๐ค Chatbot Simulation Evaluation |
๐ป ๐ฌ AI / Quality Assurance |
Simulate user interactions to evaluate chatbot performance, ensuring robustness and reliability in real-world scenarios. |
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๐ง Information Gathering via Prompting |
๐ง AI / Research & Development |
This tutorial demonstrates how to design a LangGraph workflow that utilizes prompting techniques to gather information effectively. It showcases how to structure prompts and manage the flow of information to build intelligent agents. |
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๐ง Code Assistant with LangGraph |
๐ป Software Development |
This tutorial demonstrates how to build a resilient code assistant using LangGraph. It guides you through creating a graph-based agent that can handle code generation, error checking, and iterative refinement, ensuring robust and accurate coding assistance. |
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๐งโ๐ผ Customer Support Agent |
๐งโ๐ผ Customer Support Agent |
This tutorial demonstrates how to build a customer support agent using LangGraph. It guides you through creating a graph-based agent that can handle customer inquiries, providing automated support and enhancing user experience. |
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๐ Extraction with Retries |
๐ง AI / Data Extraction |
This tutorial demonstrates how to implement retry mechanisms in LangGraph workflows, ensuring robust data extraction processes that can handle transient errors and improve reliability. |
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๐ง Multi-Agent Workflow |
๐ง AI / Workflow Orchestration |
This tutorial demonstrates how to build a multi-agent system using LangGraph's agent supervisor. It guides you through creating a supervisor agent that orchestrates multiple specialized agents, managing task delegation and communication flow. |
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๐ง Hierarchical Agent Teams |
๐ง AI / Workflow Orchestration |
This tutorial demonstrates how to build a hierarchical agent system using LangGraph. It guides you through creating a top-level supervisor agent that delegates tasks to specialized sub-agents, enabling complex workflows with clear task delegation and communication. |
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๐ค Multi-Agent Collaboration |
๐ง AI / Workflow Orchestration |
This tutorial demonstrates how to implement multi-agent collaboration using LangGraph. It guides you through creating multiple specialized agents that work together to accomplish a complex task, showcasing the power of agent collaboration in AI workflows. |
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๐ง Plan-and-Execute Agent |
๐ง AI / Workflow Orchestration |
This tutorial demonstrates how to build a "Plan-and-Execute" style agent using LangGraph. It guides you through creating an agent that first generates a multi-step plan and then executes each step sequentially, revisiting and modifying the plan as necessary. This approach is inspired by the Plan-and-Solve paper and the Baby-AGI project, aiming to enhance long-term planning and task execution in AI workflows. |
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๐ง SQL Agent |
๐ง AI / Database Interaction |
This tutorial demonstrates how to build an agent that can answer questions about a SQL database. The agent fetches available tables, determines relevance to the question, retrieves schemas, generates a query, checks for errors, executes it, and formulates a response. |
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๐ง Reflection Agent |
๐ง AI / Workflow Orchestration |
This tutorial demonstrates how to build a reflection agent using LangGraph. It guides you through creating an agent that can critique and revise its own outputs, enhancing the quality and reliability of generated content. |
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๐ง Reflexion Agent |
๐ง AI / Workflow Orchestration |
This tutorial demonstrates how to build a reflexion agent using LangGraph. It guides you through creating an agent that can reflect on its actions and outcomes, enabling iterative improvement and more accurate decision-making in complex workflows. |
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LangGraph Agentic RAG |
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๐ง Adaptive RAG |
๐ง AI / Information Retrieval |
This tutorial demonstrates how to build an Adaptive RAG system using LangGraph. It guides you through creating a dynamic retrieval process that adjusts based on query complexity, enhancing the efficiency and accuracy of information retrieval. |
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๐ง Adaptive RAG (Local) |
๐ง AI / Information Retrieval |
This tutorial focuses on implementing Adaptive RAG with local models, allowing for offline retrieval and generation, which is crucial for environments with limited internet access or privacy concerns. |
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๐ค Agentic RAG |
๐ค AI / Intelligent Agents |
Learn to build an Agentic RAG system where an agent determines the best retrieval strategy before generating a response, improving the relevance and accuracy of answers. |
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๐ค Agentic RAG (Local) |
๐ค AI / Intelligent Agents |
This tutorial extends Agentic RAG to local environments, enabling the use of local models and data sources for retrieval and generation tasks. |
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๐ง Corrective RAG (CRAG) |
๐ง AI / Information Retrieval |
Implement a Corrective RAG system that evaluates and refines retrieved documents before passing them to the generator, ensuring higher-quality outputs. |
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๐ง Corrective RAG (Local) |
๐ง AI / Information Retrieval |
This tutorial focuses on building a Corrective RAG system using local resources, allowing for offline document evaluation and refinement processes. |
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๐ง Self-RAG |
๐ง AI / Information Retrieval |
Learn to implement Self-RAG, where the system reflects on its responses and retrieves additional information if necessary, enhancing the accuracy and relevance of generated content. |
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๐ง Self-RAG (Local) |
๐ง AI / Information Retrieval |
This tutorial demonstrates how to implement Self-RAG using local models and data sources, enabling offline reflection and retrieval processes. |
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