Key insights

• Using Lang Graph with Pantic AI

  • 🔗 Lang Graph facilitates defining and connecting AI agents.
  • ↔️ Conditional edges dictate the flow of conversation in the system.
  • 💾 In-memory storage is simple; SQLite or PostgreSQL recommended for future scalability.
  • 👩‍💻 Plans to enhance Archon framework to aid non-coders in agent building.

• Managing Conversation Flow

  • 📖 Building conversation history by including messages.
  • 📊 Implementing feedback mechanisms to gather user insights.
  • 🔀 Routing decisions determine conversation continuation.

• Dynamic Scope Management in Lang Graph

  • 🌐 Global state updates in Lang Graph using key-value pairs.
  • 🔄 Dynamic injection of scope into prompts enhances performance.
  • ⏳ Message history builds over time with user feedback.
  • ⚡ Asynchronous output streaming enables real-time updates.

• Configuring Local AI Models

  • 🧠 Guide on configuring local LLM with environment variables.
  • 🔧 Defining Reasoner and Router agents for managing interactions.
  • 💬 Creating AI agents with Pantic AI and managing conversation state.

• Improving Agent Development Through Visualization

  • 📈 Integration of Pantic AI with Lang Graph improves agent workflows.
  • 📝 User feedback is critical for refining AI outputs.
  • 💡 Lessons learned from earlier versions inform better structuring.

• Iterative Development with Pantic AI

  • 🚀 Introduction to building the Archon AI agent iteratively.
  • 🔍 Version one setup based on previously built crawlers.
  • 📚 Knowledge base construction using Pantic AI documentation.
  • ⚙️ Enhanced agent functionality through refined system prompts.
  • 🔧 Need for optimization and transition to more complex versions.

• Building and Managing AI Workflows

  • 🛠️ Avoid over-engineering workflows; the Archon project aids in agile development.
  • 🎓 Archon will evolve to teach users iteratively about Pantic AI and Lang Graph.
  • 🔄 Future versions of Archon will integrate feedback loops and additional frameworks.

• Unlocking AI Potential with Pantic AI and Lang Graph

  • 🤖 Unlock the full potential of AI agents using Pantic AI and Lang Graph for customizable workflows.
  • 🛠️ Pantic AI simplifies the building of customizable AI agents.
  • 🔗 Lang Graph enables multiple AI agents to work together seamlessly.
  • ✨ The combination enhances control and customizability in AI workflows.
  • 📈 Real-world examples illustrate the non-deterministic nature of AI workflows.

Q&A

• How do I set up a chat interface with Lang Graph and Pantic AI? 💻

  Setting up a chat interface is straightforward with Lang Graph and Pantic AI. The tools provide a comprehensive overview of implementation, helping creators seamlessly connect AI agents and enhance user interaction experiences.

• What storage options are recommended for implementation? 📦

  While in-memory storage offers simplicity, it is advisable to use SQLite or PostgreSQL for more reliable long-term data management and future improvements in your AI applications.

• How is conversation flow managed in the agent system? 💬

  Conversation flow is managed by building message history, implementing human-in-the-loop feedback mechanisms, and using routing commands to determine when to continue or conclude interactions. This setup provides a clear structure for agent communication.

• What is the role of scope in Lang Graph? 🌐

  Scope in Lang Graph aids in maintaining context for agents, allowing for dynamic context updates. The scope value initialized by Reasoner persists across conversations, enhancing agent functionality with the relevant contextual information.

• How do I manage agents and their interactions? 🧠

  Managing agents involves configuring local LLMs using environment variables, defining Reasoner and Router agents, and maintaining conversation states during execution. Pantic AI facilitates fetching documentation for agent setup, supporting multiple simultaneous conversations.

• What is the importance of 'human in the loop'? 👥

  The 'human in the loop' concept is critical for refining AI outputs by incorporating user feedback directly into the workflows, ensuring that agents continuously learn and improve based on real interactions.

• How can I build an AI agent using Pantic AI? 🚀

  Building an AI agent involves setting up a knowledge base and specifying dependencies, functionality, and required tools. A refined system prompt is vital for grounding the agent in the necessary documentation, enhancing its performance.

• What does Archon project offer for AI development? 🛠️

  The Archon project focuses on creating agile AI workflows iteratively, starting with basic agent functionalities in its first version. Future updates will introduce complex features like feedback loops, tool libraries, and additional frameworks.

• What are common pitfalls when building AI agents? ⚠️

  A common pitfall is over-engineering. It's crucial to avoid complicating your workflows with unnecessary tools. The new Archon project aims to provide iterative guidance, helping users to build efficient AI workflows gradually.

• How does Lang Graph work with Pantic AI? 🔗

  Lang Graph acts as an orchestrator that facilitates seamless collaboration between multiple AI agents created with Pantic AI. It enhances control and customizability in workflows by defining their structure through nodes and edges.

• What is Pantic AI? 🤖

  Pantic AI is a Python framework designed to simplify the process of building customizable AI agents. It offers a robust set of tools that allow developers to create AI workflows tailored to their needs.

• 00:00 Unlock the full potential of AI agents using Pantic AI and Lang Graph together for powerful and customizable workflows! 🤖
• 06:31 Building AI agents using Pantic AI and Lang graph is powerful, but beware of over-engineering; the new Archon project will help create agile workflows iteratively. 🛠️
• 12:28 In this segment, the speaker discusses the iterative development of an AI agent based on the pantic AI framework, starting from version one. They outline the setup of the knowledge base, tools needed, and the functionality of the agent, while highlighting limitations that prompt the need for the next version. 🚀
• 18:42 Creating workflows using Pantic AI and Lang Graph facilitates improved agent development through visualization and iterative human feedback. 🚀
• 24:56 A tutorial on configuring local AI models with Pantic AI, how to manage agents and their interactions in the system, and strategies for defining workflows using environment variables. 🧠
• 31:25 In this segment, we explore how to update the global state in Lang graph by defining a scope with Reasoner, which helps maintain context for the coder agent. The document discusses setting up the coder agent with new dependencies and how to dynamically inject scope values into prompts for enhanced performance. 🚀
• 38:23 In this segment, the video explains how to manage conversation flow in an agent system, focusing on updating message history and implementing a human-in-the-loop feedback mechanism. It covers the process of routing user inputs to determine whether to continue or end the conversation.
• 45:03 The video discusses how to use Lang graph in conjunction with pantic AI to create and manage conversational agents effectively. It emphasizes the ease of setup, routing functions, and the benefit of chat persistence with memory integration.