GitHub - sqliteai/sqlite-agent: A SQLite extension that enables SQLite databases to run autonomous AI agents, using other SQLite AI extensions.

SQLite Agent

SQLite Agent is a cross-platform SQLite extension that brings autonomous AI agents to your database. It enables databases to run multi-step, tool-using agents that fetch data from external sources (web, APIs, etc.) through sqlite-mcp and populate database tables. The extracted data can then be analyzed by LLMs, displayed in UIs, or used in applications.

Highlights

Autonomous Agents – Run AI agents that can plan and execute multi-step tasks
MCP Integration – Works with sqlite-mcp for external tool access
AI Powered – Uses sqlite-ai for LLM inference and embeddings
Table Extraction Mode – Automatically fetch and populate database tables
Auto-Embeddings – Optionally uses sqlite-vector to generate vector embeddings automatically when inserting data for BLOB columns named *_embedding
Auto-Vector Indexing – Initialize vector search indices automatically

🚀 Quick Start

Installation

Pre-built Binaries

Download the appropriate pre-built binary for your platform from the Releases page: we do support Linux, macOS, Windows, Android and iOS.

Swift Package

You can add this repository as a package dependency to your Swift project. After adding the package, you'll need to set up SQLite with extension loading by following steps 4 and 5 of this guide.

Here's an example of how to use the package:

import agent

...

var db: OpaquePointer?
sqlite3_open(":memory:", &db)
sqlite3_enable_load_extension(db, 1)
var errMsg: UnsafeMutablePointer<Int8>? = nil
sqlite3_load_extension(db, agent.path, nil, &errMsg)
var stmt: OpaquePointer?
sqlite3_prepare_v2(db, "SELECT agent_version()", -1, &stmt, nil)
defer { sqlite3_finalize(stmt) }
sqlite3_step(stmt)
log("agent_version(): \(String(cString: sqlite3_column_text(stmt, 0)))")
sqlite3_close(db)

Android Package

Add the following to your Gradle dependencies:

implementation 'ai.sqlite:agent:0.1.3'

Here's an example of how to use the package:

SQLiteCustomExtension agentExtension = new SQLiteCustomExtension(getApplicationInfo().nativeLibraryDir + "/agent", null);
SQLiteDatabaseConfiguration config = new SQLiteDatabaseConfiguration(
    getCacheDir().getPath() + "/agent_test.db",
    SQLiteDatabase.CREATE_IF_NECESSARY | SQLiteDatabase.OPEN_READWRITE,
    Collections.emptyList(),
    Collections.emptyList(),
    Collections.singletonList(agentExtension)
);
SQLiteDatabase db = SQLiteDatabase.openDatabase(config, null, null);

Note: Additional settings and configuration are required for a complete setup. For full implementation details, see the complete Android example.

Basic Usage

-- Load required extensions
SELECT load_extension('./dist/mcp');
SELECT load_extension('./dist/ai');
SELECT load_extension('./dist/agent');

-- Initialize AI model
SELECT llm_model_load('/path/to/model.gguf', 'gpu_layers=99');

-- Connect to MCP server
SELECT mcp_connect('http://localhost:8000/mcp');

-- Run an autonomous agent (text-only mode)
SELECT agent_run('Find affordable apartments in Rome with AC', 8);
-- Agent uses MCP tools to accomplish the task and returns result

📖 API Reference

Available Functions

Function	Description
`agent_version()`	Returns extension version
`agent_run(goal, [table_name], [max_iterations], [system_prompt])`	Run autonomous AI agent

See API.md for complete API documentation with examples.

Example: AirBnb MCP Server

This example demonstrates combining agent, MCP, AI, and vector extensions:

-- Load all extensions
.load ../sqlite-mcp/dist/mcp
.load ./dist/agent
.load ../sqlite-ai/dist/ai
.load ../sqlite-vector/dist/vector

-- Initialize AI model (one-time)
SELECT llm_model_load('/path/to/model.gguf', 'gpu_layers=99');

-- Connect to MCP server (one-time)
SELECT mcp_connect('http://localhost:8000/mcp');

-- Create table with embedding column
CREATE TABLE listings (
  id INTEGER PRIMARY KEY,
  title TEXT,
  description TEXT,
  location TEXT,
  property_type TEXT,
  amenities TEXT,
  price REAL,
  rating REAL,
  embedding BLOB
);

-- Run agent to fetch and populate data
-- Embeddings and vector index are created automatically!
SELECT agent_run(
  'Find affordable apartments in Rome under 100 EUR per night',
  'listings',
  8
);
-- Returns: 5 (number of rows inserted)

-- Semantic search (works immediately!)
SELECT title, location, price, v.distance
FROM vector_full_scan('listings', 'embedding',
  llm_embed_generate('cozy modern apartment', ''), 5) v
JOIN listings l ON l.rowid = v.rowid
ORDER BY v.distance ASC;

Run the full demo:

make airbnb #first place a .gguf model inside the models folder and run your AirBnb MCP server

See USAGE.md for complete usage examples.

How It Works

The agent operates through an iterative loop:

Receives goal and available tools from MCP
Decides which tool to call
Executes tool via sqlite-mcp extension
Receives tool result
Continues until goal achieved or max iterations reached
Returns final result

Related Projects

sqlite-mcp – Model Context Protocol client for SQLite
sqlite-ai – AI/ML inference and embeddings for SQLite
sqlite-vector – Vector search capabilities for SQLite
sqlite-sync – Cloud synchronization for SQLite
sqlite-js – JavaScript engine integration for SQLite

Support

License

This project is licensed under the Elastic License 2.0. You can use, copy, modify, and distribute it under the terms of the license for non-production use. For production or managed service use, please contact SQLite Cloud, Inc for a commercial license.