Chat Logger

Log all incoming and outgoing chat messages to a SQLite database for searchable history, analytics, and auditing. Works with any chat system or agent framework.

When to use

• Building a searchable chat history system
• Auditing and reviewing past conversations
• Creating analytics on chat interactions
• Debugging chat flows and responses
• User asks to track or search conversation history

Required tools / APIs

• Python standard library (sqlite3, datetime, json)
• Any programming language with SQLite support

No external APIs or services required.

Database Schema

CREATE TABLE IF NOT EXISTS messages (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  timestamp TEXT NOT NULL,
  session_id TEXT,
  sender TEXT NOT NULL,           -- 'user', 'assistant', or identifier
  content TEXT,
  metadata TEXT,                  -- JSON: channel, tools_used, etc.
  created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_timestamp ON messages(timestamp);
CREATE INDEX idx_session ON messages(session_id);
CREATE INDEX idx_sender ON messages(sender);

-- Automatic purge: delete records older than 1 year
DELETE FROM messages WHERE created_at < datetime('now', '-1 year');

Fields:

• id

  - Auto-incrementing primary key

• timestamp

  - ISO 8601 timestamp of the message

• session_id

  - Optional session/conversation identifier

• sender

  - Message sender ('user', 'assistant', or custom ID)

• content

  - Message text content

• metadata

  - JSON field for additional data (channel, tools, context)

• created_at

  - Database insertion timestamp

Basic Implementation

Python

Initialize database:

import sqlite3
from datetime import datetime
from pathlib import Path
import json

# Configure database path
DB_PATH = Path.home() / ".chat_logs" / "messages.db"

def init_db():
    """Initialize database and create tables."""
    DB_PATH.parent.mkdir(parents=True, exist_ok=True)
    conn = sqlite3.connect(str(DB_PATH))
    conn.execute("""
        CREATE TABLE IF NOT EXISTS messages (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            timestamp TEXT NOT NULL,
            session_id TEXT,
            sender TEXT NOT NULL,
            content TEXT,
            metadata TEXT,
            created_at DATETIME DEFAULT CURRENT_TIMESTAMP
        )
    """)
    conn.execute("CREATE INDEX IF NOT EXISTS idx_timestamp ON messages(timestamp)")
    conn.execute("CREATE INDEX IF NOT EXISTS idx_session ON messages(session_id)")
    conn.execute("CREATE INDEX IF NOT EXISTS idx_sender ON messages(sender)")
    conn.commit()
    conn.close()

def purge_old_messages():
    """Delete messages older than 1 year to keep the database size sane."""
    conn = sqlite3.connect(str(DB_PATH))
    conn.execute("DELETE FROM messages WHERE created_at < datetime('now', '-1 year')")
    conn.commit()
    conn.close()

# Initialize on import and purge old records
init_db()
purge_old_messages()

Log messages:

def log_message(sender: str, content: str, session_id: str = None, metadata: dict = None):
    """Log a chat message to the database."""
    conn = sqlite3.connect(str(DB_PATH))
    try:
        conn.execute(
            """INSERT INTO messages (timestamp, session_id, sender, content, metadata)
               VALUES (?, ?, ?, ?, ?)""",
            (
                datetime.utcnow().isoformat(),
                session_id,
                sender,
                content[:10000] if content else None,  # Truncate long messages
                json.dumps(metadata) if metadata else None
            )
        )
        conn.commit()
    finally:
        conn.close()

# Usage examples
log_message("user", "Hello, how are you?", session_id="session_123")
log_message("assistant", "I'm doing well, thank you!", session_id="session_123")
log_message("user", "Help me deploy a website", session_id="session_456",
            metadata={"channel": "web", "ip": "192.168.1.1"})

Query messages:

def get_recent_messages(limit: int = 50):
    """Get recent messages."""
    conn = sqlite3.connect(str(DB_PATH))
    conn.row_factory = sqlite3.Row
    cursor = conn.execute(
        "SELECT * FROM messages ORDER BY timestamp DESC LIMIT ?",
        (limit,)
    )
    results = cursor.fetchall()
    conn.close()
    return results

def get_session_history(session_id: str):
    """Get all messages from a specific session."""
    conn = sqlite3.connect(str(DB_PATH))
    conn.row_factory = sqlite3.Row
    cursor = conn.execute(
        "SELECT * FROM messages WHERE session_id = ? ORDER BY timestamp ASC",
        (session_id,)
    )
    results = cursor.fetchall()
    conn.close()
    return results

def search_messages(query: str, limit: int = 20):
    """Search message content."""
    conn = sqlite3.connect(str(DB_PATH))
    conn.row_factory = sqlite3.Row
    cursor = conn.execute(
        "SELECT * FROM messages WHERE content LIKE ? ORDER BY timestamp DESC LIMIT ?",
        (f"%{query}%", limit)
    )
    results = cursor.fetchall()
    conn.close()
    return results

# Usage
messages = get_recent_messages(10)
for msg in messages:
    print(f"[{msg['timestamp']}] {msg['sender']}: {msg['content'][:100]}")

# Search
results = search_messages("deploy website")
print(f"Found {len(results)} messages about deploying websites")

Node.js

import sqlite3 from "sqlite3";
import { promisify } from "util";
import path from "path";
import os from "os";

const DB_PATH = path.join(os.homedir(), ".chat_logs", "messages.db");

// Initialize database
const db = new sqlite3.Database(DB_PATH);
const run = promisify(db.run.bind(db));
const all = promisify(db.all.bind(db));

await run(`
  CREATE TABLE IF NOT EXISTS messages (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp TEXT NOT NULL,
    session_id TEXT,
    sender TEXT NOT NULL,
    content TEXT,
    metadata TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
  )
`);

// Log message
async function logMessage(sender, content, sessionId = null, metadata = null) {
  await run(
    `INSERT INTO messages (timestamp, session_id, sender, content, metadata)
     VALUES (?, ?, ?, ?, ?)`,
    [
      new Date().toISOString(),
      sessionId,
      sender,
      content,
      metadata ? JSON.stringify(metadata) : null,
    ]
  );
}

// Query messages
async function getRecentMessages(limit = 50) {
  return await all(
    `SELECT * FROM messages ORDER BY timestamp DESC LIMIT ?`,
    [limit]
  );
}

// Usage
await logMessage("user", "Hello!", "session_123");
await logMessage("assistant", "Hi there!", "session_123");

const messages = await getRecentMessages(10);
console.log(messages);

Bash Quick Queries

# View recent messages
sqlite3 ~/.chat_logs/messages.db "SELECT timestamp, sender, substr(content, 1, 80) FROM messages ORDER BY timestamp DESC LIMIT 20"

# Search for specific content
sqlite3 ~/.chat_logs/messages.db "SELECT * FROM messages WHERE content LIKE '%docker%' ORDER BY timestamp DESC"

# Count messages by sender
sqlite3 ~/.chat_logs/messages.db "SELECT sender, COUNT(*) as count FROM messages GROUP BY sender"

# Export session to JSON
sqlite3 -json ~/.chat_logs/messages.db "SELECT * FROM messages WHERE session_id='session_123' ORDER BY timestamp ASC" > conversation.json

Integration Examples

Generic Chat Application

class ChatLogger:
    """Simple chat logger that can wrap any chat system."""

    def __init__(self, db_path: str = None):
        self.db_path = db_path or str(Path.home() / ".chat_logs" / "messages.db")
        self._init_db()

    def _init_db(self):
        # Same as init_db() above
        pass

    def log_user_message(self, content: str, session_id: str = None, **metadata):
        return log_message("user", content, session_id, metadata)

    def log_assistant_message(self, content: str, session_id: str = None, **metadata):
        return log_message("assistant", content, session_id, metadata)

    def get_conversation(self, session_id: str):
        return get_session_history(session_id)

# Usage in any chat system
logger = ChatLogger()

# In your chat handler
def handle_message(user_input, session_id):
    logger.log_user_message(user_input, session_id=session_id)

    # Process message...
    response = generate_response(user_input)

    logger.log_assistant_message(response, session_id=session_id)
    return response

Decorator Pattern

def with_logging(session_id: str = None):
    """Decorator to automatically log chat interactions."""
    def decorator(func):
        def wrapper(user_message, *args, **kwargs):
            # Log user message
            log_message("user", user_message, session_id=session_id)

            # Call original function
            response = func(user_message, *args, **kwargs)

            # Log assistant response
            log_message("assistant", response, session_id=session_id)

            return response
        return wrapper
    return decorator

# Usage
@with_logging(session_id="session_123")
def chat_handler(message):
    return f"You said: {message}"

Agent Prompt

You have chat logging capability. All conversations are logged to a SQLite database.

When user asks to:
- Search past conversations
- Find specific messages
- Review conversation history
- Export chat logs

Use the SQLite database at ~/.chat_logs/messages.db with this schema:
- messages table (id, timestamp, session_id, sender, content, metadata)

Query examples:
1. Recent history: SELECT * FROM messages ORDER BY timestamp DESC LIMIT 50
2. Search content: SELECT * FROM messages WHERE content LIKE '%keyword%'
3. Session history: SELECT * FROM messages WHERE session_id = ? ORDER BY timestamp ASC

Always use SQL queries to retrieve information and present results clearly to the user.

Best Practices

1. Truncate long messages to avoid database bloat (e.g., 10,000 chars)
2. Use indexes on timestamp, session_id, and sender for fast queries
3. Store metadata as JSON for flexibility
4. Use ISO 8601 timestamps for consistency
5. Session IDs help organize conversations
6. Privacy considerations: be mindful of storing sensitive data
7. Regular backups: SQLite files are easy to backup/restore

Troubleshooting

Database locked error:

• Close all connections properly with

  conn.close()

• Use connection pooling for high traffic

Large database file:

• Run

  VACUUM

  to compact:

  sqlite3 messages.db "VACUUM"

• Archive old messages periodically

Query performance:

• Ensure indexes are created (timestamp, session_id, sender)
• Use LIMIT on queries
• Consider pagination for large result sets

See also

• ../file-tracker/SKILL.md — Track file modifications
• ../web-search-api/SKILL.md — Search external content