Our latest LangChain Academy course Deep Research with LangGraph is now live. In this course, you'll learn to build your own deep research agent from scratch, using LangGraph. You'll find that it's a manageable project that you can get up and running quickly.Along the way, you'll use a multi-agent architecture and explore prompting techniques, like adding thinking steps that improve performance and offer insights into the model's decision making. Deep research has broken out as one of the most popular agent ...

Thanks for exploring LangChain Education. We offer a few different ways to learn – including courses, YouTube videos, and documentation – and I’d love to give you a quick overview to help you find the format that works for you. LangChain Academy offers three types of courses: Foundational, Project, and Quickstart.Foundational courses cover topics very methodically, taking you from introduction to mastery. These are longer courses and will take some time to complete. Project courses guide you through buildin ...

Deep agents are a form of agents that plan, reason, and act over longer time horizons. We built a dedicated UI for viewing and interacting with these agents that show its plan, the status of the file system that it uses, and any sub aents it kicks off. My name is Nick.I'm an engineer at Langchain, and today you'll learn how to set up this UI. Now, as a quick refresher, we can think of deep agents as a variant of the generic React tool calling architecture. Under the hood, deep agents still follow the same i ...

Model Performance & Capabilities - GBD5 excels in coding and agent development, demonstrating competitive pricing [1][3][8][11] - The model sets a new Pareto frontier for intelligence versus price, outperforming Gemini in this aspect [1][2][4][22] - While not a dramatic leap from GBD4, GBD5 is a strong daily driver, particularly for building agents and coding [3][8][11] - GBD5 shows state-of-the-art tool calling capabilities, especially for long-running agents [9][11] - Testing indicates a performance increase in deep research tasks when using GBD5 as a researcher agent, achieving 49.4% performance on deep research bench [18] Pricing & Availability - GBD5 is priced competitively, even lower than GPT-4 and GPT-4.01 at $1.25 per million input tokens [3][4] - Through the API, different models are available (main, mini, thinking, pro), while the Chat GBT app uses a router to automatically select the model [5] Limitations & Considerations - GBD5 is considered weaker at writing compared to GPT-40, GPT-41, and GPT-45, being more practical but less conversational [1][7][13][15] - Initial confusion existed in the open SDK regarding model names, but this is expected to be resolved [5][6]

What's up everyone. It's Brace from Langchain and in this video I am extremely excited to announce our newest project openu. Open suite is an async cloud-based open source coding agent.What that means is you connect your GitHub account to open send it a task and it does the rest. It plans, executes the plan, writes code, runs tests, runs your different scripts, and then reviews the code before putting up a poll request to make sure it's all high quality code. And when it's done and it determines that everyt ...

Setup and Configuration - Setting up tracing from Claude Code to Langsmith requires creating a Langsmith account and generating an API key [1] - Enabling telemetry for Claude Code involves setting the `CLOUD_CODE_ENABLE_TELEMETRY` environment variable to 1 [3] - Configuring the OTLP (OpenTelemetry Protocol) exporter with HTTP transport and JSON encoding is necessary for Langsmith ingestion [4] - The Langsmith Cloud endpoint needs to be specified for logs from Claude Code, or a self-hosted instance URL if applicable [5] - Setting the API key in the headers allows authentication and connection to Langsmith, along with specifying a tracing project [5] - Enabling logging of user prompts and inputs is done by setting the appropriate environment variable to true [6] Monitoring and Observability - Langsmith collects and displays events from Claude Code, providing detailed logs of Claude Code sessions [3] - Traces in Langsmith show individual actions performed by Claude Code, including model names, token usage, and latency [8] - Claude Code sends cost information associated with each request to Langsmith [8] - Langsmith's waterfall view groups runs based on timestamps, showing the sequence of user prompts and Claude Code actions [13] - Langsmith provides pre-built dashboards for monitoring general usage, including the total number of traces, token usage, and costs over time [14]

Hey there. Today you're going to learn how you can set up tracing from N8N to Langsmith in just a few minutes. NADN is an AI workflow builder.It has this really nice interface where I can quickly string together a few nodes into a simple AI agent. You can start by testing out these workflows directly in this canvas. And then once you're happy with it, you can also set up external triggers to execute these workflows automatically.Now, whether you're just getting started building or if you already have an age ...

Claude Code is a fantastic agent and has recently taken the developer community by storm and interestingly not just for coding related things but for other things in general writing books, writing reports, things like that. And part of the reason is that it is so good at general purpose and longer time horizon planning and and doing things deeply that it it can be extended to to all of these things. This isn't the first agent that has come out that has been able to do these deep work.So we have Manessa, a g ...

Hi there. Today you're going to learn all about the Langchain deep research agent and how you can use it as a starting point for your projects. It's highly configurable and allows you to add your own MCP servers and is open source so you can tailor it to your own specific use cases.Let's see how it works. So later this year, my roommates and I want to take a trip to Amsterdam and Norway. We want to leave New York on September 12th and get back on the following Sunday.I want to ask Deep Research if it can he ...

Context Engineering Overview - Context engineering is defined as the art and science of filling the context window with the right information at each step of an agent's trajectory [2][4] - The industry categorizes context engineering strategies into writing context, selecting context, compressing context, and isolating context [2][12] - Context engineering is critical for building agents because they typically handle longer contexts [10] Context Writing and Selection - Writing context involves saving information outside the context window, such as using scratch pads for note-taking or memory for retaining information across sessions [13][16][17] - Selecting context means pulling relevant context into the context window, including instructions, facts, and tools [12][19][20] - Retrieval-augmented generation (RAG) is used to augment the knowledge base of LLMs, with code agents being a large-scale application [27] Context Compression and Isolation - Compressing context involves retaining only the most relevant tokens, often through summarization or trimming [12][30] - Isolating context involves splitting up context to help an agent perform a task, with multi-agent systems being a primary example [12][35] - Sandboxing can isolate token-heavy objects from the LLM context window [39] Langraph Support for Context Engineering - Langraph, a low-level orchestration framework, supports context engineering through features like state objects for scratchpads and built-in long-term memory [44][45][48] - Langraph facilitates context selection from state or long-term memory and offers utilities for summarizing and trimming message history [50][53] - Langraph supports context isolation through multi-agent implementations and integration with sandboxes [55][56]