I have previously conducted lectures on LLM orchestration, RAG pipeline, multi-modal models, and multi-agent architecture. I am going to explain how to implement chatbot functionality by utilizing the previous lecture. A chatbot MVP is essentially: A system that takes a user message → understands it → optionally looks things up → generates a response → returns it You can express this as a simple loop: The 5 Core Components of a Chatbot MVP Break the system into 5 understandable parts: ① User Interface (UI) Chat screen (web, app, Slack, etc.) Where users type messages ② Backend Controller (Orchestrator) The “brain” that decides what to do next Routes requests between components Connect to your previous lectures: This is where **LLM orchestration logic** lives. ③ Large Language Model (LLM) Generates responses Understands natural language ④ Knowledge / Data Layer (Optional but critical for MVP+) Documents, database, APIs Used in **RAG (Retrieval-Augmented Generation)** ⑤ Memory (Optional but powerful) Conversation history User preferences User ↓ UI ↓ Orchestrator ├── LLM └── Knowledge Base (RAG) ↓ Response contact information: telegram:@kingsudo7 whatsapp:+81 80-2650-2313

And thank you for being here. If you have a minute to spare, say Hi and feel free to introduce yourself. Maybe share a picture of what you've acomplished with A0?

Apologies if this has already been covered elsewhere. And if this does not apply, please do not hesitate to contradict this information. I'm heading on vacation and wanted this community to be aware. LiteLLM (used by Agent Zero since version 0.9.1 I believe) has become the victim of a supply chain attack that is impacting many people and organizations. Security breakdown on what happened can be found here here: https://www.youtube.com/watch?v=uwSjgv4otAk Key Details of the Integration - Unified Interface: LiteLLM allows Agent Zero to interact with over 100+ LLM providers (including OpenAI, Anthropic, Google, and local providers like Ollama or LM Studio) using a single OpenAI-compatible format. ⚠️ Critical Security Alert (March 2026) As of March 24, 2026, a major supply chain attack was identified affecting the LiteLLM library. If you are currently running or installing Agent Zero, please take the precautions outlined in the attached table. Verification Command: Run pip show litellm in your terminal. If you see version 1.82.7 or 1.82.8, treat your environment as compromised, rotate all API keys and secrets immediately, and delete your virtual environment.

This setup has been working great for me. The chat and web browser models are both using the Ollama cloud. This is about as good as it gets, I think, without paying the Frontier model pricing. Chat: qwen3.5:cloud Web Browser Model: qwen3-vl:32b-cloud Embedding: sentence-transformers/all-MiniLM-L6-v2

I highly recommend giving this video a watch. The stats he shares are mind-blowing: Big companies wasted $285 billion on AI last year simply because they don't know how to actually build with it. The biggest takeaway? Only 0.05% of the world actually knows how to use AI to produce real results—like asking AI how to build something and having it do the heavy lifting for you. This video perfectly validates everything we're doing here with Agent Zero. The world is splitting into "consumers" and "producers," and by learning to build autonomous agents, we are positioning ourselves in that top 0.05% of producers. I haven't fully mastered my A0 setup yet, but seeing this data makes the learning curve totally worth it. Keep building. (P.S. Please excuse the language in the video's thumbnail. I definitely don't endorse that kind of thing, but the data inside was just too relevant to our community not to share!)