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# System prepended metadata

title: LLM for Devs - Week 4 Project

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# LLM for Devs - Week 4 Project

### Exploring Agents and Multi-Agent Systems

In this assignment, you will choose **one** of four project paths to deepen your understanding of agents and multi-agent systems powered by large language models (LLM). 

Each path includes both a Jupyter notebook component to be completed in Google Colab.

Select **one** of the following project paths based on relevance to your group project:

1. **Production Path** - Develop and deploy a multi-agent system as a web application using Chainlit, Streamlit, or a custom front-end with an optional [FastAPI](https://fastapi.tiangolo.com/) (or similar) backend
2. **Combining Techniques Path** - If your group project involves information retrieval, implement a system using [RAG](https://arxiv.org/abs/2005.11401) with a vector store and agents  
3. **Library Evaluation Path** - If you're considering using an agent library for your group project, implement a multi-agent system using three different libraries that support agents (e.g., [CrewAI](https://www.crewai.com/), [LangGraph](https://python.langchain.com/v0.1/docs/langgraph/), [Langroid](https://langroid.github.io/langroid/), [AutoGen](https://microsoft.github.io/autogen/)) and compare/contrast performance
4. **Group Project Path** - This is pretty straightforward. Use this week to continue work on your group project. 

For building multi-agent systems, we highly recommend using the [CrewAI](https://www.crewai.com/) framework. [CrewAI](https://www.crewai.com/) provides a simple, prompting-based way to build multi-agent systems. We have examples using [CrewAI](https://www.crewai.com/) in [the Week 4 repo here](https://github.com/Devs-Do-AI/Week-4---Agents). 

We have a few resources for getting started with [CrewAI](https://www.crewai.com/) in the Resources section [here](https://hackmd.io/ww9jnZm0TlSdx5c8J5EY_w?both#Resources).

## 1 -  Production Path

In this project, you will develop a multi-agent system relevant to your group project and deploy it as a user-friendly web application. We recommend [Chainlit](https://docs.chainlit.io/get-started/overview) if it is a chatbot or [Streamlit](https://streamlit.io/) if it uses a non-chat UI.

Alternatively, if you prefer to build the front-end using a different technology (e.g., a JavaScript web application), you may need to deploy your agents as an API using a library like [FastAPI](https://fastapi.tiangolo.com/).

The system should include at least 2 specialized agents that collaborate to accomplish a task.

### 📓 Jupyter Notebook Component (2-5 hours)

- Set up a Google Colab notebook
- Implement 2 agents with clearly defined roles using [CrewAI](https://www.crewai.com/) that are relevant to your group project
- Have the agents communicate and pass information between each other
- Test the system with a few example queries

### 💻 Web Application Component (2-5 hours)

- Create a user-friendly web application using [Chainlit](https://docs.chainlit.io/get-started/overview), Streamlit, or the technology of your choice
- Integrate the agent system into the web application
- Implement user input functionality and display the results generated by the agents
- Deploy the web application to Hugging Face Spaces for easy access and sharing
- [Optional] Apply appropriate styling and layout to enhance the user experience

### API Deployment Component (Optional, 2-5 hours)

If you choose to build the front-end using a different technology (e.g., a JavaScript web application), follow these steps:

- Refactor the agent system into a clean, modular FastAPI (or your preferred framework) application
- Implement API endpoints for each agent and the overall system
- Incorporate guardrails to prevent the agents from going off-track
- Deploy the API to a cloud platform like [AWS](https://aws.amazon.com/), [Google Cloud](https://cloud.google.com/), or [Hugging Face Spaces](https://huggingface.co/spaces)
- Integrate the API with your custom-built front-end application

Remember to document your code, provide clear instructions for running the application, and share the link to your deployed web application or API.

## 2 - Combining Techniques Path  

In this project, you will implement a retrieval-augmented generation (RAG) system that uses a vector database to provide knowledge to a multi-agent system relevant to your group project.

### 🗒️ Jupyter Notebook Component (4-6 hours)

- Set up a Google Colab notebook
- Choose a dataset relevant to your group project to use as the knowledge base
- Create embeddings and a vector database from the dataset using a library like [FAISS](https://github.com/facebookresearch/faiss)
- Implement a retriever agent that uses the vector database to find relevant information for a query
- Implement a generator agent that takes the retrieved information and the query to generate an answer
- Allow the agents to collaborate until a satisfactory answer is generated
- Test the system with example queries relevant to your project

### 💻 [Optional] Production Component (4-6 hours)  

- Refactor the RAG system into a [FastAPI](https://fastapi.tiangolo.com/) application
- Use a hosted vector database like [Pinecone](https://www.pinecone.io/) or [Weaviate](https://weaviate.io/)
- Implement API endpoints for queries
- Report results including example queries and answers relevant to your project  
- Stretch goal: Implement a feedback loop where user feedback is used to improve the vector database and agent prompts over time

## 3 - Library Evaluation Path

In this project, you will implement the same multi-agent system relevant to your group project using three libraries that support agents (e.g, [CrewAI](https://www.crewai.com/), [LangGraph](https://python.langchain.com/v0.1/docs/langgraph/), [Langroid](https://langroid.github.io/langroid/), [AutoGen](https://microsoft.github.io/autogen/), etc.), and compare both the developer experience and results.  

### 🗒️ Jupyter Notebook Component (4-6 hours)

- Set up a Google Colab notebook  
- Choose a task relevant to your group project for a multi-agent system to perform
- Implement the system using three libraries that support agents (e.g, [CrewAI](https://www.crewai.com/), [LangGraph](https://python.langchain.com/v0.1/docs/langgraph/), [Langroid](https://langroid.github.io/langroid/), [AutoGen](https://microsoft.github.io/autogen/), etc.)
  - Have at least 2 agents with clear roles and communication 
- For each implementation, document your experience as a developer   
  - Consider ease of use, flexibility, documentation quality, roadblocks, etc.
- Test each implementation with example queries relevant to your project
- Document and compare the quality of the results from each implementation

## 4 - Group Project Path

This is pretty straightforward. Just continue to work on your group project. 

## Deliverables

- Google Colab notebook with all code, explanations, and example outputs
- If applicable, include the following as separate blocks within your Colab notebook
  - Link to GitHub repo  
  - Link to any production deployments
  - Any architecture diagrams linked to or embedded in the notebook
  - A short writeup with key learnings and reflections

## Resources

### CrewAI
[Multi-Agent systems with CrewAI (DeepLearning.AI)](https://learn.deeplearning.ai/courses/multi-ai-agent-systems-with-crewai/lesson/1/introduction) (the most fully-featured walkthrough of the library)
[Week 4 - Agents repository with CrewAI examples](https://github.com/Devs-Do-AI/Week-4---Agents) (lessons 1-6 build from simple crews to more sophisticated crews)

#### YouTube videos
[CrewAI Tutorial: Complete Crash Course for Beginners](https://www.youtube.com/watch?v=sPzc6hMg7So)
[How I Made AI Assistants Do My Work For Me: CrewAI](https://www.youtube.com/watch?v=kJvXT25LkwA&pp=ygUHY3JldyBhaQ%3D%3D)


### LangGraph
[LangGraph playlist](https://www.youtube.com/watch?v=5h-JBkySK34&list=PLfaIDFEXuae16n2TWUkKq5PgJ0w6Pkwtg)


### Prompt Engineering
[PromptingGuide](https://promptingguide.ai/techniques/)


## Project Submission

If you chose a path that requires submission of a Google Colab notebook, submit it using the form [here](https://forms.gle/DKeRAuYkvDQGjs9P9). Please make sure sharing permissions are turned on for everyone with the link. 

The notebooks are due 11:59PM Pacific Time the evening before the class session.