Add veritasgraph-mcp to AI and Agents → Data Layer - #3290
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veritasgraph-mcp provides a data framework for LLM applications with knowledge graphs, tree-search retrieval, and citation-grounded answers.
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Pull request overview
Adds veritasgraph-mcp as a new entry under AI & ML → AI and Agents → Data Layer in README.md, which is the single source of truth for list content (the website renders from it).
Changes:
- Add a new Data Layer list entry for
veritasgraph-mcplinking tobibinprathap/VeritasGraph. - Describe it as a knowledge-graph/GraphRAG framework with tree-search retrieval and verifiable citations.
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| - [instructor](https://github.com/567-labs/instructor) - A library for extracting structured data from LLMs, powered by Pydantic. | ||
| - [llama-index](https://github.com/run-llama/llama_index) - A data framework for your LLM application. | ||
| - [mem0](https://github.com/mem0ai/mem0) - An intelligent memory layer for AI agents enabling personalized interactions. | ||
| - [veritasgraph-mcp](https://github.com/bibinprathap/VeritasGraph) - A knowledge graph framework for building GraphRAG applications with tree-search retrieval and verifiable citations. |
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Summary
veritasgraph-mcp is a Python framework for building GraphRAG applications with knowledge graphs, tree-search retrieval, and verifiable source attribution.
Why AI and Agents → Data Layer?
VeritasGraph serves the same use case as llama-index and mem0—providing a data framework for LLM applications—but specializes in graph-based RAG:
Quality Evidence
Meets All Requirements
Entry Format
Follows standard format:
- [pypi-name](github-url) - Description.