https://github.com/razamehar/hierarchical-agent-teams
https://github.com/razamehar/hierarchical-agent-teams
agentic-ai hierarchical-agents langchain langraph large-language-model multi-agent-systems openai python
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/razamehar/hierarchical-agent-teams
- Owner: razamehar
- Created: 2025-07-02T19:04:49.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-03T11:40:10.000Z (3 months ago)
- Last Synced: 2025-07-23T15:15:32.044Z (3 months ago)
- Topics: agentic-ai, hierarchical-agents, langchain, langraph, large-language-model, multi-agent-systems, openai, python
- Language: Jupyter Notebook
- Homepage:
- Size: 73.2 KB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hierarchical AI Agent Team
A modular, multi-agent AI system built using [LangGraph](https://github.com/langchain-ai/langgraph), [LangChain](https://www.langchain.com/), and OpenAI's GPT model. The system uses a **hierarchical architecture** with supervisors managing specialized agents to perform complex tasks through collaboration.
---
## Project Structure
```text
├── main.py
├── agents/
│ ├── math_agent.py
│ ├── doc_saving_agent.py
│ ├── research_agent.py
│ ├── summary_agent.py
├── teams/
│ ├── mid_supervisor.py
│ ├── supervisor.py
├── tools/
│ ├── doc_saver.py
│ ├── math_tools.py
│ ├── research_tools.py
│ ├── summarize_tool.py
├── utils/
│ ├── model_loader.py
├── schema/
│ ├── schema.py
├── config/
│ ├── config.py
├── requirements.txt
└── README.md
```
## How It Works
### `main.py`
Interactive console interface. Type a query and watch the agent hierarchy handle it. Type `exit` or `quit` to stop.
```python
Query: How has Tesla stock performed in the last year?
```
## Top-Level Supervisor
Coordinates the entire agent ecosystem:
- Delegates research and calculations to research_team
- Summarizes results via summary_agent
- Saves outputs using doc_saving_agent
## Mid-Level Supervisor: research_team
Manages:
- research_agent: performs web-based information retrieval
- math_agent: handles all arithmetic using separate tools
## Agents and Responsibilities
| Agent | Description |
| ------------------ |---------------------------------------------------------------|
| `math_agent` | Performs addition, subtraction, multiplication, and division. |
| `research_agent` | Searches online using Tavily (general + finance). |
| `summary_agent` | Summarizes large chunks of text using GPT. |
| `doc_saving_agent` | Saves results in `.pdf` or `.docx` format. |
## Tools
### Math Tools
- add_tool
- subtract_tool
- multiply_tool
- divide_tool
### Research Tools
- finance_research_tool
- general_research_tool
### Summarizer
- get_summary: Generates a 300-word summary
### Document Tools
- save_doc_tool: Save text in .pdf, .doc, or .docx format
## Requirements
Install dependencies using:
```bash
pip install -r requirements.txt
```
```nginx
langchain
langchain-community
langchain-openai
langchain_core
langgraph
langchain-tavily
python-dotenv
pydantic
wikipedia
fpdf
python-docx
openai
```
## Environment Variables
Create a .env file and add your API keys:
```ini
OPENAI_API_KEY=your-key-here
TAVILY_API_KEY=your-key-here
```
## Run the Project
```bash
python main.py
```
## Built With
- LangGraph
- LangChain
- OpenAI
- Tavily
- FPDF
- python-docx
## Contact
For any questions or clarifications, please contact Raza Mehar at [raza.mehar@gmail.com](mailto:raza.mehar@gmail.com).