# Flock Serialization System Specification

## Overview
The serialization system enables conversion of Flock components and objects to and from various formats for storage, transmission, and distribution. It provides a foundation for workflow persistence, agent sharing, and distributed execution. This specification defines the components and behavior of the serialization system.

**Core Implementation:** `src/flock/core/serialization/`

## Core Components

### 1. Serializable Base Class

**Implementation:** `src/flock/core/serialization/serializable.py`

**Purpose:**
Provides a common interface for serializable objects across the framework.

**Key Features:**
- Abstract base class with required methods
- Support for multiple serialization formats
- File I/O capabilities
- Optional format dependencies (YAML, MsgPack, Pickle)
- Utility methods for serialization tasks

**API:**
- **Core Methods** (must be implemented by subclasses):
  - `to_dict()`: Convert object to dictionary representation
  - `from_dict(data)`: Create object from dictionary representation

- **JSON Methods**:
  - `to_json(indent=2)`: Serialize to JSON string
  - `from_json(json_str)`: Create object from JSON string

- **YAML Methods**:
  - `to_yaml(sort_keys=False, default_flow_style=False)`: Serialize to YAML string
  - `from_yaml(yaml_str)`: Create object from YAML string
  - `to_yaml_file(path)`: Serialize to YAML file
  - `from_yaml_file(path)`: Create object from YAML file

- **MsgPack Methods**:
  - `to_msgpack()`: Serialize to MsgPack bytes
  - `from_msgpack(msgpack_bytes)`: Create object from MsgPack bytes
  - `to_msgpack_file(path)`: Serialize to MsgPack file
  - `from_msgpack_file(path)`: Create object from MsgPack file

- **Pickle Methods**:
  - `to_pickle()`: Serialize to Pickle bytes
  - `from_pickle(pickle_bytes)`: Create object from Pickle bytes
  - `to_pickle_file(path)`: Serialize to Pickle file
  - `from_pickle_file(path)`: Create object from Pickle file

### 2. Serialization Utilities

**Implementation:** `src/flock/core/serialization/serialization_utils.py`

**Purpose:**
Provides helper functions for handling complex object serialization and deserialization.

**Key Features:**
- Recursive serialization of nested objects
- Callable serialization using registry path strings
- Type and component reference serialization
- Support for Pydantic models
- Special handling for collections

**API:**
- `serialize_item(item)`: Recursively prepare an item for serialization
- `deserialize_item(item)`: Recursively process a deserialized item
- `deserialize_component(data, expected_base_type)`: Deserialize component instances

### 3. JSON Encoder

**Implementation:** `src/flock/core/serialization/json_encoder.py`

**Purpose:**
Extends JSON encoding to handle Flock-specific types.

**Key Features:**
- Custom JSON encoder class
- Special handling for non-serializable types
- Fallback serialization strategies

### 4. Secure Serializer

**Implementation:** `src/flock/core/serialization/secure_serializer.py`

**Purpose:**
Provides secure serialization with encryption support.

**Key Features:**
- Encryption of serialized data
- Secure storage of sensitive components
- Key management utilities

## Serialization Integration

### Registry Integration
- Serialization uses registry to resolve callable references
- Types are serialized by reference where possible
- Path strings provide stable references across environments

### Temporal Integration
- Activities and workflows require serializable components
- Context serialization for distributed execution
- Serialization supports distributed execution engines

### Storage Integration
- File-based serialization for persistence
- Support for multiple serialization formats
- Error handling for file operations

## Design Principles

1. **Format Flexibility**:
   - Multiple serialization formats supported
   - Optional dependencies for specialized formats
   - Consistent API across formats

2. **Recursive Handling**:
   - Proper handling of nested objects
   - Collection traversal
   - Special type handling

3. **Reference-Based Serialization**:
   - Callables serialized by reference
   - Component types serialized by name
   - Dynamic resolution during deserialization

4. **Graceful Degradation**:
   - Fallbacks for unserializable objects
   - Error handling for missing components
   - Optional dependency management

## Implementation Requirements

1. Serialization must handle circular references
2. Type hints must be preserved where possible
3. Component resolution must be reliable
4. Registry integration must be robust
5. File operations must have proper error handling
6. Serialization must be secure for sensitive data 