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Youtu Graphrag

Youtu Graphrag is a Python research project for graph retrieval-augmented question answering, using agents to construct and retrieve from schema-guided knowledge graphs and reason through complex queries.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Youtu Graphrag — OpenViST project profile in Agent Memory & RAG.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to research or prototype question answering and summarization where answers require multi-step reasoning over structured, private or domain-specific knowledge.

Who it is for

  • Researchers studying graph retrieval-augmented generation and agent reasoning.
  • Developers prototyping question-answering systems over structured or domain-specific knowledge.

Documented uses

  • Explore multi-hop answers that require connecting facts across a knowledge graph.
  • Build a schema-guided graph representation from domain knowledge.
  • Decompose complex queries into parallel sub-questions and iteratively refine reasoning.
  • Experiment with retrieval evaluation using the AnonyRAG dataset.
Before you choose

Requirements & limits

The public GitHub repository documents a Docker setup and a web UI setup using Python 3.10. Both require an LLM API configured in OpenAI-compatible format.

  • The README recommends Docker when environments differ; the alternative setup uses Python 3.10 and pip.
  • The framework centers on a four-level knowledge tree covering attributes, relations, keywords and communities.
  • Agentic retrieval includes schema-aware query decomposition, parallel sub-question processing and iterative reflection.
  • The repository describes Neo4j import for visualizing generated knowledge trees.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the web UI with Docker

Prepare and start the project’s web demo using the documented Docker path.

Before you start

  • Docker
  • An LLM API configured in OpenAI-compatible format

Steps

  1. Clone the Youtu-GraphRAG repository and enter its directory.

  2. Copy .env.example to .env and configure the LLM model, base URL and API key.

  3. Build the Docker image using the repository Dockerfile.

  4. Run the container with port 8000 mapped and visit http://localhost:8000.

Expected outcome

The project’s web demo is started and available at the local address specified in the README.

Guide limitations

  • The guide documents startup, not a particular question-answering workflow.
  • An LLM API configuration is required.
Documentation 1 ↗
Source-based practical guide

Set up the web UI with Python

Prepare and launch the project’s web UI using its documented Python environment.

Before you start

  • Python 3.10
  • A pip environment
  • An LLM API configured in OpenAI-compatible format

Steps

  1. Clone the Youtu-GraphRAG repository and enter its directory.

  2. Copy .env.example to .env and configure the LLM model, base URL and API key.

  3. Run ./setup_env.sh.

  4. Run ./start.sh and visit http://localhost:8000.

Expected outcome

The project’s web UI is launched at the local address specified in the README.

Guide limitations

  • The guide documents setup and launch, not a particular question-answering workflow.
  • The documented environment uses Python 3.10.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
TencentCloudADP/youtu-graphrag · Official homepage title · retained in its source language
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