agentic-kernel 0.2.10

A Python library for building agentic applications

Agentic Kernel: A Modular Framework for Autonomous AI Agents

Build, orchestrate, and manage sophisticated multi-agent systems with ease.

Agentic Kernel provides a robust and flexible foundation for creating autonomous AI agents that can collaborate, reason, and execute complex tasks. Inspired by frameworks like Semantic Kernel and Autogen, it offers a modular architecture, dynamic workflow management, and seamless integration capabilities.

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✨ Key Features

• 🤖 Modular Multi-Agent Architecture: Design systems with specialized agents, dynamic registration, and secure communication.
• ⚙️ Sophisticated Workflow Engine: Intelligently decompose tasks, track progress in real-time, handle errors gracefully, and manage concurrent execution.
• 🧠 Dynamic Planning & Orchestration: Features a powerful Orchestrator Agent capable of creating, managing, and adapting complex plans using a nested loop architecture.
• 🔌 Pluggable Components: Easily extend functionality with custom plugins, tools, and memory systems.
• 💬 Standardized Communication: Agents interact using a clear and consistent message format, compliant with Google's A2A (Agent-to-Agent) interoperability standard.
• 🖥️ Interactive UI: Includes a Chainlit-based interface for real-time interaction, task visualization, and monitoring.
• 🛠️ Rich Tooling & Integration: Leverage built-in tools and integrate with external systems (e.g., via MCP).

🚀 Getting Started

Follow these steps to get Agentic Kernel up and running on your local machine.

Prerequisites:

• Python 3.10 or higher
• uv (recommended) or pip package manager
• Git (for cloning the repository)

Installation & Setup:

1. Clone the Repository (if you haven't already):

   git clone https://github.com/qredence/agentic-kernel.git # Replace with your repo URL
   cd agentic-kernel
   

2. Create and Activate a Virtual Environment:

   • Using uv (Recommended):

     # Install uv if you don't have it (e.g., pip install uv)
     uv venv
     source .venv/bin/activate
     

   • Using standard venv:

     python -m venv .venv
     source .venv/bin/activate # On Windows use: .venv\Scripts\activate
     

3. Install Dependencies:

   # Using uv
   uv pip install -r requirements.txt
   
   # Using pip
   # pip install -r requirements.txt
   

   Note: If you plan to develop the kernel itself, you might install it in editable mode:

   # uv pip install -e .
   # pip install -e .
   

4. Configure Environment Variables:

   • Copy the example environment file:

     cp .env.example .env
     

   • Edit the .env file and add your API keys and endpoints for required services (e.g., Azure OpenAI, specific tools).

Running the Chainlit UI:

1. Ensure your virtual environment is active.

2. Run the application using the provided script or manually:

   • Using the script:

     ./scripts/run_chainlit.sh
     

     (This script conveniently handles activating the environment and setting the PYTHONPATH)

   • Manually with Chainlit:

     chainlit run src/agentic_kernel/app.py -w
     

     (The -w flag enables auto-reloading during development)

3. Access the application in your web browser, typically at http://localhost:8000.

🏛️ System Architecture

Agentic Kernel employs a modular design centered around interacting components:

src/agentic_kernel/
├── agents/         # Specialized agent implementations (e.g., Orchestrator, Worker)
├── communication/  # Protocols and message formats for inter-agent communication
├── config/        # Configuration loading and management
├── ledgers/       # State tracking for tasks and progress
├── memory/        # Systems for agent memory and knowledge storage
├── orchestrator/  # Core logic for workflow planning and execution
├── plugins/       # Extensible plugin system for adding capabilities
├── systems/       # Foundational system implementations
├── tools/         # Reusable tools agents can leverage
├── ui/           # User interface components (e.g., Chainlit app)
├── utils/        # Helper functions and utilities
└── workflows/     # Definitions and handlers for specific workflows

A2A Compliance

Agentic Kernel is compliant with Google's A2A (Agent-to-Agent) interoperability standard, which enables seamless communication and collaboration between different agent systems. Key A2A features include:

• Capability Discovery: Agents can advertise their capabilities and discover the capabilities of other agents.
• Agent Discovery: Agents can announce their presence and find other agents in the system.
• Standardized Message Format: All agent communication follows a consistent format with required A2A fields.
• Consensus Building: Agents can request and build consensus on decisions.
• Conflict Resolution: The system provides mechanisms for detecting and resolving conflicts between agents.
• Task Decomposition: Complex tasks can be broken down into subtasks and distributed among agents.
• Collaborative Memory: Agents can share and access a common memory space.

To test A2A compliance, run the provided test script:

python src/debug/test_a2a_compliance.py

Core Concepts

• Agents: Autonomous units with specific capabilities (e.g., planning, executing, validating). The OrchestratorAgent is key for managing complex tasks.
• Workflows: Sequences of steps managed by the Workflow Engine, involving task decomposition, execution, and monitoring.
• Communication Protocol: A standardized JSON format for messages exchanged between agents.
• Ledgers: Track the state and progress of tasks and workflows.
• Plugins & Tools: Extend agent functionality by providing access to external capabilities or data.

Refer to the code documentation within each directory for more detailed information.

📚 Examples & Usage

Explore the capabilities of Agentic Kernel through practical examples:

• Core Feature Examples (docs/examples/): Detailed markdown files demonstrating specific functionalities like:

  • Advanced Plugin Usage
  • Agent Communication Patterns
  • Basic Workflow Definition
  • Memory System Interaction
  • Orchestrator Features (Conditional Steps, Dynamic Planning, Error Recovery)
  • Workflow Optimization

• Multi-Agent System (examples/adk_multi_agent/): A complete example showcasing collaboration between multiple agents (Task Manager, Worker, Validator).

  • See the Multi-Agent Example README for setup and execution instructions.

🤝 Contributing

We welcome contributions! Please read our CONTRIBUTING.md guide to learn about our development process, how to propose bug fixes and improvements, and coding standards.

📜 License

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

🐛 Debugging

• The src/debug/ directory contains scripts useful for isolating and testing specific components of the kernel. Explore these scripts if you encounter issues or want to understand individual parts better.