Every AI coding session starts from scratch. You re-debug the same bugs, re-explain decisions you already made. Your agent forgets everything. ContextPool gives your agent persistent memory. It scans your past Cursor and Claude Code sessions, extracts engineering insights (bugs, fixes, design decisions, gotchas), and loads relevant context via MCP at session start. No prompting needed. Works with Claude code, Cursor, Windsurf, and Kiro. Free and open source - team sync available for $7.99/mo.
We built ContextPool because we kept hitting the same wall: every time started a new Claude Code or Cursor session, my agent had zero memory of what we'd already figured out together. Same bugs re-discovered. Same architectural decisions re-explained. Same gotchas re-learned.
It felt like working with a brilliant colleague who gets amnesia every morning.
So we built a persistent memory layer specifically for AI coding agents. Here's how it works:
1. Install with one curl command (30 seconds, single binary, no dependencies) 2. Run `cxp init` - it scans your past sessions and extracts engineering insights using an LLM 3. Your agent automatically loads relevant context via MCP at session start
What it remembers isn't conversation summaries - it's actionable engineering knowledge: → Bugs & root causes ("tokio panics on block_on in async context") → Fixes & solutions ("Use #[tokio::main] instead of manual Runtime::new()") → Design decisions ("Chose libsql over rusqlite for Turso compatibility") → Gotchas ("macOS keychain blocks in MCP subprocess context")
It works with Claude Code (zero config), Cursor, Windsurf, and Kiro. Local-first and privacy-first - raw transcripts never leave your machine, only extracted insights sync when you opt in.
The team memory feature is what we are most excited about: push insights to a shared pool, and everyone on the team pulls the collective knowledge. Your teammate debugged something last week? Your agent already knows.
Free and open source for local use. $7.99/mo for team sync.
We'd love to hear: what's the most frustrating thing you keep re-explaining to your AI coding agent? And if you try it - what insights does it extract from your sessions?
Majid Khan
Hey Product Hunt 👋
We built ContextPool because we kept hitting the same wall: every time started a new Claude Code or Cursor session, my agent had zero memory of what we'd already figured out together. Same bugs re-discovered. Same architectural decisions re-explained. Same gotchas re-learned.
It felt like working with a brilliant colleague who gets amnesia every morning.
So we built a persistent memory layer specifically for AI coding agents. Here's how it works:
1. Install with one curl command (30 seconds, single binary, no dependencies)
2. Run `cxp init` - it scans your past sessions and extracts engineering insights using an LLM
3. Your agent automatically loads relevant context via MCP at session start
What it remembers isn't conversation summaries - it's actionable engineering knowledge:
→ Bugs & root causes ("tokio panics on block_on in async context")
→ Fixes & solutions ("Use #[tokio::main] instead of manual Runtime::new()")
→ Design decisions ("Chose libsql over rusqlite for Turso compatibility")
→ Gotchas ("macOS keychain blocks in MCP subprocess context")
It works with Claude Code (zero config), Cursor, Windsurf, and Kiro. Local-first and privacy-first - raw transcripts never leave your machine, only extracted insights sync when you opt in.
The team memory feature is what we are most excited about: push insights to a shared pool, and everyone on the team pulls the collective knowledge. Your teammate debugged something last week? Your agent already knows.
Free and open source for local use. $7.99/mo for team sync.
We'd love to hear: what's the most frustrating thing you keep re-explaining to your AI coding agent? And if you try it - what insights does it extract from your sessions?
GitHub: https://github.com/syv-labs/cxp