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Recall – Persistent Memory for Claude Code via MCP Hooks

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Recall – Persistent Memory for Claude Code via MCP Hooks

Hi HN, A while back I posted about recall MCP - https://news.ycombinator.com/item?id=45516584 Since then I updated a series of times and received quite a good amount of positive response. I decided to take it a step further and make it an actual product. It has been a super interesting journey. I built Recall because I was spending 10+ minutes every Claude Code session re-explaining my project. Architecture, conventions, past decisions — all gone after a session restart or context compaction. Recall is an MCP server that gives Claude Code persistent memory. It works by running four lifecycle hooks: - session-start: fetches relevant memories and injects them as context - observe: captures key events (git commits, file changes, decisions) silently after Write/Edit/Task operations - pre-compact: saves critical state before Claude's context window gets summarised (this is the most valuable hook — compaction kills nuance) - session-end: records a session summary Install as a native Claude Code plugin: /plugin install recall@claude-plugins-official /recall:setup ← connects your API key The plugin bundles the MCP server config, all 4 hooks, and auto-updates itself in the background. Alternatively, hooks-only install is still available for those who prefer it: curl -fsSL https://recallmcp.com/install-hooks | bash Hooks are pure bash + curl. No daemon, no npm packages, no background processes. Every hook exits 0 on errors so it never blocks Claude. Memory is stored in Redis with semantic search via embeddings (Voyage AI, Cohere, OpenAI, or others). Each tenant gets isolated storage with AES-256-GCM encryption. Some features that might be interesting to this audience: 1. Native Claude Code plugin — ships as a proper plugin installable with `/plugin install recall@claude-plugins-official`. Bundles MCP server config + all 4 hooks. Auto-updates in the background. 2. Webhook ingestion — route GitHub/Stripe/any webhook into a Claude session. Your AI becomes event-aware. 3. Self-hosted — `curl -fsSL https://install.recallmcp.com | bash` gives you a Docker Compose stack with Redis + Recall. $12/mo license, air-gapped compatible. 4. Team sharing — one Claude learns, all team Claudes know. Workspace-scoped with selective sharing. Open source (MIT): https://github.com/joseairosa/recall Hosted: https://recallmcp.com (free tier: 500 memories) Technical details: TypeScript, Express 5, Redis/Valkey adapters, Drizzle ORM for billing/teams on PostgreSQL, StreamableHTTP MCP transport. 16 consolidated MCP tools exposed. Happy to discuss the architecture, MCP protocol integration, or the memory/embedding strategy.

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp, stripe · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, open source, ide · Missing: https docs, excited, just released
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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