Autonomous AI agents that run inside SQLite.
Define agents in SQL, give them tools and memory, run them locally. Built on sqlite-ai, sqlite-memory, and sqlite-mcp. No orchestration server required.
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Data:
Vector ·
Sync ·
Columnar ·
JS
AI:
AI ·
Agent ·
Memory ·
MCP
Running agents in production? SQLite-Agent works standalone; SQLite Cloud adds shared agent state, webhooks, edge functions, and observability across a fleet of agents. Free tier available.
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.
- 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
Download the appropriate pre-built binary for your platform from the Releases page: we do support Linux, macOS, Windows, Android and iOS.
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)
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.
Add the sqlite_agent package to your project:
flutter pub add sqlite_agent # Flutter projects dart pub add sqlite_agent # Dart projects
Usage with sqlite3 package:
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.
-- 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
| 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.
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 serverSee USAGE.md for complete usage examples.
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
Coordinating multiple agents in production? SQLite Cloud is the managed backbone for SQLite-Agent — shared state, webhooks, REST APIs, and observability for fleets of autonomous agents.
SQLite-Agent is one piece of a larger ecosystem that turns SQLite into a runtime for intelligent, distributed data:
Data layer
- sqlite-vector — ANN vector search inside SQLite
- sqlite-sync — Offline-first CRDT sync across devices
- sqlite-columnar — Column-oriented analytics for OLAP queries
- sqlite-js — Custom SQLite functions written in JavaScript
AI layer
- sqlite-ai — On-device LLM inference and embeddings
- sqlite-agent — Autonomous AI agents running inside SQLite (you are here)
- sqlite-memory — Persistent, searchable memory for agents
- sqlite-mcp — Call MCP tools directly from SQL queries
Managed platform
- SQLite Cloud — Hosted SQLite with sync, auth, edge functions, and analytics. Free tier →
Built by SQLite AI. Questions? Contact us.