Build intelligent agentic systems! Discover the key components of LangChain agents, including how prompts, LLMs, and tools work together for reasoning and action. You'll set up an agent with OpenAI's API, define custom tools, and tackle real-world tasks like math calculations. Plus, explore how LangChain organizes data using graphs, nodes, and edges.

Agen di LangChain

Membuat agen ReAct

Komponen agen LangChain

Membangun alat kustom

Buat tool untuk perhitungan matematika

Mengintegrasikan alat kustom dan kueri

Percakapan dengan agen ReAct

Menyiapkan percakapan

Ajukan pertanyaan tentang riwayat percakapan

The Essentials of LangChain agents

Build dynamic, tool-augmented chatbots with LangChain and LangGraph! You’ll explore how to create a chatbot that adapts based on user input by defining states and integrating external APIs for real-time information retrieval. You'll connect these components into a responsive graph structure, enabling smooth transitions between conversation and tool-assisted responses. By the end, you’ll have a visually represented chatbot framework with enhanced reasoning and multi-step workflows.

Building graphs for chatbots

Graph and agent states

Adding nodes and edges

Generating chatbot responses

Queries and responses

Generate a graph diagram

Adding external tools to a chatbot

Build a Wikipedia tool

Add a tool to a graph

Adding memory and conversation

Visualize the graph and return tool outputs

Adding graph memory

Using graph memory for conversation

Building Chatbots with LangGraph

Expand your chatbot with dynamic tools and memory! Define and integrate multiple tools into flexible workflows, build functions for dynamic tool calling, and configure your chatbot for multiple-tool handling. Organize memory and outputs to enable interleaved, multi-turn conversations. By the end, you'll have created a sophisticated chatbot capable of complex interactions.

Defining multiple tools

Build a tool that invokes the LLM

Build a tool with Python code

Binding multiple tools

Defining nodes and edges for flexible function calling

Define a function that stops the chatbot

Create a function to return an LLM response

Create the graph workflow for multiple tools

Organize chatbot outputs with memory

Configure outputs for multiple tools

Enable multi-turn conversation with memory

Congratulations!

Build Dynamic Chat Agents

Setiap hari, sistem cerdas mengubah berbagai industri—mulai dari dukungan pelanggan otomatis, rekomendasi yang dipersonalisasi, hingga pemrosesan data dinamis. Alur kerja agentic, yang mengintegrasikan model bahasa dan alat untuk menjalankan tugas kompleks, berada di garis depan transformasi ini. Kursus ini menguraikan komponen penting dari alur kerja agentic dan menjelaskan cara membangun, menyesuaikan, serta mengoptimalkannya. Anda akan mempelajari prinsip utama di balik agen LangChain, termasuk mengonfigurasi prompt, mengintegrasikan alat, dan mengelola alur kerja kompleks dengan LangGraph. Di akhir kursus, Anda akan siap membangun sistem cerdas yang mengotomatisasi tugas kompleks, meningkatkan produktivitas, dan menyediakan solusi dinamis yang disesuaikan dengan kebutuhan bisnis tertentu.

Komponen agen LangChain

Merancang Sistem Agentic dengan LangChain

Latihan interaktif praktis