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OSU-NLP-Group avatar

OSU-NLP-Group/HippoRAG

0
View on GitHub↗
3,605 stars·376 forks·Python·MIT·23 viewsarxiv.org/abs/2405.14831↗

HippoRAG

[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank.

Features

  • Agent Memory Systems - Neurobiologically inspired long-term memory for language models.
  • Knowledge Graph Integration - Neurobiologically inspired long-term memory for language models.
  • Memory and Context - Neurobiologically inspired long-term memory for language models.
  • Memory Systems - Neurobiologically inspired long-term memory for language models.
  • Model Adaptation and Merging - Non-parametric continual learning for LLMs.
  • Retrieval Augmented Generation - Memory-inspired framework for retrieval-augmented generation.

Star history

Star history chart for osu-nlp-group/hipporagStar history chart for osu-nlp-group/hipporag

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with HippoRAG

These projects share indexed features with HippoRAG. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • zhongwanjun/memorybank-siliconfriendzhongwanjun avatar

    zhongwanjun/MemoryBank-SiliconFriend

    435View on GitHub↗

    Read this in Chinese (阅读中文版本).

    Python
    View on GitHub↗435
  • cpacker/memgptcpacker avatar

    cpacker/MemGPT

    23,374View on GitHub↗

    MemGPT is a memory management framework and external memory layer for large language models. It functions as a platform for building stateful AI agents that maintain a persistent identity and continuous context across multiple sessions. The system enables agents to bypass fixed context window limitations by using a virtual context windowing approach. This allows models to manage their own memory through internal commands to search, update, and delete stored information within a hierarchical structure of short-term working context and long-term archival storage. The framework provides a local

    Python
    View on GitHub↗23,374
  • mnemox-ai/tradememory-protocolmnemox-ai avatar

    mnemox-ai/tradememory-protocol

    1,259View on GitHub↗

    The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial intelligence trading agents. It provides a structured architecture for agents to maintain episodic, semantic, and procedural knowledge across trading sessions, ensuring that decision-making is informed by long-term recall and historical context. The framework distinguishes itself through a combination of cryptographic integrity and cognitive modeling. It employs a tamper-evident logging system that uses hashed sequences to verify the history of trading decisions, alongside an outcome-wei

    Pythonai-agentsclaudecrypto
    View on GitHub↗1,259
  • anthropics/anthropic-cookbookanthropics avatar

    anthropics/anthropic-cookbook

    45,984View on GitHub↗

    This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce

    Jupyter Notebook
    View on GitHub↗45,984
Compare all 30 related projects→

Frequently asked questions

What does osu-nlp-group/hipporag do?

[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank.

What are the main features of osu-nlp-group/hipporag?

The main features of osu-nlp-group/hipporag are: Agent Memory Systems, Knowledge Graph Integration, Memory and Context, Memory Systems, Model Adaptation and Merging, Retrieval Augmented Generation.

Which projects share features with osu-nlp-group/hipporag?

Projects with overlapping indexed features include: zhongwanjun/memorybank-siliconfriend — Read this in Chinese (阅读中文版本). cpacker/memgpt — MemGPT is a memory management framework and external memory layer for large language models. It functions as a… mnemox-ai/tradememory-protocol — The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial… npubird/knowledgegraphcourse — KnowledgeGraphCourse is a structured collection of graduate-level academic materials, lecture notes, and a… anthropics/anthropic-cookbook — This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and… hkuds/deeptutor — DeepTutor is a framework for personalized AI tutoring and educational content generation. It functions as an agentic…