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ScyllaDB logoScyllaDB · Feature trail

AI Agent Database Integration

2 posts · May 2026 – Aug 2026

What this trail covers

ScyllaDB Agent Skills provides best practices and usage patterns for AI agents interacting with ScyllaDB Cloud clusters, improving reliability and performance. This integration with MCP Toolbox for Databases extends this by enabling AI agents and applications to query ScyllaDB clusters directly using the CQL protocol via the ScyllaDB Go driver, supporting both self-hosted and ScyllaDB Cloud deployments. MCP Toolbox acts as an MCP server for IDE assistants and a custom tools framework for product.

All ScyllaDB trails
  1. Building a New Rust Driver for ScyllaDB’s DynamoDB API – with 58% More ThroughputThis post details the development of a new Rust driver for ScyllaDB Alternator, which is ScyllaDB's DynamoDB-compatible API. The driver is built as a wrapper around the official `aws-sdk-dynamodb` crate and uses the SDK's Interceptor framework to inject ScyllaDB-specific optimizations. Key optimizations include a load-balancing strategy that routes requests to appropriate nodes based on cluster topology, avoiding the single-endpoint bottleneck of DynamoDB. It also describes extending ScyllaDB's Rust benchmarking tool, Latte, to measure the performance of the DynamoDB API, enabling the team to achieve approximately 58% higher throughput and steady low P99 latencies compared to using the AWS SDK directly.
  2. Build Durable Chat Memory for RAG Using ScyllaDB and LangChainThis post details the integration of ScyllaDB as a durable chat memory backend for LangChain RAG applications. It explains how to replace LangChain's in-memory chat history with ScyllaDB to retain context across restarts and scale across replicas. The post highlights ScyllaDB's benefits for AI applications, including high availability, predictable P99 latency, and horizontal scalability, and demonstrates a setup example using ScyllaDB's vector search and Cassandra connector.
  3. ScyllaDB Is Now Supported in MCP Toolbox for DatabasesThis post details the integration of ScyllaDB as a supported database source in MCP Toolbox for Databases (v1.5.0). It introduces two new resource types: 'scylladb source' for connecting to ScyllaDB clusters via CQL using the ScyllaDB Go driver, and 'scylladb-cql tool' for executing parameterized CQL statements. The post provides installation instructions for Toolbox and configuration examples for connecting to both self-hosted ScyllaDB and ScyllaDB Cloud. It highlights how this integration allows AI agents and applications to query ScyllaDB clusters directly, enabling plain English queries for schema exploration and data-aware code generation, as well as defining safe, structured CQL tools for production AI agents.
  4. Agent Memory at Monster Scale with Mem0 and ScyllaDB CloudThis post details how Mem0's memory management framework, which extracts entity-based facts and stores them as vector embeddings, can be integrated with ScyllaDB Cloud for persistent, large-scale agent memory. It highlights ScyllaDB Cloud's capabilities in handling high-throughput, low-latency vector search across multiple regions with high availability and P99 guarantees, enabling the deployment of large-scale AI agents. The post focuses on the practical application of combining Mem0's context efficiency with ScyllaDB's distributed database infrastructure for agent memory.
  5. Introducing ScyllaDB Agent SkillsThis post introduces 'ScyllaDB Agent Skills,' a set of markdown files providing best practices and domain-specific knowledge for AI agents interacting with ScyllaDB Cloud. The skills cover three areas: `scylladb-cloud-setup` (connection flow, driver selection, DC-aware load balancing), `scylladb-data-modeling` (query-first design, partition key patterns, anti-patterns, secondary indexes vs. denormalized tables), and `scylladb-vector-search` (index creation, ANN queries, filtering, quantization). The goal is to reduce AI errors related to database interaction, such as incorrect driver usage, connection failures, schema generation for NoSQL, and poorly performing queries at scale. It highlights the importance of shard-aware drivers and proper connection configurations for ScyllaDB Cloud.