WEBVTT

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<v ->Hey there, Aiden here,</v>

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and I want to show you something super useful

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when developing agents in the LangChain ecosystem.

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With LangChain, or with LangGraph, and using LangSmith.

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And if you've been coding recently,

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you are probably been using now a AI coding editor,

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like Cursor, like Claude Code,

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let me open Claude Code.

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You've been using those coding editors to write your code.

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I've been using them a lot.

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Now, with those coding editors, while they're awesome,

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they have a very annoying problem

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when it comes to ever-changing frameworks like LangChain.

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So, LangChain is very dynamic,

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it is continuously changing.

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And trust me, I know it,

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I'm updating the course continuously,

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it's very, very hard to keep up with the rate things change.

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Sometimes API break,

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sometimes objects are deprecated,

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and it's really hard to keep up with it.

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Now, LLMs, they don't keep up with it,

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because LLMs are trained in a given point of time,

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and they're trained on the LangChain version which is out

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by the time that the training data was collected.

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And we can use an LLM

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that was trained a couple of months ago,

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and the LangChain ecosystem can change dramatically.

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Now, this is true not only for LangChain,

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but for almost everything in this field,

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in this AI field, this is continuously changing.

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So if we're going to be using those coding agents

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for something very dynamic, like LangChain for example,

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if we're going to ask,

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"Write me an agent with LangChain that does X...",

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depending when we're going to be running this,

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we might get some rubbish answer,

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which is not going to be helpful,

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which is going to be based on old variations of LangChain,

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which is not going to be up to date.

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And the LangChain team are pretty aware of this.

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And if you go to their documentation,

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right over here, you can see,

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you have here this Copy page.

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So, first, with one click,

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you can go and copy this entire page,

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and you can put it in an LLM in a chat application

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and can use it.

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Now, if you go here and click this,

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you can see you can also ask to Copy MCP Server.

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Now, if I'm going to copy the MCP server,

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let's say with Cursor,

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let me open up Cursor here,

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what this is going to do,

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it's going to add the LangChain docs as an MCP server.

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You can see here,

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this is a streamable HTTP server,

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which is going to be pointing to the LangChain Docs MCP.

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So, LangChain was kind enough to create a public MCP server,

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which we can go and query without any API key,

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without anything,

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and simply take their latest and greatest documentation.

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And to use it, we need to simply install it.

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So, now this is installed.

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You can see it has one tool in it,

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SearchDocsByLangChain,

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"Search across the docs of LangChain knowledge base

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to find relevant information, code examples,

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API references, and guides.

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Use this tool when you need to answer questions

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about Docs By LangChain, find specific documentation,

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understand how features work,

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or locate implementation details.

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The search returns contextual content with titles

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and direct links to the documentation page."

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And it receives a query.

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So, and now let me go and disable for a minute context7,

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because context7 is going to do this

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for many, many libraries.

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The difference here is that DocsByLangChain

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is specifically tailor-made to LangChain

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and the LangChain ecosystem,

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and context7 is much more general.

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So if I'm going to be using a coding editor

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to write LangChain code,

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I would like to use this MCP server.

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So if I'm going to go now to the agent,

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and let me ask something like,

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"Hey, can you please tell me,

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how do I write a LangChain agent

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according to the latest docs?"

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Let me go and click Enter.

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And now the AI agent

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is supposed to invoke the LangChain MCP server.

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So now we can see it's running the SearchDocsByLangChain,

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it's refactored my question,

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latest LangChain Python agent creation.

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And now LangChain officially returned the relevant context,

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and we get the answer,

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which is to use the create_agent function.

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And let me go now and disable tavily MCP,

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which has search tool, extract, crawl tool,

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I want to disable it for this example.

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And let me now create now a new chat here.

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Let me copy the same prompt,

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and let me paste it.

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So now I don't have any MCPs connected,

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and let's see what's the answer of the LLM going to be now.

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So now we can see it's invoking

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a default search tool by Cursor.

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So it's going to be using Cursor's different search tool.

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And here we can see,

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initialized agent was actually found.

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And this is the first agent that was created,

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it's been deprecated for a very long time ago.

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Trust me, I refilled this video about it a bunch of times,

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and we can see it's still going,

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it's still searching things,

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it's still acting,

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and we can see it taking a lot of time.

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And this is because we are not using

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the LangChain MCP server.

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And let's see what result we have here.

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Here we can see,

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we have create_react_agent, which was actually deprecated.

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And we got here deprecated code.

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So if we didn't have the LangChain MCP server,

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let me go and enable it,

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so if we didn't have the LangChain MCP server,

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then the code we got Cursor to write

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is going to be the wrong code, and the deprecated code.

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So the fact that LangChain went

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and implemented a remote MCP server,

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which is going to make our lives way easier

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when using coding agents,

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is something very impressive, I have to say.

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Now, LangChain were actually one of the first companies

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to also make their entire documentation websites

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with llms.txt, which we covered in the course.

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And we can see we have other integrations,

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we can use VS Code MCP,

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we can use Claude for that,

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and this is very, very useful.

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So, if you're going to be running some LangChain code,

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I highly recommend you using this MCP server.

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And I want to show you something even cooler.

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If you go to chat.langchain.com,

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you can ask and talk to LangChain's documentation helper,

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which they created.

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And let's paste the prompt from before.

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"Hi, can you please tell me how do I write LangChain agent

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according to latest docs?"

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And what's actually going to happen,

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the tool that is going to be invoked

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to search the documents,

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searching the documentation for agents,

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I think it's actually going to be using the same MCP server

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we are using.

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I'm not sure, I need to find it out,

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but this is probably the case.

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And here we can see, we got the correct answer,

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with the create_agent text.

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And you know what, let me verify it,

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let's go to view trace here,

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they've done everything so it'll be super easy

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and super transparent to see what's happening.

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This is very, very crazy.

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Yeah, you can see here,

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we are using the SearchDocsByLangChain.

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So this is going to be the same MCP server

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which is going to power the Chat LangChain,

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official Chat LangChain.

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So this is very, very cool.

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And here you can see, by the way,

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the entire trace for the Chat LangChain run we had.

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And we can see we invoked the tool here twice.

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And, first time, it looked for agents.

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The second time,

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it went for OSS troubleshooting.

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And here we can see, it searched for create_agent.

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So, there were a bunch of tool calls here.

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So this is something which is

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really, really wonderful to see,

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how everything connects together,

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MCP, LangChain, coding agents,

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and this is really cool in my opinion.

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So, just to finish, the gist of it,

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please, I urge you, if you're going to use coding agents,

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please go here and use the LangChain MCP server.