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Top 23 Python Memory Projects
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Project mention: Scalene: A high-performance, high-precision CPU, GPU, memory profiler for Python | news.ycombinator.com | 2024-10-21
For profiling memory consider far more advanced memray.
https://github.com/bloomberg/memray
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We were given a quite big 20250312.mem file. Looking at the name of the challenge and the size of the file, it was clear it was required to use volatility.
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Project mention: Show HN: Agent S: an open agentic framework that uses computers | news.ycombinator.com | 2025-05-01
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Project mention: Introducing Potpie's Slack Integration: Bring Your Custom AI Agents to Where You Work | dev.to | 2025-04-24
Potpie is an open-source platform that allows you to build and deploy custom AI agents tailored to your specific codebase. These agents can handle a wide range of tasks including debugging, code reviewing, code generation, onboarding, etc.
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Clone the Volatility 3 repository: > git clone https://github.com/volatilityfoundation/volatility3.git
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rpmalloc
Public domain cross platform lock free thread caching 16-byte aligned memory allocator implemented in C
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Project mention: MemoRAG – Enhance RAG with memory-based knowledge discovery for long contexts | news.ycombinator.com | 2024-09-20
> AFAICT from a quick glance at the code (https://github.com/qhjqhj00/MemoRAG/blob/main/memorag/memora...), it is indeed "fine tuning" (jargon!) a model on your chosen book, presumably in the most basic/direct sense: asking it reproduce sections of text at random from the book given their surrounding context, and rewarding/penalizing the neural network based on how well it did.
Maybe your use of quotes is intentional here, but for posterity's sake there is no actual fine-tuning happening in the code you linked, insofar as the weights of the model aren't being touched at all, nor are they modifying anything else that could impact the original weights (like a LoRA adapter).
The paper details the actual process, but the TL;DR is that the memory module they use, basically a draft model, does go through a pretraining phase using the redpajama dataset, and then an SFT phase with a different objective. This all happens before and irrespective of the inference-time task (i.e. asking questions about a given text).
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This is a horrible use case for Windows Recall. Even if we ignore all the privacy implications of having a third party screenshot you every 30 seconds and making the files world readable, it's a bad idea.
Recall has lost a ton of useful metadata you already have - both URL visits and streaming are clearly discernible actions, both at the network stack level, and from your browser history. Throwing that away to trust an LLM to re-infer the same data is both reducing data fidelity and significantly increasing processing cost.
If you want to see this done reasonably well, I'd suggest looking at e.g https://beepb00p.xyz/promnesia.html (which not surprisingly bears a strong similarity to what the article discusses)
LLMs don't add much value here, outside of tightly locked down systems where screenshots are the only way of exporting.
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Project mention: ps_mem: A Lightweight Tool for Accurate Memory Usage Reporting | dev.to | 2024-09-16
For detailed information on script functionality, refer to the commit history: https://github.com/pixelb/ps_mem
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Project mention: Beyond RAG: Memobase Unlocks Scalable User Memory for Smarter AI | dev.to | 2025-01-29
User memory is perfect for most consumer apps in entertainment, lifestyle, or social domains, such as virtual companions. It makes the AI feel personal, thoughtful, and human. Check out a real-world user memory result that Memobase extracted from a public chatting dataset.
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memvid
Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed.
sadly have to agree. I asked a question about this on the issue tracker.
https://github.com/Olow304/memvid/issues/39 if anyone wants to follow along.
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memorizing-transformers-pytorch
Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
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recurrent-memory-transformer-pytorch
Implementation of Recurrent Memory Transformer, Neurips 2022 paper, in Pytorch
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mcp-memory-service
MCP server providing semantic memory and persistent storage capabilities for Claude using ChromaDB and sentence transformers.
Project mention: Supercharging Productivity with Cursor AI: A React Developer's Guide to MCP Servers and JSON Prompts | dev.to | 2025-04-17Key Takeaway: cursor10x-mcp and Repomix excel for speed and context. MCP Memory Service is great for quick wins, and Pieces organizes prompts. But tools alone don’t cut it—prompts are the real magic.
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gpt-voice-conversation-chatbot
Allows you to have an engaging and safely emotive spoken / CLI conversation with the AI ChatGPT / GPT-4 while giving you the option to let it remember things discussed.
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M.I.L.E.S
M.I.L.E.S, a GPT-4-Turbo voice assistant, self-adapts its prompts and AI model, can play any Spotify song, adjusts system and Spotify volume, performs calculations, browses the web and internet, searches global weather, delivers date and time, autonomously chooses and retains long-term memories. Available for macOS and Windows.
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Memory discussion
Python Memory related posts
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Show HN: Agent S: an open agentic framework that uses computers
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💀 Insomni'hack 2025 CTF write-up
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MemoRAG – Enhance RAG with memory-based knowledge discovery for long contexts
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Memory Dump Analysis | Kali Linux
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ps_mem: A Lightweight Tool for Accurate Memory Usage Reporting
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Show HN: I've Created the First Artificial Memory (and It's Open-Source)
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RemindAI: Revolutionize Your Note-Taking and Digital Memory
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A note from our sponsor - SaaSHub
www.saashub.com | 13 Jun 2025
Index
What are some of the best open-source Memory projects in Python? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | memray | 14,032 |
| 2 | psutil | 10,673 |
| 3 | volatility | 7,718 |
| 4 | Agent-S | 5,389 |
| 5 | potpie | 4,618 |
| 6 | volatility3 | 3,191 |
| 7 | rpmalloc | 2,298 |
| 8 | MemoRAG | 1,808 |
| 9 | promnesia | 1,807 |
| 10 | ps_mem | 1,580 |
| 11 | memobase | 1,411 |
| 12 | memvid | 7,602 |
| 13 | pointers.py | 925 |
| 14 | memorizing-transformers-pytorch | 633 |
| 15 | MalConfScan | 487 |
| 16 | recurrent-memory-transformer-pytorch | 409 |
| 17 | theine | 394 |
| 18 | mcp-memory-service | 374 |
| 19 | gpt-voice-conversation-chatbot | 306 |
| 20 | block-recurrent-transformer-pytorch | 218 |
| 21 | M.I.L.E.S | 216 |
| 22 | Proxmox-load-balancer | 191 |
| 23 | honcho | 189 |
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