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Summary

The script details a tutorial on creating an AI agent using 'agent Ops' and 'Ops' to scrape websites and summarize content into tables. It introduces 'augment' as a coding assistant, discusses utilizing 'fir crawl' for web scraping, and 'open AI' for text summarization. The process involves setting up functions, handling API requests, and monitoring agent performance through a dashboard, emphasizing the ease of tracking costs and debugging with 'agent Ops'.

Takeaways

• 🤖 The script discusses building AI agents with 'Crew AI' and 'Agent Ops' for web scraping and summarizing content into tables.
• 📝 The importance of initializing and ending sessions with Agent Ops for tracking purposes is highlighted.
• 🔍 The use of 'fir crawl', an open-source web scraping framework, is introduced for fetching web data.
• 🔑 A mention of using the 'request' library to interact with APIs, specifically for the 'fir crawl' service.
• 💡 Tips are provided for debugging, such as using 'cgbd' to explain confusing parts of the code.
• 🛠️ The script covers the implementation of functions like 'crawl_web' and 'summarize_text' using Python.
• 📈 Agent Ops is used for monitoring AI agent performance, including tracking costs and latencies of large language model (LLM) calls.
• 🔗 The integration of Agent Ops with various LLM providers for automatic tracking of requests and costs is explained.
• 📊 The script demonstrates creating a table summary using OpenAI's language model and handling errors with Agent Ops' dashboard.
• 🔍 Agent Ops provides detailed observability into agent behavior, including chat history, waterfall graphs, and cost tracking.
• 🛑 The script identifies issues such as 'yappy' agents that produce too much output, making debugging difficult without proper tools.

Q & A

What is the primary objective of the project described in the script?

The primary objective is to build an AI agent using Crew AI with Agent Ops that can scrape web data, summarize it, and present the information in a table format.

Which tool is mentioned for web scraping in the script?

The tool mentioned for web scraping is 'Fir Crawl', an easy-to-use open-source web scraping framework.

What is the alternative to Co-Pilot that the speaker is using, and why is it preferred?

The speaker is using 'Augment' as an alternative to Co-Pilot because it is faster and works better by scraping the entire database instead of just in-context.

How does the speaker suggest to handle confusion during the coding process?

The speaker suggests taking a screenshot of the confusing part and pasting it into a chatbot, asking it to explain.

What is the purpose of the 'agent ops.init' function in the script?

The 'agent ops.init' function is used to kick off the session for the AI agent, ensuring it starts correctly.

How does the speaker plan to summarize the web data into a table?

The speaker plans to use a large language model (LLM) to read through the web content and create a table summary with the help of the 'summarize text' function.

What is the significance of the 'agent ops.record' decorator mentioned in the script?

The 'agent ops.record' decorator is used to record the functions being executed, allowing Agent Ops to track which functions are happening at which moment, providing traceability.

What does the speaker mean by 'yappy agents' and why is it a problem?

'Yappy agents' refers to agents that produce a large amount of output, making it difficult to parse through the console and debug. It's a problem because it lacks observability and clarity on the agent's actions and costs.

How does the Agent Ops dashboard help in debugging and understanding agent behavior?

The Agent Ops dashboard provides a visual representation of the agent's actions, including a chat breakdown, waterfall diagram, and cost tracking, making it easier to understand and debug agent behavior.

What is the role of the 'client.chat.completion.create' in the script?

The 'client.chat.completion.create' is used to interact with the large language model, sending prompts and receiving completions that help in summarizing the web data.

How does the speaker suggest tracking and managing the costs associated with using LLMs?

The speaker suggests using Agent Ops to automatically track all the requests and costs associated with using LLMs, providing insights into the expenses and helping in managing them.