LangChain Academy New Course: LangGraph Essentials
In our newest LangChain Academy course, LangGraph Essentials, you can learn the basics of LangGraph in less than an hour in either Python or TypeScript.
LangGraph is a low-level orchestration framework designed specifically for building AI agents. It provides a durable runtime for agents with graph-based execution. LangGraph allows you to create flexible, agentic workflows with its modular components. It allows you to control execution, manage state, allow for human intervention when needed, and scale reliably.
LangGraph addresses the unique challenges of building with large language models. It addresses latency by offering streaming and parallelism. It addresses reliability with checkpointing, which allows for retries and human intervention when needed.
In this quickstart course, you’ll explore the essential building blocks of LangGraph by creating simple workflows, and then build a sample agent to tie it all together.
Enroll for free: https://bit.ly/4howDno
Learn more about LangGraph: https://bit.ly/4hq9y3Q
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Chat With Your Documents Using LangChain + JavaScript
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LangChain SQL Webinar
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LangChain "OpenAI functions" Webinar
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LangSmith Launch
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LangChain x Pinecone: Supercharging Llama-2 with RAG
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LangChain Expression Language
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Building LLM applications with LangChain with Lance
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Benchmarking Question/Answering Over CSV Data
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LangChain "RAG Evaluation" Webinar
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Fine-tuning in Your Voice Webinar
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Tabular Data Retrieval
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Building an LLM Application with Audio by AssemblyAI
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Shortwave Assistant Deepdive Webinar
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Cognitive Architectures for Language Agents
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Effectively Building with LLMs in the Browser with Jacob
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Data Privacy for LLMs
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"Theory of Mind" Webinar with Plastic Labs
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LangChain Templates
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Using Natural Language to Query Postgres with Jacob
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Building a Research Assistant from Scratch
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Benchmarking RAG over LangChain Docs
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Skeleton-of-Thought: Building a New Template from Scratch
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Benchmarking Methods for Semi-Structured RAG
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LangSmith Highlights: Getting Started
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LangSmith Highlights: Debugging
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LangSmith Highlights: Datasets
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LangSmith Highlights: Evaluation
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LangSmith Highlights: Human Annotation
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LangSmith Highlights: Monitoring
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LangSmith Highlights: Hub
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SQL Research Assistant
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Getting Started with Multi-Modal LLMs
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Build a Full Stack RAG App With TypeScript
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Auto-Prompt Builder (with Hosted LangServe)
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LangChain v0.1.0 Launch: Introduction
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LangChain v0.1.0 Launch: Observability
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LangChain v0.1.0 Launch: Integrations
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LangChain v0.1.0 Launch: Composability
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LangChain v0.1.0 Launch: Streaming
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LangChain v0.1.0 Launch: Output Parsing
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LangChain v0.1.0 Launch: Retrieval
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LangGraph: Agent Executor
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LangGraph: Chat Agent Executor
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LangGraph: Human-in-the-Loop
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LangGraph: Dynamically Returning a Tool Output Directly
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LangGraph: Respond in a Specific Format
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LangGraph: Managing Agent Steps
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LangGraph: Force-Calling a Tool
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LangGraph: Multi-Agent Workflows
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Streaming Events: Introducing a new `stream_events` method
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Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
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