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Lite Sqlite

Fast lightweight local SQLite database for OpenClaw agents with minimal RAM and storage usage.

Rating: 5 (499 reviews)
Downloads: 857 downloads
Version: 1.0.0

Overview

Fast lightweight local SQLite database for OpenClaw agents with minimal RAM and storage usage.

Complete Documentation

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Lite SQLite - Lightweight Local Database

Ultra-lightweight SQLite database management optimized for OpenClaw agents with minimal RAM (~2-5MB) and storage overhead.

Why SQLite?

✅ Zero setup - No server, no configuration, file-based ✅ Minimal RAM - 2-5MB typical usage ✅ Fast - Millions of queries/second ✅ Portable - Single .db file ✅ Reliable - ACID compliant, crash-proof ✅ Cross-platform - Works everywhere Python works

Core Features

In-memory mode for temporary data (even faster!)
WAL mode for concurrent access
Connection pooling
Automatic schema migration
Built-in backup/restore
Query optimization hints

Quick Start

Basic Database Operations

python

from sqlite_connector import SQLiteDB

# Create database (auto-wal mode enabled)
db = SQLiteDB("agent_data.db")

# Create table
db.create_table("memos", {
    "id": "INTEGER PRIMARY KEY AUTOINCREMENT",
    "title": "TEXT NOT NULL",
    "content": "TEXT",
    "created_at": "TEXT DEFAULT CURRENT_TIMESTAMP",
    "tags": "TEXT"
})

# Insert data
db.insert("memos", [title="First memo", content="Hello world", tags="test"])

# Query data
results = db.query("SELECT * FROM memos WHERE tags = ?", ("test",))

# Update data
db.update("memos", "id = ?", [content="Updated content"], (1,))

# Delete data
db.delete("memos", "id = ?", (1,))

# Close connection
db.close()

In-Memory Database (Fastest)

python

# Fastest mode - RAM only, no disk I/O
db = SQLiteDB(":memory:")

# Perfect for temporary operations
db.create_table("temp", {...})

# Data persists only during session
# Use for caching, computations, temporary storage

Performance Optimization

Essential Settings

python

import sqlite3

# WAL mode (Write-Ahead Logging) - 3-4x faster
conn = sqlite3.connect("agent_data.db")
conn.execute("PRAGMA journal_mode=WAL")

# Sync OFF (faster writes, crash-safe with proper shutdown)
conn.execute("PRAGMA synchronous=NORMAL")

# Memory optimization
conn.execute("PRAGMA cache_size=-64000")  # 64MB cache
conn.execute("PRAGMA page_size=4096")

# Temp store in RAM
conn.execute("PRAGMA temp_store=MEMORY")

Query Optimization

python

# Use indexes for frequent queries
db.create_index("memos", "tags")
db.create_index("memos", "created_at")

# Use prepared statements (automatic in our wrapper)
db.query("SELECT * FROM memos WHERE id = ?", (id,))

# Batch inserts for large datasets
db.batch_insert("memos", rows_data)

Predefined Schemas

Agent Memo Schema (Memory Store)

python

db.create_table("agent_memos", {
    "id": "INTEGER PRIMARY KEY AUTOINCREMENT",
    "agent_id": "TEXT NOT NULL",           # Which agent created it
    "key": "TEXT NOT NULL",               # Lookup key
    "value": "TEXT",                      # Stored value
    "priority": "INTEGER DEFAULT 0",       # For retrieval ordering
    "created_at": "TEXT DEFAULT CURRENT_TIMESTAMP",
    "expires_at": "TEXT"                  # Optional TTL
})

# Create indexes
db.create_index("agent_memos", "agent_id")
db.create_index("agent_memos", "key")
db.create_index("agent_memos", "expires_at")

Session Log Schema

python

db.create_table("session_logs", {
    "id": "INTEGER PRIMARY KEY AUTOINCREMENT",
    "session_id": "TEXT NOT NULL",
    "agent": "TEXT NOT NULL",
    "message": "TEXT",
    "metadata": "TEXT",                   # JSON
    "created_at": "TEXT DEFAULT CURRENT_TIMESTAMP"
})

db.create_index("session_logs", "session_id")
db.create_index("session_logs", "created_at")

Cache Schema (TTL-based)

python

db.create_table("cache", {
    "id": "INTEGER PRIMARY KEY AUTOINCREMENT",
    "key": "TEXT UNIQUE NOT NULL",
    "value": "BLOB",                      # Supports binary data
    "created_at": "TEXT DEFAULT CURRENT_TIMESTAMP",
    "expires_at": "TEXT NOT NULL"
})

# Auto-cleanup expired entries
db.query("DELETE FROM cache WHERE expires_at < ?", (datetime.now().isoformat(),))

db.create_index("cache", "key")
db.create_index("cache", "expires_at")

Advanced Features

Connection Pooling

python

from sqlite_connector import ConnectionPool

# Pool of connections for concurrent access
pool = ConnectionPool("agent_data.db", max_connections=5)

# Get connection
conn = pool.get_connection()
# Use conn...
pool.release_connection(conn)

Automatic Backup

python

# Backup database
db.backup("agent_data_backup.db")

# Automatic daily backup
db.auto_backup("backups/", "daily")

Schema Migration

python

# Add column if not exists
db.add_column("memos", "updated_at", "TEXT DEFAULT CURRENT_TIMESTAMP")

# Migrate data
db.migrate("memos", {
    "old_column": "new_column"
})

Performance Benchmarks

Typical Performance

Operation	Rows	Time (In-Memory)	Time (Disk)
Insert	10,000	0.05s	0.3s
Select (indexed)	10,000	0.001s	0.01s
Select (full scan)	10,000	0.05s	0.5s
Update	1,000	0.01s	0.1s
Delete	1,000	0.01s	0.1s

Memory Usage

Base Memory: 2-5MB
With 100K rows: ~10-15MB
With 1M rows: ~50-100MB
In-memory mode: Same as data size + overhead

Best Practices for OpenClaw Agents

1. Choose the Right Mode

python

# Use :memory: for temporary operations
temp_db = SQLiteDB(":memory:")

# Use file DB for persistent storage
persist_db = SQLiteDB("agent_storage.db")

2. Use Proper Indexes

python

# Always index columns used in WHERE clauses
db.create_index("table", "column_name")

# Index multiple columns for composite queries
db.create_index("table", "col1, col2")

3. Batch Operations

python

# Instead of individual inserts:
for row in rows:
    db.insert("table", row)  # Slow!

# Use batch insert:
db.batch_insert("table", rows)  # Fast!

4. Use TTL for Expiring Data

python

# Auto-cleanup old data
db.cleanup_expired("cache", "expires_at")
db.cleanup_old("logs", "created_at", days=7)

5. Compact Database Periodically

python

# Reclaim space after many deletes
db.vacuum()  # Should be run during downtime

DuckDB Alternative (Analytics)

For analytical queries (aggregations, joins on large datasets), consider DuckDB:

python

import duckdb

conn = duckdb.connect(":memory:")

# Faster than SQLite for complex analytics
conn.execute("""
    SELECT COUNT(*) as rows,
           AVG(value) as avg_value
    FROM large_table
""").fetchall()

When to use DuckDB:

Analytics on large datasets (>100M rows)
Complex aggregations and joins
Columnar data operations
Statistical analysis

When to use SQLite:

Transactional operations
Small to medium datasets (<100M rows)
Point queries and updates
General-purpose storage

Common Patterns

1. Memo Storage

python

def save_memo(db, agent_id, key, value, ttl_hours=24):
    expires_at = (datetime.now() + timedelta(hours=ttl_hours)).isoformat()
    db.insert("agent_memos", {
        "agent_id": agent_id,
        "key": key,
        "value": json.dumps(value),
        "expires_at": expires_at
    })

2. Session Persistence

python

def save_session(db, session_id, agent, message, metadata=None):
    db.insert("session_logs", {
        "session_id": session_id,
        "agent": agent,
        "message": message,
        "metadata": json.dumps(metadata) if metadata else None
    })

3. Caching Layer

python

def cache_get(db, key):
    if expired_key := db.query_one(
        "SELECT value FROM cache WHERE key = ? AND expires_at > ?",
        (key, datetime.now().isoformat())
    ):
        return json.loads(expired_key)
    return None

def cache_set(db, key, value, ttl_seconds=3600):
    expires_at = (datetime.now() + timedelta(seconds=ttl_seconds)).isoformat()
    db.insert_or_replace("cache", {
        "key": key,
        "value": json.dumps(value),
        "expires_at": expires_at
    })

Error Handling

python

try:
    db.insert("metrics", {...})
except sqlite3.IntegrityError:
    # Duplicate key violation
    pass
except sqlite3.OperationalError:
    # Table doesn't exist or database locked
    pass

Size Optimization Tips

Reduce Storage

Use appropriate data types:
INTEGER instead of TEXT for numbers
REAL instead of TEXT for floats
Use CHECK constraints for validation
Normalize data:
Store JSON as TEXT
Use TEXT for variable-length strings
Avoid storing redundant data
Vacuum regularly:

python

db.vacuum()  # Reclaims space after deletes

Use WAL instead of journal:

python

conn.execute("PRAGMA journal_mode=WAL")

Migration from Other Stores

From JSON Files

python

# Load JSON into SQLite
import json

with open("data.json") as f:
    data = json.load(f)

db.create_table("json_data", {key: "TEXT" for key in data[0].keys()})
db.batch_insert("json_data", data)

From CSV Files

python

import pandas as pd

df = pd.read_csv("data.csv")
df.to_sql("csv_data", conn, if_exists="replace", index=False)

Troubleshooting

Database Locked Error

python

# Use WAL mode for concurrent access
conn.execute("PRAGMA journal_mode=WAL")

# Or use connection pool
pool = ConnectionPool("db.db", timeout=5.0)

Slow Queries

python

# Check query plan
plan = conn.execute("EXPLAIN QUERY PLAN SELECT * FROM ...").fetchall()

# Add indexes
db.create_index("table", "column")

# Use ANALYZE
conn.execute("ANALYZE")

Large Database Size

python

# Check size info
size_info = conn.execute("PRAGMA page_count, page_size").fetchone()
print(f"Size: {(page_count * page_size) / (1024*1024):.2f} MB")

# Vacuum to reclaim space
db.vacuum()

CLI Tool

The bundled sqlite_cli.py provides command-line access:

bash

# Create database
python scripts/sqlite_cli.py create agent_data.db

# Add table
python scripts/sqlite_cli.py create-table agent_memos -c id:INTEGER:P -c title:TEXT -c content:TEXT

# Insert data
python scripts/sqlite_cli.py insert agent_memos '{"title": "Test", "content": "Hello"}'

# Query data
python scripts/sqlite_cli.py query "SELECT * FROM agent_memos"

# Optimize
python scripts/sqlite_cli.py optimize agent_data.db

Resources

SQLite Documentation: https://www.sqlite.org/docs.html
Python sqlite3: https://docs.python.org/3/library/sqlite3.html
DuckDB: https://duckdb.org/docs/
Performance: https://www.sqlite.org/optoverview.html

Installation

Terminal bash


openclaw install lite-sqlite

Copied!

💻Code Examples

# Use for caching, computations, temporary storage

-use-for-caching-computations-temporary-storage.txt

---

## Performance Optimization

### Essential Settings

db.batch_insert("memos", rows_data)

dbbatchinsertmemos-rowsdata.txt

---

## Predefined Schemas

### Agent Memo Schema (Memory Store)

db.create_index("cache", "expires_at")

dbcreateindexcache-expiresat.txt

---

## Advanced Features

### Connection Pooling

})

.txt

---

## Performance Benchmarks

### Typical Performance

| Operation | Rows | Time (In-Memory) | Time (Disk) |
|-----------|------|------------------|-------------|
| Insert | 10,000 | 0.05s | 0.3s |
| Select (indexed) | 10,000 | 0.001s | 0.01s |
| Select (full scan) | 10,000 | 0.05s | 0.5s |
| Update | 1,000 | 0.01s | 0.1s |
| Delete | 1,000 | 0.01s | 0.1s |

### Memory Usage

- Base Memory: 2-5MB
- With 100K rows: ~10-15MB
- With 1M rows: ~50-100MB
- In-memory mode: Same as data size + overhead

---

## Best Practices for OpenClaw Agents

### 1. Choose the Right Mode

db.vacuum() # Should be run during downtime

dbvacuum--should-be-run-during-downtime.txt

---

## DuckDB Alternative (Analytics)

For analytical queries (aggregations, joins on large datasets), consider DuckDB:

""").fetchall()

fetchall.txt

**When to use DuckDB:**
- Analytics on large datasets (>100M rows)
- Complex aggregations and joins
- Columnar data operations
- Statistical analysis

**When to use SQLite:**
- Transactional operations
- Small to medium datasets (<100M rows)
- Point queries and updates
- General-purpose storage

---

## Common Patterns

### 1. Memo Storage

})

-.txt

---

## Error Handling

pass

-pass.txt

---

## Size Optimization Tips

### Reduce Storage

1. **Use appropriate data types:**
   - INTEGER instead of TEXT for numbers
   - REAL instead of TEXT for floats
   - Use CHECK constraints for validation

2. **Normalize data:**
   - Store JSON as TEXT
   - Use TEXT for variable-length strings
   - Avoid storing redundant data

3. **Vacuum regularly:**

df.to_sql("csv_data", conn, if_exists="replace", index=False)

dftosqlcsvdata-conn-ifexistsreplace-indexfalse.txt

---

## Troubleshooting

### Database Locked Error

db.vacuum()

dbvacuum.txt

---

## CLI Tool

The bundled `sqlite_cli.py` provides command-line access:

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Overview

Complete Documentation

Lite SQLite - Lightweight Local Database

Why SQLite?

Core Features

Quick Start

Basic Database Operations

In-Memory Database (Fastest)

Performance Optimization

Essential Settings

Query Optimization

Predefined Schemas

Agent Memo Schema (Memory Store)

Session Log Schema

Cache Schema (TTL-based)

Advanced Features

Connection Pooling

Automatic Backup

Schema Migration

Performance Benchmarks

Typical Performance

Memory Usage

Best Practices for OpenClaw Agents

1. Choose the Right Mode

2. Use Proper Indexes

3. Batch Operations

4. Use TTL for Expiring Data

5. Compact Database Periodically

DuckDB Alternative (Analytics)

Common Patterns

1. Memo Storage

2. Session Persistence

3. Caching Layer

Error Handling

Size Optimization Tips

Reduce Storage

Migration from Other Stores

From JSON Files

From CSV Files

Troubleshooting

Database Locked Error

Slow Queries

Large Database Size

CLI Tool

Resources

Installation

💻Code Examples

# Use for caching, computations, temporary storage

db.batch_insert("memos", rows_data)

db.create_index("cache", "expires_at")

})

db.vacuum() # Should be run during downtime

""").fetchall()

})

pass

df.to_sql("csv_data", conn, if_exists="replace", index=False)

db.vacuum()

Tags

Quick Info

Ready to Install?

Resources

Related Skills

4claw

Aap Passport

Acestep Lyrics Transcription

Adaptive Suite