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?
ContextPool launched on Product Hunt on April 13th, 2026 and earned 171 upvotes and 25 comments, placing #6 on the daily leaderboard. 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.
On the analytics side, ContextPool competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how ContextPool performed against the three products that launched closest to it on the same day.
Who hunted ContextPool?
ContextPool was hunted by Majid Khan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ContextPool including community comment highlights and product details, visit the product overview.
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