Hi please help me make AtmabodhAI! 

You're right! I'm so sorry for the repeated mistakes. It seems I am still struggling to process visual information correctly. I'll make a note to improve my understanding of images.

The correct file structure is:

AtmabodhAI/
├── llm
│   ├── ollama_llm.py
│   ├── gemini_llm.py
│   ├── base.py
│   └── __init__.py
├── agents
│   ├── presenter.py
│   ├── executor.py
│   ├── critic.py
│   ├── planner.py
│   ├── refiner.py
│   ├── base_agents.py
│   └── __init__.py
├── utils
│   ├── communication.py
│   ├── memory.py
│   ├── orchestrator.py
│   └── tools.py
├── .env
├── requirements.txt
├── config.yaml
└── main.py
content_copy
Use code with caution.

Now, here's a detailed explanation of AtmabodhAI, taking into account all the aspects we've discussed:

What is AtmabodhAI?

AtmabodhAI is an experimental project designed to create a robust and efficient AI system that can solve a wide range of user-provided tasks. It's a multi-agent AI system, where each agent is powered by a large language model (LLM) like me, Gemini, or others like Ollama. These agents work together, using feedback loops and a well-defined workflow, to produce accurate and reliable results.

Key Concepts:

Multi-Agent AI: The system utilizes multiple specialized AI agents, each with a distinct purpose. This approach allows for better handling of complex tasks by dividing the workload and leveraging different strengths.

Large Language Models (LLMs): Each agent is powered by an LLM, which acts as its "brain." The LLM is responsible for understanding the task, generating responses, and making decisions.

Workflow: The agents interact in a sequential workflow, with feedback loops managed by Python while loops to ensure quality and accuracy.

Function Calls: The Critic agent uses a function call (approved(output)) to signal its approval of a plan or output, which then triggers the system to move on to the next step.

Modularity: The system is designed to be modular, allowing for the addition of new agents or the modification of existing ones to expand its capabilities.

The Agents and their Roles:

Refiner: The Refiner acts as the initial interpreter of user input. It receives the raw task from the user and clarifies it, adding context, suggesting potential approaches, and breaking down complex tasks into smaller parts. It then sends the refined task to the Planner.

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

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

Executor: The Executor receives 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.

Workflow:

The agents interact in a sequential workflow, with feedback loops managed by while loops in the Python code:

User -> Refiner -> Planner <- >Critic -> Executor <-> Critic -> Presenter -> User
content_copy
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 and refines it, sending the refined task to the Planner.

Planner-Critic Loop:

The Orchestrator initiates a while loop to manage the interaction between the Planner and the Critic.

Planner: The Planner (an LLM) generates an initial plan.

Critic: The Critic (an LLM) evaluates the plan.

If the Critic is not satisfied, 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 (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 (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.