Today we're launching deep agent CLI. Deep agent CLI is an open- source coding tool built on top of the deep agents package. It allows you to write, edit, and understand code.One key differentiator is that it has memory baked in. What this means is that it will learn alongside you and you can save these memory profiles as different agents that you can then access at different points in time and port across projects or across uh terminal windows or anything like that. So, let me show you how it works.First, ...

There's so many agents and workflows and automations that that I want for myself, but I would never spend the time or I don't have the time to build them out. But with agent builder, I can spend a minute to describe what I want and then instantly get an agent that that can run for me. At Lang Chain, we've historically focused on developer tools.That's what we're known for. And so that's why it's pretty exciting to be here today talking about something different out of our wheelhouse, a noode agent builder. ...

Today we're excited to launch Langsmith Agent Builder. Langmith Agent Builder is a completely new type of noode agent experience. You build and describe your agent in natural language incorporating memory so it adapts and learns over time alongside you.There's no drag and drop. There's no visual workflow complexity. It's all just natural language.It's dead simple, easy for anyone to pick up. The hardest part about building an agent in Langmith Agent Builder is writing the prompt. needs to be pretty detailed ...

We’re releasing a new LangChain Academy course, LangGraph Essentials, where you can learn the basics of LangGraph in less than an hour. 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 add ...

LangChain Essentials Course Highlights - LangChain releases a new LangChain Essentials course for learning the basics of LangChain in an hour [1] - The course focuses on building agents using the `create_agent` abstraction [2] - The pre-built agent utilizes a ReAct-style architecture for reasoning and acting with tools [3] Agent Architecture and Scalability - The agent is built on LangGraph to balance flexibility with pre-built abstraction benefits [4] - The agent is designed to be scalable, resilient to failures, and allows for human intervention [3] - The agent can dynamically select prompts and models, with optional middleware for customization [4] Course Content - The course covers features of the `create_agent` abstraction through building increasingly sophisticated agents [5] - The course utilizes LangChain building blocks including messages, tools, and models [5]

Hey, I'm Tanishi from Langchain. I'm excited to share a new feature that we're launching called multi-turn evaluations. These are used to run online evaluations over end to end user interactions.If you're already using evals and linksmith, these complement those and should be used when your evaluator needs the context of an entire thread or an entire user conversation rather than just the next message. So to walk through a quick example, let's say you have a chat app like a customer support agent. This is a ...

And so you have this iterative process of creating the right prompt and shaping the right guard rails and other code in order to get it to be like really useful in those situations. Open source has been a huge part of lang chain from ever since we got started. Obviously it started as an open source package and it's evolved a lot over the years.We now have Typescript packages. We now have lang chain and langraph. And so you know as we release 1.0% know of these packages.It's a huge moment for us as a company ...

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Hi there, this is Bogattor from Lang Chain and today I'm really excited to introduce the new insights agent in Langmith. Let's say you've just shipped your first agent to production. You're tracing it with Langmith and you're starting to see an uptick in traffic. You're excited and you're really curious to understand how end users are engaging with your agent.What questions are they asking. What tools is your agent using. What subpar responses is your agent returning.And more. To try and answer these questi ...

Clay's Core Offering & Vision - Clay is a creative tool designed to help companies turn growth ideas into reality, functioning as an IDE for revenue generation [1] - Clay's vision is to empower users to leverage data about companies and people to find new customers or expand existing accounts [1] - Clay positions itself as providing tools for users to get data, experiment, and review it quickly, rather than guaranteeing data correctness [2] - Clay emphasizes the importance of flexibility in data analysis, allowing users to ask any question about a company or person and get an answer, leveraging LLMs [2] GTM Engineer & Go-to-Market Strategy - Clay created the role of the GTM (Go-To-Market) engineer, an AI-native role that treats go-to-market activities like an engineer would, focusing on systems, data, and tactics [1] - The go-to-market strategy emphasizes being unique and constantly changing tactics to maintain an edge, as success raises the baseline [1] - The new go-to-market playbook involves finding a go-to-market alpha by implementing a strategy specific to the company quickly, emphasizing hyper-specificity in customer targeting [11][12] Clay's Technology & Architecture - Clay's architecture is built for integrations, treating them as first-class citizens, with integrations living in AWS Lambda functions for on-demand spin-up [1] - Clay uses agents like Legent (account researching agent) and Navigator (computer user agent) to interact with the web and gather information [1] - Clay's architecture allows users to bring their own API keys, which can lead to complexities in calculating rate limits for LLM runs and debugging issues [3] Product Development & User Experience - Clay prioritizes shipping and iterating quickly, focusing on what works now to build momentum for future developments [3] - Session replay is a key feature, allowing users to see how the agent obtained information, identify errors, and provide feedback [2][3] - Clay's user experience is designed around the principle that data is not always accurate, providing tools to review and improve it [2][3] New Products & Features - Clay introduced "Audiences," which treats companies and people as first-class citizens, aggregating signals on them [3] - Clay launched its own sequencer designed to send AI-made messages with spot-checking capabilities [3] - Clay released Sculptor, an agent that helps users build things in Clay and answer questions about tables built in Clay, understanding business context from sources like Notion and CRM [3][4] Metrics & Evaluation - Clay tracks metrics such as the number of Plagent runs, which are on track to reach 2 billion this year, and the usage distribution across integrations [4][5] - Clay categorizes customers by use case and creates health scores to identify expansion opportunities [5]