HippoRAG
A RAG framework enabling LLMs to integrate knowledge from external documents
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
HippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory, and supports personalized page rank and knowledge graphs.
Good fit when
- When you need a framework that integrates knowledge from external documents into LLMs in a manner inspired by human long-term memory.
- If your project requires the use of personalized page rank and knowledge graphs for enhancing the retrieval and integration of knowledge.
Avoid when
- If your project does not require the integration of knowledge from external documents into LLMs in a manner inspired by human long-term memory.
- When personalized page rank and knowledge graphs are not necessary for your project's knowledge retrieval and integration needs.
- Requirements:
- Ensure you have Python 3.10 installed.; Set environment variables such as `CUDA_VISIBLE_DEVICES`, `HF_HOME`, and `OPENAI_API_KEY` as needed.; Install HippoRAG using Conda or `uv` for a project-local environment.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (14d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 18, 2026
- Security (OSV)
- 96 low (96 low)
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install HippoRAGSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
HippoRAG is a RAG framework that integrates knowledge from external documents into LLMs, inspired by human long-term memory. It supports personalized page rank and knowledge graphs.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 18, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
export OPENAI_API_KEY=<your openai api key> # if you want to use OpenAI modelSource link
Source: README excerpt (regex_v1, Sep 18, 2026)
Use Conda or `uv` to create a Python 3.10 environment. A project-local `.venv` is recommended for development.Source link
Tags
README
Installation Use Conda or to create a Python 3.10 environment. A project local is recommended for development. Set only the environment variables required by the models you use: For a project local environment managed by : Install optional local model support only when needed, fo...
For agents
This page has a .md twin and JSON over the API.