Layout

Detailed framework structure and component organization

Framework Layout and Organization

The Agentic Framework follows a carefully organized structure that promotes modularity, extensibility, and clear separation of concerns. Below is the detailed layout of the framework:

agentic/
├── __init__.py
├── agent/                 # Agent core: Base agent implementations and interfaces
│   ├── __init__.py
│   ├── base.py           # Abstract base classes and interfaces for all agents
│   ├── memory.py         # Agent-specific memory management and state persistence
│   └── storage.py        # Storage mechanisms for agent state and data
├── api/                  # API Layer: External interface definitions
│   ├── __init__.py
│   ├── routes.py         # API endpoint definitions and routing logic
│   └── schemas.py        # Data validation schemas and API models
├── document/            # Document Processing: Text and file handling
│   ├── __init__.py
│   ├── loader.py        # Document loading from various sources (PDF, HTML, etc.)
│   └── parser.py        # Document parsing and content extraction
├── embedder/            # Embedding Engine: Text vectorization
│   ├── __init__.py
│   ├── base.py          # Abstract embedding interfaces
│   └── openai.py        # OpenAI-specific embedding implementations
├── knowledge/           # Knowledge Management: Information storage and retrieval
│   ├── __init__.py
│   ├── base.py          # Base knowledge store interfaces
│   └── vector.py        # Vector-based knowledge storage implementation
├── llm/                 # Language Model Integration: LLM providers and interfaces
│   ├── __init__.py
│   ├── anthropic.py     # Anthropic Claude integration
│   ├── openai.py        # OpenAI GPT integration
│   └── base.py          # Common LLM interfaces and abstractions
├── memory/             # Memory Systems: Long-term and working memory
│   ├── __init__.py
│   ├── conversation.py  # Conversation history and context management
│   └── vector.py        # Vector-based memory storage
├── model/              # Data Models: Core data structures
│   ├── __init__.py
│   ├── base.py         # Base model classes and interfaces
│   └── config.py       # Configuration and settings models
├── prompt/             # Prompt Engineering: Template management
│   ├── __init__.py
│   └── templates.py    # Prompt templates and generation logic
├── reasoning/          # Reasoning Engines: Logic and inference
│   ├── __init__.py
│   └── chain.py        # Reasoning chain implementation
├── storage/            # Storage Layer: Persistence backends
│   ├── __init__.py
│   ├── redis.py        # Redis storage implementation
│   └── vector.py       # Vector storage implementation
├── swarm/              # Swarm Intelligence: Multi-agent coordination
│   ├── __init__.py
│   ├── base.py         # Base swarm interfaces and abstractions
│   ├── coordinator/    # Coordination and Orchestration
│   │   ├── __init__.py
│   │   ├── base.py     # Base coordinator interfaces
│   │   └── router.py   # Message and task routing logic
│   ├── hierarchy/      # Hierarchical Organization
│   │   ├── __init__.py
│   │   ├── base.py     # Hierarchy interfaces
│   │   └── manager.py  # Hierarchy management and relationships
│   ├── messaging/      # Communication System
│   │   ├── __init__.py
│   │   ├── base.py     # Messaging interfaces
│   │   └── bus.py      # Message bus implementation
│   └── scheduler/      # Task Management
│       ├── __init__.py
│       ├── base.py     # Scheduler interfaces
│       └── engine.py   # Task scheduling and distribution
├── tools/              # Tool Framework: External tool integration
│   ├── __init__.py
│   ├── base.py         # Base tool interfaces
│   └── examples/       # Example tool implementations
├── utils/              # Utilities: Helper functions and common code
│   ├── __init__.py
│   ├── logger.py       # Logging configuration and utilities
│   └── helpers.py      # General helper functions
├── vectordb/          # Vector Database: Vector storage and search
│   ├── __init__.py
│   ├── base.py        # Vector database interfaces
│   └── pinecone.py    # Pinecone implementation
└── workflow/           # Workflow Engine: Task execution patterns
    ├── __init__.py
    ├── base.py        # Base workflow interfaces
    ├── concurrent.py  # Parallel execution patterns
    ├── sequential.py  # Sequential execution patterns
    └── graph.py       # Graph-based workflow execution

Component Details

1. Core Agent Layer (`agent/`)

Contains the fundamental agent implementations
Handles agent state management and persistence
Defines core interfaces that all agents must implement
Integrates with model/ for core data structures and configurations

Key files:

base.py: Abstract base classes and interfaces defining agent behavior
memory.py: Agent-specific memory management and state persistence
storage.py: Storage mechanisms for agent state and data

2. Intelligence Components

Language Models (`llm/`)

Integration with multiple LLM providers
Standardized interface for model interactions
Provider-specific optimizations

Embedding Engine (`embedder/`)

Text vectorization capabilities
Semantic understanding interfaces
Provider-specific implementations (e.g., OpenAI)

Knowledge Management (`knowledge/`)

Information storage and retrieval systems
Vector-based knowledge storage
Base interfaces for knowledge stores

3. Memory and Storage Systems

Conversation history management through memory/
Vector-based memory storage
Multiple storage backends (Redis, vector stores)
State persistence mechanisms

4. Document Processing (`document/`)

Handles various document formats (PDF, HTML)
Document parsing and content extraction
Text processing capabilities

5. Swarm Intelligence (`swarm/`)

Multi-agent coordination system with components:

coordinator/: Orchestration and routing
hierarchy/: Agent relationship management
messaging/: Inter-agent communication
scheduler/: Task distribution and management

6. Tool and Workflow Systems

tools/: Framework for external tool integration
workflow/: Task execution patterns (sequential, concurrent, graph-based)

7. Infrastructure Components

api/: External interface layer
vectordb/: Vector database implementations
utils/: Common utilities and helpers

Key Design Features

1. Modular Design

Each directory represents a distinct functional domain, ensuring clean separation of concerns and making the codebase easier to maintain and extend.

2. Clear Hierarchy

The framework maintains a flat top-level structure with focused submodules, making it intuitive to navigate and understand the system’s organization.

3. Extensibility

Every component includes base interfaces and abstract classes, facilitating easy extension and customization of functionality.

4. Separation of Concerns

Clear boundaries between different system aspects ensure that components can evolve independently while maintaining system cohesion.

5. Swarm Intelligence

A dedicated swarm module provides comprehensive support for multi-agent coordination and collaboration.

6. Flexible Storage

Multiple storage backends accommodate different use cases and performance requirements.

7. Tool Integration

A robust framework for adding and managing external tool capabilities, enabling seamless integration with external systems and services.

This organized structure ensures that developers can quickly locate components, understand their relationships, and extend the framework to meet specific needs while maintaining consistency and reliability.