diff --git a/examples/agent-kit-nextjs-langchain b/examples/agent-kit-nextjs-langchain
deleted file mode 160000
index 0d53c48..0000000
--- a/examples/agent-kit-nextjs-langchain
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 0d53c48c0353dc4652b338eb7803147570907040
diff --git a/examples/agent-kit-nextjs-langchain/.env.example b/examples/agent-kit-nextjs-langchain/.env.example
new file mode 100644
index 0000000..99650b7
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/.env.example
@@ -0,0 +1,4 @@
+LANGCHAIN_CALLBACKS_BACKGROUND=false
+OPENAI_API_KEY=
+RPC_URL=
+SOLANA_PRIVATE_KEY=
diff --git a/examples/agent-kit-nextjs-langchain/.eslintrc.json b/examples/agent-kit-nextjs-langchain/.eslintrc.json
new file mode 100644
index 0000000..bffb357
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/.eslintrc.json
@@ -0,0 +1,3 @@
+{
+ "extends": "next/core-web-vitals"
+}
diff --git a/examples/agent-kit-nextjs-langchain/.gitignore b/examples/agent-kit-nextjs-langchain/.gitignore
new file mode 100644
index 0000000..d1e196e
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/.gitignore
@@ -0,0 +1,43 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# local env files
+.env*.local
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
+
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/sdks
+!.yarn/versions
+.env
\ No newline at end of file
diff --git a/examples/agent-kit-nextjs-langchain/.prettierrc.json b/examples/agent-kit-nextjs-langchain/.prettierrc.json
new file mode 100644
index 0000000..0967ef4
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/.prettierrc.json
@@ -0,0 +1 @@
+{}
diff --git a/examples/agent-kit-nextjs-langchain/LICENSE b/examples/agent-kit-nextjs-langchain/LICENSE
new file mode 100644
index 0000000..136a251
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 LangChain
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/examples/agent-kit-nextjs-langchain/README.md b/examples/agent-kit-nextjs-langchain/README.md
new file mode 100644
index 0000000..35a905d
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/README.md
@@ -0,0 +1,56 @@
+# SolanaAgentKit 🦜️🔗 LangChain + Next.js Starter Template
+
+[](https://codespaces.new/michaelessiet/solana-agent-nextjs-starter-langchain)
+[](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fmichaelessiet%2Fsolana-agent-nextjs-starter-langchain)
+
+This template scaffolds a SolanaAgentKit + LangChain.js + Next.js starter app.
+
+The agent uses [LangGraph.js](https://langchain-ai.github.io/langgraphjs/), LangChain's framework for building agentic workflows. They use preconfigured helper functions to minimize boilerplate, but you can replace them with custom graphs as desired.
+
+
+
+It's free-tier friendly too! Check out the [bundle size stats below](#-bundle-size).
+
+## 🚀 Getting Started
+
+First, clone this repo and download it locally.
+
+Next, you'll need to set up environment variables in your repo's `.env.local` file. Copy the `.env.example` file to `.env.local`.
+To start, you'll just need to add your OpenAI API key, Solana RPC URL and wallet private key in base 58 string form.
+
+Next, install the required packages using your preferred package manager (e.g. `pnpm`).
+
+```bash
+pnpm install
+```
+
+Now you're ready to run the development server:
+
+```bash
+pnpm dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result! Ask the bot something and you'll see a streamed response:
+
+
+
+You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
+
+Backend logic lives in `app/api/chat/route.ts`. From here, you can change the prompt and model, or add other modules and logic.
+
+## 📚 Learn More
+
+To learn more about what you can do with SolanaAgentKit and LangChain.js, check out the docs here:
+
+- https://github.com/sendaifun/solana-agent-kit
+- https://js.langchain.com/docs/
+
+## ▲ Deploy on Vercel
+
+When ready, you can deploy your app on the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme).
+
+Check out the [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
+
+## Thank You!
+
+Thanks for reading! If you have any questions or comments, please drop an issue on this repo or reach out to us on [X](https://x.com/sendaifun)
diff --git a/examples/agent-kit-nextjs-langchain/app/api/chat/route.ts b/examples/agent-kit-nextjs-langchain/app/api/chat/route.ts
new file mode 100644
index 0000000..06e6911
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/app/api/chat/route.ts
@@ -0,0 +1,71 @@
+import { NextRequest, NextResponse } from "next/server";
+import { ChatOpenAI } from "@langchain/openai";
+import { MemorySaver } from "@langchain/langgraph";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { SolanaAgentKit, createSolanaTools } from "solana-agent-kit";
+
+const llm = new ChatOpenAI({
+ temperature: 0.7,
+ model: "gpt-4o-mini",
+});
+
+const solanaAgent = new SolanaAgentKit(
+ process.env.SOLANA_PRIVATE_KEY!,
+ process.env.RPC_URL,
+ process.env.OPENAI_API_KEY!,
+);
+
+const tools = createSolanaTools(solanaAgent);
+const memory = new MemorySaver();
+
+const agent = createReactAgent({
+ llm,
+ tools,
+ checkpointSaver: memory,
+ messageModifier: `
+ You are a helpful agent that can interact onchain using the Solana Agent Kit. You are
+ empowered to interact onchain using your tools. If you ever need funds, you can request them from the
+ faucet. If not, you can provide your wallet details and request funds from the user. If there is a 5XX
+ (internal) HTTP error code, ask the user to try again later. If someone asks you to do something you
+ can't do with your currently available tools, you must say so, and encourage them to implement it
+ themselves using the Solana Agent Kit, recommend they go to https://www.solanaagentkit.xyz for more information. Be
+ concise and helpful with your responses. Refrain from restating your tools' descriptions unless it is explicitly requested.
+ `,
+});
+
+export async function POST(req: NextRequest) {
+ try {
+ const body = await req.json();
+ const messages = body.messages ?? [];
+
+ const eventStream = agent.streamEvents(
+ {
+ messages,
+ },
+ {
+ version: "v2",
+ configurable: {
+ thread_id: "Solana Agent Kit!",
+ },
+ },
+ );
+
+ const textEncoder = new TextEncoder();
+ const transformStream = new ReadableStream({
+ async start(controller) {
+ for await (const { event, data } of eventStream) {
+ if (event === "on_chat_model_stream") {
+ if (!!data.chunk.content) {
+ controller.enqueue(textEncoder.encode(data.chunk.content));
+ }
+ }
+ }
+ controller.close();
+ },
+ });
+
+ return new Response(transformStream);
+ } catch (e: any) {
+ return NextResponse.json({ error: e.message }, { status: e.status ?? 500 });
+ }
+}
diff --git a/examples/agent-kit-nextjs-langchain/app/globals.css b/examples/agent-kit-nextjs-langchain/app/globals.css
new file mode 100644
index 0000000..bbdc80a
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/app/globals.css
@@ -0,0 +1,33 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+body {
+ color: #f8f8f8;
+ background: #131318;
+}
+
+body input,
+body textarea {
+ color: black;
+}
+
+a {
+ color: #2d7bd4;
+}
+
+a:hover {
+ border-bottom: 1px solid;
+}
+
+p {
+ margin: 8px 0;
+}
+
+code {
+ color: #ffa500;
+}
+
+li {
+ padding: 4px;
+}
diff --git a/examples/agent-kit-nextjs-langchain/app/layout.tsx b/examples/agent-kit-nextjs-langchain/app/layout.tsx
new file mode 100644
index 0000000..0b627ec
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/app/layout.tsx
@@ -0,0 +1,45 @@
+import "./globals.css";
+import { Public_Sans } from "next/font/google";
+
+const publicSans = Public_Sans({ subsets: ["latin"] });
+
+export default function RootLayout({
+ children,
+}: {
+ children: React.ReactNode;
+}) {
+ return (
+
+
+ SolanaAgentKit + LangChain + Next.js Template
+
+
+
+
+
+
+
+
+
+
+
+ {children}
+
+
+ );
+}
diff --git a/examples/agent-kit-nextjs-langchain/app/page.tsx b/examples/agent-kit-nextjs-langchain/app/page.tsx
new file mode 100644
index 0000000..a9eb072
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/app/page.tsx
@@ -0,0 +1,75 @@
+import { ChatWindow } from "@/components/ChatWindow";
+
+export default function Home() {
+ const InfoCard = (
+
+
+ SolanaAgentKit + LangChain.js 🦜🔗 + Next.js
+
+
+
+ 🤝
+
+ This template showcases a simple agent chatbot using{" "}
+ SolanaAgentKit
+ {", "}
+
+ LangChain.js
+ {" "}
+ and the Vercel{" "}
+
+ AI SDK
+ {" "}
+ in a{" "}
+
+ Next.js
+ {" "}
+ project.
+
+
+
+ 💻
+
+ You can find the prompt and model logic for this use-case in{" "}
+ app/api/chat/route.ts.
+
+
+
+ 🎨
+
+ The main frontend logic is found in app/page.tsx.
+
+
+
+ 🐙
+
+ This template is open source - you can see the source code and
+ deploy your own version{" "}
+
+ from the GitHub repo
+
+ !
+
+
+
+ 👇
+
+ Try asking e.g. What is my wallet address? below!
+
+
+
+
+ );
+ return (
+
+ );
+}
diff --git a/examples/agent-kit-nextjs-langchain/components/ChatMessageBubble.tsx b/examples/agent-kit-nextjs-langchain/components/ChatMessageBubble.tsx
new file mode 100644
index 0000000..8d1beca
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/components/ChatMessageBubble.tsx
@@ -0,0 +1,39 @@
+import type { Message } from "ai/react";
+
+export function ChatMessageBubble(props: { message: Message, aiEmoji?: string, sources: any[] }) {
+ const colorClassName =
+ props.message.role === "user" ? "bg-sky-600" : "bg-slate-50 text-black";
+ const alignmentClassName =
+ props.message.role === "user" ? "ml-auto" : "mr-auto";
+ const prefix = props.message.role === "user" ? "🧑" : props.aiEmoji;
+ return (
+
+
+ {prefix}
+
+
+
{props.message.content}
+ {props.sources && props.sources.length ? <>
+
+
+ 🔍 Sources:
+
+
+
+ {props.sources?.map((source, i) => (
+
+ {i + 1}. "{source.pageContent}"{
+ source.metadata?.loc?.lines !== undefined
+ ?
Lines {source.metadata?.loc?.lines?.from} to {source.metadata?.loc?.lines?.to}
+ : ""
+ }
+
+ ))}
+
+ > : ""}
+
+
+ );
+}
\ No newline at end of file
diff --git a/examples/agent-kit-nextjs-langchain/components/ChatWindow.tsx b/examples/agent-kit-nextjs-langchain/components/ChatWindow.tsx
new file mode 100644
index 0000000..b1f0d90
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/components/ChatWindow.tsx
@@ -0,0 +1,248 @@
+"use client";
+
+import { ToastContainer, toast } from "react-toastify";
+import "react-toastify/dist/ReactToastify.css";
+
+import { Message } from "ai";
+import { useChat } from "ai/react";
+import { useRef, useState, ReactElement } from "react";
+import type { FormEvent } from "react";
+
+import { ChatMessageBubble } from "@/components/ChatMessageBubble";
+import { IntermediateStep } from "./IntermediateStep";
+
+export function ChatWindow(props: {
+ endpoint: string;
+ emptyStateComponent: ReactElement;
+ placeholder?: string;
+ titleText?: string;
+ emoji?: string;
+ showIntermediateStepsToggle?: boolean;
+}) {
+ const messageContainerRef = useRef(null);
+
+ const {
+ endpoint,
+ emptyStateComponent,
+ placeholder,
+ titleText = "An LLM",
+ showIntermediateStepsToggle,
+ emoji,
+ } = props;
+
+ const [showIntermediateSteps, setShowIntermediateSteps] = useState(false);
+ const [intermediateStepsLoading, setIntermediateStepsLoading] =
+ useState(false);
+ const intemediateStepsToggle = showIntermediateStepsToggle && (
+
+ setShowIntermediateSteps(e.target.checked)}
+ >
+ Show intermediate steps
+
+ );
+
+ const [sourcesForMessages, setSourcesForMessages] = useState<
+ Record
+ >({});
+
+ const {
+ messages,
+ input,
+ setInput,
+ handleInputChange,
+ handleSubmit,
+ isLoading: chatEndpointIsLoading,
+ setMessages,
+ } = useChat({
+ api: endpoint,
+ onResponse(response) {
+ const sourcesHeader = response.headers.get("x-sources");
+ const sources = sourcesHeader
+ ? JSON.parse(Buffer.from(sourcesHeader, "base64").toString("utf8"))
+ : [];
+ const messageIndexHeader = response.headers.get("x-message-index");
+ if (sources.length && messageIndexHeader !== null) {
+ setSourcesForMessages({
+ ...sourcesForMessages,
+ [messageIndexHeader]: sources,
+ });
+ }
+ },
+ streamMode: "text",
+ onError: (e) => {
+ toast(e.message, {
+ theme: "dark",
+ });
+ },
+ });
+
+ async function sendMessage(e: FormEvent) {
+ e.preventDefault();
+ if (messageContainerRef.current) {
+ messageContainerRef.current.classList.add("grow");
+ }
+ if (!messages.length) {
+ await new Promise((resolve) => setTimeout(resolve, 300));
+ }
+ if (chatEndpointIsLoading ?? intermediateStepsLoading) {
+ return;
+ }
+ if (!showIntermediateSteps) {
+ handleSubmit(e);
+ // Some extra work to show intermediate steps properly
+ } else {
+ setIntermediateStepsLoading(true);
+ setInput("");
+ const messagesWithUserReply = messages.concat({
+ id: messages.length.toString(),
+ content: input,
+ role: "user",
+ });
+ setMessages(messagesWithUserReply);
+ const response = await fetch(endpoint, {
+ method: "POST",
+ body: JSON.stringify({
+ messages: messagesWithUserReply,
+ show_intermediate_steps: true,
+ }),
+ });
+ const json = await response.json();
+ setIntermediateStepsLoading(false);
+ if (response.status === 200) {
+ const responseMessages: Message[] = json.messages;
+ // Represent intermediate steps as system messages for display purposes
+ // TODO: Add proper support for tool messages
+ const toolCallMessages = responseMessages.filter(
+ (responseMessage: Message) => {
+ return (
+ (responseMessage.role === "assistant" &&
+ !!responseMessage.tool_calls?.length) ||
+ responseMessage.role === "tool"
+ );
+ },
+ );
+ const intermediateStepMessages = [];
+ for (let i = 0; i < toolCallMessages.length; i += 2) {
+ const aiMessage = toolCallMessages[i];
+ const toolMessage = toolCallMessages[i + 1];
+ intermediateStepMessages.push({
+ id: (messagesWithUserReply.length + i / 2).toString(),
+ role: "system" as const,
+ content: JSON.stringify({
+ action: aiMessage.tool_calls?.[0],
+ observation: toolMessage.content,
+ }),
+ });
+ }
+ const newMessages = messagesWithUserReply;
+ for (const message of intermediateStepMessages) {
+ newMessages.push(message);
+ setMessages([...newMessages]);
+ await new Promise((resolve) =>
+ setTimeout(resolve, 1000 + Math.random() * 1000),
+ );
+ }
+ setMessages([
+ ...newMessages,
+ {
+ id: newMessages.length.toString(),
+ content: responseMessages[responseMessages.length - 1].content,
+ role: "assistant",
+ },
+ ]);
+ } else {
+ if (json.error) {
+ toast(json.error, {
+ theme: "dark",
+ });
+ throw new Error(json.error);
+ }
+ }
+ }
+ }
+
+ return (
+ 0 ? "border" : ""}`}
+ >
+
0 ? "" : "hidden"} text-2xl`}>
+ {emoji} {titleText}
+
+ {messages.length === 0 ? emptyStateComponent : ""}
+
+ {messages.length > 0
+ ? [...messages].reverse().map((m, i) => {
+ const sourceKey = (messages.length - 1 - i).toString();
+ return m.role === "system" ? (
+
+ ) : (
+
+ );
+ })
+ : ""}
+
+
+
+
+
+ );
+}
diff --git a/examples/agent-kit-nextjs-langchain/components/IntermediateStep.tsx b/examples/agent-kit-nextjs-langchain/components/IntermediateStep.tsx
new file mode 100644
index 0000000..5ec1fc0
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/components/IntermediateStep.tsx
@@ -0,0 +1,50 @@
+import { useState } from "react";
+import type { Message } from "ai/react";
+
+export function IntermediateStep(props: { message: Message }) {
+ const parsedInput = JSON.parse(props.message.content);
+ const action = parsedInput.action;
+ const observation = parsedInput.observation;
+ const [expanded, setExpanded] = useState(false);
+ return (
+
+
setExpanded(!expanded)}
+ >
+
+ 🛠 {action.name}
+
+ 🔽
+ 🔼
+
+
+
+
+ Tool Input:
+
+
+ {JSON.stringify(action.args)}
+
+
+
+
+ {observation}
+
+
+
+
+ );
+}
diff --git a/examples/agent-kit-nextjs-langchain/data/DefaultRetrievalText.ts b/examples/agent-kit-nextjs-langchain/data/DefaultRetrievalText.ts
new file mode 100644
index 0000000..898acba
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/data/DefaultRetrievalText.ts
@@ -0,0 +1,540 @@
+export default `# QA and Chat over Documents
+
+Chat and Question-Answering (QA) over \`data\` are popular LLM use-cases.
+
+\`data\` can include many things, including:
+
+* \`Unstructured data\` (e.g., PDFs)
+* \`Structured data\` (e.g., SQL)
+* \`Code\` (e.g., Python)
+
+Below we will review Chat and QA on \`Unstructured data\`.
+
+
+
+\`Unstructured data\` can be loaded from many sources.
+
+Check out the [document loader integrations here](/docs/modules/data_connection/document_loaders/) to browse the set of supported loaders.
+
+Each loader returns data as a LangChain \`Document\`.
+
+\`Documents\` are turned into a Chat or QA app following the general steps below:
+
+* \`Splitting\`: [Text splitters](/docs/modules/data_connection/document_transformers/) break \`Documents\` into splits of specified size
+* \`Storage\`: Storage (e.g., often a [vectorstore](/docs/modules/data_connection/vectorstores/)) will house [and often embed](https://www.pinecone.io/learn/vector-embeddings/) the splits
+* \`Retrieval\`: The app retrieves splits from storage (e.g., often [with similar embeddings](https://www.pinecone.io/learn/k-nearest-neighbor/) to the input question)
+* \`Output\`: An [LLM](/docs/modules/model_io/models/llms/) produces an answer using a prompt that includes the question and the retrieved splits
+
+
+
+## Quickstart
+
+Let's load this [blog post](https://lilianweng.github.io/posts/2023-06-23-agent/) on agents as an example \`Document\`.
+
+We'll have a QA app in a few lines of code.
+
+First, set environment variables and install packages required for the guide:
+
+\`\`\`shell
+> yarn add cheerio
+# Or load env vars in your preferred way:
+> export OPENAI_API_KEY="..."
+\`\`\`
+
+## 1. Loading, Splitting, Storage
+
+### 1.1 Getting started
+
+Specify a \`Document\` loader.
+
+\`\`\`typescript
+// Document loader
+import { CheerioWebBaseLoader } from "langchain/document_loaders/web/cheerio";
+
+const loader = new CheerioWebBaseLoader(
+ "https://lilianweng.github.io/posts/2023-06-23-agent/"
+);
+const data = await loader.load();
+\`\`\`
+
+Split the \`Document\` into chunks for embedding and vector storage.
+
+
+\`\`\`typescript
+import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
+
+const textSplitter = new RecursiveCharacterTextSplitter({
+ chunkSize: 500,
+ chunkOverlap: 0,
+});
+
+const splitDocs = await textSplitter.splitDocuments(data);
+\`\`\`
+
+Embed and store the splits in a vector database (for demo purposes we use an unoptimized, in-memory example but you can [browse integrations here](/docs/modules/data_connection/vectorstores/integrations/)):
+
+
+\`\`\`typescript
+import { OpenAIEmbeddings } from "langchain/embeddings/openai";
+import { MemoryVectorStore } from "langchain/vectorstores/memory";
+
+const embeddings = new OpenAIEmbeddings();
+
+const vectorStore = await MemoryVectorStore.fromDocuments(splitDocs, embeddings);
+\`\`\`
+
+Here are the three pieces together:
+
+
+
+### 1.2 Going Deeper
+
+#### 1.2.1 Integrations
+
+\`Document Loaders\`
+
+* Browse document loader integrations [here](/docs/modules/data_connection/document_loaders/).
+
+* See further documentation on loaders [here](/docs/modules/data_connection/document_loaders/).
+
+\`Document Transformers\`
+
+* All can ingest loaded \`Documents\` and process them (e.g., split).
+
+* See further documentation on transformers [here](/docs/modules/data_connection/document_transformers/).
+
+\`Vectorstores\`
+
+* Browse vectorstore integrations [here](/docs/modules/data_connection/vectorstores/integrations/).
+
+* See further documentation on vectorstores [here](/docs/modules/data_connection/vectorstores/).
+
+## 2. Retrieval
+
+### 2.1 Getting started
+
+Retrieve [relevant splits](https://www.pinecone.io/learn/what-is-similarity-search/) for any question using \`similarity_search\`.
+
+
+\`\`\`typescript
+const relevantDocs = await vectorStore.similaritySearch("What is task decomposition?");
+
+console.log(relevantDocs.length);
+
+// 4
+\`\`\`
+
+
+### 2.2 Going Deeper
+
+#### 2.2.1 Retrieval
+
+Vectorstores are commonly used for retrieval.
+
+But, they are not the only option.
+
+For example, SVMs (see thread [here](https://twitter.com/karpathy/status/1647025230546886658?s=20)) can also be used.
+
+LangChain [has many retrievers and retrieval methods](/docs/modules/data_connection/retrievers/) including, but not limited to, vectorstores.
+
+All retrievers implement some common methods, such as \`getRelevantDocuments()\`.
+
+
+## 3. QA
+
+### 3.1 Getting started
+
+Distill the retrieved documents into an answer using an LLM (e.g., \`gpt-3.5-turbo\`) with \`RetrievalQA\` chain.
+
+
+\`\`\`typescript
+import { RetrievalQAChain } from "langchain/chains";
+import { ChatOpenAI } from "langchain/chat_models/openai";
+
+const model = new ChatOpenAI({ model: "gpt-3.5-turbo" });
+const chain = RetrievalQAChain.fromLLM(model, vectorstore.asRetriever());
+
+const response = await chain.call({
+ query: "What is task decomposition?"
+});
+console.log(response);
+
+/*
+ {
+ text: 'Task decomposition refers to the process of breaking down a larger task into smaller, more manageable subgoals. By decomposing a task, it becomes easier for an agent or system to handle complex tasks efficiently. Task decomposition can be done through various methods such as using prompting or task-specific instructions, or through human inputs. It helps in planning and organizing the steps required to complete a task effectively.'
+ }
+*/
+\`\`\`
+
+### 3.2 Going Deeper
+
+#### 3.2.1 Integrations
+
+\`LLMs\`
+
+* Browse LLM integrations and further documentation [here](/docs/modules/model_io/models/).
+
+#### 3.2.2 Customizing the prompt
+
+The prompt in \`RetrievalQA\` chain can be customized as follows.
+
+
+\`\`\`typescript
+import { RetrievalQAChain } from "langchain/chains";
+import { ChatOpenAI } from "langchain/chat_models/openai";
+import { PromptTemplate } from "langchain/prompts";
+
+const model = new ChatOpenAI({ model: "gpt-3.5-turbo" });
+
+const template = \`Use the following pieces of context to answer the question at the end.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+Use three sentences maximum and keep the answer as concise as possible.
+Always say "thanks for asking!" at the end of the answer.
+{context}
+Question: {question}
+Helpful Answer:\`;
+
+const chain = RetrievalQAChain.fromLLM(model, vectorstore.asRetriever(), {
+ prompt: PromptTemplate.fromTemplate(template),
+});
+
+const response = await chain.call({
+ query: "What is task decomposition?"
+});
+
+console.log(response);
+
+/*
+ {
+ text: 'Task decomposition is the process of breaking down a large task into smaller, more manageable subgoals. This allows for efficient handling of complex tasks and aids in planning and organizing the steps needed to achieve the overall goal. Thanks for asking!'
+ }
+*/
+\`\`\`
+
+
+#### 3.2.3 Returning source documents
+
+The full set of retrieved documents used for answer distillation can be returned using \`return_source_documents=True\`.
+
+
+\`\`\`typescript
+import { RetrievalQAChain } from "langchain/chains";
+import { ChatOpenAI } from "langchain/chat_models/openai";
+
+const model = new ChatOpenAI({ model: "gpt-3.5-turbo" });
+
+const chain = RetrievalQAChain.fromLLM(model, vectorstore.asRetriever(), {
+ returnSourceDocuments: true
+});
+
+const response = await chain.call({
+ query: "What is task decomposition?"
+});
+
+console.log(response.sourceDocuments[0]);
+
+/*
+Document {
+ pageContent: 'Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.',
+ metadata: [Object]
+}
+*/
+\`\`\`
+
+
+#### 3.2.4 Customizing retrieved docs in the LLM prompt
+
+Retrieved documents can be fed to an LLM for answer distillation in a few different ways.
+
+\`stuff\`, \`refine\`, and \`map-reduce\` chains for passing documents to an LLM prompt are well summarized [here](/docs/modules/chains/document/).
+
+\`stuff\` is commonly used because it simply "stuffs" all retrieved documents into the prompt.
+
+The [loadQAChain](/docs/modules/chains/document/) methods are easy ways to pass documents to an LLM using these various approaches.
+
+
+\`\`\`typescript
+import { loadQAStuffChain } from "langchain/chains";
+
+const stuffChain = loadQAStuffChain(model);
+
+const stuffResult = await stuffChain.call({
+ input_documents: relevantDocs,
+ question: "What is task decomposition
+});
+
+console.log(stuffResult);
+/*
+{
+ text: 'Task decomposition is the process of breaking down a large task into smaller, more manageable subgoals or steps. This allows for efficient handling of complex tasks by focusing on one subgoal at a time. Task decomposition can be done through various methods such as using simple prompting, task-specific instructions, or human inputs.'
+}
+*/
+\`\`\`
+
+## 4. Chat
+
+### 4.1 Getting started
+
+To keep chat history, we use a variant of the previous chain called a \`ConversationalRetrievalQAChain\`.
+First, specify a \`Memory buffer\` to track the conversation inputs / outputs.
+
+
+\`\`\`typescript
+import { ConversationalRetrievalQAChain } from "langchain/chains";
+import { BufferMemory } from "langchain/memory";
+import { ChatOpenAI } from "langchain/chat_models/openai";
+
+const memory = new BufferMemory({
+ memoryKey: "chat_history",
+ returnMessages: true,
+});
+\`\`\`
+
+Next, we initialize and call the chain:
+
+\`\`\`typescript
+const model = new ChatOpenAI({ model: "gpt-3.5-turbo" });
+const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorstore.asRetriever(), {
+ memory
+});
+
+const result = await chain.call({
+ question: "What are some of the main ideas in self-reflection?"
+});
+console.log(result);
+
+/*
+{
+ text: 'Some main ideas in self-reflection include:\n' +
+ '\n' +
+ '1. Iterative Improvement: Self-reflection allows autonomous agents to improve by continuously refining past action decisions and correcting mistakes.\n' +
+ '\n' +
+ '2. Trial and Error: Self-reflection plays a crucial role in real-world tasks where trial and error are inevitable. It helps agents learn from failed trajectories and make adjustments for future actions.\n' +
+ '\n' +
+ '3. Constructive Criticism: Agents engage in constructive self-criticism of their big-picture behavior to identify areas for improvement.\n' +
+ '\n' +
+ '4. Decision and Strategy Refinement: Reflection on past decisions and strategies enables agents to refine their approach and make more informed choices.\n' +
+ '\n' +
+ '5. Efficiency and Optimization: Self-reflection encourages agents to be smart and efficient in their actions, aiming to complete tasks in the least number of steps.\n' +
+ '\n' +
+ 'These ideas highlight the importance of self-reflection in enhancing performance and guiding future actions.'
+}
+*/
+\`\`\`
+
+
+The \`Memory buffer\` has context to resolve \`"it"\` ("self-reflection") in the below question.
+
+
+\`\`\`typescript
+const followupResult = await chain.call({
+ question: "How does the Reflexion paper handle it?"
+});
+console.log(followupResult);
+
+/*
+{
+ text: "The Reflexion paper introduces a framework that equips agents with dynamic memory and self-reflection capabilities to improve their reasoning skills. The approach involves showing the agent two-shot examples, where each example consists of a failed trajectory and an ideal reflection on how to guide future changes in the agent's plan. These reflections are then added to the agent's working memory as context for querying a language model. The agent uses this self-reflection information to make decisions on whether to start a new trial or continue with the current plan."
+}
+*/
+\`\`\`
+
+
+### 4.2 Going deeper
+
+The [documentation](/docs/modules/chains/popular/chat_vector_db) on \`ConversationalRetrievalQAChain\` offers a few extensions, such as streaming and source documents.
+
+
+# Conversational Retrieval Agents
+
+This is an agent specifically optimized for doing retrieval when necessary while holding a conversation and being able
+to answer questions based on previous dialogue in the conversation.
+
+To start, we will set up the retriever we want to use, then turn it into a retriever tool. Next, we will use the high-level constructor for this type of agent.
+Finally, we will walk through how to construct a conversational retrieval agent from components.
+
+## The Retriever
+
+To start, we need a retriever to use! The code here is mostly just example code. Feel free to use your own retriever and skip to the next section on creating a retriever tool.
+
+\`\`\`typescript
+import { FaissStore } from "langchain/vectorstores/faiss";
+import { OpenAIEmbeddings } from "langchain/embeddings/openai";
+import { TextLoader } from "langchain/document_loaders/fs/text";
+import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
+
+const loader = new TextLoader("state_of_the_union.txt");
+const docs = await loader.load();
+const splitter = new RecursiveCharacterTextSplitter({
+ chunkSize: 1000,
+ chunkOverlap: 0
+});
+
+const texts = await splitter.splitDocuments(docs);
+
+const vectorStore = await FaissStore.fromDocuments(texts, new OpenAIEmbeddings());
+
+const retriever = vectorStore.asRetriever();
+\`\`\`
+
+## Retriever Tool
+
+Now we need to create a tool for our retriever. The main things we need to pass in are a \`name\` for the retriever as well as a \`description\`. These will both be used by the language model, so they should be informative.
+
+\`\`\`typescript
+import { createRetrieverTool } from "langchain/agents/toolkits";
+
+const tool = createRetrieverTool(retriever, {
+ name: "search_state_of_union",
+ description: "Searches and returns documents regarding the state-of-the-union.",
+});
+\`\`\`
+
+## Agent Constructor
+
+Here, we will use the high level \`create_conversational_retrieval_agent\` API to construct the agent.
+Notice that beside the list of tools, the only thing we need to pass in is a language model to use.
+
+Under the hood, this agent is using the OpenAIFunctionsAgent, so we need to use an ChatOpenAI model.
+
+\`\`\`typescript
+import { createConversationalRetrievalAgent } from "langchain/agents/toolkits";
+import { ChatOpenAI } from "langchain/chat_models/openai";
+
+const model = new ChatOpenAI({
+ temperature: 0,
+});
+
+const executor = await createConversationalRetrievalAgent(model, [tool], {
+ verbose: true,
+});
+\`\`\`
+
+We can now try it out!
+
+\`\`\`typescript
+const result = await executor.call({
+ input: "Hi, I'm Bob!"
+});
+
+console.log(result);
+
+/*
+ {
+ output: 'Hello Bob! How can I assist you today?',
+ intermediateSteps: []
+ }
+*/
+
+const result2 = await executor.call({
+ input: "What's my name?"
+});
+
+console.log(result2);
+
+/*
+ { output: 'Your name is Bob.', intermediateSteps: [] }
+*/
+
+const result3 = await executor.call({
+ input: "What did the president say about Ketanji Brown Jackson in the most recent state of the union?"
+});
+
+console.log(result3);
+
+/*
+ {
+ output: "In the most recent state of the union, President Biden mentioned Ketanji Brown Jackson. He nominated her as a Circuit Court of Appeals judge and described her as one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. He mentioned that she has received a broad range of support, including from the Fraternal Order of Police and former judges appointed by Democrats and Republicans.",
+ intermediateSteps: [
+ {...}
+ ]
+ }
+*/
+
+const result4 = await executor.call({
+ input: "How long ago did he nominate her?"
+});
+
+console.log(result4);
+
+/*
+ {
+ output: 'President Biden nominated Ketanji Brown Jackson four days before the most recent state of the union address.',
+ intermediateSteps: []
+ }
+*/
+\`\`\`
+
+Note that for the final call, the agent used previously retrieved information to answer the query and did not need to call the tool again!
+
+Here's a trace showing how the agent fetches documents to answer the question with the retrieval tool:
+
+https://smith.langchain.com/public/1e2b1887-ca44-4210-913b-a69c1b8a8e7e/r
+
+## Creating from components
+
+What actually is going on underneath the hood? Let's take a look so we can understand how to modify things going forward.
+
+### Memory
+
+In this example, we want the agent to remember not only previous conversations, but also previous intermediate steps.
+For that, we can use \`OpenAIAgentTokenBufferMemory\`. Note that if you want to change whether the agent remembers intermediate steps,
+how the long the retained buffer is, or anything like that you should change this part.
+
+\`\`\`typescript
+import { OpenAIAgentTokenBufferMemory } from "langchain/agents/toolkits";
+
+const memory = new OpenAIAgentTokenBufferMemory({
+ llm: model,
+ memoryKey: "chat_history",
+ outputKey: "output"
+});
+\`\`\`
+
+You should make sure \`memoryKey\` is set to \`"chat_history"\` and \`outputKey\` is set to \`"output"\` for the OpenAI functions agent.
+This memory also has \`returnMessages\` set to \`true\` by default.
+
+You can also load messages from prior conversations into this memory by initializing it with a pre-loaded chat history:
+
+\`\`\`typescript
+import { ChatOpenAI } from "langchain/chat_models/openai";
+import { OpenAIAgentTokenBufferMemory } from "langchain/agents/toolkits";
+import { HumanMessage, AIMessage } from "langchain/schema";
+import { ChatMessageHistory } from "langchain/memory";
+
+const previousMessages = [
+ new HumanMessage("My name is Bob"),
+ new AIMessage("Nice to meet you, Bob!"),
+];
+
+const chatHistory = new ChatMessageHistory(previousMessages);
+
+const memory = new OpenAIAgentTokenBufferMemory({
+ llm: new ChatOpenAI({}),
+ memoryKey: "chat_history",
+ outputKey: "output",
+ chatHistory,
+});
+\`\`\`
+
+### Agent executor
+
+We can recreate the agent executor directly with the \`initializeAgentExecutorWithOptions\` method.
+This allows us to customize the agent's system message by passing in a \`prefix\` into \`agentArgs\`.
+Importantly, we must pass in \`return_intermediate_steps: true\` since we are recording that with our memory object.
+
+\`\`\`typescript
+import { initializeAgentExecutorWithOptions } from "langchain/agents";
+
+const executor = await initializeAgentExecutorWithOptions(tools, llm, {
+ agentType: "openai-functions",
+ memory,
+ returnIntermediateSteps: true,
+ agentArgs: {
+ prefix:
+ prefix ??
+ \`Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.\`,
+ },
+});
+\`\`\`
+`;
\ No newline at end of file
diff --git a/examples/agent-kit-nextjs-langchain/next.config.js b/examples/agent-kit-nextjs-langchain/next.config.js
new file mode 100644
index 0000000..654e669
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/next.config.js
@@ -0,0 +1,4 @@
+const withBundleAnalyzer = require('@next/bundle-analyzer')({
+ enabled: process.env.ANALYZE === 'true',
+})
+module.exports = withBundleAnalyzer({})
\ No newline at end of file
diff --git a/examples/agent-kit-nextjs-langchain/package.json b/examples/agent-kit-nextjs-langchain/package.json
new file mode 100644
index 0000000..241a8d4
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/package.json
@@ -0,0 +1,44 @@
+{
+ "name": "langchain-nextjs-template",
+ "version": "0.0.0",
+ "private": true,
+ "scripts": {
+ "dev": "next dev",
+ "build": "next build",
+ "start": "next start",
+ "lint": "next lint",
+ "format": "prettier --write \"app\""
+ },
+ "engines": {
+ "node": ">=18"
+ },
+ "dependencies": {
+ "@langchain/community": "^0.3.11",
+ "@langchain/core": "^0.3.17",
+ "@langchain/langgraph": "^0.2.20",
+ "@langchain/openai": "^0.3.11",
+ "@next/bundle-analyzer": "^13.4.19",
+ "@supabase/supabase-js": "^2.32.0",
+ "@types/node": "20.12.12",
+ "@types/react": "18.3.2",
+ "@types/react-dom": "18.3.0",
+ "ai": "^3.1.12",
+ "autoprefixer": "10.4.14",
+ "eslint": "8.46.0",
+ "eslint-config-next": "13.4.12",
+ "langchain": "^0.3.5",
+ "next": "^14.2.3",
+ "postcss": "8.4.27",
+ "react": "^18.3.1",
+ "react-dom": "^18.3.1",
+ "react-toastify": "^9.1.3",
+ "solana-agent-kit": "^1.3.0",
+ "tailwindcss": "3.3.3",
+ "typescript": "5.1.6",
+ "zod": "^3.22.3",
+ "zod-to-json-schema": "^3.21.4"
+ },
+ "devDependencies": {
+ "prettier": "3.0.0"
+ }
+}
diff --git a/examples/agent-kit-nextjs-langchain/pnpm-lock.yaml b/examples/agent-kit-nextjs-langchain/pnpm-lock.yaml
new file mode 100644
index 0000000..966aa8b
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/pnpm-lock.yaml
@@ -0,0 +1,7733 @@
+lockfileVersion: '9.0'
+
+settings:
+ autoInstallPeers: true
+ excludeLinksFromLockfile: false
+
+importers:
+
+ .:
+ dependencies:
+ '@langchain/community':
+ specifier: ^0.3.11
+ version: 0.3.20(@browserbasehq/sdk@2.0.0)(@browserbasehq/stagehand@1.8.0(@playwright/test@1.49.1)(bufferutil@4.0.8)(deepmerge@4.3.1)(dotenv@16.4.7)(openai@4.77.0(zod@3.24.1))(utf-8-validate@5.0.10)(zod@3.24.1))(@ibm-cloud/watsonx-ai@1.3.0)(@langchain/core@0.3.26(openai@4.77.0(zod@3.24.1)))(@langchain/groq@0.1.2(@langchain/core@0.3.26(openai@4.77.0(zod@3.24.1))))(@supabase/supabase-js@2.47.10(bufferutil@4.0.8)(utf-8-validate@5.0.10))(axios@1.7.4)(ibm-cloud-sdk-core@5.1.0)(ignore@5.3.2)(jsonwebtoken@9.0.2)(lodash@4.17.21)(openai@4.77.0(zod@3.24.1))(playwright@1.49.1)(ws@8.18.0(bufferutil@4.0.8)(utf-8-validate@5.0.10))
+ '@langchain/core':
+ specifier: ^0.3.17
+ version: 0.3.26(openai@4.77.0(zod@3.24.1))
+ '@langchain/langgraph':
+ specifier: ^0.2.20
+ version: 0.2.36(@langchain/core@0.3.26(openai@4.77.0(zod@3.24.1)))
+ '@langchain/openai':
+ specifier: ^0.3.11
+ version: 0.3.16(@langchain/core@0.3.26(openai@4.77.0(zod@3.24.1)))
+ '@next/bundle-analyzer':
+ specifier: ^13.4.19
+ version: 13.5.8(bufferutil@4.0.8)(utf-8-validate@5.0.10)
+ '@supabase/supabase-js':
+ specifier: ^2.32.0
+ version: 2.47.10(bufferutil@4.0.8)(utf-8-validate@5.0.10)
+ '@types/node':
+ specifier: 20.12.12
+ version: 20.12.12
+ '@types/react':
+ specifier: 18.3.2
+ version: 18.3.2
+ '@types/react-dom':
+ specifier: 18.3.0
+ version: 18.3.0
+ ai:
+ specifier: ^3.1.12
+ version: 3.4.33(openai@4.77.0(zod@3.24.1))(react@18.3.1)(sswr@2.1.0(svelte@5.16.0))(svelte@5.16.0)(vue@3.5.13(typescript@5.1.6))(zod@3.24.1)
+ autoprefixer:
+ specifier: 10.4.14
+ version: 10.4.14(postcss@8.4.27)
+ eslint:
+ specifier: 8.46.0
+ version: 8.46.0
+ eslint-config-next:
+ specifier: 13.4.12
+ version: 13.4.12(eslint@8.46.0)(typescript@5.1.6)
+ langchain:
+ specifier: ^0.3.5
+ version: 0.3.8(@langchain/core@0.3.26(openai@4.77.0(zod@3.24.1)))(@langchain/groq@0.1.2(@langchain/core@0.3.26(openai@4.77.0(zod@3.24.1))))(axios@1.7.4)(openai@4.77.0(zod@3.24.1))
+ next:
+ specifier: ^14.2.3
+ version: 14.2.21(@opentelemetry/api@1.9.0)(@playwright/test@1.49.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+ postcss:
+ specifier: 8.4.27
+ version: 8.4.27
+ react:
+ specifier: ^18.3.1
+ version: 18.3.1
+ react-dom:
+ specifier: ^18.3.1
+ version: 18.3.1(react@18.3.1)
+ react-toastify:
+ specifier: ^9.1.3
+ version: 9.1.3(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+ solana-agent-kit:
+ specifier: ^1.3.0
+ version: 1.3.0(@noble/hashes@1.6.1)(axios@1.7.4)(borsh@2.0.0)(buffer@6.0.3)(bufferutil@4.0.8)(fastestsmallesttextencoderdecoder@1.0.22)(typescript@5.1.6)(utf-8-validate@5.0.10)(zod@3.24.1)
+ tailwindcss:
+ specifier: 3.3.3
+ version: 3.3.3
+ typescript:
+ specifier: 5.1.6
+ version: 5.1.6
+ zod:
+ specifier: ^3.22.3
+ version: 3.24.1
+ zod-to-json-schema:
+ specifier: ^3.21.4
+ version: 3.24.1(zod@3.24.1)
+ devDependencies:
+ prettier:
+ specifier: 3.0.0
+ version: 3.0.0
+
+packages:
+
+ '@ai-sdk/provider-utils@1.0.22':
+ resolution: {integrity: sha512-YHK2rpj++wnLVc9vPGzGFP3Pjeld2MwhKinetA0zKXOoHAT/Jit5O8kZsxcSlJPu9wvcGT1UGZEjZrtO7PfFOQ==}
+ engines: {node: '>=18'}
+ peerDependencies:
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+ peerDependenciesMeta:
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+
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+ resolution: {integrity: sha512-dQkfBDs2lTYpKM8389oopPdQgIU007GQyCbuPPrV+K6MtSII3HBfE0stUIMXUb44L+LK1t6GXPP7wjSzjO6uKg==}
+ engines: {node: '>=18'}
+
+ '@ai-sdk/react@0.0.70':
+ resolution: {integrity: sha512-GnwbtjW4/4z7MleLiW+TOZC2M29eCg1tOUpuEiYFMmFNZK8mkrqM0PFZMo6UsYeUYMWqEOOcPOU9OQVJMJh7IQ==}
+ engines: {node: '>=18'}
+ peerDependencies:
+ react: ^18 || ^19 || ^19.0.0-rc
+ zod: ^3.0.0
+ peerDependenciesMeta:
+ react:
+ optional: true
+ zod:
+ optional: true
+
+ '@ai-sdk/solid@0.0.54':
+ resolution: {integrity: sha512-96KWTVK+opdFeRubqrgaJXoNiDP89gNxFRWUp0PJOotZW816AbhUf4EnDjBjXTLjXL1n0h8tGSE9sZsRkj9wQQ==}
+ engines: {node: '>=18'}
+ peerDependencies:
+ solid-js: ^1.7.7
+ peerDependenciesMeta:
+ solid-js:
+ optional: true
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+
+ strip-json-comments@3.1.1: {}
+
+ strtok3@6.3.0:
+ dependencies:
+ '@tokenizer/token': 0.3.0
+ peek-readable: 4.1.0
+
+ styled-jsx@5.1.1(react@18.3.1):
+ dependencies:
+ client-only: 0.0.1
+ react: 18.3.1
+
+ sucrase@3.35.0:
+ dependencies:
+ '@jridgewell/gen-mapping': 0.3.8
+ commander: 4.1.1
+ glob: 10.4.5
+ lines-and-columns: 1.2.4
+ mz: 2.7.0
+ pirates: 4.0.6
+ ts-interface-checker: 0.1.13
+
+ superstruct@0.15.5: {}
+
+ superstruct@2.0.2: {}
+
+ supports-color@7.2.0:
+ dependencies:
+ has-flag: 4.0.0
+
+ supports-preserve-symlinks-flag@1.0.0: {}
+
+ svelte@5.16.0:
+ dependencies:
+ '@ampproject/remapping': 2.3.0
+ '@jridgewell/sourcemap-codec': 1.5.0
+ '@types/estree': 1.0.6
+ acorn: 8.14.0
+ acorn-typescript: 1.4.13(acorn@8.14.0)
+ aria-query: 5.3.2
+ axobject-query: 4.1.0
+ clsx: 2.1.1
+ esm-env: 1.2.1
+ esrap: 1.3.2
+ is-reference: 3.0.3
+ locate-character: 3.0.0
+ magic-string: 0.30.17
+ zimmerframe: 1.1.2
+
+ swr@2.3.0(react@18.3.1):
+ dependencies:
+ dequal: 2.0.3
+ react: 18.3.1
+ use-sync-external-store: 1.4.0(react@18.3.1)
+
+ swrev@4.0.0: {}
+
+ swrv@1.0.4(vue@3.5.13(typescript@5.1.6)):
+ dependencies:
+ vue: 3.5.13(typescript@5.1.6)
+
+ tailwindcss@3.3.3:
+ dependencies:
+ '@alloc/quick-lru': 5.2.0
+ arg: 5.0.2
+ chokidar: 3.6.0
+ didyoumean: 1.2.2
+ dlv: 1.1.3
+ fast-glob: 3.3.2
+ glob-parent: 6.0.2
+ is-glob: 4.0.3
+ jiti: 1.21.7
+ lilconfig: 2.1.0
+ micromatch: 4.0.8
+ normalize-path: 3.0.0
+ object-hash: 3.0.0
+ picocolors: 1.1.1
+ postcss: 8.4.27
+ postcss-import: 15.1.0(postcss@8.4.27)
+ postcss-js: 4.0.1(postcss@8.4.27)
+ postcss-load-config: 4.0.2(postcss@8.4.27)
+ postcss-nested: 6.2.0(postcss@8.4.27)
+ postcss-selector-parser: 6.1.2
+ resolve: 1.22.10
+ sucrase: 3.35.0
+ transitivePeerDependencies:
+ - ts-node
+
+ tapable@2.2.1: {}
+
+ text-encoding-utf-8@1.0.2: {}
+
+ text-table@0.2.0: {}
+
+ thenify-all@1.6.0:
+ dependencies:
+ thenify: 3.3.1
+
+ thenify@3.3.1:
+ dependencies:
+ any-promise: 1.3.0
+
+ throttleit@2.1.0: {}
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+
+ tiny-inflate@1.0.3: {}
+
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+
+ to-regex-range@5.0.1:
+ dependencies:
+ is-number: 7.0.0
+
+ toformat@2.0.0: {}
+
+ token-types@4.2.1:
+ dependencies:
+ '@tokenizer/token': 0.3.0
+ ieee754: 1.2.1
+
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+
+ totalist@1.1.0: {}
+
+ tough-cookie@4.1.4:
+ dependencies:
+ psl: 1.15.0
+ punycode: 2.3.1
+ universalify: 0.2.0
+ url-parse: 1.5.10
+
+ tr46@0.0.3: {}
+
+ trim-lines@3.0.1: {}
+
+ ts-interface-checker@0.1.13: {}
+
+ ts-log@2.2.7: {}
+
+ tsconfig-paths@3.15.0:
+ dependencies:
+ '@types/json5': 0.0.29
+ json5: 1.0.2
+ minimist: 1.2.8
+ strip-bom: 3.0.0
+
+ tsconfig-paths@4.2.0:
+ dependencies:
+ json5: 2.2.3
+ minimist: 1.2.8
+ strip-bom: 3.0.0
+
+ tslib@1.14.1: {}
+
+ tslib@2.8.1: {}
+
+ tsutils@3.21.0(typescript@5.1.6):
+ dependencies:
+ tslib: 1.14.1
+ typescript: 5.1.6
+
+ tweetnacl@1.0.3: {}
+
+ type-check@0.4.0:
+ dependencies:
+ prelude-ls: 1.2.1
+
+ type-fest@0.20.2: {}
+
+ typed-array-buffer@1.0.3:
+ dependencies:
+ call-bound: 1.0.3
+ es-errors: 1.3.0
+ is-typed-array: 1.1.15
+
+ typed-array-byte-length@1.0.3:
+ dependencies:
+ call-bind: 1.0.8
+ for-each: 0.3.3
+ gopd: 1.2.0
+ has-proto: 1.2.0
+ is-typed-array: 1.1.15
+
+ typed-array-byte-offset@1.0.4:
+ dependencies:
+ available-typed-arrays: 1.0.7
+ call-bind: 1.0.8
+ for-each: 0.3.3
+ gopd: 1.2.0
+ has-proto: 1.2.0
+ is-typed-array: 1.1.15
+ reflect.getprototypeof: 1.0.9
+
+ typed-array-length@1.0.7:
+ dependencies:
+ call-bind: 1.0.8
+ for-each: 0.3.3
+ gopd: 1.2.0
+ is-typed-array: 1.1.15
+ possible-typed-array-names: 1.0.0
+ reflect.getprototypeof: 1.0.9
+
+ typedoc@0.26.11(typescript@5.1.6):
+ dependencies:
+ lunr: 2.3.9
+ markdown-it: 14.1.0
+ minimatch: 9.0.5
+ shiki: 1.24.4
+ typescript: 5.1.6
+ yaml: 2.6.1
+
+ typescript@5.1.6: {}
+
+ uc.micro@2.1.0: {}
+
+ unbox-primitive@1.1.0:
+ dependencies:
+ call-bound: 1.0.3
+ has-bigints: 1.1.0
+ has-symbols: 1.1.0
+ which-boxed-primitive: 1.1.1
+
+ undici-types@5.26.5: {}
+
+ unicode-trie@2.0.0:
+ dependencies:
+ pako: 0.2.9
+ tiny-inflate: 1.0.3
+
+ unist-util-is@6.0.0:
+ dependencies:
+ '@types/unist': 3.0.3
+
+ unist-util-position@5.0.0:
+ dependencies:
+ '@types/unist': 3.0.3
+
+ unist-util-stringify-position@4.0.0:
+ dependencies:
+ '@types/unist': 3.0.3
+
+ unist-util-visit-parents@6.0.1:
+ dependencies:
+ '@types/unist': 3.0.3
+ unist-util-is: 6.0.0
+
+ unist-util-visit@5.0.0:
+ dependencies:
+ '@types/unist': 3.0.3
+ unist-util-is: 6.0.0
+ unist-util-visit-parents: 6.0.1
+
+ universalify@0.2.0: {}
+
+ universalify@2.0.1: {}
+
+ update-browserslist-db@1.1.1(browserslist@4.24.3):
+ dependencies:
+ browserslist: 4.24.3
+ escalade: 3.2.0
+ picocolors: 1.1.1
+
+ uri-js@4.4.1:
+ dependencies:
+ punycode: 2.3.1
+
+ url-parse@1.5.10:
+ dependencies:
+ querystringify: 2.2.0
+ requires-port: 1.0.0
+
+ use-sync-external-store@1.4.0(react@18.3.1):
+ dependencies:
+ react: 18.3.1
+
+ utf-8-validate@5.0.10:
+ dependencies:
+ node-gyp-build: 4.8.4
+ optional: true
+
+ util-deprecate@1.0.2: {}
+
+ util@0.12.5:
+ dependencies:
+ inherits: 2.0.4
+ is-arguments: 1.2.0
+ is-generator-function: 1.0.10
+ is-typed-array: 1.1.15
+ which-typed-array: 1.1.18
+
+ uuid@10.0.0: {}
+
+ uuid@8.3.2: {}
+
+ uuid@9.0.1: {}
+
+ vfile-message@4.0.2:
+ dependencies:
+ '@types/unist': 3.0.3
+ unist-util-stringify-position: 4.0.0
+
+ vfile@6.0.3:
+ dependencies:
+ '@types/unist': 3.0.3
+ vfile-message: 4.0.2
+
+ vue@3.5.13(typescript@5.1.6):
+ dependencies:
+ '@vue/compiler-dom': 3.5.13
+ '@vue/compiler-sfc': 3.5.13
+ '@vue/runtime-dom': 3.5.13
+ '@vue/server-renderer': 3.5.13(vue@3.5.13(typescript@5.1.6))
+ '@vue/shared': 3.5.13
+ optionalDependencies:
+ typescript: 5.1.6
+
+ web-streams-polyfill@3.3.3: {}
+
+ web-streams-polyfill@4.0.0-beta.3: {}
+
+ webidl-conversions@3.0.1: {}
+
+ webpack-bundle-analyzer@4.7.0(bufferutil@4.0.8)(utf-8-validate@5.0.10):
+ dependencies:
+ acorn: 8.14.0
+ acorn-walk: 8.3.4
+ chalk: 4.1.2
+ commander: 7.2.0
+ gzip-size: 6.0.0
+ lodash: 4.17.21
+ opener: 1.5.2
+ sirv: 1.0.19
+ ws: 7.5.10(bufferutil@4.0.8)(utf-8-validate@5.0.10)
+ transitivePeerDependencies:
+ - bufferutil
+ - utf-8-validate
+
+ whatwg-url@5.0.0:
+ dependencies:
+ tr46: 0.0.3
+ webidl-conversions: 3.0.1
+
+ which-boxed-primitive@1.1.1:
+ dependencies:
+ is-bigint: 1.1.0
+ is-boolean-object: 1.2.1
+ is-number-object: 1.1.1
+ is-string: 1.1.1
+ is-symbol: 1.1.1
+
+ which-builtin-type@1.2.1:
+ dependencies:
+ call-bound: 1.0.3
+ function.prototype.name: 1.1.8
+ has-tostringtag: 1.0.2
+ is-async-function: 2.0.0
+ is-date-object: 1.1.0
+ is-finalizationregistry: 1.1.1
+ is-generator-function: 1.0.10
+ is-regex: 1.2.1
+ is-weakref: 1.1.0
+ isarray: 2.0.5
+ which-boxed-primitive: 1.1.1
+ which-collection: 1.0.2
+ which-typed-array: 1.1.18
+
+ which-collection@1.0.2:
+ dependencies:
+ is-map: 2.0.3
+ is-set: 2.0.3
+ is-weakmap: 2.0.2
+ is-weakset: 2.0.4
+
+ which-typed-array@1.1.18:
+ dependencies:
+ available-typed-arrays: 1.0.7
+ call-bind: 1.0.8
+ call-bound: 1.0.3
+ for-each: 0.3.3
+ gopd: 1.2.0
+ has-tostringtag: 1.0.2
+
+ which@2.0.2:
+ dependencies:
+ isexe: 2.0.0
+
+ word-wrap@1.2.5: {}
+
+ wrap-ansi@7.0.0:
+ dependencies:
+ ansi-styles: 4.3.0
+ string-width: 4.2.3
+ strip-ansi: 6.0.1
+
+ wrap-ansi@8.1.0:
+ dependencies:
+ ansi-styles: 6.2.1
+ string-width: 5.1.2
+ strip-ansi: 7.1.0
+
+ wrappy@1.0.2: {}
+
+ ws@7.5.10(bufferutil@4.0.8)(utf-8-validate@5.0.10):
+ optionalDependencies:
+ bufferutil: 4.0.8
+ utf-8-validate: 5.0.10
+
+ ws@8.18.0(bufferutil@4.0.8)(utf-8-validate@5.0.10):
+ optionalDependencies:
+ bufferutil: 4.0.8
+ utf-8-validate: 5.0.10
+
+ yaml@2.6.1: {}
+
+ yocto-queue@0.1.0: {}
+
+ zimmerframe@1.1.2: {}
+
+ zod-to-json-schema@3.24.1(zod@3.24.1):
+ dependencies:
+ zod: 3.24.1
+
+ zod@3.24.1: {}
+
+ zwitch@2.0.4: {}
diff --git a/examples/agent-kit-nextjs-langchain/postcss.config.js b/examples/agent-kit-nextjs-langchain/postcss.config.js
new file mode 100644
index 0000000..33ad091
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/postcss.config.js
@@ -0,0 +1,6 @@
+module.exports = {
+ plugins: {
+ tailwindcss: {},
+ autoprefixer: {},
+ },
+}
diff --git a/examples/agent-kit-nextjs-langchain/public/images/favicon.ico b/examples/agent-kit-nextjs-langchain/public/images/favicon.ico
new file mode 100644
index 0000000..4c29611
Binary files /dev/null and b/examples/agent-kit-nextjs-langchain/public/images/favicon.ico differ
diff --git a/examples/agent-kit-nextjs-langchain/public/images/og-image.png b/examples/agent-kit-nextjs-langchain/public/images/og-image.png
new file mode 100644
index 0000000..088b4bb
Binary files /dev/null and b/examples/agent-kit-nextjs-langchain/public/images/og-image.png differ
diff --git a/examples/agent-kit-nextjs-langchain/public/images/title-card.png b/examples/agent-kit-nextjs-langchain/public/images/title-card.png
new file mode 100644
index 0000000..f501bca
Binary files /dev/null and b/examples/agent-kit-nextjs-langchain/public/images/title-card.png differ
diff --git a/examples/agent-kit-nextjs-langchain/tailwind.config.js b/examples/agent-kit-nextjs-langchain/tailwind.config.js
new file mode 100644
index 0000000..8c4d1b2
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/tailwind.config.js
@@ -0,0 +1,18 @@
+/** @type {import('tailwindcss').Config} */
+module.exports = {
+ content: [
+ './pages/**/*.{js,ts,jsx,tsx,mdx}',
+ './components/**/*.{js,ts,jsx,tsx,mdx}',
+ './app/**/*.{js,ts,jsx,tsx,mdx}',
+ ],
+ theme: {
+ extend: {
+ backgroundImage: {
+ 'gradient-radial': 'radial-gradient(var(--tw-gradient-stops))',
+ 'gradient-conic':
+ 'conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))',
+ },
+ },
+ },
+ plugins: [],
+}
diff --git a/examples/agent-kit-nextjs-langchain/tsconfig.json b/examples/agent-kit-nextjs-langchain/tsconfig.json
new file mode 100644
index 0000000..23ba4fd
--- /dev/null
+++ b/examples/agent-kit-nextjs-langchain/tsconfig.json
@@ -0,0 +1,28 @@
+{
+ "compilerOptions": {
+ "target": "es5",
+ "lib": ["dom", "dom.iterable", "esnext"],
+ "allowJs": true,
+ "skipLibCheck": true,
+ "strict": true,
+ "forceConsistentCasingInFileNames": true,
+ "noEmit": true,
+ "esModuleInterop": true,
+ "module": "esnext",
+ "moduleResolution": "bundler",
+ "resolveJsonModule": true,
+ "isolatedModules": true,
+ "jsx": "preserve",
+ "incremental": true,
+ "plugins": [
+ {
+ "name": "next"
+ }
+ ],
+ "paths": {
+ "@/*": ["./*"]
+ }
+ },
+ "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+ "exclude": ["node_modules"]
+}