#langchain (49)
- Build AI Agents with LangGraph
Step-by-step tutorial: Build AI Agents with LangGraph. A functional and robust AI agentic system using LangGraph, capable of executing multi-step workflows and utilizing external tools. Time: ~90 min.
- Prompt Engineering and Agentic AI for Production
Learn to build and deploy advanced AI applications using prompt engineering and agentic AI workflows, focusing on practical, production-ready techniques for developers.
- Build Your First RAG System Using Embeddings and Vector Databases
Readers will build a RAG system, integrating document chunking, text embeddings, and vector databases to enhance LLM knowledge with external data.
- Developing Robust Agents: Design Patterns for Production Readiness
Dive into advanced design patterns for building robust, scalable, and reliable AI agents ready for production environments.
- Agentic AI: LLM, Memory, Tools, Planning Explained
Learn to deconstruct Agentic AI systems by understanding how LLMs, memory, tools, and planning combine to create dynamic, problem-solving agents.
- Empowering Agents with Custom Tools and API Integrations
Extend your AI agents' capabilities by integrating custom tools and external APIs to access real-time data and perform actions beyond their core LLM knowledge.
- Orchestrating Agents with Frameworks: LangChain and LlamaIndex
Learn how to orchestrate complex AI agents using popular frameworks like LangChain and LlamaIndex, integrating LLMs, tools, and memory for production-ready applications.
- Build AI Agents with Persistent Short-Term and Long-Term Memory
Learn to equip AI agents with persistent short-term context and long-term knowledge, making them more capable, consistent, and useful in applications.
- Design and Build AI Agents for Complex Workflows
Discover how to build advanced AI applications that manage complex, multi-step tasks, maintain context, and seamlessly integrate with various external tools.
- Building Your First Agent: A Hands-On Autonomous System Project
Get hands-on building your first autonomous AI agent using Python and LangChain. Learn to integrate LLMs, tools, and memory to create a smart research assistant.
- Advanced Tooling for AI Agents and External API Integration
Learn to build robust tools for AI agents, integrating external APIs, managing complex data flows, and handling errors to enable real-world interactions.
- Debugging, Testing, and Monitoring: Building Reliable Agent Systems
Master debugging, testing, and monitoring strategies for AI agent systems built with LangGraph, AutoGen, CrewAI, and Semantic Kernel to ensure reliability and performance.
- Choosing the Right AI Agent Framework for Your Projects
You will learn to evaluate leading AI agent frameworks like LangGraph, AutoGen, and CrewAI, enabling you to choose the best fit for your multi-agent applications.
- LangGraph: Building State Machines for Dynamic Agent Workflows
Dive into LangGraph to build dynamic, stateful AI agent workflows. Learn about state machines, graph nodes, and edges for complex agent orchestration with practical Python examples.
- Architect Multi-Step AI Agent Workflows with Patterns
Learn to design robust multi-step AI agent workflows by structuring interactions, managing state, and orchestrating communication across diverse patterns.
- Persistent Memory & Context Management: Remembering the Past
Explore how AI agent frameworks manage short-term and long-term memory, and track workflow state to build intelligent, conversational, and persistent applications.
- Mastering Modern AI Agent Frameworks
Explore leading AI agent frameworks like LangGraph, AutoGen, CrewAI, and Semantic Kernel. Master multi-step workflows, memory, and tool orchestration for complex AI applications.
- Build a RAG Agent with Memory for Contextual AI Responses
You will learn to build a RAG agent that uses memory and conversational history to provide accurate, context-aware AI responses.
- Implement Distributed Tracing in AI Workflows with OpenTelemetry
Implement distributed tracing in AI systems using OpenTelemetry, instrumenting LLM calls to track prompts, responses, and latency for debugging.
- LLM Planning for Agentic Retrieval in RAG 2.0
Learn to implement agentic retrieval systems where LLMs plan and execute complex information retrieval tasks, advancing your RAG 2.0 capabilities.
- Akka Agentic AI vs LangChain: Complete Comparison 2026
Comprehensive comparison of Akka Agentic AI and LangChain - features, performance, pros & cons, and when to use each for LLM orchestration and agentic AI development.
- LlamaIndex vs LangChain: Complete Comparison 2026
Comprehensive comparison of LlamaIndex and LangChain - features, performance, pros & cons, and when to use each.
- Memory & State Management for Persistent AI Interactions
Learn how to implement memory and state management in AI applications using LangChain, Vector Stores, and RAG.
- Build a Smart AI Research Assistant Agent Project
Design and implement a multi-agent AI system to build a smart research assistant that searches, synthesizes, and summarizes information.
- LangChain Catalyst - LLM Orchestration Essentials
Guide to using LangChain for LLM orchestration, including core syntax and essential patterns.
- LangExtract Practical Field Guide
Learn how to use LangExtract, a Python library for extracting structured data from text using LLMs.
- Getting Started – Installation and First Run
Learn to install and use LangExtract for structured data extraction from unstructured text.
- Connecting to LLM Providers
Learn how to connect LangExtract to LLM providers, securely manage API keys, and configure your development environment for advanced data extraction.
- Defining Your Extraction Task and Schema
Learn how to define extraction tasks and schemas for data extraction using LangExtract and Pydantic.
- Basic Extraction and Understanding Results
Learn how to use LangExtract for basic data extraction and understand the results.
- Handling Different Document Types – Text, HTML, PDF
Learn how to use LangExtract for extracting data from various document types including text, HTML, and PDF.
- Advanced Schema Design and Data Types
Learn how to design advanced schemas for data extraction using LangExtract, including nested structures and rich data types.
- Interactive Visualization and Debugging
Learn how to use interactive visualization and debugging techniques with LangExtract for accurate data extraction.
- The LangExtract API: Core Functions and Parameters
Learn how to use the LangExtract API for structured information extraction with Python.
- Tackling Long Documents with Chunking Strategies
Learn how to use LangExtract's chunking strategies for efficient data extraction from long documents.
- Multi-Pass Extraction and Refinement
Learn how to use LangExtract for multi-pass extraction and refinement of complex documents.
- Error Handling, Robustness, and Retries
Learn how to handle errors, ensure robustness, and implement retries in your LangExtract pipelines for reliable data extraction.
- Performance Tuning and Optimization
Learn how to optimize LangExtract for efficient data extraction with smart chunking, parallel processing, and optimized prompt engineering.
- Custom LLM Providers and Integrations
Learn how to extend LangExtract with custom LLM providers for specialized, fine-tuned, or open-source models.
- Project: Extracting Key Information from Legal Contracts
Learn how to use LangExtract and Pydantic to extract key information from legal contracts.
- Project: Summarizing and Structuring Financial Reports
Learn to build a LangExtract solution for extracting structured financial data from reports.
- Project: Data Extraction for E-commerce Product Listings
Learn how to extract structured data from e-commerce product listings using LangExtract and Pydantic.
- Best Practices for Prompt Engineering with LangExtract
Learn advanced techniques for prompt engineering with LangExtract to achieve accurate data extraction.
- Comparison with Alternative NLP Extraction Methods
Learn how LangExtract compares to other NLP extraction methods, including rule-based systems and LLM-centric frameworks.
- Common Pitfalls and How to Avoid Them
Learn to avoid common pitfalls in data extraction using LangExtract and Large Language Models.
- Mastering Data Extraction with LangExtract and LLMs
Master LangExtract to efficiently extract data using LLMs, applying foundational concepts to advanced deployment and optimization for diverse real-world applications.
- Deploying LangExtract for Production
Learn how to deploy LangExtract in a production environment for reliable, efficient, and scalable data extraction.
- Integrating with Existing Agent Frameworks
Learn how to integrate existing AI agents from popular frameworks with Agentic Lightening for training and optimization.
- Project 2: Enhancing a LangChain Agent with Reinforcement Learning
Learn how to enhance a LangChain agent with Reinforcement Learning using Agentic Lightening for better decision-making and tool usage in multi-step problems.