docs: remove deprecated Graph Memory references

Graph Memory was deprecated on April 16, 2026. This removes remaining
mentions across docs that could mislead readers into thinking it is still
a supported feature.

- Platform pages (overview, quickstart, faqs, platform-vs-oss, vibecoding):
  remove graph memory from feature lists, comparison table, and intro copy
- Platform features (platform-overview, mcp-integration,
  advanced-memory-operations): rephrase "graph-powered retrieval" to
  "advanced retrieval"; drop "Enable graph memories" tip; remove graph-edge
  verification step from the metadata example
- Core concepts (add, search, memory-types, memory-evaluation): remove
  "optional graph storage" / "graph features" / "graph writes" / "graph
  toggles"; drop "graph" from Entity Search description; remove outdated
  add/search architecture diagrams (replacement pending)
- Open source features (overview, rest-api): remove "graph relationships"
  and "graph backend(s)" from REST server config narrative
- API reference (organizations-projects): drop "graph settings" from
  Update Project Settings description
- Cookbooks: remove "Graph Memory on Neptune" card (page removed) and
  Neptune mention from AWS Bedrock card; remove "Graph Capabilities"
  bullet from Gemini cookbook; remove graph-memory conditional bullet from
  controlling-memory-ingestion
- CLI: remove --graph / --no-graph flags from mem0 add / mem0 search
  flag tables and MEM0_ENABLE_GRAPH env var
- Images: delete orphan add_architecture.png and search_architecture.png

docs/api-reference/organizations-projects.mdx

@@ -79,7 +79,7 @@ new_project = client.project.create(
 
 ### Update Project Settings
 
-Modify project configuration including custom instructions, categories, graph settings, and language preferences:
+Modify project configuration including custom instructions, categories, and language preferences:
 
 ```python
 # Update project with custom categories

docs/cookbooks/essentials/controlling-memory-ingestion.mdx

@@ -323,10 +323,6 @@ Metadata: {'verified': True, 'updated_date': '2025-04-02'}
 That “no duplicates” promise comes from the inference pipeline. Keep `infer=True` when you rely on automatic updates. Raw imports (`infer=False`) skip conflict checks, so mixing the two modes for the same fact will create duplicates.
 </Warning>
 
-**Maintains relationships:**
-
-- If using graph memory, connections to other entities persist
-
 ### Pick the right inference mode
 
 | Mode | What it does | Best for | Watch out for |

docs/cookbooks/frameworks/gemini-3-with-mem0-mcp.mdx

@@ -216,7 +216,6 @@ This information was retrieved from your memory history where you previously men
 
 - **Smart Memory Management** - Organizes memories into searchable information *without setting up vector databases*
 - **Fast Retrieval** - Instant lookups with *sub-millisecond ping*, handles large datasets
-- **Graph Capabilities** - Builds knowledge *automatically* as you push information
 - **Simple Integration** - Uses Mem0 API in the backend, works with *any MCP client* with just a few lines of code
 
 ### Gemini 3 + Mem0 Benefits

docs/cookbooks/overview.mdx

@@ -156,14 +156,7 @@ Here are some examples of how Mem0 can be integrated into various applications:
     icon="aws"
     href="/cookbooks/integrations/aws-bedrock"
   >
-    Mem0 with AWS Bedrock and Neptune.
-  </Card>
-  <Card
-    title="Graph Memory on Neptune"
-    icon="network-wired"
-    href="/cookbooks/integrations/neptune-analytics"
-  >
-    Graph memory with Neptune Analytics.
+    Mem0 with AWS Bedrock.
   </Card>
 </CardGroup>
 

docs/core-concepts/memory-evaluation.mdx

@@ -47,7 +47,7 @@ When a query arrives, the retrieval pipeline scores candidates across three sign
 
 1. **Semantic Search** — Vector similarity scoring against memory embeddings
 2. **Keyword Search** — Normalized term matching via BM25 with verb-form lemmatization
-3. **Entity Search** — Entity graph matching boosts memories linked to query entities
+3. **Entity Search** — Entity matching boosts memories linked to query entities
 
 Results are fused via rank scoring into a final top-K set. Different query types lean on different signals:
 

docs/core-concepts/memory-operations/add.mdx

@@ -27,15 +27,11 @@ Adding memory is how Mem0 captures useful details from a conversation so your ag
 
 Mem0 offers two flows:
 
-- **Mem0 Platform** – Fully managed API with dashboard, scaling, and graph features.
+- **Mem0 Platform** – Fully managed API with dashboard and scaling.
 - **Mem0 Open Source** – Local SDK that you run in your own environment.
 
 Both flows take the same payload and pass it through the same pipeline.
 
-<Frame caption="Architecture diagram illustrating the process of adding memories.">
-  <img src="../../images/add_architecture.png" />
-</Frame>
-
 <Steps>
 <Step title="Information extraction">
 Mem0 sends the messages through an LLM that pulls out key facts, decisions, or preferences to remember.
@@ -44,7 +40,7 @@ Mem0 sends the messages through an LLM that pulls out key facts, decisions, or p
 Existing memories are checked for duplicates or contradictions so the latest truth wins.
 </Step>
 <Step title="Storage">
-The resulting memories land in managed vector storage (and optional graph storage) so future searches return them quickly.
+The resulting memories land in managed vector storage so future searches return them quickly.
 </Step>
 </Steps>
 
@@ -177,7 +173,7 @@ For full list of supported fields, required formats, and advanced options, see t
 
 ## Put it into practice
 
-- Review the <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> guide to layer metadata, rerankers, and graph toggles.
+- Review the <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> guide to layer metadata and rerankers.
 - Explore the <Link href="/api-reference/memory/add-memories">Add Memories API reference</Link> for every request/response field.
 
 ## See it live

docs/core-concepts/memory-operations/search.mdx

@@ -25,10 +25,6 @@ Mem0's search operation lets agents ask natural-language questions and get back
 
 ## Architecture
 
-<Frame caption="Architecture diagram illustrating the memory search process.">
-  <img src="../../images/search_architecture.png" />
-</Frame>
-
 <Steps>
 <Step title="Query processing">
 Mem0 cleans and enriches your natural-language query so the downstream embedding search is accurate.

docs/core-concepts/memory-types.mdx

@@ -104,7 +104,7 @@ results = memory.search(
 ## Put it into practice
 
 - Use the <Link href="/core-concepts/memory-operations/add">Add Memory</Link> guide to persist user preferences.
-- Follow <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> to tune metadata and graph writes.
+- Follow <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> to tune metadata and retrieval.
 
 ## See it live
 

docs/open-source/features/overview.mdx

@@ -6,7 +6,7 @@ icon: "list"
 
 # Self-Hosting Features Overview
 
-Mem0 Open Source ships with capabilities that adapt memory behavior for production workloads—async operations, graph relationships, multimodal inputs, and fine-tuned retrieval. Configure these features with code or YAML to match your application's needs.
+Mem0 Open Source ships with capabilities that adapt memory behavior for production workloads—async operations, multimodal inputs, and fine-tuned retrieval. Configure these features with code or YAML to match your application's needs.
 
 <Info>
   Start with the <Link href="/open-source/python-quickstart">Python quickstart</Link> to validate basic memory operations, then enable the features below when you need them.

docs/open-source/features/rest-api.mdx

@@ -119,7 +119,7 @@ docker build -t mem0-api-server .
 </Tip>
 
 <Note>
-  The REST server reads the same configuration you use locally, so you can point it at your preferred LLM, vector store, graph backend, and reranker without changing code.
+  The REST server reads the same configuration you use locally, so you can point it at your preferred LLM, vector store, and reranker without changing code.
 </Note>
 
 ---
@@ -319,7 +319,7 @@ The `/auth/*`, `/api-keys`, `/requests`, and `/entities` routes are new to the s
 
 <CardGroup cols={2}>
   <Card title="Configure OSS Components" icon="sliders" href="/open-source/configuration">
-    Fine-tune LLMs, vector stores, and graph backends that power the REST server.
+    Fine-tune LLMs, vector stores, and rerankers that power the REST server.
   </Card>
   <Card title="Automate Agent Integrations" icon="plug" href="/cookbooks/integrations/agents-sdk-tool">
     See how services call the REST endpoints as part of an automation pipeline.

docs/platform/advanced-memory-operations.mdx

@@ -64,7 +64,7 @@ const memory = new Memory({ apiKey: process.env.MEM0_API_KEY!, async: true });
   </Tab>
 </Tabs>
 
-## Add memories with metadata and graph context
+## Add memories with metadata
 
 <Tabs>
   <Tab title="Python">
@@ -109,7 +109,7 @@ const result = await memory.add(conversation, {
 </Tabs>
 
 <Info icon="check">
-  Successful calls return memories tagged with the metadata you passed. In the dashboard, confirm a graph edge between “Morgan” and “Tokyo” and verify the `trip=japan-2025` tag exists.
+  Successful calls return memories tagged with the metadata you passed. In the dashboard, verify the `trip=japan-2025` tag exists on the new memory.
 </Info>
 
 ## Retrieve and refine
@@ -201,7 +201,7 @@ await memory.deleteAll({ userId: "traveler-42", runId: "planning-call-1" });
   />
   <Card
     title="Explore Reranker Search"
-    description="See how rerankers boost accuracy after vector + graph retrieval."
+    description="See how rerankers boost accuracy after advanced retrieval."
     icon="sparkles"
     href="/open-source/features/reranker-search"
   />

docs/platform/cli.mdx

@@ -121,7 +121,6 @@ echo "Loves hiking on weekends" | mem0 add --user-id alice
 | `-f, --file` | Read messages from a JSON file |
 | `-m, --metadata` | Custom metadata as JSON |
 | `--categories` | Categories (JSON array or comma-separated) |
-| `--graph / --no-graph` | Enable or disable graph memory extraction |
 | `-o, --output` | Output format: `text`, `json`, `quiet` |
 
 ### `mem0 search`
@@ -141,7 +140,6 @@ mem0 search "preferred tools" --user-id alice --output json --top-k 5
 | `--rerank` | Enable reranking |
 | `--keyword` | Use keyword search instead of semantic |
 | `--filter` | Advanced filter expression (JSON) |
-| `--graph / --no-graph` | Enable or disable graph in search |
 | `-o, --output` | Output format: `text`, `json`, `table` |
 
 ### `mem0 list`
@@ -436,7 +434,6 @@ For non-interactive environments (CI, agent runtimes), set credentials via `mem0
 | `MEM0_AGENT_ID` | Default agent ID |
 | `MEM0_APP_ID` | Default app ID |
 | `MEM0_RUN_ID` | Default run ID |
-| `MEM0_ENABLE_GRAPH` | Enable graph memory (`true` / `false`) |
 
 Environment variables take precedence over values in the config file, which take precedence over defaults.
 

docs/platform/faqs.mdx

@@ -9,7 +9,7 @@ iconType: "solid"
     <Accordion title="How does Mem0 work?">
         Mem0 utilizes a sophisticated hybrid database system to efficiently manage and retrieve memories for AI agents and assistants. Each memory is linked to a unique identifier, such as a user ID or agent ID, enabling Mem0 to organize and access memories tailored to specific individuals or contexts.
 
-        When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them across various data stores: a vector database and a graph database. This hybrid strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
+        When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them in a managed vector store. This strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
 
         When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.
 

docs/platform/features/mcp-integration.mdx

@@ -130,7 +130,6 @@ The Mem0 MCP server enables powerful memory capabilities for your AI application
 
 ## Performance tips
 
-- Enable graph memories for relationship-aware recall
 - Use specific filters when searching large memory sets
 - Batch operations when adding multiple memories
 - Monitor memory usage in the Mem0 dashboard

docs/platform/features/platform-overview.mdx

@@ -1,10 +1,10 @@
 ---
 title: Overview
-description: "See how Mem0 Platform features evolve from baseline filters to graph-powered retrieval."
+description: "See how Mem0 Platform features evolve from baseline filters to advanced retrieval."
 icon: "list"
 ---
 
-Mem0 Platform features help managed deployments scale from basic filtering to graph-powered retrieval and data governance. Use this page to pick the right feature lane for your team.
+Mem0 Platform features help managed deployments scale from basic filtering to advanced retrieval and data governance. Use this page to pick the right feature lane for your team.
 
 <Info>
   New to the platform? Start with the <Link href="/platform/quickstart">Platform quickstart</Link>,

docs/platform/overview.mdx

@@ -11,7 +11,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
 ## Why it matters
 
 - **Personalized replies**: Memories persist across users and agents, cutting prompt bloat and repeat questions.
-- **Hosted stack**: Mem0 runs the vector store, graph services, and rerankers—no provisioning, tuning, or maintenance.
+- **Hosted stack**: Mem0 runs the vector store and rerankers—no provisioning, tuning, or maintenance.
 - **Enterprise controls**: Audit logs and workspace governance ship by default for production readiness.
 
 <AccordionGroup>
@@ -21,7 +21,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
     | --- | --- |
     | Fast setup | Add a few lines of code and you’re production-ready—no vector database or LLM configuration required. |
     | Production scale | Automatic scaling, high availability, and managed infrastructure so you focus on product work. |
-    | Advanced features | Graph memory, webhooks, multimodal support, and custom categories are ready to enable. |
+    | Advanced features | webhooks, multimodal support, and custom categories are ready to enable. |
     | Enterprise ready | Audit logs, workspace governance, and dedicated support keep security and governance covered. |
   </Accordion>
 </AccordionGroup>
@@ -49,7 +49,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
     Add, search, update, and delete workflows.
   </Card>
   <Card title="Explore Platform Features" icon="sparkles" href="/platform/features/platform-overview">
-    Graph memory, async clients, and rerankers.
+    async clients and rerankers.
   </Card>
   <Card title="Configure Advanced Operations" icon="bolt" href="/platform/advanced-memory-operations">
     Metadata filters and per-request toggles.

docs/platform/platform-vs-oss.mdx

@@ -57,7 +57,6 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
   <Accordion title="Advanced Capabilities" icon="sparkles">
     | Feature | Platform | Open Source |
     |---------|----------|-------------|
-    | **Graph Memory** | ✅ (Managed) | ✅ (Self-configured) |
     | **Multimodal support** | ✅ | ✅ |
     | **Custom categories** | ✅ | Limited |
     | **Advanced retrieval** | ✅ | ✅ |

docs/platform/quickstart.mdx

@@ -155,7 +155,7 @@ Learn how to search, update, and delete memories with complete CRUD operations
 </Card>
 
 <Card title="Platform Features" icon="star" href="/platform/features/platform-overview">
-  Explore advanced features like metadata filtering, graph memory, and webhooks
+  Explore advanced features like metadata filtering and webhooks
 </Card>
 
 <Card title="API Reference" icon="code" href="/api-reference/memory/add-memories">

docs/vibecoding.mdx

@@ -96,7 +96,7 @@ applications that gives agents persistent context across sessions.
 Mem0 is a memory layer for AI apps — managed (Mem0 Platform) or self-hosted
 (Open Source). It stores, retrieves, and manages user memories so agents
 remember preferences, learn from interactions, and personalize over time.
-Sub-50ms retrieval. Dual storage: vector embeddings + graph databases.
+Sub-50ms retrieval. Storage: vector embeddings.
 
 **Architecture Overview:**
 - Memory is scoped by user_id, agent_id, or run_id