Discussion on agentic LLM adoption in production, focusing on LangChain and LlamaIndex tooling, production readiness, tools, evaluation and observability, safety and guardrails.

Dialogue on deploying agentic LLMs in production, focusing on LangChain and LlamaIndex usage, tools, evaluation and observability, safety and guardrails.

Components: planning, memory, tools, and environment; Frameworks overview: LangChain, CrewAI, LlamaIndex; The Agent Loop: perception → reasoning → action → feedback; Examples of orchestration and coordination.

Discussion on agentic LLM adoption, including LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails.

Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails)

Teams define their problem statement and build core agent workflows; Mentors available for architecture and framework support.

Introduction to Google AI Developer Kit (ADK) and Gemini models; Setting up the environment and connecting to Google AI Studio APIs; Example: Deploying an agent using ADK tools for retrieval and tool integration; Optional track for participants interested in Google’s ecosystem; Mentors assist others starting on different frameworks (e.g., LangChain or CrewAI)

Introduction to Google AI Developer Kit (ADK) and Gemini models; setting up the environment and connecting to Google AI Studio APIs; example: deploying an agent using ADK tools for retrieval and tool integration; optional track for participants interested in Google’s ecosystem; mentors assist others starting on different frameworks (e.g., LangChain or CrewAI).

Introduction to Google AI Developer Kit (ADK) and Gemini models; Setting up the environment and connecting to Google AI Studio APIs; Example: Deploying an agent using ADK tools for retrieval and tool integration; Optional track for participants interested in Google's ecosystem; Mentors assist others starting on different frameworks (e.g., LangChain or CrewAI)

Introduction to Google AI Developer Kit (ADK) and Gemini models; Setting up the environment and connecting to Google AI Studio APIs; Example: Deploying an agent using ADK tools for retrieval and tool integration; Optional track for participants interested in Google’s ecosystem; Mentors assist others starting on different frameworks (e.g., LangChain or CrewAI).

Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails).

Dialogue 2: Agentic LLM adoption (LangChain/LlamaIndex in production, tools, evaluation and observability, safety and guardrails)

Agentic LLM adoption in production, including LangChain and LlamaIndex in production contexts, tools, evaluation and observability, safety and guardrails.

Many websites have a way to search through listings. However the UI often includes many filters, check boxes, dropdowns and other UX nightmares. What if the user could just write in plain English what they wanted to find and get suitable results? We explore it as we build a version of linkedin's AI job search in Django using LangChain!

Dialogue on agentic LLM adoption, including production use cases, tooling and observability, safety and guardrails, with input from LangChain and LlamaIndex.

Discussion on LangChain and LlamaIndex in production, tools, evaluation and observability, safety and guardrails for agentic LLM workflows.

In this session, I'll walk you through how to build a smart, context-aware agent in just 45 minutes. You'll see how OpenAI APIs, LangChain, and Python can work together to create an agent that goes beyond basic chat. With a demo and easy-to-follow steps, you’ll leave with the confidence to start building and customizing your own AI Assistant. We'll cover: Core principles of AI agents and what makes them different from simple chatbots Step-by-step walkthrough of building an agent with LangChain and OpenAI APIs Demo of an AI agent Practical ways to customize agents for your own use cases

Discussion on agentic LLM adoption in production, featuring LangChain and LlamaIndex; topics include tools, evaluation, observability, safety and guardrails.

In this session, you’ll learn how to: Set up SurrealDB as both a graph and vector store—one connection, one system; Use LangChain to ingest documents; Use LLMs to infer keywords; Tune retrieval (k, thresholds) and compare vector-only, graph-only, and intersected results. We’ll walk through an experiment: a chatbot answering questions over chat-style conversations, showing when vector retrieval wins, when lightweight graphs help, and how to handle tricky bits like time awareness.

In this session, you’ll learn how to set up SurrealDB as both a graph and vector store—one connection, one system; use LangChain to ingest documents; use LLMs to infer keywords; tune retrieval (k, thresholds) and compare vector-only, graph-only, and intersected results.