I want to creaete AtmabodhAI, I have created this explanation which gives the vision of it, but please do change any of the details if you think you have a better implementation of some part of the idea, just keep the vision:

mabodhAI, incorporating all the details we've discussed, including the LLM integration, the while loops, the approved function call, and the roles of each agent:

AtmabodhAI: A Multi-Agent AI System Powered by Large Language Models

Goal: AtmabodhAI is an experimental project aimed at creating a robust and efficient AI system that can solve a wide range of user-provided tasks. It achieves this by leveraging the power of multiple specialized AI agents, each powered by a large language model (LLM).

Agents and Their Roles:

Refiner: The Refiner acts as the initial interpreter of user input. It takes the raw task provided by the user and clarifies it, adding context, suggesting potential approaches, and breaking down complex tasks into smaller, more manageable steps.

Planner: The Planner receives the refined task from the Refiner and creates a step-by-step plan to address the task. It draws upon its knowledge base and past experiences to devise an effective plan.

Critic: The Critic is the quality control expert of the system. Its role is to evaluate both the Planner's plans and the Executor's outputs, ensuring they are of high quality, accurate, and aligned with the user's original request. The Critic provides feedback for improvement and uses a special function call, approved(output), to signal when a plan or output is satisfactory and can move to the next stage.

Executor: The Executor takes the approved plan from the Planner and puts it into action. Depending on the task, the Executor might generate and execute Python code or provide a direct answer if code is not necessary.

Presenter: The Presenter receives the final, approved output from the Executor and formats it into a clear, concise, and user-friendly presentation for the user.

Agents as Large Language Models (LLMs):

Each agent in AtmabodhAI is not a traditional coded agent. Instead, each agent is powered by a large language model (LLM).

The LLM acts as the "brain" of the agent, enabling it to understand instructions, process information, generate responses (plans, code, evaluations), and make decisions (such as calling the approved function).

We use carefully crafted prompts to provide the LLMs with context, instructions, and examples to guide their behavior within each agent's role.

AtmabodhAI is designed to be flexible and supports different LLM providers:

Gemini: Currently, the system is primarily set up to use Google's Gemini family of LLMs. You can specify the desired Gemini model (e.g., gemini-pro) in the config.yaml file.

Ollama: The framework also includes support for Ollama, an open-source LLM platform. You can switch to using Ollama by changing the LLM provider setting in the config.yaml file.

LLM Abstraction: The code utilizes an abstract BaseLLM class and concrete implementations for Gemini (GeminiLLM) and Ollama (OllamaLLM). This abstraction allows you to easily integrate and experiment with other LLMs in the future.

Workflow and Interactions:

The agents interact in a sequential workflow, with feedback loops managed by while loops in the Python code. Here's a visual representation of the flow:

User -> Refiner -> Planner - Critic -> Executor - Critic -> Presenter -> User
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Use code with caution.

User Input: The user provides a task to AtmabodhAI.

Refiner: The Refiner agent, powered by an LLM, receives the raw task. It uses its LLM to clarify the task, add context, suggest potential approaches, and decompose complex tasks into smaller parts. The Refiner then sends the refined task to the Planner.

Planner-Critic Loop:

The Orchestrator (a Python class that manages the workflow) initiates a while loop to handle the interaction between the Planner and the Critic.

Planner: The Planner, powered by an LLM, receives the refined task and generates an initial plan.

Critic: The Critic, also powered by an LLM, evaluates the plan.

If the Critic is not satisfied with the plan, it provides feedback. The while loop continues automatically, sending the feedback back to the Planner for revisions.

If the Critic is satisfied, it calls the approved(plan) function, passing the plan as an argument. This function call acts as a signal to break the while loop and move on to the Executor.

Executor-Critic Loop:

The Orchestrator starts another while loop to manage the interaction between the Executor and the Critic.

Executor: The Executor, powered by an LLM, receives the approved plan and determines the best course of action. If the plan requires code, the Executor generates and executes the Python code. If not, it provides a direct answer.

Critic: The Critic evaluates the Executor's output (code/result or answer).

If the Critic is not satisfied, it provides feedback, and the while loop continues, sending the feedback back to the Executor for revisions.

If the Critic is satisfied, it calls the approved(output) function, passing the output as an argument. This function call breaks the while loop, and the approved output is sent to the Presenter.

Presenter: The Presenter, powered by an LLM, receives the final, approved output from the Executor. It formats the output using markdown or other techniques to make it clear, concise, and easy for the user to understand.

System Termination: The AtmabodhAI workflow concludes after the Presenter presents the final output to the user.

Key Mechanisms:

while Loops: The while loops in the Orchestrator ensure that the Planner and Executor iteratively refine their work based on the Critic's feedback until the Critic is satisfied. This helps to overcome the randomness of LLMs and produce more reliable results.

approved(output) Function Call: The Critic's approved function call acts as a clear signal to break the while loop and move the workflow forward. This function is described to the Critic LLM so it understands its purpose and how to use it. The Orchestrator handles the execution of this function, forwarding the approved output to the next agent.