PdfToMem

alinvdu/PdfToMem

3.4

If you are the rightful owner of PdfToMem and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to dayong@mcphub.com.

PdfToMem is a system that transforms PDFs into structured, queryable memory representations optimized for Large Language Models (LLMs).

Tools
1
Resources
0
Prompts
0

🧠 PdfToMem

Turn PDFs into structured, queryable memory—built for LLMs.

Large Language Models struggle with memory. PdfToMem makes it effortless.
By combining reasoning-powered ingestion, structured retrieval, and a multi-agent architecture, it transforms unstructured PDFs into rich memory representations.

It exposes a powerful MCP Server to coordinate ingestion, reasoning, storage, and querying—using state-of-the-art tools like LlamaIndex and LangGraph.

🎥 Video Walkthrough

Video Icon

🧠 MCP Server — Memory Control Plane

The MCP Server is the brain of PdfToMem. It leverages tool-based reasoning to:

  • 🏗️ Design ingestion pipelines
  • 🧩 Choose the right memory representation
  • 📊 Index and structure PDF content for retrieval

Use tools like determine_memory_architecture to automatically infer the optimal memory structure using LlamaIndex abstractions:

  • VectorIndex
  • QueryEngine
  • LLMSelector

🛠️ Tool-Driven Parsing — Powered by LangGraph

PdfToMem employs modular tools to extract structured data from PDFs:

  • 📄 Text Extraction
  • 📊 Table Detection
  • 🧾 OCR for Scanned Docs
  • 🖼️ Image Extraction
  • 📸 PDF Screenshots

These tools feed into the multi-agent system, enabling intelligent, context-aware processing.

🤖 Multi-Agent Architecture

PdfToMem uses a LangGraph-based orchestrator to coordinate specialized agents. Each agent performs a distinct task:

  • 🕵️ Extractor Agent – pulls raw content
  • 🧠 Semantic Agent – applies embedding & understanding
  • ✂️ Segmenter Agent – splits content intelligently
  • 🕸️ Relationship Agent – builds semantic links

The orchestrator decides when and how to invoke these agents based on the content type and reasoning goals.

🧭 Memory Planning — Reasoning Over Representation

Once the data is structured, a planning model (currently o3-mini) determines the best memory format using LlamaIndex components:

  • 🧱 SimpleSentenceIndex
  • 🌐 AutoMergingIndex
  • 🧮 Custom tool-enhanced indices

This reasoning-driven approach ensures optimal retrieval performance tailored to the document.

💾 Storage — Flexible, Dynamic, Queryable

Based on the selected memory representation, a FastAPI-powered Storage Service builds a tailored query engine for each PDF.

  • 🔍 Built-in search and retrieval
  • 🧠 Vector and hybrid index support
  • 🧪 Modular for experimentation

The storage is designed to support scalable memory systems across domains.

🖥️ MCP Client — Full Control with a Friendly UI

The MCP Client is a React-based interface for controlling the full lifecycle:

  • ⚙️ Configure agents, tools, memory plans
  • 📂 Upload and ingest PDFs
  • 🔎 Preview structured chunks
  • 🧪 Query memory and view responses

Everything is interactive, inspectable, and customizable.

🚀 Why PdfToMem?

  • ✅ LLM-optimized ingestion & memory
  • 🧩 Modular tools & agents
  • 🧠 Reasoning-based memory planning
  • 💬 Queryable representations via LlamaIndex
  • 🌐 UI + API for full pipeline control

🛠️ Contributions welcome! Help us build the future of intelligent memory systems.
🔗