Memoryport

Memoryport fills a gap that few other tools address: a local-first, persistent memory layer that works across multiple LLMs and is fully open-source. For developers juggling Claude Code, Cursor, and Ollama, the practical benefit is real — you no longer have to repeat context or lose threads when switching tools. The three-gate retrieval system feels smart; it doesn't dump everything into context but picks what's relevant, which keeps responses clean. On benchmarks, the 97.9% recall on LongMemEval is impressive, and sub-100ms retrieval means you won't notice the overhead. Where Memoryport shines is the free tier: unlimited local storage with no catch. That's rare. The Pro tier is cheap at $9.99/month, and the promise that memories persist even after cancellation is a strong differentiator — Arweave backup means your data lives on decentralized storage. But it's not for everyone. Setup requires the command line; there's no GUI, no mobile app, no web interface. If you're not comfortable with curl pipes and config wizards, you'll struggle. The integrations are developer-focused: MCP servers, proxy for Ollama/OpenAI. It doesn't plug into ChatGPT's web UI or any Chrome extension. Enterprises may also pause at the lack of a managed cloud option or SLA. The closest alternative is something like Mem.ai or personal AI memory services, but those are cloud-first and typically require subscriptions. Memoryport's local-first approach gives it a privacy edge, but sacrifices accessibility. In practice, we'd reach for this when we want all our development LLMs to share context without sending data to a third party. It's a sharp tool for a specific job — not a mass-market product.