'Wait..this AI Agent does research for you 24hrs without hallucination?!' - Here is how

🔗 Links

⏱️ Timestamps
0:00 Why AI Research agent?
3:12 Step1 Setup search tool
5:48 Step2 Setup Scraping tool #1
6:56 Tips: How to handle token limit
8:13 Step 2 Setup Scraping tool #2
11:24 Step3 Create agent
14:02 Tips: Setup GUI with streamlit
15:12 Research Agent Demo
16:13 Step4 Deploy

👋🏻 About Me

#gpt #autogpt #ai #artificialintelligence #tutorial #stepbystep #openai #llm #langchain #largelanguagemodels #largelanguagemodel #bestaiagent #chatgpt #agentgpt #agent #babyagi

[MarcelousTV]

I’m excited to watch this as many times as necessary to comprehend and implement. This was a concise yet thorough walk through of a complex topic with zero fluff or dead time. Very, very well done. This video leaves me feeling empowered! I’m really looking forward to following along step by step. Thank you for this! 🙏🏼

[HyperUpscale]

Finally you released another outstanding video... I was waiting for it.

[gustavopelissaro]

Probably the best channel on AI LangChain actual useful applications tutorials on youtube, i always rush to see your videos, much value provided as always, thanks Jason!

[therealunclexavier]

Jason. DON'T STOP!!! Every single video of your is remarkable. Please continue to produce these and come up with amazing ways of using AI. Use AI to "Audit/Verify" that someone has filled up a printed form correctly (for example, with their name, address and so on) and their signature matches with another document for example an ID to verify the person completed a printed document correctly.

[matt-collins]

Great video, Jason. I love that, as others have said, you're demonstrating how to build something actually useful.

[photon]

The research agent from your link is fantastic at doing market research! The links actually work and are from reputable places. Im blown away!

[Churchofexponentialgrowth]

🎯 Key Takeaways for quick navigation:

00:00 🗣️ The video discusses how an AI research agent works, crafting AI workflows, and executing tasks. The AI agent uses large language models for its operations, with the help of tools that facilitate task completion.
00:56 🎛️ The AI agent's efficiency depends on the quality of task execution and its ability to correctly reprioritize tasks, and hence building agents for specific tasks can be more effective.
02:05 📨 An AI research agent can be used in various ways, such as triggering a workflow when someone receives an email. The agent can analyze the incoming communication, derive insights, and compile it into a specific format such as a prioritized list.
02:34 🔄 The video suggests that an AI research agent can stitch together information from different systems, providing additional decision-making support for humans.
03:18 🖥️ The host showcases how to set up a research agent, deploy it as a web service and use it in business workflows. Steps include creating tools for the agent to search the internet, scrape websites, and summarizing content.
06:07 🛠️ Details on how to create a tool for scripting website data are provided, including using functions, headers, and APIs.
07:02 📚 One of the challenges addressed in the video relates to the token limit inherent in large language models. Two strategies to handle this include the MapReduce method of summarizing content, and VectorSearch, which creates a vector embedding of a large content piece.
14:17 🌐 The host then converts the AI research agent app into a web app by using streamlit, setting page configurations, and creating text inputs. Testing is done on the research results the agent can fetch.
16:37 💻 To make the research agent more versatile, it is suggested to deploy it as a web service to be embedded in a company's business workflow. The AI research agent can be turned into an API endpoint using FastAPI. Finally, the API endpoint is tested and deployed using a cloud platform.
19:52 💻 The API endpoint developed for the research agent is live and can be called via the API. The video also proposes embedding it within the business workflow.
20:20 📩 Automating business processes using make.com is demonstrated. For example, an incoming email can trigger information extraction about sender and company, then fed into the research agent for a background check.
21:02 🎯 The video walks through the process of setting up Gmail in make.com to watch incoming emails and trigger the research agent workflow.
23:51 🧩 The integration of OpenAI is shown to extract name and company information from emails, using chat completion methods.
24:19 🌐 The extracted information is then used in the Google Cloud platform and passed on to the research agent through an API endpoint.
25:02 👨‍💻 The whole system is automated to run every 15 minutes, checking for emails and triggering workflows for each email. The resulting data from the AI research agent is then organized in an Airtable.
25:29 🔄 Links to other workflow templates are provided that allow the research agent to be triggered from various points like Telegram, Google Forms, ClickFunnels and even Shopify.

Made with HARPA AI

[bwaldo]

Thank you very much. After going through everything, and getting this app to actually work, you've inspired me to build my first AI agent with this video, and now my mind is firing on all cylinders with the world of possibilities. Thank you for doing what you're doing.

[jasonfinance]

just tried the AI research agent in your link, and it worked like magic! getting the research agent that dont hallucinate is so useful

[farfromerease]

This use case is really amazing. I am so inspired with your level of skill and knowledge in this topic. It was very informative and entertaining to watch and I can definitely see how useful this can be in day to day operation of research and business! Wow

[heagandev]

Excellent content Jason, this is easily one of my favorite AI youtube channels

[Taskade]

Hey Jason, just watched your AI research agent tutorial and man, it's game-changing! Given how Botpress allows for custom chatbot training, it'd be cool to see a comparison or integration with Langchain AI agents. Keep those awesome insights coming! Cheers! 👍😃

[nateslo]

Uncredibly good and easy to understand explanation. Thanks Jason!

[AndrewLiu]

Your content is so unique and definitely one of the best channels!

[sixpathsphotography]

You will be one of very incredible person contributing to advancement of AI. Great job. You are so authentic

[slavrgo]

This is amazing man! Thanks for sharing in a so detailed way 🔥🔥 especially thanks for sharing the git. And this is the best Make integration I ever seen. Used them a lot, and here’s one more use case.

[zacboyles]

Bravo, very good walkthrough. I like how you pieced everything together and browserless is good for not getting blacklisted.

[fasaipuengudom]

In the world of junk get rich quick AI content, your content is the one I never want to miss

[jessedbrown]

What I think is hilarious, is that some day, AI Jason will be read and watched by an AI that is learning how to do suff. and it will say. Wow What a great site. Save that in my long term.

[SarahStair]

You are AMAZING! Such a great video -thanks for sharing! I love how simple the the code is, and how you broke down every section and what it does. Plan on trying it with some of my own use cases soon! :)