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+# Neo4j Context Providers
+
+Neo4j offers two context providers for the Agent Framework, each serving a different purpose:
+
+| | [Neo4j Memory](../neo4j_memory/README.md) | [Neo4j GraphRAG](../../../05-end-to-end/neo4j_graphrag/README.md) |
+|---|---|---|
+| **What it does** | Read-write memory — stores conversations, builds knowledge graphs, learns from interactions | Read-only retrieval from a pre-existing knowledge base with optional graph traversal |
+| **Data source** | Agent interactions (grows over time) | Pre-loaded documents and indexes |
+| **Python package** | [`neo4j-agent-memory`](https://pypi.org/project/neo4j-agent-memory/) | [`agent-framework-neo4j`](https://pypi.org/project/agent-framework-neo4j/) |
+| **Database setup** | Empty — creates its own schema | Requires pre-indexed documents with vector or fulltext indexes |
+| **Example use case** | "Remember my preferences", "What did we discuss last time?" | "Search our documents", "What risks does Acme Corp face?" |
+
+## Which should I use?
+
+**Use [Neo4j Memory](../neo4j_memory/README.md)** when your agent needs to remember things across sessions — user preferences, past conversations, extracted entities, and reasoning traces. The memory provider writes to the database on every interaction, building a knowledge graph that grows over time.
+
+**Use [Neo4j GraphRAG](../../../05-end-to-end/neo4j_graphrag/README.md)** when your agent needs to search an existing knowledge base — documents, articles, product catalogs — and optionally enrich results by traversing graph relationships. The GraphRAG provider is read-only and does not modify your data.
+
+You can use both together: GraphRAG for domain knowledge retrieval, Memory for personalization and learning.
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+# Neo4j Memory Context Provider
+
+[Neo4j Agent Memory](https://github.com/neo4j-labs/agent-memory) is a graph-native memory system for AI agents that stores conversations, builds knowledge graphs from interactions, and lets agents learn from their own reasoning — all backed by Neo4j.
+
+For full documentation, installation instructions, code examples, and configuration details, see the [Neo4j Memory integration guide on Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/neo4j-memory).
+
+For a runnable example, see the [retail assistant sample](https://github.com/neo4j-labs/agent-memory/tree/main/examples/microsoft_agent_retail_assistant).
+
+For help choosing between the Memory and GraphRAG providers, see the [Neo4j Context Providers overview](../neo4j/README.md).
diff --git a/python/samples/05-end-to-end/neo4j_graphrag/README.md b/python/samples/05-end-to-end/neo4j_graphrag/README.md
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 This sample keeps setup lightweight by using a pre-built Neo4j fulltext index plus a graph-enrichment query.
 
+For full documentation, see the [Neo4j GraphRAG integration guide on Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/neo4j-graphrag).
+
 ## Example
 
 | File | Description |