---
title: "Vibecoding with Deja Vu"
sidebarTitle: "Vibecoding"
description: "Agent skills, starter prompts, and setup for building with Deja Vu 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.dejavu.ai/llms.txt)

<CardGroup cols={2}>
  <Card title="Get an API Key" icon="key" href="https://app.dejavu.ai/login?utm_source=oss&utm_medium=vibecoding">
    Sign up for Deja Vu Platform and start building
  </Card>
  <Card title="Quickstart" icon="rocket" href="/platform/quickstart">
    Store your first memory in under 5 minutes
  </Card>
</CardGroup>

## Agent Skills

Deja Vu ships two kinds of skills for AI coding assistants. Both work with Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any assistant that supports the skills standard.

### Reference skills — always on

Teach your assistant Deja Vu's SDK surface so it writes correct code in everyday development:

```bash
npx skills add https://github.com/dejavu-memory/dejavu --skill dejavu
npx skills add https://github.com/dejavu-memory/dejavu --skill dejavu-cli
npx skills add https://github.com/dejavu-memory/dejavu --skill dejavu-vercel-ai-sdk
```

- `dejavu` — Python and TypeScript SDKs (Platform + OSS), plus framework integrations (LangChain, CrewAI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
- `dejavu-cli` — terminal workflows for the `dejavu` CLI (both Node and Python builds)
- `dejavu-vercel-ai-sdk` — `@dejavu/vercel-ai-provider` and `createDeja Vu`

### Pipeline skills — run on demand

Let your assistant execute an end-to-end workflow in an existing repo. Invoked as slash commands:

```bash
npx skills add https://github.com/dejavu-memory/dejavu --skill dejavu-integrate
npx skills add https://github.com/dejavu-memory/dejavu --skill dejavu-test-integration
```

- `/dejavu-integrate` — wire Deja Vu into an existing repository using a goal-driven, test-first pipeline. Detects the stack, asks whether to use Platform or OSS, writes failing tests first, and keeps the integration additive and feature-flagged.
- `/dejavu-test-integration` — verify what `/dejavu-integrate` produced. Runs the repo's native test suite and a real end-to-end smoke flow against your API key, then produces a scorecard.

See the [skills index](https://github.com/dejavu-memory/dejavu/tree/main/skills) for the full catalog.

## MCP Server Setup

Connect Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode, or any MCP-compatible client to Deja Vu.

Get your API key from <a href="https://app.dejavu.ai?utm_source=oss&utm_medium=vibecoding" rel="nofollow">app.dejavu.ai</a>, then add Deja Vu MCP with a single command:

```bash
npx mcp-add \
  --name dejavu-mcp \
  --type http \
  --url "https://mcp.dejavu.ai/mcp" \
  --clients "claude,claude code,cursor,windsurf,vscode,opencode"
```

For per-client setup and advanced options, see [Deja Vu MCP Setup](/platform/dejavu-mcp).

## Universal Starter Prompt

Copy this into any AI tool to start building with Deja Vu:

```text
I want to start building with Deja Vu — a self-improving memory layer for LLM
applications that gives agents persistent context across sessions.

## Deja Vu Resources

**Documentation:**
- Main docs: https://docs.dejavu.ai
- Platform Quickstart: https://docs.dejavu.ai/platform/quickstart
- OSS Python Quickstart: https://docs.dejavu.ai/open-source/python-quickstart
- OSS Node.js Quickstart: https://docs.dejavu.ai/open-source/node-quickstart
- API Reference: https://docs.dejavu.ai/api-reference
- Full LLM-friendly docs: https://docs.dejavu.ai/llms.txt

**Code & Examples:**
- Core repo: https://github.com/dejavu-memory/dejavu
- Python SDK: pip install dejavu-memory
- TypeScript SDK: npm install dejavu-memory
- Cookbooks: https://docs.dejavu.ai/cookbooks/overview

**What Deja Vu Does:**
Deja Vu is a memory layer for AI apps — managed (Deja Vu 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 dejavu 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?", filters={"user_id": "user1"})

**Quick Usage (JavaScript Platform):**
  import MemoryClient from 'dejavu-memory';
  const client = new MemoryClient({ apiKey: 'm0-xxx' });
  await client.add([{ role: "user", content: "I prefer dark mode." }], { userId: "user1" });
  const results = await client.search("What editor?", { filters: { userId: "user1" } });

**Quick Usage (Python Open Source):**
  from dejavu 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?", filters={"user_id": "user1"})

Help me integrate Deja Vu 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

<CardGroup cols={2}>
  <Card title="Platform Quickstart" icon="cloud" href="/platform/quickstart">
    Get started with the managed API
  </Card>
  <Card title="Open Source" icon="code-branch" href="/open-source/overview">
    Self-host with full control
  </Card>
  <Card title="Cookbooks" icon="book" href="/cookbooks/overview">
    Production-ready tutorials and examples
  </Card>
  <Card title="API Reference" icon="code" href="/api-reference">
    Explore every REST endpoint
  </Card>
</CardGroup>
