Building LangChain and LangGraph 1.0

LangChain · Beginner ·🤖 AI Agents & Automation ·6mo ago
LangChain CEO Harrison Chase sits down with open source engineers Sydney, Hunter, and Will for an in-depth technical discussion on the major 1.0 releases of LangChain and LangGraph. The team explores the evolution to production-ready agent frameworks, including the new create_agent abstraction, middleware system, and why controllability matters for building reliable AI applications. Learn how to build with LangChain and LangGraph on LangChain Academy: https://bit.ly/3Lhaj3q Check out the docs: https://bit.ly/42MylJi Learn more about LangChain & LangGraph 1.0 in the blog post: https://bit.ly/4qkNhs7 00:00 - Introductions and team overview 01:15 - The origins of LangChain 03:00 - Why we built LangGraph 05:30 - What's in an agent runtime 08:45 - Rewriting LangChain on top of LangGraph for 1.0 10:20 - The create_agent abstraction in LangChain 1.0 12:00 - Middleware in LangChain 1.0 14:30 - Pre-built middleware for summarization, human-in-the-loop 16:15 - The end of the "champion model" era 18:00 - Content blocks for standardizing model outputs 20:30 - When to use LangChain vs LangGraph (agents vs workflows) 23:00 - Context engineering and composability with middleware 25:15 - What's next for LangChain 27:30 - Closing thoughts and getting started with 1.0
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1 Chat With Your Documents Using LangChain + JavaScript
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2 LangChain SQL Webinar
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3 LangChain "OpenAI functions" Webinar
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4 LangSmith Launch
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5 LangChain x Pinecone: Supercharging Llama-2 with RAG
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6 LangChain Expression Language
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7 Building LLM applications with LangChain with Lance
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8 Benchmarking Question/Answering Over CSV Data
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9 LangChain "RAG Evaluation" Webinar
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10 Fine-tuning in Your Voice Webinar
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11 Tabular Data Retrieval
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12 Building an LLM Application with Audio by AssemblyAI
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13 Superagent Deepdive Webinar
Superagent Deepdive Webinar
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14 Lessons from Deploying LLMs with LangSmith
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15 Shortwave Assistant Deepdive Webinar
Shortwave Assistant Deepdive Webinar
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16 Cognitive Architectures for Language Agents
Cognitive Architectures for Language Agents
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17 Effectively Building with LLMs in the Browser with Jacob
Effectively Building with LLMs in the Browser with Jacob
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18 Data Privacy for LLMs
Data Privacy for LLMs
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19 "Theory of Mind" Webinar with Plastic Labs
"Theory of Mind" Webinar with Plastic Labs
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20 LangChain Templates
LangChain Templates
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21 Using Natural Language to Query Postgres with Jacob
Using Natural Language to Query Postgres with Jacob
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22 Building a Research Assistant from Scratch
Building a Research Assistant from Scratch
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23 Benchmarking RAG over LangChain Docs
Benchmarking RAG over LangChain Docs
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24 Skeleton-of-Thought: Building a New Template from Scratch
Skeleton-of-Thought: Building a New Template from Scratch
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25 Benchmarking Methods for Semi-Structured RAG
Benchmarking Methods for Semi-Structured RAG
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26 LangSmith Highlights: Getting Started
LangSmith Highlights: Getting Started
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27 LangSmith Highlights: Debugging
LangSmith Highlights: Debugging
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28 LangSmith Highlights: Datasets
LangSmith Highlights: Datasets
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29 LangSmith Highlights: Evaluation
LangSmith Highlights: Evaluation
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30 LangSmith Highlights: Human Annotation
LangSmith Highlights: Human Annotation
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31 LangSmith Highlights: Monitoring
LangSmith Highlights: Monitoring
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32 LangSmith Highlights: Hub
LangSmith Highlights: Hub
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33 SQL Research Assistant
SQL Research Assistant
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34 Getting Started with Multi-Modal LLMs
Getting Started with Multi-Modal LLMs
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35 Build a Full Stack RAG App With TypeScript
Build a Full Stack RAG App With TypeScript
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36 Auto-Prompt Builder (with Hosted LangServe)
Auto-Prompt Builder (with Hosted LangServe)
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37 LangChain v0.1.0 Launch: Introduction
LangChain v0.1.0 Launch: Introduction
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38 LangChain v0.1.0 Launch: Observability
LangChain v0.1.0 Launch: Observability
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39 LangChain v0.1.0 Launch: Integrations
LangChain v0.1.0 Launch: Integrations
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40 LangChain v0.1.0 Launch: Composability
LangChain v0.1.0 Launch: Composability
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41 LangChain v0.1.0 Launch: Streaming
LangChain v0.1.0 Launch: Streaming
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42 LangChain v0.1.0 Launch: Output Parsing
LangChain v0.1.0 Launch: Output Parsing
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43 LangChain v0.1.0 Launch: Retrieval
LangChain v0.1.0 Launch: Retrieval
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44 LangChain v0.1.0 Launch: Agents
LangChain v0.1.0 Launch: Agents
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45 Build and Deploy a RAG app with Pinecone Serverless
Build and Deploy a RAG app with Pinecone Serverless
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46 Hosted LangServe + LangChain Templates
Hosted LangServe + LangChain Templates
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47 LangGraph: Intro
LangGraph: Intro
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48 LangGraph: Agent Executor
LangGraph: Agent Executor
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49 LangGraph: Chat Agent Executor
LangGraph: Chat Agent Executor
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50 LangGraph: Human-in-the-Loop
LangGraph: Human-in-the-Loop
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51 LangGraph: Dynamically Returning a Tool Output Directly
LangGraph: Dynamically Returning a Tool Output Directly
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52 LangGraph: Respond in a Specific Format
LangGraph: Respond in a Specific Format
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53 LangGraph: Managing Agent Steps
LangGraph: Managing Agent Steps
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54 LangGraph: Force-Calling a Tool
LangGraph: Force-Calling a Tool
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55 LangGraph: Multi-Agent Workflows
LangGraph: Multi-Agent Workflows
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56 Streaming Events: Introducing a new `stream_events` method
Streaming Events: Introducing a new `stream_events` method
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57 Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
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58 OpenGPTs
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59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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60 LangGraph: Persistence
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Chapters (14)

Introductions and team overview
1:15 The origins of LangChain
3:00 Why we built LangGraph
5:30 What's in an agent runtime
8:45 Rewriting LangChain on top of LangGraph for 1.0
10:20 The create_agent abstraction in LangChain 1.0
12:00 Middleware in LangChain 1.0
14:30 Pre-built middleware for summarization, human-in-the-loop
16:15 The end of the "champion model" era
18:00 Content blocks for standardizing model outputs
20:30 When to use LangChain vs LangGraph (agents vs workflows)
23:00 Context engineering and composability with middleware
25:15 What's next for LangChain
27:30 Closing thoughts and getting started with 1.0
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