This pseudocode outlines the creation of a state machine similar to LangGraph using Pydantic for data validation. The Node class represents tasks or agents with their specific functions. The StateGraph manages nodes and transitions based on conditions. This abstract implementation can be extended with more sophisticated logic for conditions, nod…

PseudoLanggraphStateMachine.md

To create an abstracted LangGraph-like state machine using Pydantic, we will design a system that allows for the creation of nodes (agents) and edges (transitions) in a graph that represents a workflow. The agents will have specific roles or functions, and the transitions between these agents will be dictated by the outcomes of their actions. Pydantic will be used for data validation and settings management, ensuring that the components of our state machine are well-defined and interact correctly.

Step 1: Define the Base Models with Pydantic

First, we need to define our base models for Nodes (Agents) and Edges (Transitions). Pydantic models will ensure that each component of our graph adheres to a specific structure and type, facilitating error handling and data consistency.

Step 2: Implement the Graph Structure

We will create a class for the graph itself, which holds the nodes and edges and manages the workflow's execution. This includes methods for adding nodes, adding edges, and determining the next node based on the current state and the outcome of the node's action.

Step 3: Execution Logic

The execution logic will involve initializing the graph with a starting node and then, based on the input and the outcomes of each node's action, transitioning through the graph according to the defined edges until an end condition is met.

Pseudocode for Implementation:

from pydantic import BaseModel
from typing import Callable, Dict, List, Optional, Union

# Step 1: Define the Base Models
class Node(BaseModel):
    id: str
    function: Callable
    next: Optional[Dict[str, 'Node']] = None  # Using string literals for forward declaration

Node.update_forward_refs()

class Edge(BaseModel):
    source_node: Node
    condition: Callable
    target_node: Node

class StateGraph(BaseModel):
    nodes: Dict[str, Node] = {}
    edges: List[Edge] = []
    start_node_id: Optional[str] = None

    def add_node(self, node: Node):
        self.nodes[node.id] = node

    def add_edge(self, source_id: str, target_id: str, condition: Optional[Callable] = None):
        source_node = self.nodes.get(source_id)
        target_node = self.nodes.get(target_id)
        if source_node and target_node:
            self.edges.append(Edge(source_node=source_node, condition=condition, target_node=target_node))
        else:
            raise ValueError("Source or Target node not found")

    def set_start_node(self, node_id: str):
        if node_id in self.nodes:
            self.start_node_id = node_id
        else:
            raise ValueError("Node not found")

    def execute(self, initial_data):
        if not self.start_node_id:
            raise ValueError("Start node not set")

        current_node = self.nodes[self.start_node_id]
        result = initial_data
        while current_node:
            result = current_node.function(result)
            next_node_id = self.determine_next_node(current_node, result)
            current_node = self.nodes.get(next_node_id)

    def determine_next_node(self, current_node: Node, result) -> Optional[str]:
        for edge in self.edges:
            if edge.source_node == current_node and (not edge.condition or edge.condition(result)):
                return edge.target_node.id
        return None

# Example usage
# Define node functions and conditions here
def start_function(input):
    # Implement the start function logic
    pass

def condition_function(result):
    # Implement condition logic to decide transitions
    pass

# Initialize the graph
graph = StateGraph()

# Add nodes and edges to the graph
graph.add_node(Node(id="start", function=start_function))
# Add more nodes and define their functions

# Define transitions between nodes
graph.add_edge("start", "next_node_id", condition=condition_function)
# Set the starting node
graph.set_start_node("start")

# Execute the graph with initial data
graph.execute(initial_data={})

This pseudocode outlines the creation of a state machine similar to LangGraph using Pydantic for data validation. The Node class represents tasks or agents with their specific functions. The StateGraph manages nodes and transitions based on conditions. This abstract implementation can be extended with more sophisticated logic for conditions, node functions, and integrating streaming or asynchronous processing as needed.