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Works with the tools you already use

Claude CodeCLI
GitHub CopilotIDE
CursorIDE
WindsurfIDE
ClineCLI
Continue.devIDE
AiderCLI
JetBrains AIIDE
ZedIDE
Claude DesktopApp
+ any MCP or API client

6.6k+

CLI downloads

10+

tool integrations

< 5 min

to set up

0

raw transcripts stored

See the Difference

Same task. Completely different experience.

Claude Code
>I need to add webhook notifications to the API
I'd be happy to help you add webhook notifications! To provide the best solution, I have a few questions:
1. What's your current tech stack and framework? 2. How do you handle async jobs currently? 3. Do you have any existing notification patterns I should follow? 4. What's your testing approach?
>Node.js with Express, we use Bull for queues...
Got it. And what retry strategy do you prefer? Do you have an existing notification service I could look at for patterns?
>Check src/jobs/ for our email notifier
Readsrc/jobs/
● No persistent memoryclaude-sonnet-4

Back-and-forth to establish context every session

Claude Code+ MemNexus
>I need to add webhook notifications to the API. Use MX to recall previous work we've done and our established process for creating, testing, and deploying new features.
Memory"notification patterns" "feature development" "testing process"
Found 4 relevant memories from your codebase patterns
I can help with that. Based on your established patterns:
• Your email notifier uses Bull with 3 retries + exponential backoff → src/jobs/email-notifier.ts • New features follow: route → service → job → tests • You use Vitest + MSW for API mocking • Webhooks should include HMAC signatures (per your security decisions)
I'll create the webhook job following your email notifier structure. Starting with the Bull job definition?
● Context loaded from 4 memoriesclaude-sonnet-4

Instant context from your coding history

Sound familiar?

Every developer knows these frustrations

🔄

You've explained your coding style 47 times

🔍

You can't find that conversation where you solved the exact same bug

📋

Your AI gives generic answers, not ones tailored to your workflow

🚀

Every new project starts from zero

Six patterns of AI memory failure — documented with solutions. See the full picture →

Or read: Why AI assistants lose context — and what to do about it

What MemNexus Does

Three simple outcomes, no technical jargon

Remembers Everything

Every conversation, every decision, every preference. Find past discussions instantly and build a searchable history of your work.

Learns How You Work

Automatically adapts to your style. Applies your preferences without being asked. Gets better the more you use it.

Gets Smarter Over Time

The more you use it, the better it gets. MemNexus builds a knowledge graph of your decisions, patterns, and preferences -- compounding its usefulness with every interaction.

Works however you work

🖥️CLICommand-line interface
🔌MCPCursor, Windsurf, Claude Desktop
📦SDKPython, TypeScript, JS
🔗APIRESTful API

See how it works → · Building with the SDK

Why Not Just Use Built-In Memory?

ChatGPT Memory, Cursor context, and Copilot workspace indexing are useful starting points -- but they keep your knowledge trapped in one tool. Learn the architectural difference.

Cross-Tool

Built-in memory

Locked to one tool. ChatGPT memory only works in ChatGPT. Cursor context only works in Cursor.

MemNexus

Works across Claude, GPT, Cursor, Copilot, and any tool that supports MCP or REST APIs. Switch tools without losing context.

Persistent

Built-in memory

Memory resets between sessions, disappears when you switch machines, and gets wiped by tool updates.

MemNexus

Survives session resets, tool switches, and machine changes. Your knowledge graph lives in the cloud, accessible from anywhere.

Learns Over Time

Built-in memory

A flat list of facts with no relationships. Can't connect that your Redis caching decision relates to your scaling strategy.

MemNexus

Builds a knowledge graph that links decisions, patterns, and context. Understands relationships between your memories, not just individual facts.

How It Works

Three simple steps to AI that actually remembers

1

Connect Your AI

Use the CLI with Claude Code, the MCP server with Cursor or Claude Desktop, or the SDK to build memory into your own AI apps. One-time setup — your existing workflow stays the same.

2

Work Normally

Debug, plan, and build with your AI as you normally do. MemNexus records decisions, architectural choices, and debugging paths from your conversations — and makes them searchable and reusable across sessions.

3

Your AI Stops Forgetting

Your AI references past decisions without being asked, applies your preferred patterns, and brings up relevant context automatically. No more re-explaining your stack, your conventions, or what you tried last week.

terminal

Setup guides: Claude Code · Cursor · Windsurf · Full walkthrough →

Common questions

Everything you need to know before getting started.

Simple, transparent pricing

Full learning capabilities in Free tier

Free

Experience the magic

$0
  • 200 memories/month
  • Full memory & learning
  • 60-day retention
Most Popular

Pro

Unlimited for power users

$10/month
  • Unlimited memories
  • Forever retention
  • Advanced search

Enterprise

For teams that ship

Custom
  • Team collaboration
  • SSO/SAML
  • Self-hosting option

Ready to build AI that remembers?

We're onboarding developers in small batches. Request early access.

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