Curlens

The Problem

You use Cursor CLI across multiple projects. After a few days, you have dozens of chats scattered across different workspaces. You remember discussing “flink job optimization” somewhere, but:

• Which folder was it in?
• What was the chat called?
• How do you resume it?

Cursor stores all chats internally using MD5 hashes of workspace paths - not human-readable at all!

The Solution

Curlens indexes your chats with AI-generated summaries and lets you search by description:

$ curlens -d "flink optimization"

Found 2 matching chat(s):

[1] Flink Job Tuning
    Dir: /Users/you/workspace/data-pipeline
    Optimized Flink checkpointing and parallelism settings...

[2] Stream Processing Debug
    Dir: /Users/you/workspace/analytics
    Fixed watermark issues in Flink streaming job...

Select chat [1-2]: 1
→ Resuming: Flink Job Tuning

How It Works

1. Hooks fire after Cursor CLI actions (shell commands, file edits, MCP calls)
2. Summarizer reads chat messages and generates summaries via LLM
3. Search ranks results by keyword match (or LLM with --smart flag)
4. Resume selected chat with cursor agent --resume

Install

pip install curlens

Features

• Backfill existing chats with curlens --backfill
• Auto-index new chats via Cursor hooks
• Smart search with LLM ranking (--smart flag)
• Configurable models and search window