amsdal_workflow 0.1.0

amsdal_workflow plugin for AMSDAL Framework

AMSDAL Workflow

A LangGraph checkpoint persistence plugin for the AMSDAL Framework. This plugin enables persistent and recoverable LangGraph workflows by storing checkpoint state in AMSDAL-managed databases.

Features

• Persistent Workflow State: Store LangGraph checkpoints in any AMSDAL-supported database (SQLite, PostgreSQL, etc.)
• Dual Mode Support: Both synchronous and asynchronous operations
• Thread-based Organization: Manage multiple workflow threads with checkpoint namespacing
• Drop-in Replacement: Compatible with LangGraph's BaseCheckpointSaver interface
• Production Ready: Built on the robust AMSDAL ORM with comprehensive testing

Installation

Install via pip:

pip install amsdal-workflow

Or with optional dependencies:

# With OpenAI support
pip install amsdal-workflow[openai]

Quick Start

Basic Usage

from langgraph.graph import StateGraph
from amsdal_workflow.checkpoint import AmsdalCheckpointSaver

# Initialize the checkpoint saver
checkpointer = AmsdalCheckpointSaver()

# Create your LangGraph workflow
workflow = StateGraph(...)
# ... define your workflow nodes and edges ...

# Compile with checkpoint support
app = workflow.compile(checkpointer=checkpointer)

# Run with persistence
config = {'configurable': {'thread_id': 'user-123'}}
result = app.invoke(input_data, config=config)

Async Usage

from amsdal_workflow.checkpoint import AmsdalCheckpointSaver

# Same checkpointer works for async
checkpointer = AmsdalCheckpointSaver()

# Compile async workflow
app = workflow.compile(checkpointer=checkpointer)

# Run async with persistence
config = {'configurable': {'thread_id': 'user-123'}}
result = await app.ainvoke(input_data, config=config)

Advanced Configuration

from langchain_core.runnables import RunnableConfig
from amsdal_workflow.checkpoint import AmsdalCheckpointSaver

checkpointer = AmsdalCheckpointSaver()

# Configuration with checkpoint namespace
config: RunnableConfig = {
    'configurable': {
        'thread_id': 'conversation-456',
        'checkpoint_ns': 'production',  # Optional namespace
    }
}

# Run workflow
result = app.invoke(input_data, config=config)

# Resume from checkpoint
checkpoint_tuple = checkpointer.get_tuple(config)
if checkpoint_tuple:
    # Continue from last checkpoint
    result = app.invoke(input_data, config=config)

Architecture

Core Components

• AmsdalCheckpointSaver: Main class implementing LangGraph's BaseCheckpointSaver protocol
• Checkpoint Model: Stores checkpoint snapshots with metadata
• CheckpointWrites Model: Stores pending write operations for each checkpoint

Data Models

Checkpoint

Stores workflow state snapshots:

• thread_id: Workflow thread identifier
• checkpoint_ns: Optional namespace for organization
• checkpoint_id: Unique checkpoint identifier
• parent_checkpoint_id: Reference to parent checkpoint
• checkpoint: Serialized checkpoint data
• meta: Checkpoint metadata

CheckpointWrites

Stores pending writes associated with checkpoints:

• thread_id, checkpoint_ns, checkpoint_id: Links to checkpoint
• task_id: Task identifier
• idx: Write operation index
• channel: Channel name
• value: Serialized write value

API Reference

AmsdalCheckpointSaver

Methods

Synchronous Methods

• get_tuple(config: RunnableConfig) -> CheckpointTuple | None

  • Retrieve a checkpoint tuple by configuration

• list(config: RunnableConfig | None, *, filter: dict | None = None, before: RunnableConfig | None = None, limit: int | None = None) -> Iterator[CheckpointTuple]

  • List checkpoints with optional filtering

• put(config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, new_versions: ChannelVersions) -> RunnableConfig

  • Store a new checkpoint

• put_writes(config: RunnableConfig, writes: Sequence[tuple[str, Any]], task_id: str) -> None

  • Store pending writes for a checkpoint

• delete_thread(thread_id: str) -> None

  • Delete all checkpoints and writes for a thread

Asynchronous Methods

All synchronous methods have async equivalents prefixed with a:

• aget_tuple(...)
• alist(...)
• aput(...)
• aput_writes(...)
• adelete_thread(...)

Configuration

Database Setup

AMSDAL Workflow uses your existing AMSDAL configuration. Ensure you have configured your database connection:

from amsdal.manager import AmsdalManager

# Initialize AMSDAL
manager = AmsdalManager()
manager.setup()

# Now use AmsdalCheckpointSaver
checkpointer = AmsdalCheckpointSaver()

Migration

The plugin includes migration files for creating the required tables. Run migrations before first use:

amsdal migrate

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/amsdal/amsdal-workflow.git
cd amsdal-workflow

# Install dependencies
hatch run sync

Running Tests

# Run all tests
hatch run test

# Run with coverage
hatch run cov

# Run specific test file
hatch run test tests/test_checkpoint.py

# Run with verbose output
hatch run test -v

Code Quality

# Format code
hatch run fmt

# Check code style
hatch run style

# Run type checking
hatch run typing

# Run all checks
hatch run all

Project Structure

amsdal_workflow/
├── amsdal_workflow/           # Main package
│   ├── __init__.py
│   ├── checkpoint.py          # AmsdalCheckpointSaver implementation
│   ├── utils.py               # Utility functions
│   ├── models/                # Data models
│   │   ├── checkpoint.py      # Checkpoint model
│   │   └── checkpoint_writes.py  # CheckpointWrites model
│   └── migrations/            # Database migrations
├── tests/                     # Test suite
│   ├── conftest.py            # Test fixtures
│   └── test_checkpoint.py     # Checkpoint tests
├── pyproject.toml             # Project configuration
├── README.md                  # This file

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests and code quality checks
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Code Standards

• Python 3.11+ required
• Follow PEP 8 style guide (enforced by Ruff)
• Add type hints to all functions
• Write tests for new features
• Maintain test coverage above 90%

License

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

Acknowledgments

• Built on LangGraph by LangChain
• Powered by AMSDAL Framework

Support

• Documentation: AMSDAL Docs
• Issues: GitHub Issues
• Discussions: GitHub Discussions

Changelog

See CHANGELOG.md for a list of changes in each release.