aneequrrehman

aneequrrehman / recall

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AI memory layer that lives in your stack

recall-docs-sand.vercel.app ai ai-agent ai-memory ai-tools embeddings
21
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100% credibility
Found Feb 18, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

Recall provides composable libraries for adding persistent, AI-powered memory extraction, storage, and retrieval to applications using existing infrastructure.

How It Works

1
💭 Realize your AI chat forgets users

You're building a helpful chatbot but it keeps forgetting what users tell it, like their job or favorite foods.

2
🔍 Discover Recall

You find Recall, a simple way to give your AI a long-term memory that fits right into what you already have.

3
🧩 Add memory magic in minutes

You easily connect a safe spot to store memories and link smart helpers to understand conversations.

4
💬 Share conversations

As users chat, you pass their messages to Recall, which pulls out key facts like 'User loves pizza' or 'Works at Acme'.

5
🧠 AI remembers everything

When users ask questions, Recall finds the right memories instantly, so your AI responds like an old friend.

6
📈 Grow smarter over time

Memories build up automatically, staying fresh and duplicate-free, powering better chats and insights.

🎉 Personal AI companion ready

Your app now has a perfect memory layer – users feel truly understood, no extra hassle for you.

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AI-Generated Review

What is recall?

Recall is a TypeScript library providing an AI memory layer that integrates into your existing stack for persistent user memory in AI apps. It extracts facts from conversations via LLM, deduplicates intelligently, and enables vector search or structured SQL queries—all stored in your SQLite, Postgres, or MySQL database without deploying new services. Pick adapters for embeddings (OpenAI, Cohere, Voyage) and extractors (GPT, Claude) to query like "What does the user prefer?" and get relevant memories back.

Why is it gaining traction?

It stands out by avoiding vendor lock-in and github memory limit headaches—plug it into your background jobs and DB for seamless memory layer for ai agents, unlike heavy vector services. The Vercel AI SDK middleware auto-injects memories into prompts, while MCP tools and local recall github setups work offline or with github memory mcp for unattended agents. Intelligent consolidation (add/update/delete/none) keeps memory layers at scale clean without manual cleanup.

Who should use this?

AI developers building personalized chatbots or agents needing user fact recall, like tracking preferences in customer support bots. Backend teams managing memory layer for llm apps on Linux, frustrated with github memory allocator complexity or wanting github memory optimizer simplicity over full RAG stacks. Full-stack devs prototyping memory github copilot extensions or github recall for linux workflows.

Verdict

Solid start for composable AI memory with great docs and examples, but 12 stars and 1.0% credibility score signal early maturity—core is stable, structured memory experimental, so test thoroughly before prod. Star it if github recall ai fits your stack.

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