AI-Agents: Automation & Business with LangChain & LLM Apps

Explore the basics and theory of AI agents with a broad overview of what AI agents are. Delve into LMS tokens, function calling, vector databases, embedding models, and API.

Explore how AI agents orchestrate multiple GPTs with distinct tools through function calling, enabling web search, image creation, and local file saving, guided by frameworks like Lang Chain.

Explore what LLMs are and how they work, including ChatGPT, Claude, Gemini, Llama, Mistral. Learn open-source options you can run locally with parameter file and run file, plus token basics.

discover how function calling makes an llm an operating system that invokes calculators, diffusion models, browsers, and Python tools to automate tasks.

Learn how embeddings models turn documents into vectors stored in vector databases, enabling retrieval-augmented generation as llms browse uploaded pdfs without hitting context-window limits.

Define ai agents as supervisor systems that coordinate sub-experts and tools to complete tasks, and explore LangChain-based tools like LangFlow, LangGraph, VectorShift, Autogen, Crew AI, and Agency Swarm.

Discover function calling with APIs, enabling AI agents to access weather, news, Gmail, YouTube, and more via JavaScript, while API definitions describe client–server communication as a contract.

Explore how ai agents and llms, guided by a supervisor, coordinate tasks with function calling via api requests, using vector databases and embeddings to train them.

Build AI agents with a no-code flow wise layer on LangChain and Lang Graph, using JavaScript for function calling to create four agents: stories, social posts, YouTube scripts, and emails.

Switch from version one to version two in Agent Flows to review updates, including the unpublished Agents version two, and stay current as the AI world moves fast.

Learn to run Flowise locally by installing Node.js and using the Node.js command prompt, with a simple pre-built installer for Windows or Mac, before cloud deployment on Render or AWS.

Install flow wise locally with node.js, start a local server on port 3000 using npx flow wise start, and update the installation with npm update -g flow wise.

Fix Flowise installation in Node.js by downgrading to a compatible version (18–20) with NVM for Windows, then switch versions using nvm and verify the active version with NVM list.

Learn to navigate the flow wise interface to create and manage chat flows and agent flows, using nodes, models, tools, credentials, the marketplace, and templates.

Build your first ai agent with a supervisor and two workers: a storyteller and a title generator. Learn how to connect llms and prompts to generate stories and titles.

Discover how to deploy multiple AI agents to generate blog posts, YouTube scripts, YouTube titles, and X posts, using prompt engineering and agent flows for rapid, scalable social media content.

Explore function calling with multi-agent workflows that fetch web data, craft profiles, and write personal emails using tools, prompts, and server API integrations.

Master function calling by linking a calculator and Python interpreter to calculate investments and generate data visualizations, then save results locally with the write file tool.

Master ai agents with LangChain and Flowise, building practical workflows from chat models to social media automation, with supervisor and workers, using function calling, web search, and prompt engineering.

Discover flow wise agents version two, featuring a new interface, simplified setup with OpenRouter and cloud models, memory, prompts, tools, and Slack integration for building advanced workflows.

Build an agent v2 workflow from scratch, connecting a chat input to an agent using OpenAI GPT four mini, with tools like calculator, memory, vector databases, and Brave search MCP.

Build an AI-driven sentiment analysis workflow that collects customer feedback via a form, routes happy and not happy outcomes with conditional logic, and tailors responses using OpenAI models.

Learn to build custom tools with rack technology, use a vector database and embeddings models, connect APIs via JavaScript, and deploy webhooks and automations with flow wise and Make.

Train a rack-based llm on your own data using vector databases, embeddings, and uploaded pdfs to power knowledge explorer, content creator, and data organizer workflows, with local saving.

Learn how Fire Crawl converts websites into markdown text for training RAG apps, enabling easy integration with LangChain, APIs, and flow wise to train LLM-powered chatbots.

Prepare data for llm apps by converting PDFs, CSVs, and docs to markdown with Llama Bars and Llama Index. Use Llama Parse to generate markdown summaries for rec pipelines.

Parse PDFs with llama index via llama parse to extract content as markdown, text, or JSON in llama cloud. Drag and drop uploads, set page ranges, and get llm-ready data.

Learn to tune chunk size and chunk overlap for better rec applications, using vector databases, embeddings, and text splitters to respect token limits and context windows.

Discover how to use custom tools in flow wise, connect any API with a JavaScript function, and configure API keys for HubSpot, Morningstar, and other services.

Connect any API to Flowise with custom tools and JavaScript functions. Build, test, and log API calls using Node.js fetch, an example Bitcoin price tool, and RapidAPI integrations.

Develop an AI agent with Gmail access using a custom send mail tool and Make webhook, enabling web research, summaries, and automated email outreach.

Learn how to build a Gmail automation scenario in Make using webhooks and Google API keys, connect your Google Cloud credentials, test sending emails, and run the workflow forever.

Explore building a rack-based AI app with vector databases, embeddings, and text splitters; learn data preparation with fire crawl and llama bars, plus local testing of flow wise tools.

Discover how ai agents boost business with customer support and collecting leads, while hosting in the cloud and embedding in websites or apps, with branding and audio model integration.

Learn how to leverage ai agents for business, from collecting leads and automations to selling agents and chatbots, while hosting on render and offering consulting or courses.

Learn to build a simple, cost-effective AI agent using an OpenAI assistant template and a single tool, train it with uploaded knowledge and a vector store, then host for sale.

Learn to host and deploy your chatbot in the cloud with render, via a GitHub fork and environment variables, enabling client hosting and cross-device access.

Host and test your Node.js chatbot on render, managing deployments and flow wise instances. Embed or standalone, integrate via HTML, curl, JavaScript, or Python in web pages or WordPress.

Learn to refine a standalone application by customizing its title, avatar, welcome message, colors, and font size, then share or publish the chatbot with authorization for public or cloud use.

Learn to brand and style a website chatbot pop up by editing the embed code, adjusting colors, text, and size. Add a company link and position the widget for visibility.

Explore lead capture, voice integration, and audio model features for ai agents on web pages, including whisper speech-to-text. View messages, export chats, and manage leads and chat flows.

Identify the right customer, prototype a tailored AI agent, and present a compelling live-demo offer. Use warm and cold outreach, content, and a two-week guarantee to boost sales.

Explore how ai agents power business tasks like customer support and lead generation, and learn to host, embed, and brand chatbots across web pages, WordPress, and apps.

Learn to build a Microsoft-style copilot with a recall feature by wiring OpenAI Whisper, text-to-speech, and vision APIs in Python, VS Code, and GitHub, while prioritizing safety.

Explore an open source Python project on GitHub for building an ai assistant, detailing setup with dot env keys, running the code, and core components like LangChain and tts.

Install and customize Visual Studio Code for multi-language development, exploring debugging, intelligent code completion snippets, code refactoring, and built-in git integration with themes and extensions to boost productivity.

Install git to enable cloning GitHub repositories for your projects, following a straightforward download and setup across Windows, Linux, and other operating systems.

Clone and run a Python AI assistant that transcribes speech with Whisper locally, queries OpenAI, and streams camera or desktop screenshots in base64.

Learn how to customize your ai assistant by changing voices, reducing latency with Gemini Flash, and switching OpenAI or Google models. Modify the system prompt and test Allo voices.

Compare speed, costs, pricing, and hardware needs when using the OpenAI API with models like GPT-4 Omni, GPT-4 Mini, and Gemini Flash, while noting base64 security and whisper.

Copy the provided python code and run the program with a dot env OpenAI API key. Install pillow and numpy, or use the Gemini flash model variant with ai api.

Build a practical AI assistant workflow using VSCode and GitHub; use Whisper for transcription and text-to-speech with GPT for Omni or Gemini Flash, and learn by doing.

Explore open source lms to build ai agents locally, with private uncensored models, and learn upsides, downsides, chat flows, queues, chains, and grok api.

Assess the advantages and drawbacks of open-source LLMs, including data privacy, cost savings, offline and customizable operation, and current performance gaps versus closed-source models.

Install Ollama to run a local LLM server, download open-source llama models, choose quantized variants (Q4, Q2, Q8) for your hardware, and run offline on your machine.

Create an open-source ai agent with llama 3.1 and ollama in a flow wise setup, pairing a storyteller with a title generator to generate stories and titles via function calling.

Build a local open-source mail agent that reads, summarizes, and writes emails in my style, saves outputs locally, using llama 3.1 8-bit with offline privacy.

Build a local retrieval-augmented chatbot using Lang Chain and llama 3 with Ollama, connecting an in-memory vector store, embeddings, document loading, and a conversational retrieval QA chain.

Explore insanely fast inference with the Groq API, swapping in a faster chat model for local agents, demonstrating speeds up to 1000 tokens per second and easy integration.

Introduce Ornit 1.0, a compact open-source llm family with 9b, 31b dense, 35b moe, and 397b variants, featuring self-scaffolding and runnable on consumer hardware via OLAMA or LM Studio.

evaluate llama 3.1 open-source models across 405, 70B, and 8B sizes, compare benchmarks with closed models, explore grok pricing, and discuss running quantized 4–5 GB locally.

Learn to install a private, local open source lms with olama and build agents like a storyteller and a mail agent. Explore fast inference with grok and llama 3.1.

Examine security, data privacy, and copyright issues in AI agents, discuss output quality, rising API costs with multi-llm setups, and risks like jail breaks, prompt injections, and data poisoning.

Explore how prompts and sequences can jailbreak LLMs, using few-shot and zero-shot methods, and review research on safeguards and potential misuse.

Examine prompt injections as a key security vulnerability in LLMs, with examples of white-text prompts, phishing tactics, and caution for internet-based tasks.

Explore data poisoning and backdoor attacks in LLMs, including fine-tuned open-source models from Hugging Face, and the risks to data security and privacy in AI tools.

Explore copyrights and generated data in AI agents, including OpenAI's copyright shields for developers, licensing nuances with llama models, diffusion and voice tools, and implications for selling outputs.

Learn how to protect client and private data in AI agents using OpenAI and other APIs, covering data ownership, encryption, retention, and local models.

This lecture examines the tradeoffs between API costs and local llms, security and data privacy risks, and copyright concerns, emphasizing that llm outputs are not guaranteed.

Learn to build ai agents with LangChain and line graph, using function calling, vector databases, embeddings, and OpenAI API to enable practical business automation.