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Add MCP to AI docs #1285

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25 changes: 13 additions & 12 deletions pages/ai-ecosystem.mdx
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Expand Up @@ -13,20 +13,21 @@ natural language processing (NLP) systems and beyond. These technologies
frequently intersect, enabling the creation of powerful applications with
Generative AI (GenAI), including advanced chatbots and agents.

## What You'll Find Here
## What you'll find here

This section of Memgraph’s documentation is your guide to using Memgraph for AI:

- [Building GenAI apps with GraphRAG](/ai-ecosystem/graph-rag): See how knowledge graphs enable more
efficient and scalable RAG systems.
- [AI integrations with Memgraph](/ai-ecosystem/integrations): We have several integrations with popular
AI frameworks to help you customize and build your own GenAI application from
scratch. Some of the libraries that support Memgraph
include **LangChain** and **LlamaIndex**.
- [GraphChat in Memgraph Lab](/ai-ecosystem/graphchat): Explore how natural language querying
(GraphChat) ties into the GraphRAG ecosystem, making complex graphs accessible
to everyone.
- [Machine learning with Memgraph](/ai-ecosystem/machine-learning): Learn how Memgraph powers ML workflows
with graph-powered insights.
- [Building GenAI apps with GraphRAG](/ai-ecosystem/graph-rag): See how
knowledge graphs enable more efficient and scalable RAG systems.
- [AI integrations with Memgraph](/ai-ecosystem/integrations): We have several
integrations with popular AI frameworks to help you customize and build your
own GenAI application from scratch. Some of the libraries that support
Memgraph include **Model Context Protocol (MCP)**, **LangChain**
and **LlamaIndex**.
- [GraphChat in Memgraph Lab](/ai-ecosystem/graphchat): Explore how natural
language querying (GraphChat) ties into the GraphRAG ecosystem, making complex
graphs accessible to everyone.
- [Machine learning with Memgraph](/ai-ecosystem/machine-learning): Learn how
Memgraph powers ML workflows with graph-powered insights.

<CommunityLinks/>