llama-scheduler

Llama Scheduler (llama-scheduler) provides job scheduling capabilities within the LlamaSearch AI ecosystem. It allows defining, scheduling, and managing recurring or one-off tasks.

Key Features

• Job Scheduling: Core engine for scheduling tasks based on time or events (scheduler.py).
• Job Definitions: Allows defining the functions or tasks to be executed (job_functions.py).
• Configuration Schema: Defines the structure for configuring jobs and schedules (config_schema.py).
• Command-Line Interface: Provides tools to manage schedules and jobs via CLI (cli.py).
• Core Module: Manages the scheduler lifecycle and job execution (core.py).
• Configurable: Allows defining schedules, job parameters, and backend settings (config.py).

Installation

pip install llama-scheduler
# Or install directly from GitHub for the latest version:
# pip install git+https://github.com/llamasearchai/llama-scheduler.git

Usage

Command-Line Interface (CLI)

(CLI usage examples for adding, removing, and listing scheduled jobs will be added here.)

llama-scheduler add-job --name "daily-report" --schedule "0 8 * * *" --task "generate_report"
llama-scheduler list-jobs

Python Client / Embedding

(Python usage examples for programmatically scheduling jobs will be added here.)

# Placeholder for Python client usage
# from llama_scheduler import Scheduler, Job
# from my_tasks import backup_database # Assuming backup_database is in job_functions or imported

# scheduler = Scheduler(config_path="config.yaml")

# # Define a job
# backup_job = Job(
#     name="database_backup",
#     func=backup_database,
#     trigger="cron",
#     hour=2, # Run at 2 AM daily
#     args=["/path/to/backup/dir"]
# )

# # Add and start the job
# scheduler.add_job(backup_job)
# scheduler.start()

Architecture Overview

graph TD
    A[User / CLI (cli.py)] --> B{Core Module (core.py)};
    B --> C{Scheduler Engine (scheduler.py)};
    C -- Loads Job Definitions --> D[Job Functions (job_functions.py)];
    C -- Reads Schedule --> E{Job Store / Schedule DB};
    C -- Triggers Job --> F[Job Executor];
    F -- Executes --> D;

    G[Configuration (config.py, config_schema.py)] -- Configures --> B;
    G -- Configures --> C;
    G -- Defines Schema for --> E;

    style C fill:#f9f,stroke:#333,stroke-width:2px
    style E fill:#ccf,stroke:#333,stroke-width:1px

1. Interface: Users interact via the CLI or potentially a programmatic API managed by the Core Module.
2. Core Module: Handles requests and controls the scheduler engine.
3. Scheduler Engine: The heart of the system, responsible for tracking time/events and triggering jobs based on the schedule.
4. Job Store: Stores the defined jobs and their schedules (could be in memory, a file, or a database).
5. Job Functions: Contains the actual Python code that gets executed for each job.
6. Executor: The component responsible for running the job function when triggered.
7. Configuration: Defines the schedule, job parameters, backend storage, etc.

Configuration

(Details on configuring job schedules (cron syntax, intervals), job parameters, backend persistence, etc., will be added here.)

Development

Setup

# Clone the repository
git clone https://github.com/llamasearchai/llama-scheduler.git
cd llama-scheduler

# Install in editable mode with development dependencies
pip install -e ".[dev]"

Testing

pytest tests/

Contributing

Contributions are welcome! Please refer to CONTRIBUTING.md and submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.