Stateful architectures leveraging short-term working memory, semantic episodic memory, and long-term vector stores.
This Streamlit app implements an AI-powered customer support agent for synthetic data generated using GPT-4o. The agent uses OpenAI's GPT-4o model and maintains a memory of past interactions using the Mem0 library with Qdrant as the...
Demonstrates fundamental session memory management with SQLiteSession for automatic conversation history. cp ../env.example .env from agent import inmemorysessionexample, persistentsessionexample.
Demonstrates advanced session memory operations including item manipulation, conversation corrections, and session management. cp ../env.example .env.
InMemorySessionService stores session data in your computer's RAM (memory). This means: from google.adk.sessions import InMemorySessionService.
DatabaseSessionService stores session data in a SQLite database file. This means: from google.adk.sessions import DatabaseSessionService ┌─────────────────────────────────────────────────────────────┐.
Sessions provide automatic conversation memory that eliminates the need to manually handle .toinputlist() between turns. Think of sessions as a smart conversation database that.
This Streamlit app implements a multi-agent career coaching assistant built with Google's Agent Development Kit (ADK). A career orchestrator agent delegates each question to the right specialist sub-agent (resume, interview practice,...
This Streamlit app implements an AI-powered research assistant that helps users search for academic papers on arXiv while maintaining a memory of user interests and past interactions. It utilizes OpenAI's GPT-4o-mini model for...
This Streamlit app implements an AI-powered travel assistant that remembers user preferences and past interactions. It utilizes OpenAI's GPT-4o for generating responses and Mem0 with Qdrant for maintaining conversation history.
This Streamlit app is an AI-powered chatbot that uses OpenAI's GPT-4o model with a persistent memory feature. It allows users to have conversations with the AI while maintaining context across multiple interactions.
This Streamlit application demonstrates a multi-LLM system with a shared memory layer, allowing users to interact with different language models while maintaining conversation history and context across sessions.
