---
name: agent-supervisor
version: 1.0.0
description: Demonstrate a supervisor-worker architecture for intelligent task delegation
  and real-time decision-making.
inputs:
- name: OPENAI_API_KEY
  description: OpenAI API key for language models.
- name: TAVILY_API_KEY
  description: Tavily API key for search functionality.
steps:
- step: 1
  action: Load environment variables.
  details: Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.
- step: 2
  action: Configure LangChain tools.
  details: Initialize TavilySearchResults and PythonREPLTool.
- step: 3
  action: Define agent nodes.
  details: Create functions for the Researcher and Coder agents that process state
    through their respective tasks.
- step: 4
  action: Set up supervisor agent.
  details: Create a supervisor agent function that decides which worker should act
    next based on user input.
- step: 5
  action: Build state graph.
  details: Construct the state graph with nodes for each agent and edges connecting
    them to the supervisor node.
- step: 6
  action: Add conditional edges.
  details: Define conditions for transitioning between agents based on their responses.
- step: 7
  action: Compile graph.
  details: Compile the state graph into a runnable workflow.
- step: 8
  action: Run example queries.
  details: Stream through the workflow with example inputs to demonstrate its functionality.
outputs:
- name: 'Example 1: Code Hello World'
  description: A demonstration of coding a simple hello world program.
- name: 'Example 2: Research Report'
  description: A demonstration of researching and writing a brief report on pikas.
tags: []
metadata:
  source_repo: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
  extracted_at: ''
  confidence: 0.9
---

# agent-supervisor

Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.

## Steps

1. {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'}
2. {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'}
3. {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
4. {'step': 4, 'action': 'Set up supervisor agent.', 'details': 'Create a supervisor agent function that decides which worker should act next based on user input.'}
5. {'step': 5, 'action': 'Build state graph.', 'details': 'Construct the state graph with nodes for each agent and edges connecting them to the supervisor node.'}
6. {'step': 6, 'action': 'Add conditional edges.', 'details': 'Define conditions for transitioning between agents based on their responses.'}
7. {'step': 7, 'action': 'Compile graph.', 'details': 'Compile the state graph into a runnable workflow.'}
8. {'step': 8, 'action': 'Run example queries.', 'details': 'Stream through the workflow with example inputs to demonstrate its functionality.'}

## Inputs

- {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}
- {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}

## Outputs

- {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}
- {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}

## Failure Modes

- {'mode': 'Invalid API keys', 'description': 'The workflow may fail if the provided API keys are invalid or expired.'}
- {'mode': 'Insufficient permissions', 'description': 'The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality.'}

## Source

Extracted from: [https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git](https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git)
Confidence: 0.9