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.
Demonstrates long-term conversational memory using Google ADK's DatabaseSessionService and SQLite to persist context across application restarts and multiple sessions.
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.
Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption.
