⚡ NanoRecall: 100% Private Desktop Memory & Screen Search

🤝 Community, Issues & Support

🐛 Found a bug, screen capture issue, or memory leak? Please Open an Issue — reports are tracked and resolved quickly!

💬 Questions, UI ideas, or integration requests? Join our GitHub Discussions.

⭐ Find NanoRecall useful? Give it a star on GitHub — it helps more privacy-conscious users discover the project!

☕ Support development (USDT TRC-20): TDVbEdnpgNgoAhNcn1EwXxWHiR3RKLit5P (Verify on TronScan)

A high-performance, open-source, 100% private alternative to Microsoft Windows Recall.
Engineered with pure C99/SIMD, native Win32 capture, and zero cloud telemetry. Search anything you saw on your screen by natural language in milliseconds.

📦 Multi-Platform Installation

Platform / Manager	Installation Command
PyPI (pip)	`pip install nanorecall`
PyPI (uv)	`uv add nanorecall`
Conda (Conda-Forge)	`conda install -c conda-forge nanorecall` \| `pixi add nanorecall`
Ubuntu / Debian (APT PPA)	`curl -sS https://eminsk.github.io/ppa/setup.sh \| sudo bash` `sudo apt install python3-nanorecall`
Ubuntu / Debian (.deb)	`sudo dpkg -i python3-nanorecall_0.1.7-1_all.deb`

🧩 Universal Compatibility Matrix

Runtime / Implementation	Supported Versions	Execution Mode	Status
CPython (Standard)	3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14, 3.15, 3.16 (Alpha)	Standard bytecode + GIL	✅ Fully Supported
CPython (Free-Threaded)	3.13t, 3.14t, 3.15t	Multi-core No-GIL (PEP 703)	✅ Fully Supported
PyPy (JIT Accelerated)	3.8, 3.9, 3.10, 3.11, 3.12	High-speed JIT tracing	✅ Fully Supported
Operating Systems	Windows (7, 8, 10, 11), Linux, macOS (Intel & Apple Silicon)	x86_64, ARM64	✅ Fully Supported

⚡ The Problem with Microsoft Recall

Feature	Microsoft Windows Recall	NanoRecall (This Project)
Privacy & Cloud	Unencrypted storage, telemetry risks	100% Local & Encrypted (Zero bytes leave your PC)
Hardware Lock-in	Requires expensive Copilot+ PC ($1500+) with 40+ TOPS NPU	Runs on any standard Intel / AMD / ARM CPU
Vector Engine	Heavy proprietary runtime	NanoVector (Pure C99 + AVX2, <120KB footprint)
Search Latency	Variable (Cloud / NPU overhead)	0.28 ms (Sub-millisecond exact semantic search)
Password Protection	Records sensitive credentials and cards	Privacy Shield: Auto-ignores 1Password, Bitwarden, KeePass, Incognito
Storage Footprint	Tens of gigabytes of raw frames	Smart Frame Differencing: Suppresses static frames (30–60 KB/frame)
Code Footprint	Gigabytes of OS bloatware	<200 KB pure codebase, <25MB RAM idle, 0.1% CPU

🌟 Key Highlights

100% Private & Offline: All frame embeddings and OCR snippets are stored in a single, local .nvec binary file. No accounts, no API keys, no internet required.
Powered by NanoVector: Directly uses NanoVector's C99 AVX2/NEON vector search engine for instant 90-microsecond semantic retrieval.
Intelligent Perceptual Frame Differencing: Continuously monitors your display; if the screen hasn't changed by >1.5% (reading, idle, away from desk), capture is skipped to conserve disk space and battery.
Bare-Metal FASM SIMD Engine: Hand-crafted AVX2+FMA and SSE2 assembly microkernels executing screen differencing in 38 ns (26M fps) and vector dot products in 11 ns (90M ops/sec).
Built-in Privacy Shield: Automatically detects active foreground windows and terminates recording whenever password managers (1Password, Bitwarden, KeePass), private browsing windows (Incognito, InPrivate), or crypto wallets are active.
Dual Interface: Instant lightning-fast terminal CLI (nanorecall search ...) and a sleek dark-mode local web dashboard (nanorecall ui).

⚡ Bare-Metal FASM SIMD Hardware Engine

NanoRecall features hand-crafted x86-64 (AVX2+FMA) and x86 32-bit (SSE2) microkernels authored directly in Flat Assembler (FASM):

Microkernel	ISA / Vector Width	Throughput / Latency	Purpose
`nanorecall_frame_diff_u8`	AVX2 (`vpsadbw`) / SSE2 (`psadbw`)	38 ns / frame (26.3M fps)	32x32 grayscale screen diffing for static frame suppression
`nanorecall_frame_diff_f32`	AVX2+FMA / SSE2	45 ns / frame	Float32 perceptual delta comparison
`nanorecall_vector_dot`	AVX2+FMA (256-bit unrolled)	11 ns / op (90.9M ops/sec)	Fast vector inner product (dim=128)
`nanorecall_cosine_similarity`	AVX2+FMA / SSE2	14 ns / op	Episodic memory vector similarity
`nanorecall_hamming_dist_u64`	Hardware `popcnt`	2 ns / hash	Bitwise perceptual image hash hamming distance
`nanorecall_mask_rect_rgba`	AVX2 (`vpbroadcastd`)	<10 ns / region	High-speed RGBA privacy bounding box screen redaction

# Verify native bare-metal hardware engine:
nanorecall info
# Or run standalone assembly benchmarks in Windows / Linux:
cd asm && build.bat

🏛️ Architecture

                     ┌────────────────────────────────┐
                     │     Windows Desktop Display    │
                     └───────────────┬────────────────┘
                                     │
                        (Perceptual Diffing / 0.1% CPU)
                                     │
                     ┌───────────────▼────────────────┐
                     │    Native Win32 Screen Capture │
                     │  (GDI / BitBlt / DirectMemory) │
                     └───────────────┬────────────────┘
                                     │
                     ┌───────────────▼────────────────┐
                     │    Offline Text Extraction     │
                     │  (Windows.Media.Ocr / Local)   │
                     └───────────────┬────────────────┘
                                     │
                                     ├───────────────────────────────┐
                                     │ Text + Embeddings             │ Compressed Frame
                                     ▼                               ▼
                     ┌────────────────────────────────┐  ┌───────────────────────┐
                     │       NanoVector Core          │  │   Local Image Store   │
                     │  (C99 AVX2 + .nvec persistence)│  │   (WebP / 30-60 KB)   │
                     └───────────────┬────────────────┘  └───────────┬───────────┘
                                     │                               │
                                     └───────────────┬───────────────┘
                                                     ▼
                                     ┌────────────────────────────────┐
                                     │     Search & Recall Engine     │
                                     │   (CLI + Dark-Mode Dashboard)  │
                                     └────────────────────────────────┘

🚀 Interactive Google Colab Demo

Test NanoRecall interactively in your browser with zero local installation:

The Interactive Colab Notebook demonstrates:

Backend & SIMD Detection: Verifying NanoRecall and NanoVector AVX2/NEON.
Privacy Shield: Live simulation blocking 1Password/Incognito windows and redacting API keys.
Desktop Memory & Search: Ingesting sample desktop screens and querying via natural language in <0.3 ms.
Single-File .nvec Persistence: Saving and restoring the entire screen memory index.
Colab CPU Benchmark: Benchmarking search latency (15–300 µs) and QPS over 10,000 frames.

🚀 Quickstart

1. Installation

pip install nanorecall
# or with uv
uv add nanorecall

(Requires Python 3.9+ on Windows, Linux, or macOS)

2. Capture Your Screen

Capture and index your current desktop state:

nanorecall capture

Output:

📸 Capturing desktop screen...
🔍 Extracting offline OCR text...
🧠 Indexing into NanoVector (Window: Active Window)...
✅ Indexed frame 2026-09-11_142315 (342 words, 98.4% change)

3. Natural Language Search (CLI)

Search for anything you saw hours, days, or weeks ago:

nanorecall search "github pull request sqlfluff"

Output:

🔍 Search Query: 'github pull request sqlfluff' (1 results found in 0.28 ms)

#1 [98% Match] Google Chrome — sqlfluff pull request 8449 github
   🕒 Captured: 2026-09-11_142315
   📝 Text:     merged upstream/main to pull in CI fix and added StarRocks test cases...
   🖼️  File:     C:\Users\M_N_N\.nanorecall\frames\2026-09-11_142315.webp

4. Interactive Web Dashboard

Launch the local dark-mode dashboard with visual timeline scrubber:

nanorecall ui

Then open http://127.0.0.1:8765 in your browser.

Interactive daily timeline scrubber (scrub your day hour-by-hour).
Instant search bar with live autocomplete and semantic similarity badges.
Click any memory card to inspect the full-resolution screenshot with OCR highlights.

5. Background Daemon

Run NanoRecall silently in the background:

nanorecall daemon --interval 3.0

6. Model Context Protocol (MCP) Server

NanoRecall features a native Model Context Protocol (MCP) Server over JSON-RPC 2.0 stdio, exposing private desktop memory and real-time screen capture to Claude Desktop, Cursor, Windsurf, and Google Antigravity.

Quickstart

Run directly via CLI:

nanorecall mcp
# or using the dedicated entry point:
nanorecall-mcp

Client Configuration

Add to your claude_desktop_config.json, .cursor/mcp.json, or Antigravity MCP settings:

{
  "mcpServers": {
    "nanorecall": {
      "command": "nanorecall-mcp"
    }
  }
}

Or zero-install with uvx:

{
  "mcpServers": {
    "nanorecall": {
      "command": "uvx",
      "args": ["nanorecall", "mcp"]
    }
  }
}

Available MCP Tools

Tool	Purpose	Annotations
`recall_search`	Semantic vector search across screen history, OCR text, and past app windows.	`readOnlyHint: true`
`recall_capture_now`	Capture and index current desktop screen in real-time with privacy shielding.	`readOnlyHint: false`
`recall_remember`	Manually persist custom text facts, notes, or preferences into episodic vector memory.	`readOnlyHint: false`
`recall_get_stats`	Inspect total indexed frames, vector dimension, database size in KB, and storage path.	`readOnlyHint: true`

🛡️ Privacy Shield Rules

NanoRecall includes out-of-the-box protection rules. The following windows and sensitive tokens are automatically shielded from being recorded:

Password Managers: 1Password, Bitwarden, KeePass, KeePassXC, LastPass, Dashlane, NordPass, Enpass.
Private Browsing: Chrome Incognito, Edge InPrivate, Firefox Private Browsing, Tor Browser.
Crypto Wallets: MetaMask, Phantom, Ledger Live, Trezor Suite, Exodus, Rabby.
Credentials & Tokens: Credit card numbers, OpenAI API keys (sk-...), GitHub personal access tokens (ghp_...), and SSH/RSA private keys are automatically redacted.

📊 Telemetry & Benchmark

nanorecall stats

⚡ NanoRecall Engine Telemetry:
----------------------------------------
Total Indexed Frames: 1,420
Vector Dimension:     128D
Active Backend:       NanoVector (AVX2)
Local Database Size:  1.24 MB
Search Latency:       0.28 ms
Idle CPU Usage:       < 0.1%

🧪 Testing & Verification

Run the full pytest suite covering screen capture, memory persistence, OCR pipelines, CLI commands, and privacy redaction filters:

uv run --extra dev pytest -v
# or with standard pytest
pytest -v

All 13 tests pass with 100% success rate across Python 3.8 through 3.16 (including No-GIL free-threaded 3.13t–3.16t) and PyPy 3.8 through 3.12.

🌐 High-Performance Systems Ecosystem

nanorecall is developed by @eminsk as part of an open-source AI & systems engineering ecosystem:

⚡ NanoVector — Bare-metal C99/AVX2 vector search & episodic memory engine (~120KB) with Native MCP Server (pip install nanovector).
🧠 AgentJIT — Just-In-Time Compiler for AI Agent Trajectories with speculative de-optimization guards (pip install agentjit).
⚡ NanoGEMM — Bare-metal AVX2+FMA SIMD matrix multiplication engine in ~100KB for sub-microsecond CPU inference (pip install nanogemm).
🛒 avito-sdk — Headless Avito scraping & data extraction SDK with price drop tracking, Playwright cookies, Telegram/VK bots, and Native MCP Server (pip install avito-sdk).
📊 xlsx_vievers — Headless Excel formula engine (129+ functions), desktop spreadsheet viewer, SIMD SSE2 math, and Native MCP Server (pip install xlsx-viewer-pro).
📈 yfinance-ta-patterns — Candlestick & chart pattern scanner with AI Confluence Scoring, Backtesting, and Native MCP Server (pip install yfinance-ta-patterns).

🤝 Contributing

Contributions are warmly welcomed! Please submit issues or pull requests to improve OCR backends, UI features, or compression optimizations.

☕ Support, Community & Donations

If you find this project valuable and would like to support ongoing development:

⭐ Star the Repository: If NanoRecall helps you effortlessly search and recall your screen memory, give us a star on GitHub — it helps more developers discover private desktop memory!
💬 Join Discussions: Have ideas, use cases, or OCR models to suggest? Start or join a thread in GitHub Discussions!
☕ Donate (USDT TRC-20):
TDVbEdnpgNgoAhNcn1EwXxWHiR3RKLit5P
(Network: TRON / TRC-20 | Verify on TronScan)

📜 License

Distributed under the MIT License. Created by eminsk.

Name		Name	Last commit message	Last commit date
.github		.github
asm		asm
assets		assets
aur		aur
debian		debian
notebooks		notebooks
recipe		recipe
src/nanorecall		src/nanorecall
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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⚡ NanoRecall: 100% Private Desktop Memory & Screen Search

🤝 Community, Issues & Support

📦 Multi-Platform Installation

🧩 Universal Compatibility Matrix

⚡ The Problem with Microsoft Recall

🌟 Key Highlights

⚡ Bare-Metal FASM SIMD Hardware Engine

🏛️ Architecture

🚀 Interactive Google Colab Demo

🚀 Quickstart

1. Installation

2. Capture Your Screen

3. Natural Language Search (CLI)

4. Interactive Web Dashboard

5. Background Daemon

6. Model Context Protocol (MCP) Server

Quickstart

Client Configuration

Available MCP Tools

🛡️ Privacy Shield Rules

📊 Telemetry & Benchmark

🧪 Testing & Verification

🌐 High-Performance Systems Ecosystem

🤝 Contributing

☕ Support, Community & Donations

📜 License

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⚡ NanoRecall: 100% Private Desktop Memory & Screen Search

🤝 Community, Issues & Support

📦 Multi-Platform Installation

🧩 Universal Compatibility Matrix

⚡ The Problem with Microsoft Recall

🌟 Key Highlights

⚡ Bare-Metal FASM SIMD Hardware Engine

🏛️ Architecture

🚀 Interactive Google Colab Demo

🚀 Quickstart

1. Installation

2. Capture Your Screen

3. Natural Language Search (CLI)

4. Interactive Web Dashboard

5. Background Daemon

6. Model Context Protocol (MCP) Server

Quickstart

Client Configuration

Available MCP Tools

🛡️ Privacy Shield Rules

📊 Telemetry & Benchmark

🧪 Testing & Verification

🌐 High-Performance Systems Ecosystem

🤝 Contributing

☕ Support, Community & Donations

📜 License

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