I built an AI front-desk multi-agent orchestration

# Receptics: AI Front-Desk Multi-Agent System Demo Based on LangGraph **TL;DR:** Developer ranger0004 demonstrated an AI receptionist system called Receptics, built with Python's LangGraph framework. It uses an orchestrator agent to coordinate a knowledge base agent and a booking agent, enabling 24/7 automated customer service and appointment scheduling. ## Project Overview & Core Architecture Receptics is an AI receptionist system designed to provide businesses with round-the-clock online customer service—even at 3 AM. Its core is a multi-agent collaborative system implemented using **LangGraph**, a Python framework for building intelligent agents that can be considered a sub-module of LangChain. The system includes three primary agent roles: 1. **Orchestrator Agent**: Acts as the central dispatcher, receiving requests and deciding which sub-agent to invoke. 2. **Knowledge Base Agent**: Responsible for answering frequently asked questions (FAQs) based on documents uploaded by the business. 3. **Booking Agent**: Handles reservation requests like scheduling appointments. ## Agent Orchestration Flow in LangGraph In LangGraph, agents are defined as "nodes." The presenter detailed the workflow: 1. **Routing Decision**: The orchestrator agent calls a "tool call node," which routes the task to the appropriate sub-agent (knowledge base or booking agent) based on the user's request content. 2. **Agent Execution & State Update**: The sub-agent receives the task, performs computation, and outputs the result to a "tool node." The workflow then checks back to see if any other agent needs to be called. 3. **Iteration & Completion**: If necessary, the orchestrator invokes other agents. Once all agents have finished their work, the results return to the orchestrator. The orchestrator then determines if the process is complete. 4. **Structured Output & State Management**: Agents use the **instructor** library (or a similar library like Pydantic) to output structured JSON data. These results are stored in LangGraph's global "state." As each agent completes its task, the field representing that agent is removed from the state. When no agent fields remain in the state, all subtasks are considered complete. ## Main Features & Interface ### Business Dashboard After business users register and log in, they see a dashboard interface. Here: * Businesses can import documents containing their business-related materials for the knowledge base agent to use in responses. * Businesses can configure, test, and interact with their own AI agents. * Businesses can publish a configured agent, and the system generates a unique URL. Businesses can send this URL directly to customers or (plan to) embed it on their own website, allowing customers to solve common problems or make appointments directly via the AI. ### Client Interface Through the generated URL, customers can access a standalone conversational interface to interact with the published AI agent, ask questions, or schedule appointments. ## System Evaluation & Testing The presenter created **60 evaluation scenarios** for the orchestrator node to test whether the system invokes the correct agent under different queries. These scenarios include: * Whether the knowledge base agent was correctly invoked. * Whether the booking agent was correctly invoked. * Whether the orchestrator can provide an answer on its own when no other agent needs to be called. * How the system handles irrelevant questions. Currently, these evaluations mainly focus on the behavior of the orchestrator node. ## Development Status & Current Demo The project is still in active development ("building every day"). The presenter emphasized that the frontend code is manually written, not generated with a code generator. * **Implemented Features**: The demo showed login, dashboard interaction, document import, agent publishing, URL generation, client-side dialogue, and a successful meeting booking flow (though with a time display bug). * **Issues to Fix**: * A known frontend bug causes an "internal error" when interacting with an unpublished agent. * The booking agent has a logic error in parsing time (booked 13:00-15:00 but displayed as 18:00-20:00). The presenter stated they are using **LangSmith** for log tracing to investigate the frontend logic issue. * The booking form should not expose the email field to end-users and needs optimization. ## Analysis of Key Source Code Components The presenter showed the backend code structure. Key components include: * **Main Agent Workflow Class**: The entry point, integrating all LLM clients, agent definitions, graph setup, etc. * **Orchestrator Node**: Implements the core routing logic. * **Tool Call Node**: Executes the actual invocation of sub-agents. * **RAG Pipeline**: Provides retrieval tools for the knowledge base agent, working based on imported documents. * **Agent Tool Set**: For example, tools for the booking agent to fetch and update room data. * **Authentication & Session Management**: Handled using Supabase, with endpoints checking session status and returning access tokens, refresh tokens, and business user information (like business ID and email). ## Conclusion Receptics demonstrates the potential of using LangGraph to build complex multi-agent AI applications. Although still in development, the system already possesses a core framework for agent collaboration, document integration, evaluation testing, and user interaction, offering a scalable solution for automating front-desk business processes. Source: I built an AI front-desk multi-agent orchestration (https://www.youtube.com/watch?v=WkthJS-O92c)