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Multi-Agent LLM Scheduling Co-Agent
This talk covers building an intelligent appointment scheduling co-agent using LLM multi-agent systems to resolve conflicts, optimize availability, and send email confirmations.
In this presentation, we will explore how Large Language Model (LLM) multi-agentic systems can be leveraged to build an intelligent appointment scheduling co-agent. We’ll dive into the architecture, agent coordination, and how these systems autonomously resolve scheduling conflicts, optimize availability, and seamlessly send confirmations via email.
P.S. - You can find architecture and Slight overview in article, although the article doesn’t cover entire codebase for original functionality.
Demonstrates LangGraph-based multi-agent LLM system for doctor appointment scheduling.
- PythonPython: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .
- LangGraphA low-level orchestration framework for building long-running, stateful, and cyclic multi-agent systems using a graph-based architecture.LangGraph is the specialized, low-level runtime for developing complex AI agents, extending the LangChain ecosystem to handle intricate, stateful workflows. It models the agent's logic as a directed graph: nodes represent actions (LLM calls, tool use), and conditional edges dictate the flow, enabling critical features like cycles (loops) for iterative reasoning. This graph-based approach ensures durable execution, allowing agents to persist through failures and resume operations. Key capabilities include comprehensive memory management via a shared state object and built-in human-in-the-loop functionality (interrupts) for external oversight. This robust framework is trusted by production teams at companies like Klarna and Replit for deploying scalable, resilient agent architectures.
- CromaDBChromaDB is the open-source, AI-native vector database engineered for Large Language Model (LLM) applications and Retrieval-Augmented Generation (RAG).ChromaDB acts as the critical storage and retrieval layer for vector embeddings, enabling semantic search and context injection for LLMs. It is an open-source project, licensed under Apache 2.0, and has quickly scaled its community, boasting over 24k GitHub stars and exceeding 8 million monthly downloads. The company secured $18 million in seed funding in April 2023, underscoring its market traction. Developers leverage its Python and JavaScript SDKs for streamlined integration, focusing on speed and developer productivity for building high-performance AI systems.
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