Mem0
Mem
Pieces for Developers
Mengram
Unabyss
OpenMemory
Memori
ai second brain for all your saved stuff

Markitdown Online
Doc2Markdown
Docling
MarkItDownai.org
Markovo
MarkItDown
Markdown.free
Upload a document and turn it into clean, token-efficient Markdown for ChatGPT, Claude, Gemini, Cursor and RAG workflows.
Which is more popular?
Based on our record, Supermemory seems to be more popular. It has been mentioned 4 times since March 2021.
Website, pricing, platforms and company facts side by side.
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Supermemory
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| Website | supermemory.ai | tokenpig.co |
| Pricing | — | |
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| Listed in |
In their own words, as submitted to SaaSHub.

No description of Supermemory yet.
TokenPig converts documents into clean, structured Markdown built specifically for LLM and RAG workflows — ChatGPT, Claude, Gemini, and retrieval pipelines. The problem Raw PDF, Word, PowerPoint and Excel exports carry a lot of formatting noise — repeated headers, broken tables, inconsistent...
What each product offers, as listed by its team.

No features have been listed yet.
An editorial look at what each product does well and who it suits.

Overall verdict
Why this product is good
Recommended for
No analysis of TokenPig yet.
How often each product is chosen within a category, 0–100% relative to the other.

As answered by people managing Supermemory and TokenPig.
TokenPig's answer:
TokenPig started from a recurring frustration: pasting PDF or Word exports into an LLM and watching layout noise — repeated headers, broken tables, stray whitespace — burn through the context window before the actual content even got read. TokenPig was built to solve that specific problem: clean, structured Markdown output plus visibility into the tokens saved.
TokenPig's answer:
Two main groups: individuals who regularly feed documents into ChatGPT or Claude and want cleaner, cheaper context (researchers, consultants, students), and developers/teams building RAG pipelines who need reliable document-to-Markdown conversion via API.
TokenPig's answer:
TokenPig focuses specifically on token efficiency, not just format conversion. Alongside clean Markdown output, it shows an estimated token savings for every conversion, so users can see exactly how much context window they're recovering before pasting a document into ChatGPT, Claude or Gemini — something general-purpose converters don't surface.
TokenPig's answer:
TokenPig runs entirely in the browser — no Python setup, no libraries to install, no code to maintain. That makes it accessible to non-developers (consultants, researchers, students) while still offering batch processing and an API for teams that want to automate document ingestion at scale.
TokenPig's answer:
TokenPig's answer:
Built as a modern web application using Next.js and TypeScript, with a focus on fast, reliable document processing entirely server-side — no client installation required.
Share your experience with using Supermemory and TokenPig. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.

This is usually a challenge that any developer has to take care of when building an AI agent. In fact, managing the context is one of the hardest problems when working with AI agents and there are many companies like SuperMemory, Mem0... - Source: dev.to / 2 months ago
Memory. I use Supermemory for this. Before, Pipa loaded context files and knew to update them. A memory tool adds teammate-like recall: goals, preferences, latest business state, and small details that should carry across runs. Good... - Source: dev.to / 4 months ago
We wire everything up with Vision Agents as the voice agent framework, Stream for WebRTC audio and video, OpenAI Realtime for speech in and speech out, Anam so the agent shows up as a face on the video, and Supermemory so answers come... - Source: dev.to / 5 months ago
Tracking TokenPig since Aug 2026.
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