diff --git a/src/docs.json b/src/docs.json
index 70bd51f3ef..944cd19c1b 100644
--- a/src/docs.json
+++ b/src/docs.json
@@ -360,6 +360,7 @@
                       "group": "Conceptual overviews",
                       "icon": "book",
                       "pages": [
+                        "oss/python/langchain/component-architecture",
                         "oss/python/concepts/memory",
                         "oss/python/concepts/context",
                         "oss/python/langgraph/graph-api",
@@ -723,6 +724,7 @@
                       "group": "Conceptual overviews",
                       "icon": "book",
                       "pages": [
+                        "oss/javascript/langchain/component-architecture",
                         "oss/javascript/concepts/memory",
                         "oss/javascript/concepts/context",
                         "oss/javascript/langgraph/graph-api",
diff --git a/src/oss/langchain/component-architecture.mdx b/src/oss/langchain/component-architecture.mdx
new file mode 100644
index 0000000000..a66a5838ae
--- /dev/null
+++ b/src/oss/langchain/component-architecture.mdx
@@ -0,0 +1,121 @@
+---
+title: Component architecture
+---
+
+LangChain's power comes from how its components work together to create sophisticated AI applications. This page provides diagrams showcasing the relationships between different components.
+
+## Core component ecosystem
+
+The diagram below shows how LangChain's major components connect to form complete AI applications:
+
+```mermaid
+graph TD
+    %% Input processing
+    subgraph "📥 Input processing"
+        A[Text input] --> B[Document loaders]
+        B --> C[Text splitters]
+        C --> D[Documents]
+    end
+
+    %% Embedding & storage
+    subgraph "🔢 Embedding & storage"
+        D --> E[Embedding models]
+        E --> F[Vectors]
+        F --> G[(Vector stores)]
+    end
+
+    %% Retrieval
+    subgraph "🔍 Retrieval"
+        H[User Query] --> I[Embedding models]
+        I --> J[Query vector]
+        J --> K[Retrievers]
+        K --> G
+        G --> L[Relevant context]
+    end
+
+    %% Generation
+    subgraph "🤖 Generation"
+        M[Chat models] --> N[Tools]
+        N --> O[Tool results]
+        O --> M
+        L --> M
+        M --> P[AI response]
+    end
+
+    %% Orchestration
+    subgraph "🎯 Orchestration"
+        Q[Agents] --> M
+        Q --> N
+        Q --> K
+        Q --> R[Memory]
+    end
+```
+
+### How components connect
+
+Each component layer builds on the previous ones:
+
+1. **Input processing** – Transform raw data into structured documents
+2. **Embedding & storage** – Convert text into searchable vector representations
+3. **Retrieval** – Find relevant information based on user queries
+4. **Generation** – Use AI models to create responses, optionally with tools
+5. **Orchestration** – Coordinate everything through agents and memory systems
+
+## Component categories
+
+LangChain organizes components into these main categories:
+
+| Category | Purpose | Key Components | Use Cases |
+|----------|---------|---------------|-----------|
+| **[Models](/oss/langchain/models)** | AI reasoning and generation | Chat models, LLMs, Embedding models | Text generation, reasoning, semantic understanding |
+| **[Tools](/oss/langchain/tools)** | External capabilities | APIs, databases, etc. | Web search, data access, computations |
+| **[Agents](/oss/langchain/agents)** | Orchestration and reasoning | ReAct agents, tool calling agents | Nondeterministic workflows, decision making |
+| **[Memory](/oss/langchain/short-term-memory)** | Context preservation | Message history, custom state | Conversations, stateful interactions |
+| **[Retrievers](/oss/integrations/retrievers)** | Information access | Vector retrievers, web retrievers | RAG, knowledge base search |
+| **[Document processing](/oss/integrations/document_loaders)** | Data ingestion | Loaders, splitters, transformers | PDF processing, web scraping |
+| **[Vector Stores](/oss/integrations/vectorstores)** | Semantic search | Chroma, Pinecone, FAISS | Similarity search, embeddings storage |
+
+## Common patterns
+
+### RAG (Retrieval-Augmented Generation)
+```mermaid
+graph LR
+    A[User question] --> B[Retriever]
+    B --> C[Relevant docs]
+    C --> D[Chat model]
+    A --> D
+    D --> E[Informed response]
+```
+
+### Agent with tools
+```mermaid
+graph LR
+    A[User request] --> B[Agent]
+    B --> C{Need tool?}
+    C -->|Yes| D[Call tool]
+    D --> E[Tool result]
+    E --> B
+    C -->|No| F[Final answer]
+```
+
+### Multi-agent system
+```mermaid
+graph LR
+    A[Complex Task] --> B[Supervisor agent]
+    B --> C[Specialist agent 1]
+    B --> D[Specialist agent 2]
+    C --> E[Results]
+    D --> E
+    E --> B
+    B --> F[Coordinated response]
+```
+
+## Learn more
+
+Now that you understand how components relate to each other, explore specific areas:
+
+- [Building your first RAG system](/oss/langchain/knowledge-base)
+- [Creating agents](/oss/langchain/agents)
+- [Working with tools](/oss/langchain/tools)
+- [Setting up memory](/oss/langchain/short-term-memory)
+- [Browse integrations](/oss/integrations/providers/overview)