LangSmith Studio: The first agent IDE

LangChain · Beginner ·🤖 AI Agents & Automation ·1y ago
Note: LangGraph Studio is now LangSmith Studio. LLMs have paved the way for the development of new types of agentic applications — and as LLM applications evolve, so must the tooling needed to efficiently develop them. Today, we're announcing LangSmith Studio - the first IDE designed specifically for agent development - in open beta. LangSmith Studio offers a new way to develop LLM applications, providing a specialized agent IDE for visualizing, interacting with, and debugging complex agentic applications. In this blog, we'll give a brief overview of LangGraph and then explore how LangSmith Studio streamlines the development of agentic applications. Download LangGraph Studio: https://github.com/langchain-ai/langgraph-studio Sign up for LangSmith: https://smith.langchain.com/ Read the blog: blog.langchain.dev/langgraph-studio-the-first-agent-ide/
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1 Chat With Your Documents Using LangChain + JavaScript
Chat With Your Documents Using LangChain + JavaScript
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 2 LangChain SQL Webinar
LangChain SQL Webinar
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 3 LangChain "OpenAI functions" Webinar
LangChain "OpenAI functions" Webinar
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 4 LangSmith Launch
LangSmith Launch
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 5 LangChain x Pinecone: Supercharging Llama-2 with RAG
LangChain x Pinecone: Supercharging Llama-2 with RAG
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 6 LangChain Expression Language
LangChain Expression Language
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 7 Building LLM applications with LangChain with Lance
Building LLM applications with LangChain with Lance
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 8 Benchmarking Question/Answering Over CSV Data
Benchmarking Question/Answering Over CSV Data
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 9 LangChain "RAG Evaluation" Webinar
LangChain "RAG Evaluation" Webinar
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 10 Fine-tuning in Your Voice Webinar
Fine-tuning in Your Voice Webinar
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 11 Tabular Data Retrieval
Tabular Data Retrieval
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 12 Building an LLM Application with Audio by AssemblyAI
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
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
OpenGPTs
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 59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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 60 LangGraph: Persistence
LangGraph: Persistence
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