ADK

AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind

Jul 11, 2025 · 1:44:51

In this workshop, Philipp Schmid from Google DeepMind demonstrates AI Engineering with the Gemini 2.5 model family, focusing on using Gemini 2.5 Flash via a free API tier for hands-on coding tasks including text generation, multimodal processing of images, audio, and PDFs, function calling with structured outputs, and integration with MCP servers. The session covers setting up API keys in AI Studio, uploading files via the Files API (free for 1 day), and controlling thinking budgets (0–24,000 tokens) to manage cost and reasoning depth. Schmid shows how Gemini natively processes videos at 1 frame per second for accurate timestamp extraction and how PDFs are handled by combining OCR text with image understanding. He introduces native tools like Google Search with grounding metadata, code execution, and URL context, and explains how MCP servers can be used seamlessly with the Gemini SDK for tool calling. The workshop also covers parallel vs sequential function calling, the Agent Development Kit (ADK), and the upcoming asynchronous function calling for the Live API, providing a practical path from simple generation to agentic workflows.

Memory Masterclass: Make Your AI Agents Remember What They Do! — Mark Bain, AIUS

Jun 27, 2025 · 51:25

Mark Bain, Vasilia Markovits, Alex Gilmore, and Daniel Chalev demonstrate that AI memory requires causal relationships and graph databases, not just vector similarity, to solve hallucinations and enable agentic workflows. Bain argues that memory is any data affecting change, and that attention, diffusion, and VAEs follow the same geometric principles as gravity and entropy. Alex shows Neo4j's MCP server storing semantic memory as entities and relationships retrieved across conversations. Vasilia demos Cognee building semantic graphs from GitHub data for agentic hiring decisions. Daniel presents Graphiti's domain-aware memory using custom Pydantic schemas to filter irrelevant facts. Bain introduces a GraphRAG chat arena that switches between memory solutions on a single Neo4j graph, testing different implementations for episodic and temporal recall.

Agentic GraphRAG: Simplifying Retrieval Across Structured & Unstructured Data — Zach Blumenfeld

Jun 27, 2025 · 15:25

Zach Blumenfeld (Neo4j) demonstrates how a knowledge graph simplifies agentic retrieval across structured and unstructured data, using an employee skill graph as an example. He shows that a plain document search fails to answer questions like "how many Python developers" (returns 5 instead of 28) or "who is most similar to Lucas Martinez" — but after extracting entities into a Neo4j graph, the agent can run precise Cypher queries via an MCP server, correctly reporting 28 Python devs and identifying Sarah as the closest match via skill overlap. He also shows how to flexibly add new relationships (e.g., project collaborators from an HR system) without schema changes, enabling questions like "which individuals collaborated most on AI projects" to return exact pairings like Sarah and Amanda. The talk emphasizes accuracy, explainability, and the ability to start with a simple data model and expand as agents ingest more data.