tomldiary 0.2.0

A lightweight TOML-based memory system for AI agents

TOMLDiary

Memory, Simplified: TOML-Driven, Agent-Approved.

TOMLDiary is a dead-simple, customizable memory system for agentic applications. It stores data in human-readable TOML files so your agents can keep a tidy diary of only the useful stuff.

Key Benefits

• Human-readable TOML storage – easy to inspect, debug and manage.
• Fully customizable – define your own memory schema with simple Pydantic models.
• Smart deduplication – prevents duplicate preferences with FuzzyWuzzy similarity detection (70% threshold).
• Enhanced limit enforcement – visual indicators and pre-flight checking prevent failed operations.
• Force creation mechanism – bypass similarity detection when needed with id="new" parameter.
• Built-in observability – comprehensive metrics for monitoring queue health, throughput, and error rates in production.
• Minimal overhead – lightweight design, backend agnostic and easy to integrate.
• Atomic, safe writes – ensures data integrity with proper file locking.

Storage Backends

TOMLDiary supports multiple storage backends for different deployment scenarios:

• LocalBackend (included) – File-based storage with path-level locking. Perfect for development, local applications, and single-server deployments.
• FirestoreBackend (optional) – Google Cloud Firestore for cloud-based storage with multi-region replication, automatic scaling, and real-time sync. Requires tomldiary[firestore] installation.

See the Backend Options section below for configuration examples.

Installation

Requires Python 3.11+

uv add tomldiary pydantic-ai

Optional: Firestore Backend

To use the Firestore backend for cloud storage:

uv add 'tomldiary[firestore]'
# or with pip
pip install 'tomldiary[firestore]'

Quick Start

from pydantic import BaseModel
from typing import Dict
from tomldiary import Diary, PreferenceItem
from tomldiary.backends import LocalBackend

# Be as specific as possible in your preference schema, it passed to the system prompt of the agent extracting the data!
# This of the fields as the "slots" to organize facts into and tell the agent what to remember.
class MyPrefTable(BaseModel):
    """
    likes    : What the user enjoys
    dislikes : Things user avoids
    allergies: Substances causing reactions
    routines : User’s typical habits
    biography: User’s personal details
    """

    likes: Dict[str, PreferenceItem] = {}
    dislikes: Dict[str, PreferenceItem] = {}
    allergies: Dict[str, PreferenceItem] = {}
    routines: Dict[str, PreferenceItem] = {}
    biography: Dict[str, PreferenceItem] = {}


diary = Diary(
    backend=LocalBackend(path="./memories"),
    pref_table_cls=MyPrefTable,
    max_prefs_per_category=100,
    max_conversations=50,
)

await diary.ensure_session(user_id, session_id)
await diary.update_memory(
    user_id,
    session_id,
    user_msg="I'm allergic to walnuts.",
    assistant_msg="I'll remember you're allergic to walnuts.",
)

TOML Memory Example

[_meta]
version = "0.3"
schema_name = "MyPrefTable"

[allergies.walnuts]
text = "allergic to walnuts"
contexts = ["diet", "health"]
_count = 1
_created = "2024-01-01T00:00:00Z"
_updated = "2024-01-01T00:00:00Z"

Conversations File (alice_conversations.toml)

[_meta]
version = "0.3"
schema_name = "MyPrefTable"

[conversations.chat_123]
_created = "2024-01-01T00:00:00Z"
_turns = 5
summary = "Discussed food preferences and dietary restrictions"
keywords = ["food", "allergy", "italian"]

Advanced Usage

Custom Preference Categories

Create your own preference schema:

class DetailedPrefTable(BaseModel):
    """
    dietary     : Food preferences and restrictions
    medical     : Health conditions and medications
    interests   : Hobbies and topics of interest
    goals       : Personal objectives and aspirations
    family      : Family members and relationships
    work        : Professional information
    """
    dietary: Dict[str, PreferenceItem] = {}
    medical: Dict[str, PreferenceItem] = {}
    interests: Dict[str, PreferenceItem] = {}
    goals: Dict[str, PreferenceItem] = {}
    family: Dict[str, PreferenceItem] = {}
    work: Dict[str, PreferenceItem] = {}

Smart Preference Management

The system includes enhanced tools for intelligent preference management:

# The extraction agent uses these enhanced tools automatically:
# - list_preferences(category) - shows limits with visual indicators (✅/⚠️/❌)  
# - upsert_preference() with smart workflows:
#   * Similarity detection prevents duplicates
#   * Auto-increment counts on updates  
#   * Force creation with id="new" when needed
#   * Intelligent error messages with match percentages

# Examples of enhanced error messages:
# "❌ Similar preferences found:
#   • likes/pref001: 'black blazers for work' (85% match)
#   • likes/pref003: 'dark blazers' (72% match)
# 
# To update existing: upsert_preference('likes', id='pref001')
# To force create anyway: upsert_preference('likes', id='new', text='black blazers')"

Backend Options

The library supports different storage backends:

Local Filesystem (Default)

from pathlib import Path
from tomldiary.backends import LocalBackend

backend = LocalBackend(Path("./memories"))

Firestore (Cloud Storage)

Install first: uv add 'tomldiary[firestore]'

from tomldiary.backends import FirestoreBackend

# Using default credentials (Application Default Credentials)
backend = FirestoreBackend(
    project_id="my-gcp-project",
    base_path="app/memory"  # Must have EVEN number of segments
)

# Or with explicit credentials
backend = FirestoreBackend(
    project_id="my-gcp-project",
    base_path="app/memory",
    credentials_path="/path/to/service-account.json",
    database="my-database"  # Optional, defaults to "(default)"
)

Important: The base_path must have an even number of segments due to Firestore's collection/document structure requirements. Examples:

• ✅ "users/data" (2 segments)
• ✅ "app/memory" (2 segments)
• ✅ "prod/app/v1/memory" (4 segments)
• ❌ "users" (1 segment - will raise ValueError)
• ❌ "app/prod/memory" (3 segments - will raise ValueError)

Firestore Structure:

{base_path}/
  {user_id}/
    preferences.toml    # Document with TOML content
    conversations.toml  # Document with TOML content

Test your setup with uv run --extra firestore scripts/firestore_test_connection.py or uv run --extra firestore examples/firestore_example.py.

Other Backends (Custom Implementation)

# S3 backend (implement your own S3Backend)
# backend = S3Backend(bucket="my-memories")

# Redis backend (implement your own RedisBackend)
# backend = RedisBackend(host="localhost")

Memory Writer Configuration

# Configure the background writer
writer = MemoryWriter(
    diary=diary,
    workers=8,        # Number of background workers (default: 8 or 2×CPU)
    qsize=1000,       # Queue size (default: 1000)
)

Observability and Monitoring

The MemoryWriter includes built-in observability for production deployments:

# Get real-time statistics
stats = writer.stats()

# Returns comprehensive metrics:
{
    "queue_size": 5,              # Current items in queue
    "queue_capacity": 1000,       # Maximum queue size
    "queue_utilization": 0.005,   # Queue fullness (0.0 to 1.0)
    "total_workers": 8,           # Number of worker tasks
    "active_workers": 2,          # Workers currently processing
    "idle_workers": 6,            # Workers waiting for tasks
    "submitted": 1247,            # Total tasks submitted
    "completed": 1240,            # Total tasks completed
    "failed": 2,                  # Total tasks failed
    "pending": 5,                 # Tasks in flight
    "error_rate": 0.0016,         # Failure ratio
    "is_running": True            # Accepting new tasks
}

# Check if writer is running
if writer.is_running:
    await writer.submit(...)

Production Use Cases

Health Check Endpoints:

@app.get("/health/memory")
async def memory_health():
    stats = writer.stats()
    status = "healthy" if stats["queue_utilization"] < 0.9 else "degraded"
    return {"status": status, "metrics": stats}

Monitoring and Alerting:

# Alert on queue backpressure
stats = writer.stats()
if stats["queue_utilization"] > 0.8:
    alert("MemoryWriter queue depth high")

# Alert on error rate
if stats["error_rate"] > 0.1:
    alert(f"MemoryWriter error rate: {stats['error_rate']:.1%}")

# Alert on worker saturation
if stats["idle_workers"] == 0:
    alert("All MemoryWriter workers busy")

Graceful Degradation:

# Reject requests if queue is near capacity
stats = writer.stats()
if stats["queue_utilization"] > 0.95:
    raise HTTPException(503, "Memory writer at capacity")

Integration with Logfire:

import logfire

# Log periodic metrics
logfire.info("memory_writer_stats", **writer.stats())

API Reference

Diary

Main class for memory operations:

• preferences(user_id): Get user preferences as TOML string
• last_conversations(user_id, limit): Get last N conversation summaries
• ensure_session(user_id, session_id): Create session if needed
• update_memory(user_id, session_id, user_msg, assistant_msg): Process and store memory

Automated compaction sweeps

Use CompactionConfig to schedule background clean-up passes that trim redundant preferences or stale conversation summaries. The configuration persists progress inside _meta.compaction so counters survive restarts.

from tomldiary.compaction import CompactionConfig

compaction = CompactionConfig(
    enabled=True,
    total_char_threshold=4000,      # trigger when serialized store exceeds N characters
    segment_char_threshold=600,     # or if any single block exceeds this size
    user_turn_interval=25,          # also run every 25 user turns
    cooldown_seconds=900,           # minimum gap between runs
    compact_preferences=True,       # target preference store
    compact_conversations=False,    # skip conversation summaries for this diary
)

diary = Diary(
    backend=backend,
    pref_table_cls=MyPrefTable,
    agent=extractor,
    compaction_config=compaction,
)

The compactor uses dedicated tools (list_preference_blocks, rewrite_*, delete_*) and will loop through every block during a sweep. When disabled, the diary still records char counts and turn statistics so triggers fire immediately once compaction is re-enabled.

MemoryWriter

Background queue for non-blocking writes:

• submit(user_id, session_id, user_message, assistant_response): Queue memory update
• stats(): Get comprehensive statistics for monitoring and observability
• is_running: Property to check if writer is accepting tasks
• close(): Graceful shutdown

Models

• PreferenceItem: Single preference with text, contexts, and metadata
• ConversationItem: Conversation with summary, keywords, and turn count
• MemoryDeps: Container for preferences and conversations

Examples

See the examples/ directory for:

• simple_example.py: Basic usage with educational agent (no LLM required)
• example_cooking_show.py: Advanced AI-powered cooking show with celebrity chef interviews
• culinary_prefs.py: Custom preference schema for culinary applications

Note: Examples use custom agents for educational purposes. The built-in extraction agent automatically uses the enhanced smart deduplication and limit enforcement tools described above.

Development

# Install dev dependencies
uv sync --group dev

# Run tests
pytest

# Run tests with Firestore backend (optional)
uv add 'tomldiary[firestore]'
pytest  # Firestore tests will be included automatically

# Test Firestore backend with live credentials
# Set environment variables: FIREBASE_ADMIN_CREDS, FIREBASE_ADMIN_PROJECT_ID, FIREBASE_WINDOW_SHOP_DB_NAME
python scripts/test_firestore.py

# Format code
ruff format .

# Lint code
ruff check .

License

MIT License - see LICENSE file for details.