peewee

Simple, expressive Python ORM — small codebase, minimal dependencies, supports SQLite/PostgreSQL/MySQL with a clean model definition API. peewee features: Model subclass for schema definition, CharField/IntegerField/BooleanField/DateTimeField/ForeignKeyField, database.connect()/close(), Model.create(), Model.get(), Model.select(), Model.update(), Model.delete(), where() clauses, join(), fn() for SQL functions, transaction via database.atomic(), migration via playhouse.migrate, raw SQL via database.execute_sql(), and many extensions in playhouse.

Evaluated Mar 06, 2026 (0d ago) v3.x
Homepage ↗ Repo ↗ Developer Tools python peewee orm sqlite postgresql mysql simple lightweight
⚙ Agent Friendliness
66
/ 100
Can an agent use this?
🔒 Security
85
/ 100
Is it safe for agents?
⚡ Reliability
86
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
85
Error Messages
80
Auth Simplicity
95
Rate Limits
99

🔒 Security

TLS Enforcement
88
Auth Strength
85
Scope Granularity
82
Dep. Hygiene
90
Secret Handling
82

ORM library. Parameterized queries by default — safe from SQL injection. Raw SQL: database.execute_sql('SELECT * FROM t WHERE id = ?', (id,)) with parameters. Store database path/credentials in environment variables. SQLite file permissions for access control.

⚡ Reliability

Uptime/SLA
85
Version Stability
88
Breaking Changes
85
Error Recovery
85
AF Security Reliability

Best When

Simple Python applications with SQLite or small PostgreSQL/MySQL databases — peewee's simplicity and small footprint make it ideal for scripts, tools, and small applications.

Avoid When

Async applications (use SQLAlchemy async), complex schemas (use SQLAlchemy), enterprise needs, or when Alembic migration maturity is required.

Use Cases

  • Agent SQLite database — from peewee import SqliteDatabase, Model, CharField, IntegerField; db = SqliteDatabase('data.db'); class Task(Model): name = CharField(); status = CharField(default='pending'); priority = IntegerField(default=0); class Meta: database = db; db.connect(); db.create_tables([Task]); Task.create(name='Do work', priority=1) — SQLite; agent creates local SQLite-backed data store
  • Agent query — tasks = Task.select().where(Task.status == 'pending').order_by(Task.priority.desc()).limit(10); for task in tasks: process(task.name) — query; agent queries with peewee's expressive query DSL; lazy execution until iteration; .dicts() for dict output
  • Agent update — Task.update(status='done').where(Task.name == 'task_name').execute(); task = Task.get(Task.id == 1); task.status = 'done'; task.save() — update; agent updates records; class method or instance method; execute() required for class-method updates
  • Agent transaction — with db.atomic(): Task.create(name='a'); Task.create(name='b'); Subtask.create(task=task, name='sub') — atomic; agent wraps multiple writes in transaction; atomic() is context manager and decorator; nested atomic() creates savepoints
  • Agent aggregate queries — from peewee import fn; result = Task.select(Task.status, fn.COUNT(Task.id).alias('count')).group_by(Task.status).dicts(); for row in result: print(row['status'], row['count']) — aggregation; agent runs GROUP BY queries with COUNT/SUM/AVG using fn()

Not For

  • Async applications — peewee is synchronous; for async use SQLAlchemy async or tortoise-orm
  • Complex enterprise schemas — peewee is simple by design; for complex ORM with advanced features use SQLAlchemy
  • Large teams with migration needs — peewee's migration tooling (playhouse.migrate) is less mature than Alembic

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
No

Authentication

Methods: none
OAuth: No Scopes: No

No auth — ORM library. Database credentials in connection string.

Pricing

Model: open_source
Free tier: Yes
Requires CC: No

peewee is MIT licensed. Free for all use.

Agent Metadata

Pagination
offset
Idempotent
Partial
Retry Guidance
Not documented

Known Gotchas

  • Database connection must be managed — SqliteDatabase() does not connect automatically; call db.connect() before use; or use db.connection_context() context manager; in web apps: connect/close per request via before/after hooks; in scripts: connect at start, close at end; missing connect: OperationalError: database not open
  • Model.get() raises DoesNotExist on miss — Task.get(Task.id == 99) raises Task.DoesNotExist if not found; use Model.get_or_none() for None return; or try/except Task.DoesNotExist; agent code: task = Task.get_or_none(Task.id == id); if task is None: handle_missing(); do not catch generic Exception
  • Query execution is lazy — Task.select().where(Task.status == 'active') builds query but does NOT execute; execution happens on iteration (for task in query:), .count(), .get(), .execute(), .dicts(), etc.; agent code: do not check if query is truthy; check: query.count() > 0 or use any(query.iterator())
  • update().execute() required for bulk update — Task.update(status='done').where(...) without .execute() does nothing; .execute() returns number of rows updated; agent code: ALWAYS call .execute() after update()/delete() class-method queries; instance.save() vs Model.update().where().execute() are different paths
  • ForeignKeyField creates backref — class Post(Model): author = ForeignKeyField(User, backref='posts'); author.posts returns SelectQuery; author.posts_id returns raw FK id; agent code: use .posts to get related objects (executes query); use ._pk for raw ID; prefetch(User.select(), Post.select()) for N+1 prevention
  • SQLite concurrent writes need WAL — SqliteDatabase('db.sqlite3', pragmas={'journal_mode': 'wal'}) for better concurrent read performance; WAL allows concurrent reads with one write; default journal mode causes lock contention; agent code with multiple processes: enable WAL and use db.atomic() for all writes

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Scores are editorial opinions as of 2026-03-06.

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