What is Llama Stack

The Llama Stack is an emerging framework in the development of agentic applications using Large Language Models (LLMs) like LLaMA (Large Language Model Meta AI). When you refer to "Llama stack app agentic flow", you're likely talking about a system where multiple agents interact with each other to accomplish tasks within an application. These agents rely on the capabilities of LLMs and are orchestrated through frameworks or methodologies like Langchain to handle complex workflows.

Here's an overview of what agentic flow in a Llama stack app might involve:

Components in the Llama Stack:

LLM Core (LLaMA Model):

This is the foundational large language model that powers the decision-making, language understanding, and task execution in the agents. The LLaMA model would be fine-tuned or customized for your specific application.

Agents:

Agents are specialized modules or entities in the system, each responsible for handling specific tasks or subtasks within the overall workflow. Agents may include:

Planning agents: Decompose complex requests into sub-goals.

Execution agents: Perform specific tasks (e.g., call APIs, run computations).

Query agents: Interact with databases, gather information, and respond to queries.

Translation agents: Map instructions or information into another system or language.

Agentic Flow: In an agentic flow, tasks are dynamically assigned and executed by the agents based on the input and the current state of the application. This is similar to a Multi-Agent System (MAS) where agents collaboratively work to achieve a larger goal, like generating configuration templates, running workflows, or decision-making.

A typical flow might look like:

User Input: The user provides a request to the system.

Planning Agent Activation: The system activates a planning agent that breaks the request down into smaller, manageable tasks.

Task Delegation: The tasks are handed over to the relevant agents (e.g., an execution agent calls an API, a query agent fetches data from a database).

Coordination & Feedback: The agents may communicate back and forth, sharing intermediate results, and updating their status as they work towards the goal.

Final Output: Once the agents complete their tasks, the results are aggregated and presented to the user.

Langchain/Llama Integration: The Llama Stack could integrate Langchain, which offers agent-driven interaction with LLMs, allowing agents to reason, break down problems, and use tools like databases or external APIs. Langchain's role would be orchestrating how agents communicate with each other and interact with the LLM.