Conversational
How to Create Reliable Conversational AI Agents Using Parlant?
Parlant is a framework designed to help developers build production-ready AI agents that behave consistently and reliably. A common challenge when deploying large language model (LLM) agents is that they often perform well in testing but fail when interacting with real users. They may ignore carefully designed system prompts, generate inaccurate or irrelevant responses at…
How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints
In this tutorial, we aim to understand how LangGraph enables us to manage conversation flows in a structured manner, while also providing the power to “time travel” through checkpoints. By building a chatbot that integrates a free Gemini model and a Wikipedia tool, we can add multiple steps to a dialogue, record each checkpoint, replay…
Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API
class GeminiAutoGenFramework: “”” Complete AutoGen framework using free Gemini API Supports multi-agent conversations, code execution, and retrieval “”” def __init__(self, gemini_api_key: str): “””Initialize with Gemini API key””” self.gemini_api_key = gemini_api_key self.setup_gemini_config() self.agents: Dict[str, autogen.Agent] = {} self.group_chats: Dict[str, GroupChat] = {} def setup_gemini_config(self): “””Configure Gemini for AutoGen””” os.environ[“GOOGLE_API_KEY”] = self.gemini_api_key self.llm_config = { “config_list”: […
A Coding Guide to Build an Intelligent Conversational AI Agent with Agent Memory Using Cognee and Free Hugging Face Models
In this tutorial, we delve into building an advanced AI agent with agent memory using Cognee and Hugging Face models, utilizing entirely free, open-source tools that work seamlessly in Google Colab and other notebook. We configure Cognee for memory storage and retrieval, integrate a lightweight conversational model for generating responses, and bring it all together…