https://github.com/ksm26/ai-agents-in-langgraph
Master the art of building and enhancing AI agents. Learn to develop flow-based applications, implement agentic search, and incorporate human-in-the-loop systems using LangGraph's powerful components.
https://github.com/ksm26/ai-agents-in-langgraph
agent-accuracy agent-components agent-development agentic-search ai-agents ai-enhancement essay-writing-agent flow-based-applications human-in-the-loop langchain langgraph python-llm-integration state-management task-division
Last synced: about 2 months ago
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Master the art of building and enhancing AI agents. Learn to develop flow-based applications, implement agentic search, and incorporate human-in-the-loop systems using LangGraph's powerful components.
- Host: GitHub
- URL: https://github.com/ksm26/ai-agents-in-langgraph
- Owner: ksm26
- Created: 2024-06-08T19:31:31.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-06-20T13:07:03.000Z (11 months ago)
- Last Synced: 2024-06-21T05:13:24.255Z (11 months ago)
- Topics: agent-accuracy, agent-components, agent-development, agentic-search, ai-agents, ai-enhancement, essay-writing-agent, flow-based-applications, human-in-the-loop, langchain, langgraph, python-llm-integration, state-management, task-division
- Language: Jupyter Notebook
- Homepage: https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/
- Size: 460 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🤖 [AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)
💡 Welcome to the "AI Agents in LangGraph" course! The course will equip you with the knowledge and skills to build and enhance AI agents using the LangGraph extension of LangChain.
## Course Summary
In this course, you'll explore key principles of designing AI agents with LangGraph, learning how to build flow-based applications and enhance agent capabilities. Here's what you can expect to learn and experience:
1. 🛠️ **Building from Scratch**: Learn to build an agent from scratch using Python and an LLM, understanding the division of tasks between the LLM and the code around it.
2. 🔄 **LangGraph Implementation**: Rebuild your agent using LangGraph, learning about its components and how to combine them effectively.
3. 🔍 **Agentic Search**: Explore agentic search, which retrieves multiple answers in a predictable format, enhancing the agent’s built-in knowledge.
4. 💾 **Persistence**: Implement persistence in agents, enabling state management across multiple threads, conversation switching, and the ability to reload previous states.
5. 👥 **Human-in-the-Loop**: Incorporate human-in-the-loop into agent systems to ensure accuracy and reliability.
6. ✍️ **Essay Writing Agent**: Develop an agent for essay writing, replicating the workflow of a researcher to enhance productivity and quality.
By the end of the course, you’ll have hands-on experience with LangGraph’s core components and a solid understanding of how to build and enhance AI agents effectively.
## Key Points
- 🧩 Learn about LangGraph’s components and how they enable the development, debugging, and maintenance of AI agents.
- 📈 Integrate agentic search capabilities to enhance agent knowledge and performance.
- 🌟 Learn directly from LangChain founder Harrison Chase and Tavily founder Rotem Weiss.
## About the Instructors
🌟 **Harrison Chase** is the Co-Founder and CEO of LangChain, bringing extensive expertise in AI and agent systems to guide you through this course.
🌟 **Rotem Weiss** is the Co-founder and CEO of Tavily, specializing in AI agent design and implementation, to help you master the use of LangGraph.
🔗 To enroll in the course or for further information, visit [deeplearning.ai](https://www.deeplearning.ai/short-courses/).