Persistent AI Memory for Cursor

Showcase Built for Cursor
1

I want to introduce Recallium a self-hosted memory system that fundamentally changes how you work with AI coding assistants. It’s a game-changer for any developer using Cursor alongside other AI tools.

Recallium provides persistent, intelligent memory across all your AI tools through the Model Context Protocol (MCP). It’s like giving your AI assistants a shared brain that learns and remembers.

1. Seamless Cross-Tool Integration

  • Works with Cursor, Claude Desktop/code, VS Code and any MCP-compatible tool.

  • Store context once, access everywhere.

  • No more tool isolation your AI remembers across all platforms.

  • Unified knowledge base for your entire development workflow.

2. Intelligent Semantic Search

  • Sub-second retrieval: 0.31s average search time.

  • 88% first-result accuracy: Finds exactly what you need.

  • Conceptual understanding: Searches by meaning, not just keywords.

  • Hybrid search: Combines semantic intelligence with keyword precision.

  • Handles vague queries like “that timezone bug” and returns “UTC timestamp parsing issue”.

3. Cross-Project Intelligence

  • Automatically surfaces patterns across your entire codebase history.

  • Learns from every bug fix and architectural decision.

  • Connects solutions from different projects you didn’t know were related.

  • Identifies inconsistencies and opportunities for standardization.

  • Builds compound knowledge that gets smarter over time.

4. Team Knowledge Preservation

  • Shared memory for entire engineering teams.

  • Institutional knowledge persists when team members leave.

  • New developers onboard instantly with searchable context.

  • Consistent code reviews based on established standards.

  • Production incidents become permanent learning opportunities.

5. Enterprise-Grade Privacy & Security

  • 100% self-hosted via Docker.

  • Your code never leaves your infrastructure.

  • Full control over data storage and access.

  • No external API calls or cloud dependencies.

  • Perfect for compliance-sensitive environments.

6. Production-Ready Performance

  • Scales to thousands of memories with sub-second search.

  • Minimal resource usage: ~1GB RAM, low CPU.

  • Tested with 2,847 memories across 12 projects.

  • 95th percentile latency: 0.5s.

  • No performance degradation as memory grows.

Website: https://www.recallium.ai

4

I work on Recallium, so I am obviously biased, but I genuinely believe it solves a real problem for developers. Recallium is completely free. We built it to solve a real problem we faced, and we are sharing it with the community.
Try it out and let me know what you think. If you run into issues or have suggestions, please share them here or open a GitHub issue.

5

Hello! I am using Recallium and loving it. Last week, I started this discussion. What are you using for AI memory across tools?

1 Like
6

Thank you! if you need any asssitance please let me know.

7

I keep hearing about people setting up MCP servers just for memory, and I’m honestly not sure why. Cursor already has a built-in memory feature, so why wouldn’t I just use that instead?

What does an MCP server actually give me in practice that Cursor’s own memory doesn’t? Is it really worth the extra setup and complexity?

8

The short answer is cursor’s built in memory isn’t sufficient when you get to a certain size of application. You need a development diary for a repo in order to save tokens and ensure the ai agent stays on track.

9

Great discussion!

@jabooth is absolutely right about scale. The “development diary” concept is key for larger applications.

@vibe-coder - to directly answer your question with context from @jabooth point:

Cursor’s built-in memory limitations:

  • Doesn’t scale to large applications.
  • No structured “development diary” capability.
  • Limited token management.
  • Can’t handle complex project histories.

When you need more, you have options:

Option A: File-based (simpler)

  • Keep structured docs/memory in your repo.
  • Reference via .cursorrules.
  • Git versioned.
  • Transparent and portable.
  • Perfect for Cursor-only workflows.
  • Lower complexity.

Option B: MCP memory servers (more powerful)

  • Semantic search across thousands of entries.
  • Cross-tool (Cursor, Claude, VS Code, etc.)
  • Team knowledge sharing.
  • Better token efficiency through smart retrieval.
  • Higher initial complexity.

The key insight from @jabooth At scale, you need structured memory with smart retrieval, not just Cursor’s basic memory feature.

What’s your project complexity? That will determine which approach fits better.