Agentic AI applications have become the talk of the AI town lately. Today, enterprises seek to build agentic applications to automate complex tasks that were once considered impossible. By deploying multiple AI agents, organizations can now create advanced multi-agent applications that enhance workflows with essential features and capabilities.

These AI agents are no longer reserved exclusively for software developers; even non-coders are empowered to build innovative applications using no-code platforms. Agentic systems and applications are now more relevant than ever, offering transformative value for businesses and individuals alike.

In this article, discover how you can build powerful agentic applications using an all-in-one data platform like SingleStore, which streamlines integration with modern frameworks and brings these capabilities directly to your development workflow.

Why SingleStore for Agentic AI Applications?

Agentic AI thrives when intelligence meets speed, scale, and flexibility—and that’s exactly where SingleStore shines. Instead of juggling multiple databases for structured, unstructured, and vector data, SingleStore brings everything under one roof. It’s a single engine that can power real-time reasoning, decision-making, and autonomous execution for your AI agents. With its distributed architecture, you get both performance and reliability at scale. Whether it’s responding to user queries, planning multi-step workflows, or analyzing streams of data, SingleStore ensures your agents don’t just think smarter—they act faster. This unified approach makes building production-ready agentic AI far simpler and more efficient.

Unified Querying & Hybrid Search

One of the biggest challenges in agentic AI is seamlessly combining structured business data with unstructured context and embeddings. SingleStore solves this with unified querying and hybrid search. It natively supports both SQL operations and vector similarity search in a single system, eliminating the need for clunky integrations between separate databases. This means your AI agents can fetch customer records using SQL while simultaneously retrieving semantically similar documents, embeddings, or chat history—all in real time. The result is contextually rich, precise, and fast responses. With hybrid search, you’re not limited to keyword or vector lookups alone; you can blend them to achieve higher accuracy. For developers, this means less plumbing and more focus on building smarter, context-aware agents.

Scalability, Low Latency & Cost Efficiency

For agentic AI, speed and scalability aren’t just nice-to-haves—they’re essential. SingleStore is built to deliver ultra-low latency, ensuring agents can execute tasks and respond in milliseconds, even under heavy workloads. Its distributed architecture scales effortlessly as your data or user base grows, without slowing down. On top of performance, SingleStore helps keep costs under control by consolidating multiple data engines into one platform. No need for separate databases for vectors, time series, or analytics—it’s all handled in a single system. That efficiency translates directly into lower infrastructure costs while still empowering agents to operate at production-grade speed and reliability.

Simplified Architecture & Enterprise Benefits

What makes SingleStore stand out for agentic AI is its simplicity and robustness. Instead of patching together multiple systems, you get a clean, streamlined architecture that reduces operational complexity. It works equally well across cloud environments or local deployments, giving teams flexibility based on their infrastructure needs. Most importantly, it’s fully ACID compliant—something you don’t often see in vector databases—ensuring your agent workflows run with enterprise-grade consistency and reliability. This combination of simplified architecture, strong compliance, and deployment flexibility makes SingleStore the perfect foundation for real-world agentic applications, where reliability and trust matter just as much as speed and intelligence.

SingleStore Integration Overview

SingleStore (formerly MemSQL) has emerged as a compelling hybrid transactional/analytical platform well suited for AI and LLM applications, especially those requiring real-time ingestion, hybrid search (vector + full-text), and scale. Below is an overview of how SingleStore integrates with major LLM/AI frameworks, together with a survey of connection methods (drivers, APIs, etc.).

Integration with LLM / AI Frameworks & Tooling

LangChain supports SingleStore as both a vector store and a provider in Python. For vector usage, the SingleStoreVectorStore class enables storing embeddings, executing nearest-neighbor similarity queries (via dot_product or euclidean_distance), and combining vector and full-text filtering. LangChain also offers wrappers such as SingleStoreLoader (to load documents from a SingleStore table) and SingleStoreChatMessageHistory / SingleStoreSemanticCache for chat/memory use cases. See the official LangChain docs at:

https://python.langchain.com/docs/integrations/vectorstores/singlestore/

https://python.langchain.com/docs/integrations/providers/singlestore/

Typical use cases include:

Persistent chat / memory: You can store chat messages or embeddings in SingleStore, enabling agents to revisit prior turns via similarity queries (e.g. “retrieve last K relevant messages”) across sessions.

Semantic cache: The system may cache query ↔ embedding results, so repeated LLM queries can bypass embedding or scoring when a similar query was seen before.

Document loaders / retrieval: You can ingest documents (PDFs, text) into SingleStore tables, embed them, and then use LangChain’s document loader patterns to populate the vector store. LangChain also supports wrapper utilities like SingleStoreLoader or history store classes.

There are community and tutorial resources: e.g. “Implementing RAG using LangChain and SingleStore: A Step-by-Step Guide” walks through end-to-end setup.

In practice, this integration streamlines your AI workflow by collapsing the retrieval, storage, and metadata logic into a single unified system, enabling fewer moving parts and lower latency between query ↔ data access.

LangChain & SingleStore Integration Package Usage:

To access SingleStore vector stores you’ll need to install the langchain-singlestore integration package.

1!pip install -qU "langchain-singlestore

Build an AI application/system following this package from here: https://python.langchain.com/docs/integrations/vectorstores/singlestore/

LlamaIndex

LlamaIndex offers a SingleStore-backed vector / storage integration via a storage interface (often named “SingleStoreDB”). This integration enables storing and querying document embeddings directly in SingleStore. The LlamaIndex docs (Python) cover this under their storage / vector store API. (E.g. “Storage / vector_store / SingleStoreDB” in LlamaIndex API reference).

See more from the official LlamaIndex docs:

https://developers.llamaindex.ai/python/framework-api-reference/storage/vector_store/singlestoredb/

Developers building multi-agent or orchestrated RAG systems can use this integration to ensure each agent or sub-engine can address different domains (e.g. finance, sales) but use the same SingleStore instance, enabling cross-agent linking or shared memory. A SingleStore blog post describes how LlamaIndex + SingleStore scale with growing data and maintain real-time query performance. Also, you can find hands-on walkthroughs (e.g. “How to Build a GenAI App with LlamaIndex”) that include repository code.

Overall, LlamaIndex + SingleStore enables a unified storage and retrieval layer for RAG systems, minimizing data duplication, ensuring fresh updates, and allowing seamless, scalable semantic retrieval across agents or modules.

LlamaIndex & SingleStore Integration Package Usage:

SingleStore used as a vector store that stores embeddings within a SingleStore database table.

During query time, the index uses SingleStore to query for the top k most similar nodes.

1!pip install llama-index-vector-stores-singlestoredb

CrewAI supports integrating with SingleStore both via direct query tools and by wrapping LlamaIndex tools into CrewAI workflows. For instance, the SingleStoreSearchTool safely executes read-only SELECT/SHOW queries with connection pooling. Also, the LlamaIndexTool wrapper enables using LlamaIndex-based query engines as tools within CrewAI agents.

More on CrewAI database tooling:

https://docs.crewai.com/en/tools/database-data/singlestoresearchtool

These integrations illustrate a pattern: SingleStore can serve as (a) a vector store or embedding store layer, (b) a document store or “knowledge base” for retrieval, or (c) a queryable backend for agents / tools in multi-agent systems.

In agentic RAG pipelines, CrewAI + SingleStore allow agents to share a common persistence layer, enabling cross-agent memory, state passing, or tool chaining while maintaining safety and pooled access to the database.

CrewAI & SingleStore Integration Package Usage:

1uv add crewai-tools[singlestore]

Build an AI application/system following this package from here: https://docs.crewai.com/en/tools/database-data/singlestoresearchtool

Phidata

Phidata integrates with SingleStore to provide a fast, scalable vector database for AI and LLM-powered applications. Using the SingleStoreVectorStore connector, developers can store and query embeddings directly in SingleStore to power RAG systems, chatbots, and AI agents with real-time context.This integration combines SingleStore’s hybrid SQL + vector search with Phidata’s AI workflow capabilities, enabling efficient retrieval, filtering, and reasoning across structured and unstructured data. It supports similarity searches (cosine, dot-product) and metadata filtering while leveraging SingleStore’s low-latency performance for dynamic and streaming workloads.Getting started is simple: install phidata, configure your SingleStore credentials, and initialize the vector store in just a few lines of Python. This setup allows developers to build production-grade AI assistants and multi-agent systems with unified memory and context.Know more about this integration in the official docs: https://docs.phidata.com/vectordb/singlestore Python Connectors & Native Drivers:To connect your Python/LLM code to SingleStore, you have several options:MySQL-compatible clients / connectors: Because SingleStore offers MySQL wire-protocol compatibility, many existing MySQL drivers (e.g. mysqlclient, pymysql) will work, though they may not fully leverage vector/analytic optimizations.Native drivers: SingleStore publishes its own ODBC and JDBC drivers optimized for its architecture. The SingleStore ODBC driver works across platforms (Windows, Linux, macOS) and supports both Unicode and ANSI modes. The SingleStore JDBC driver (JDBC 4.2) is available for Java applications and can also underpin JVM-based tooling. Spark connector: For data-at-scale pipelines, SingleStore provides a Spark connector. Other client drivers: SingleStore’s Helios portal bundles recommended client drivers (MySQL, MariaDB, etc.). Integration Methods & Trade-offsODBC / JDBC: These standard APIs are broadly supported, enabling BI, ETL, and legacy tool integration. The trade-off is often higher latency and less optimized vector operations compared to embedding-native paths.Native API / Python integration via LangChain / LlamaIndex: Using the integration layers (e.g. langchain-singlestore, LlamaIndex’s SingleStore interface) allows leveraging built-in support for vector indexing, similarity search, and hybrid filtering. This path often offers better ergonomics and performance for AI workloads.Direct SQL + UDF / AI Services: In use cases demanding minimal latency, you can embed embeddings in SingleStore and execute vector similarity search directly via SQL built-in functions (dot_product, euclidean_distance), sometimes augmented by user-defined functions (UDFs). SingleStore’s AI Services also aim to reduce data movement by enabling model execution within the database. Hybrid or fallback architectures: In some systems, you may prefer to keep vector indexing in a specialized DB (e.g. Milvus, Qdrant) but store metadata and fallback search hits in SingleStore via the connectors above.SingleStore emerges as a game-changing platform for building agentic AI applications, eliminating the complexity of managing multiple databases while delivering the speed and scalability modern AI demands. Its unified architecture seamlessly combines structured data, unstructured content, and vector embeddings, enabling developers to focus on building intelligent agents rather than wrestling with infrastructure. With native integrations across LangChain, LlamaIndex, and CrewAI, plus enterprise-grade reliability through ACID compliance, SingleStore provides everything needed to deploy production-ready agentic systems. Whether you’re building autonomous workflows, multi-agent applications, or context-aware AI assistants, SingleStore simplifies the journey from prototype to production, making sophisticated agentic AI accessible and practical for organizations of all sizes. Try SingleStore Today!