How to Build a UI for Your CrewAI AI Agent using Streamlit

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AI Summary

Video Summary: Building a UI for AI Agents with Streamlit

Architectural Overview

• The project involves creating a simple AI agent that performs basic research and displays results on a Streamlit UI.
• The user interacts with a Streamlit app, which communicates with an AI agent.
• The agent uses LLM (Language Learning Model) for decision-making and output generation.
• The agent also utilizes EXA, an AI search engine, to answer queries and conduct research.
• EXA provides endpoints for answering and searching, which the agent uses to gather information and citations.
• The final response is edited by the LLM and displayed on the Streamlit UI.
• Users have the option to download the research report in markdown format.

Codebase and Implementation

• The codebase is organized into modules for modularity and best practices.
• Secrets like API keys are stored in streamlit configuration files.
• The researcher.py file contains the AI agent code, which uses EXA as a tool for research.
• The agent is defined with a role, goal, backstory, and tools it can use.
• The agent’s task is to generate a comprehensive research report with specific sections and details relevant to the current year.
• The streamlit side of the code handles the UI, including the sidebar for configuration and the main body for displaying results.
• The utils module captures the terminal output and displays it in the Streamlit UI.
• The streamlit app.py file sets up the page configuration, input areas, research button, and output display.
• Running the app is done via the command streamlit run streamit_app.py, which launches the app on a local server.

Demonstration

• The presenter demonstrates the app by inputting a research topic and observing the agent’s process in the UI.
• The agent reframes the query, uses EXA to conduct research, and generates a final summary with citations.
• The final report is displayed in the UI and can be downloaded as a markdown file.

Additional Notes

• The presenter suggests potential improvements and variations, such as converting the UI into a chat application or adding more agents.
• The code for the demo is available in the video description for viewers to experiment with.

Conclusion

• The video concludes with an encouragement to build upon the demonstrated project and share any creations with the community.