. Grant explicit access — integrations don’t auto-access pages
  6. Notion + AI = queryable second brain

Your Notion workspace is already structured knowledge. Connect it to your AI.

Want automatic Notion + AI sync? MyDeepBrain integrates with Notion workspaces out of the box. Join the waitlist.

Want early access to MyDeepBrain?

We're building a self-hosted memory platform for AI assistants. Join the waitlist to be notified when we launch.

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Tags: Notion AI integration PKM personal knowledge management API