What is Agent DB?
AgentDB is a specialized database system built from the ground up to support the extreme scale and unique demands of AI applications and autonomous agents. It solves the critical bottleneck of traditional database provisioning by offering instant, truly serverless deployment with just a unique ID, eliminating setup time and infrastructure management. Developers and agents can now provision databases instantly, ensuring optimal performance and cost-efficiency for every tool call and context requirement.
AgentDB is optimized for the unique workflows of AI agents, which often generate thousands of ephemeral databases during conversations and tool execution.
🚀 Instant, Zero-Setup Provisioning
Generate databases instantly using only a unique identifier, scaling effortlessly from zero to millions of requests without requiring manual configuration, user management, or pre-allocated compute. This serverless architecture ensures that AI agents can provision the necessary data resources instantly, supporting the high-volume, rapid deployment needs of advanced AI workflows.
🧠 AI-Ready Templates and MCP Support
AgentDB functions as a Model Context Protocol (MCP) server, utilizing dynamic templates that enforce schemas and migration definitions. This feature provides agents with immediate, accurate context on database structure, eliminating the time-consuming discovery overhead. Agents can properly write data and perform complex operations from the start, significantly reducing the potential for errors and maximizing efficiency.
🔍 Built-in Vector Search
Quickly integrate Retrieval-Augmented Generation (RAG) systems and semantic search directly into your application using the integrated sqlite-vec extension. You can store embeddings and perform high-performance vector searches out of the box, removing the complexity and cost of configuring and managing a separate, dedicated vector database stack.
⚙️ Dual Engine Flexibility: SQLite & DuckDB
Choose the optimal database engine for your task. AgentDB supports SQLite for fast, reliable transactional operations (OLTP) and DuckDB for powerful, high-speed analytical queries (OLAP). This flexibility allows AI applications to select the right tool based on the specific data requirements of the task at hand.
Use Cases
AgentDB enables developers and agents to rethink how data is managed at scale by removing infrastructure constraints.
1. Managing Ephemeral Agent Tool Calls
An autonomous agent decides it needs to store intermediate state or aggregate data across several steps of a complex user task (e.g., compiling a multi-source report). The agent instantly creates a new database via the SDK using a unique ID, performs its necessary operations, and then either deletes the database or downloads the resulting data file. This workflow minimizes cost, as the system only pays for the brief queries and storage used, rather than idle compute time.
2. Building Scalable Multi-Tenant Applications
For SaaS developers building AI-powered platforms, true tenant isolation is critical for security and compliance. AgentDB allows the application to automatically provision millions of fully isolated databases, one for each user or tenant. Because isolation occurs at the filesystem level, you achieve strong data provenance, ensuring that malicious queries cannot cross user boundaries, while drastically simplifying data deletion for compliance requirements.
3. Rapid Prototyping of RAG Systems
A data science team needs to rapidly iterate on different RAG strategies using various embedding models and proprietary source documents. Instead of provisioning and configuring a separate vector database and synchronizing it with a transactional store, they leverage AgentDB to store both the raw text/metadata and the vector embeddings within the same instantly provisioned, serverless database, accelerating the path from prototype to production.
Why Choose AgentDB?
AgentDB is designed to solve the structural and cost challenges that traditional database systems impose on high-scale, dynamic AI applications.
| Feature | AgentDB Approach (Optimized for AI Scale) | Traditional Database Approach |
|---|---|---|
| Setup Time | No Setup Time: Databases are ready instantly. Provision 1 or 1,000,000 instances with just a unique ID. | Manual setup required: involves database creation, user management, certificate issuance, and network configuration. |
| Cost Model | Minimized Cost: Pay only for data stored and queries executed. No charge for idle compute or server capacity. | Requires forecasting usage and paying for idle compute time, leading to high costs for low-access or ephemeral databases. |
| Portability | True Portability: Download any isolated database as a single file (SQLite/DuckDB) that can be run anywhere instantly. | Exporting requires complex scripts or full database dumps, often requiring a full server spin-up to utilize the data. |
| Data Provenance | True Tenant Isolation: User data is separated at the filesystem level for easy compliance, deletion, and robust security. | Unified database structure complicates finding and acting on specific data regulation requirements; security breaches risk widespread data access. |
Conclusion
AgentDB fundamentally changes how AI applications manage data, offering the speed, scale, isolation, and cost structure that modern autonomous agents and high-growth platforms demand. Stop managing complex infrastructure and start leveraging instant, scalable data solutions.
Explore how AgentDB can help you achieve truly scalable, data-driven AI applications.
More information on Agent DB
Launched
Pricing Model
Freemium
Starting Price
$29/month
Global Rank
Follow
Month Visit
<5k
Tech used
Top 5 Countries
100%
United States
Traffic Sources
100%
direct
Source: Similarweb (Oct 30, 2025)
Agent DB was manually vetted by our editorial team and was first featured on 2025-10-30.
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