diff --git a/docs/docs.json b/docs/docs.json
index c09af9b83..feb95cfdb 100644
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -40,6 +40,7 @@
"icon": "rocket",
"pages": [
"platform/overview",
+ "vibecoding",
"platform/mem0-mcp",
"platform/platform-vs-oss",
"platform/quickstart"
@@ -142,6 +143,7 @@
"icon": "rocket",
"pages": [
"open-source/overview",
+ "vibecoding",
"open-source/python-quickstart",
"open-source/node-quickstart"
]
@@ -302,6 +304,7 @@
"icon": "square-terminal",
"pages": [
"openmemory/overview",
+ "vibecoding",
"openmemory/quickstart",
"openmemory/integrations"
]
diff --git a/docs/vibecoding.mdx b/docs/vibecoding.mdx
new file mode 100644
index 000000000..e79351168
--- /dev/null
+++ b/docs/vibecoding.mdx
@@ -0,0 +1,181 @@
+---
+title: "Vibecoding with Mem0"
+sidebarTitle: "Vibecoding"
+description: "Agent skills, starter prompts, and setup for building with Mem0 using AI coding tools."
+icon: "wand-magic-sparkles"
+---
+
+These docs are designed to be easily consumable by LLMs. Each page has a button that lets you copy the page as Markdown or paste directly into ChatGPT, Claude, or any AI coding tool.
+
+We follow the llms.txt standard:
+
+- [llms.txt](https://docs.mem0.ai/llms.txt)
+
+
+
+ Sign up for Mem0 Platform and start building
+
+
+ Store your first memory in under 5 minutes
+
+
+
+## Agent Skills
+
+Teach your coding assistant how to build with Mem0:
+
+```bash
+npx skills add https://github.com/mem0ai/mem0 --skill mem0
+```
+
+Works with Claude Code, Cursor, Windsurf, and any assistant that supports skills. Once installed, your assistant understands Mem0's full API, framework integrations, and common patterns.
+
+## Claude Code Plugin
+
+The [OpenMemory plugin](https://github.com/mem0ai/claude-code-plugin) gives Claude Code **persistent memory across sessions, projects, and teams** — automatically.
+
+
+
+Sign up at [app.openmemory.dev](https://app.openmemory.dev).
+
+
+
+```bash
+/plugin add mem0ai/claude-code-plugin
+```
+
+
+
+```bash
+export OPENMEMORY_API_KEY="your-key-here"
+```
+
+
+
+The plugin activates automatically. It captures decisions at session end, preserves context during compaction, and retrieves relevant memories at session start.
+
+
+
+## MCP Server Setup
+
+Connect Cursor, Windsurf, Claude Desktop, or any MCP-compatible client to Mem0.
+
+
+
+Sign up at [app.openmemory.dev](https://app.openmemory.dev), then pick your client:
+
+
+```bash Claude Desktop
+npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key
+```
+
+```bash Cursor
+npx @openmemory/install --client cursor --env OPENMEMORY_API_KEY=your-key
+```
+
+```bash Windsurf
+npx @openmemory/install --client windsurf --env OPENMEMORY_API_KEY=your-key
+```
+
+
+For full setup options, see [OpenMemory Quickstart](/openmemory/quickstart).
+
+
+
+Get your API key from [app.mem0.ai](https://app.mem0.ai), then add to your MCP config:
+
+```json
+{
+ "mcpServers": {
+ "mem0": {
+ "command": "uvx",
+ "args": ["mem0-mcp-server"],
+ "env": {
+ "MEM0_API_KEY": "m0-...",
+ "MEM0_DEFAULT_USER_ID": "your-handle"
+ }
+ }
+ }
+}
+```
+
+For Docker, Smithery, and advanced options, see [Mem0 MCP Setup](/platform/mem0-mcp).
+
+
+
+## Universal Starter Prompt
+
+Copy this into any AI tool to start building with Mem0:
+
+```text
+I want to start building with Mem0 — a self-improving memory layer for LLM
+applications that gives agents persistent context across sessions.
+
+## Mem0 Resources
+
+**Documentation:**
+- Main docs: https://docs.mem0.ai
+- Platform Quickstart: https://docs.mem0.ai/platform/quickstart
+- OSS Python Quickstart: https://docs.mem0.ai/open-source/python-quickstart
+- OSS Node.js Quickstart: https://docs.mem0.ai/open-source/node-quickstart
+- API Reference: https://docs.mem0.ai/api-reference
+- Full LLM-friendly docs: https://docs.mem0.ai/llms.txt
+
+**Code & Examples:**
+- Core repo: https://github.com/mem0ai/mem0
+- Python SDK: pip install mem0ai
+- TypeScript SDK: npm install mem0ai
+- Cookbooks: https://docs.mem0.ai/cookbooks/overview
+
+**What Mem0 Does:**
+Mem0 is a memory layer for AI apps — managed (Mem0 Platform) or self-hosted
+(Open Source). It stores, retrieves, and manages user memories so agents
+remember preferences, learn from interactions, and personalize over time.
+Sub-50ms retrieval. Dual storage: vector embeddings + graph databases.
+
+**Architecture Overview:**
+- Memory is scoped by user_id, agent_id, or run_id
+- Core operations: add, search, update, delete
+- Memory types: factual (preferences, facts), episodic (past interactions),
+ semantic (concept relationships), working (session state)
+- Integration pattern: retrieve relevant memories → generate response → store
+ new memories
+
+**Quick Usage (Python Platform):**
+ from mem0 import MemoryClient
+ client = MemoryClient(api_key="m0-xxx")
+ client.add("I prefer dark mode and use VS Code.", user_id="user1")
+ results = client.search("What editor do they use?", user_id="user1")
+
+**Quick Usage (JavaScript Platform):**
+ import MemoryClient from 'mem0ai';
+ const client = new MemoryClient({ apiKey: 'm0-xxx' });
+ await client.add([{ role: "user", content: "I prefer dark mode." }], { user_id: "user1" });
+ const results = await client.search("What editor?", { user_id: "user1" });
+
+**Quick Usage (Python Open Source):**
+ from mem0 import Memory
+ m = Memory()
+ m.add("I prefer dark mode and use VS Code.", user_id="user1")
+ results = m.search("What editor do they use?", user_id="user1")
+
+Help me integrate Mem0 into my project. Start by asking what I'm building,
+what language/framework I'm using, and whether I want managed or self-hosted.
+```
+
+## Go Deeper
+
+
+
+ Get started with the managed API
+
+
+ Self-host with full control
+
+
+ Production-ready tutorials and examples
+
+
+ Explore every REST endpoint
+
+