Oracle AI Database Integrations

Use this overview to choose an Oracle AI Database integration for retrieval, agent state, and AI application persistence.

Oracle AI Database integrations connect AI application frameworks to database capabilities such as vector search, document loading, checkpointing, and durable memory.

Available Integrations

Use the available integrations to match framework features with the database capabilities that your application needs.

Integration	Use For	Primary Oracle Capabilities
LangChain	Retrieval-augmented generation, semantic search, chat history, semantic caching, and agent memory.	Oracle AI Vector Search, Oracle Text, in-database embeddings, vector stores, and message history.
LangChain Deep Agents	Long-running research agents that plan, delegate work, use files, and retrieve grounded Oracle data.	OCI Generative AI, Oracle AI Vector Search, Oracle Text hybrid retrieval, LangGraph checkpoints, application-managed long-term memory, and agent file persistence.
LangGraph	Durable agent state, checkpoint persistence, long-term memory, and agent workflows.	Checkpoint tables, JSON stores, connection pooling, and vector search.
Spring AI	Spring Boot applications that need vector stores, similarity search, metadata filtering, and chat memory.	Oracle AI Vector Search, JDBC connectivity, vector indexes, document metadata filters, and chat memory persistence.
Mem0	AI applications that need persistent, searchable long-term memory.	Oracle AI Vector Search, JSON metadata filtering, vector indexes, and connection pooling.
n8n	Workflow automation, AI retrieval, in-database embeddings, and Oracle SQL operations.	Oracle AI Vector Search, ONNX embeddings, node-oracledb connectivity, and SQL data operations.

Choose an Integration

Use LangChain when an application needs a framework for retrieval and model orchestration. LangChain works well when the main database tasks are document ingestion, chunking, embedding, vector search, retrieval, chat history, or semantic caching.

Use LangChain Deep Agents when an agent needs to plan multi-step work, delegate to subagents, preserve intermediate files, or research across Oracle datastores. The langchain-oci factory combines the upstream LangChain Deep Agents harness with generated Oracle retrieval tools and optional Oracle-backed persistence.

Use LangGraph when an application needs durable state across agent steps. LangGraph works well when the application must pause, resume, checkpoint, or search long-term memory across graph executions.

Use Spring AI when a Spring Boot application needs to store document embeddings, run similarity search through a Spring-native VectorStore abstraction, or persist chat memory with JDBC.

Use Mem0 when an AI application needs to extract, persist, and retrieve long-term user or agent memories. Mem0 stores its memory vectors and payloads in Oracle AI Database through the oracledb vector store provider.

Use n8n when a workflow needs to automate Oracle SQL operations or connect Oracle AI Vector Search to document loaders, retrievers, chains, tools, or AI agents. n8n provides separate Oracle AI Database, Oracle AI Database Vector Store, and Oracle AI Database Embeddings nodes.

Use LangChain and LangGraph together when a stateful agent needs retrieval and durable workflow state. For example, an agent can retrieve context through LangChain components and persist graph state through LangGraph components.

Shared Oracle AI Database Capabilities

Oracle AI Database provides shared capabilities that support these integration patterns:

• Vector search: Store embedding vectors with source data and query them with semantic similarity search.
• Document processing: Load and process files with Oracle Text when a retrieval workflow needs source documents.
• Hybrid retrieval: Combine semantic and keyword signals when an application needs more precise retrieval.
• Durable persistence: Store application state, messages, and memory in database tables with transactional guarantees.
• Operational control: Use existing database security, backup, monitoring, and lifecycle processes for AI application data.

Related Guides

Use the related guides for implementation details, setup steps, and examples.

Guide	Scope
LangChain and Oracle AI Database Integration Guide	Build retrieval-augmented generation, agentic search, semantic caching, chat history, and memory-augmented AI applications.
LangChain Deep Agents and Oracle Integration Guide	Build planning and research agents with Oracle retrieval, generated tools, checkpoints, application-managed memory, and persistent files.
LangGraph and Oracle AI Database Integration Guide	Persist checkpoints, stores, agent state, and searchable long-term memory for LangGraph applications.
Spring AI and Oracle AI Database Integration Guide	Configure Spring AI Oracle vector stores and chat memory for Spring Boot applications.
Mem0 and Oracle AI Database Integration Guide	Configure Mem0 persistent memory with Oracle AI Vector Search.
n8n and Oracle AI Database Integration Guide	Automate Oracle AI Database operations and build n8n AI retrieval workflows with Oracle AI Vector Search and in-database embeddings.