https://github.com/sqliteai/sqlite-agent
A SQLite extension that enables SQLite databases to run autonomous AI agents, using other SQLite AI extensions.
https://github.com/sqliteai/sqlite-agent
agent ai sqlite sqlite-agent
Last synced: 24 days ago
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A SQLite extension that enables SQLite databases to run autonomous AI agents, using other SQLite AI extensions.
- Host: GitHub
- URL: https://github.com/sqliteai/sqlite-agent
- Owner: sqliteai
- License: other
- Created: 2025-11-20T14:28:13.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-04-01T11:58:25.000Z (about 1 month ago)
- Last Synced: 2026-04-01T13:34:53.362Z (about 1 month ago)
- Topics: agent, ai, sqlite, sqlite-agent
- Language: C
- Homepage: https://sqlite.ai
- Size: 2.56 MB
- Stars: 23
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://github.com/sqliteai/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](https://github.com/sqliteai/sqlite-mcp) for external tool access
* **AI Powered** – Uses [sqlite-ai](https://github.com/sqliteai/sqlite-ai) for LLM inference and embeddings
* **Table Extraction Mode** – Automatically fetch and populate database tables
* **Auto-Embeddings** – Optionally uses [sqlite-vector](https://github.com/sqliteai/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](https://github.com/sqliteai/sqlite-agent/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](https://developer.apple.com/documentation/xcode/adding-package-dependencies-to-your-app#Add-a-package-dependency). After adding the package, you'll need to set up SQLite with extension loading by following steps 4 and 5 of [this guide](https://github.com/sqliteai/sqlite-extensions-guide/blob/main/platforms/ios.md#4-set-up-sqlite-with-extension-loading).
Here's an example of how to use the package:
```swift
import agent
...
var db: OpaquePointer?
sqlite3_open(":memory:", &db)
sqlite3_enable_load_extension(db, 1)
var errMsg: UnsafeMutablePointer? = 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](https://central.sonatype.com/artifact/ai.sqlite/agent) to your Gradle dependencies:
```gradle
implementation 'ai.sqlite:agent:0.1.3'
```
Here's an example of how to use the package:
```java
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](https://github.com/sqliteai/sqlite-extensions-guide/blob/main/examples/android/README.md).
#### Flutter Package
Add the [sqlite_agent](https://pub.dev/packages/sqlite_agent) package to your project:
```bash
flutter pub add sqlite_agent # Flutter projects
dart pub add sqlite_agent # Dart projects
```
Usage with `sqlite3` package:
```dart
import 'package:sqlite3/sqlite3.dart';
import 'package:sqlite_agent/sqlite_agent.dart';
sqlite3.loadSqliteAgentExtension();
final db = sqlite3.openInMemory();
print(db.select('SELECT agent_version()'));
```
For a complete example, see the [Flutter example](https://github.com/sqliteai/sqlite-extensions-guide/blob/main/examples/flutter/README.md).
### Basic Usage
```sql
-- 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](API.md) for complete API documentation with examples.
## Example: AirBnb MCP Server
This example demonstrates combining agent, MCP, AI, and vector extensions:
```sql
-- 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:
```bash
make airbnb #first place a .gguf model inside the models folder and run your AirBnb MCP server
```
See [USAGE.md](USAGE.md) for complete usage examples.
## How It Works
The agent operates through an iterative loop:
1. Receives goal and available tools from MCP
2. Decides which tool to call
3. Executes tool via sqlite-mcp extension
4. Receives tool result
5. Continues until goal achieved or max iterations reached
6. Returns final result
## Support
- [GitHub Issues](https://github.com/sqliteai/sqlite-agent/issues)
- [SQLite Extension Load Guide](https://github.com/sqliteai/sqlite-extensions-guide)
---
## License
This project is licensed under the [Elastic License 2.0](./LICENSE). 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](mailto:info@sqlitecloud.io) for a commercial license.
---
## Part of the SQLite AI Ecosystem
This project is part of the **SQLite AI** ecosystem, a collection of extensions that bring modern AI capabilities to the world’s most widely deployed database. The goal is to make SQLite the default data and inference engine for Edge AI applications.
Other projects in the ecosystem include:
- **[SQLite-AI](https://github.com/sqliteai/sqlite-ai)** — On-device inference and embedding generation directly inside SQLite.
- **[SQLite-Memory](https://github.com/sqliteai/sqlite-memory)** — Markdown-based AI agent memory with semantic search.
- **[SQLite-Vector](https://github.com/sqliteai/sqlite-vector)** — Ultra-efficient vector search for embeddings stored as BLOBs in standard SQLite tables.
- **[SQLite-Sync](https://github.com/sqliteai/sqlite-sync)** — Local-first CRDT-based synchronization for seamless, conflict-free data sync and real-time collaboration across devices.
- **[SQLite-Agent](https://github.com/sqliteai/sqlite-agent)** — Run autonomous AI agents directly from within SQLite databases.
- **[SQLite-MCP](https://github.com/sqliteai/sqlite-mcp)** — Connect SQLite databases to MCP servers and invoke their tools.
- **[SQLite-JS](https://github.com/sqliteai/sqlite-js)** — Create custom SQLite functions using JavaScript.
- **[Liteparser](https://github.com/sqliteai/liteparser)** — A highly efficient and fully compliant SQLite SQL parser.
Learn more at **[SQLite AI](https://sqlite.ai)**.