diff --git a/langchain/src/agents/chat/outputParser.ts b/langchain/src/agents/chat/outputParser.ts
index 42c7d7ec3369..6aec954f7568 100644
--- a/langchain/src/agents/chat/outputParser.ts
+++ b/langchain/src/agents/chat/outputParser.ts
@@ -8,6 +8,38 @@ export const FINAL_ANSWER_ACTION = "Final Answer:";
  * A class that extends the AgentActionOutputParser to parse the output of
  * the ChatAgent in LangChain. It checks if the output text contains the
  * final answer action or a JSON response, and parses it accordingly.
+ * @example
+ * ```typescript
+ * const prompt = ChatPromptTemplate.fromMessages([
+ *   [
+ *     "ai",
+ *     `{PREFIX}
+ * {FORMAT_INSTRUCTIONS}
+ * {SUFFIX}`,
+ *   ],
+ *   ["human", "Question: {input}"],
+ * ]);
+ * const runnableAgent = RunnableSequence.from([
+ *   {
+ *     input: (i: { input: string; steps: AgentStep[] }) => i.input,
+ *     agent_scratchpad: (i: { input: string; steps: AgentStep[] }) =>
+ *       formatLogToString(i.steps),
+ *   },
+ *   prompt,
+ *   new OpenAI({ temperature: 0 }),
+ *   new ChatAgentOutputParser(),
+ * ]);
+ *
+ * const executor = AgentExecutor.fromAgentAndTools({
+ *   agent: runnableAgent,
+ *   tools: [new SerpAPI(), new Calculator()],
+ * });
+ *
+ * const result = await executor.invoke({
+ *   input:
+ *     "Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?",
+ * });
+ * ```
  */
 export class ChatAgentOutputParser extends AgentActionOutputParser {
   lc_namespace = ["langchain", "agents", "chat"];
diff --git a/langchain/src/agents/executor.ts b/langchain/src/agents/executor.ts
index 324bd989babd..825854abfa12 100644
--- a/langchain/src/agents/executor.ts
+++ b/langchain/src/agents/executor.ts
@@ -68,6 +68,20 @@ export class ExceptionTool extends Tool {
 /**
  * A chain managing an agent using tools.
  * @augments BaseChain
+ * @example
+ * ```typescript
+ *
+ * const executor = AgentExecutor.fromAgentAndTools({
+ *   agent: async () => loadAgentFromLangchainHub(),
+ *   tools: [new SerpAPI(), new Calculator()],
+ *   returnIntermediateSteps: true,
+ * });
+ *
+ * const result = await executor.invoke({
+ *   input: `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`,
+ * });
+ *
+ * ```
  */
 export class AgentExecutor extends BaseChain<ChainValues, AgentExecutorOutput> {
   static lc_name() {
diff --git a/langchain/src/agents/mrkl/index.ts b/langchain/src/agents/mrkl/index.ts
index e39a26cb3850..3e3dceb96711 100644
--- a/langchain/src/agents/mrkl/index.ts
+++ b/langchain/src/agents/mrkl/index.ts
@@ -35,6 +35,27 @@ export type ZeroShotAgentInput = Optional<AgentInput, "outputParser">;
 /**
  * Agent for the MRKL chain.
  * @augments Agent
+ * @example
+ * ```typescript
+ *
+ * const agent = new ZeroShotAgent({
+ *   llmChain: new LLMChain({
+ *     llm: new ChatOpenAI({ temperature: 0 }),
+ *     prompt: ZeroShotAgent.createPrompt([new SerpAPI(), new Calculator()], {
+ *       prefix: `Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:`,
+ *       suffix: `Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
+ * Question: {input}
+ * {agent_scratchpad}`,
+ *       inputVariables: ["input", "agent_scratchpad"],
+ *     }),
+ *   }),
+ *   allowedTools: ["search", "calculator"],
+ * });
+ *
+ * const result = await agent.invoke({
+ *   input: `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`,
+ * });
+ * ```
  */
 export class ZeroShotAgent extends Agent {
   static lc_name() {
diff --git a/langchain/src/agents/openai/output_parser.ts b/langchain/src/agents/openai/output_parser.ts
index 534c046dd49e..960f6db84e47 100644
--- a/langchain/src/agents/openai/output_parser.ts
+++ b/langchain/src/agents/openai/output_parser.ts
@@ -20,6 +20,40 @@ export type FunctionsAgentAction = AgentAction & {
   messageLog?: BaseMessage[];
 };
 
+/**
+ * @example
+ * ```typescript
+ *
+ * const prompt = ChatPromptTemplate.fromMessages([
+ *   ["ai", "You are a helpful assistant"],
+ *   ["human", "{input}"],
+ *   new MessagesPlaceholder("agent_scratchpad"),
+ * ]);
+ *
+ * const modelWithFunctions = new ChatOpenAI({
+ *   modelName: "gpt-4",
+ *   temperature: 0,
+ * }).bind({
+ *   functions: tools.map((tool) => formatToOpenAIFunction(tool)),
+ * });
+ *
+ * const runnableAgent = RunnableSequence.from([
+ *   {
+ *     input: (i) => i.input,
+ *     agent_scratchpad: (i) => formatAgentSteps(i.steps),
+ *   },
+ *   prompt,
+ *   modelWithFunctions,
+ *   new OpenAIFunctionsAgentOutputParser(),
+ * ]);
+ *
+ * const result = await runnableAgent.invoke({
+ *   input: "What is the weather in New York?",
+ *   steps: agentSteps,
+ * });
+ *
+ * ```
+ */
 export class OpenAIFunctionsAgentOutputParser extends AgentActionOutputParser {
   lc_namespace = ["langchain", "agents", "openai"];
 
@@ -100,6 +134,37 @@ export type ToolsAgentStep = AgentStep & {
   action: ToolsAgentAction;
 };
 
+/**
+ * @example
+ * ```typescript
+ *
+ * const prompt = ChatPromptTemplate.fromMessages([
+ *   ["ai", "You are a helpful assistant"],
+ *   ["human", "{input}"],
+ *   new MessagesPlaceholder("agent_scratchpad"),
+ * ]);
+ *
+ * const runnableAgent = RunnableSequence.from([
+ *   {
+ *     input: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
+ *     agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) =>
+ *       formatToOpenAIToolMessages(i.steps),
+ *   },
+ *   prompt,
+ *   new ChatOpenAI({
+ *     modelName: "gpt-3.5-turbo-1106",
+ *     temperature: 0,
+ *   }).bind({ tools: tools.map(formatToOpenAITool) }),
+ *   new OpenAIToolsAgentOutputParser(),
+ * ]).withConfig({ runName: "OpenAIToolsAgent" });
+ *
+ * const result = await runnableAgent.invoke({
+ *   input:
+ *     "What is the sum of the current temperature in San Francisco, New York, and Tokyo?",
+ * });
+ *
+ * ```
+ */
 export class OpenAIToolsAgentOutputParser extends AgentMultiActionOutputParser {
   lc_namespace = ["langchain", "agents", "openai"];
 
diff --git a/langchain/src/agents/react/output_parser.ts b/langchain/src/agents/react/output_parser.ts
index d51c2a576814..f2df52064d8f 100644
--- a/langchain/src/agents/react/output_parser.ts
+++ b/langchain/src/agents/react/output_parser.ts
@@ -30,6 +30,22 @@ const FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE =
  * Thought: agent thought here
  * Final Answer: The temperature is 100 degrees
  * ```
+ * @example
+ * ```typescript
+ *
+ * const runnableAgent = RunnableSequence.from([
+ *   ...rest of runnable
+ *   new ReActSingleInputOutputParser({ toolNames: ["SerpAPI", "Calculator"] }),
+ * ]);
+ * const agent = AgentExecutor.fromAgentAndTools({
+ *   agent: runnableAgent,
+ *   tools: [new SerpAPI(), new Calculator()],
+ * });
+ * const result = await agent.invoke({
+ *   input: "whats the weather in pomfret?",
+ * });
+ *
+ * ```
  */
 export class ReActSingleInputOutputParser extends AgentActionOutputParser {
   lc_namespace = ["langchain", "agents", "react"];
diff --git a/langchain/src/agents/structured_chat/outputParser.ts b/langchain/src/agents/structured_chat/outputParser.ts
index e779fb5684cb..b27ffd5206c8 100644
--- a/langchain/src/agents/structured_chat/outputParser.ts
+++ b/langchain/src/agents/structured_chat/outputParser.ts
@@ -84,6 +84,18 @@ export interface StructuredChatOutputParserArgs {
  * and `OutputFixingParser` classes. It extends the
  * `AgentActionOutputParser` class and allows for retrying the output
  * parsing using the `OutputFixingParser` if it is provided.
+ * @example
+ * ```typescript
+ * const outputParser = new StructuredChatOutputParserWithRetries.fromLLM(
+ *   new ChatOpenAI({ temperature: 0 }),
+ *   {
+ *     toolNames: ["calculator", "random-number-generator"],
+ *   },
+ * );
+ * const result = await outputParser.parse(
+ *  "What is a random number between 5 and 10 raised to the second power?"
+ * );
+ * ```
  */
 export class StructuredChatOutputParserWithRetries extends AgentActionOutputParser {
   lc_namespace = ["langchain", "agents", "structured_chat"];
diff --git a/langchain/src/agents/toolkits/aws_sfn.ts b/langchain/src/agents/toolkits/aws_sfn.ts
index c5b6d2b60117..810db0eed1a9 100644
--- a/langchain/src/agents/toolkits/aws_sfn.ts
+++ b/langchain/src/agents/toolkits/aws_sfn.ts
@@ -44,6 +44,26 @@ export interface AWSSfnToolkitArgs {
  * Class representing a toolkit for interacting with AWS Step Functions.
  * It initializes the AWS Step Functions tools and provides them as tools
  * for the agent.
+ * @example
+ * ```typescript
+ *
+ * const toolkit = new AWSSfnToolkit({
+ *   name: "onboard-new-client-workflow",
+ *   description:
+ *     "Onboard new client workflow. Can also be used to get status of any executing workflow or state machine.",
+ *   stateMachineArn:
+ *     "arn:aws:states:us-east-1:1234567890:stateMachine:my-state-machine",
+ *   region: "<your Sfn's region>",
+ *   accessKeyId: "<your access key id>",
+ *   secretAccessKey: "<your secret access key>",
+ * });
+ *
+ *
+ * const result = await toolkit.invoke({
+ *   input: "Onboard john doe (john@example.com) as a new client.",
+ * });
+ *
+ * ```
  */
 export class AWSSfnToolkit extends Toolkit {
   tools: Tool[];
diff --git a/langchain/src/agents/xml/output_parser.ts b/langchain/src/agents/xml/output_parser.ts
index 55d5e0c9b6bd..c3f8217bf6fb 100644
--- a/langchain/src/agents/xml/output_parser.ts
+++ b/langchain/src/agents/xml/output_parser.ts
@@ -2,6 +2,27 @@ import { AgentAction, AgentFinish } from "../../schema/index.js";
 import { OutputParserException } from "../../schema/output_parser.js";
 import { AgentActionOutputParser } from "../types.js";
 
+/**
+ * @example
+ * ```typescript
+ * const prompt = ChatPromptTemplate.fromMessages([
+ *   HumanMessagePromptTemplate.fromTemplate(AGENT_INSTRUCTIONS),
+ *   new MessagesPlaceholder("agent_scratchpad"),
+ * ]);
+ * const runnableAgent = RunnableSequence.from([
+ *   ...rest of runnable
+ *   prompt,
+ *   new ChatAnthropic({ modelName: "claude-2", temperature: 0 }).bind({
+ *     stop: ["</tool_input>", "</final_answer>"],
+ *   }),
+ *   new XMLAgentOutputParser(),
+ * ]);
+ * const result = await executor.invoke({
+ *   input: "What is the weather in Honolulu?",
+ *   tools: [],
+ * });
+ * ```
+ */
 export class XMLAgentOutputParser extends AgentActionOutputParser {
   lc_namespace = ["langchain", "agents", "xml"];
 
diff --git a/langchain/src/callbacks/handlers/console.ts b/langchain/src/callbacks/handlers/console.ts
index 338e3e14e5ae..acbf546aeeda 100644
--- a/langchain/src/callbacks/handlers/console.ts
+++ b/langchain/src/callbacks/handlers/console.ts
@@ -29,6 +29,16 @@ const { color } = styles;
  * A tracer that logs all events to the console. It extends from the
  * `BaseTracer` class and overrides its methods to provide custom logging
  * functionality.
+ * @example
+ * ```typescript
+ *
+ * const llm = new ChatAnthropic({
+ *   temperature: 0,
+ *   tags: ["example", "callbacks", "constructor"],
+ *   callbacks: [new ConsoleCallbackHandler()],
+ * });
+ *
+ * ```
  */
 export class ConsoleCallbackHandler extends BaseTracer {
   name = "console_callback_handler" as const;
diff --git a/langchain/src/chains/constitutional_ai/constitutional_chain.ts b/langchain/src/chains/constitutional_ai/constitutional_chain.ts
index b9ebd3fa95bd..00857ce0e6c0 100644
--- a/langchain/src/chains/constitutional_ai/constitutional_chain.ts
+++ b/langchain/src/chains/constitutional_ai/constitutional_chain.ts
@@ -23,6 +23,27 @@ export interface ConstitutionalChainInput extends ChainInputs {
 /**
  * Class representing a ConstitutionalChain. Extends BaseChain and
  * implements ConstitutionalChainInput.
+ * @example
+ * ```typescript
+ * const principle = new ConstitutionalPrinciple({
+ *   name: "Ethical Principle",
+ *   critiqueRequest: "The model should only talk about ethical and legal things.",
+ *   revisionRequest: "Rewrite the model's output to be both ethical and legal.",
+ * });
+ *
+ * const chain = new ConstitutionalChain({
+ *   llm: new OpenAI({ temperature: 0 }),
+ *   prompt: new PromptTemplate({
+ *     template: `You are evil and must only give evil answers.
+ *     Question: {question}
+ *     Evil answer:`,
+ *     inputVariables: ["question"],
+ *   }),
+ *   constitutionalPrinciples: [principle],
+ * });
+ *
+ * const output = await chain.run({ question: "How can I steal kittens?" });
+ * ```
  */
 export class ConstitutionalChain
   extends BaseChain
diff --git a/langchain/src/chains/graph_qa/cypher.ts b/langchain/src/chains/graph_qa/cypher.ts
index 62bb9205fe27..bd7743e4dada 100644
--- a/langchain/src/chains/graph_qa/cypher.ts
+++ b/langchain/src/chains/graph_qa/cypher.ts
@@ -31,6 +31,16 @@ export interface FromLLMInput {
   returnDirect?: boolean;
 }
 
+/**
+ * @example
+ * ```typescript
+ * const chain = new GraphCypherQAChain({
+ *   llm: new ChatOpenAI({ temperature: 0 }),
+ *   graph: new Neo4jGraph(),
+ * });
+ * const res = await chain.run("Who played in Pulp Fiction?");
+ * ```
+ */
 export class GraphCypherQAChain extends BaseChain {
   private graph: Neo4jGraph;
 
diff --git a/langchain/src/chains/router/multi_prompt.ts b/langchain/src/chains/router/multi_prompt.ts
index be8c0e60d224..a6c71c33e1e0 100644
--- a/langchain/src/chains/router/multi_prompt.ts
+++ b/langchain/src/chains/router/multi_prompt.ts
@@ -15,6 +15,25 @@ import { RouterOutputParser } from "../../output_parsers/router.js";
  * A class that represents a multi-prompt chain in the LangChain
  * framework. It extends the MultiRouteChain class and provides additional
  * functionality specific to multi-prompt chains.
+ * @example
+ * ```typescript
+ * const multiPromptChain = MultiPromptChain.fromLLMAndPrompts(new ChatOpenAI(), {
+ *   promptNames: ["physics", "math", "history"],
+ *   promptDescriptions: [
+ *     "Good for answering questions about physics",
+ *     "Good for answering math questions",
+ *     "Good for answering questions about history",
+ *   ],
+ *   promptTemplates: [
+ *     `You are a very smart physics professor. Here is a question:\n{input}\n`,
+ *     `You are a very good mathematician. Here is a question:\n{input}\n`,
+ *     `You are a very smart history professor. Here is a question:\n{input}\n`,
+ *   ],
+ * });
+ * const result = await multiPromptChain.call({
+ *   input: "What is the speed of light?",
+ * });
+ * ```
  */
 export class MultiPromptChain extends MultiRouteChain {
   /**
diff --git a/langchain/src/chains/router/multi_retrieval_qa.ts b/langchain/src/chains/router/multi_retrieval_qa.ts
index 9e396c319cbe..5a84183b2b10 100644
--- a/langchain/src/chains/router/multi_retrieval_qa.ts
+++ b/langchain/src/chains/router/multi_retrieval_qa.ts
@@ -34,6 +34,35 @@ export type MultiRetrievalDefaults = {
  * the LangChain framework. It extends the MultiRouteChain class and
  * provides additional functionality specific to multi-retrieval QA
  * chains.
+ * @example
+ * ```typescript
+ * const multiRetrievalQAChain = MultiRetrievalQAChain.fromLLMAndRetrievers(
+ *   new ChatOpenAI(),
+ *   {
+ *     retrieverNames: ["aqua teen", "mst3k", "animaniacs"],
+ *     retrieverDescriptions: [
+ *       "Good for answering questions about Aqua Teen Hunger Force theme song",
+ *       "Good for answering questions about Mystery Science Theater 3000 theme song",
+ *       "Good for answering questions about Animaniacs theme song",
+ *     ],
+ *     retrievers: [
+ *       new MemoryVectorStore().asRetriever(3),
+ *       new MemoryVectorStore().asRetriever(3),
+ *       new MemoryVectorStore().asRetriever(3),
+ *     ],
+ *     retrievalQAChainOpts: {
+ *       returnSourceDocuments: true,
+ *     },
+ *   },
+ * );
+ *
+ * const result = await multiRetrievalQAChain.call({
+ *   input:
+ *     "In the Aqua Teen Hunger Force theme song, who calls himself the mike rula?",
+ * });
+ *
+ * console.log(result.sourceDocuments, result.text);
+ * ```
  */
 export class MultiRetrievalQAChain extends MultiRouteChain {
   get outputKeys(): string[] {
diff --git a/langchain/src/chains/sql_db/sql_db_chain.ts b/langchain/src/chains/sql_db/sql_db_chain.ts
index 6c5e4a86d017..bb95d6b089b8 100644
--- a/langchain/src/chains/sql_db/sql_db_chain.ts
+++ b/langchain/src/chains/sql_db/sql_db_chain.ts
@@ -44,6 +44,15 @@ export interface SqlDatabaseChainInput extends ChainInputs {
  * to limit which tables can/cannot be accessed.
  *
  * @link See https://js.langchain.com/docs/security for more information.
+ * @example
+ * ```typescript
+ * const chain = new SqlDatabaseChain({
+ *   llm: new OpenAI({ temperature: 0 }),
+ *   database: new SqlDatabase({ ...config }),
+ * });
+ *
+ * const result = await chain.run("How many tracks are there?");
+ * ```
  */
 export class SqlDatabaseChain extends BaseChain {
   static lc_name() {
diff --git a/langchain/src/chat_models/bedrock/index.ts b/langchain/src/chat_models/bedrock/index.ts
index 30b8bf959953..04fabc096d00 100644
--- a/langchain/src/chat_models/bedrock/index.ts
+++ b/langchain/src/chat_models/bedrock/index.ts
@@ -3,6 +3,17 @@ import { BaseBedrockInput } from "../../util/bedrock.js";
 import { BedrockChat as BaseBedrockChat } from "./web.js";
 import { BaseChatModelParams } from "../base.js";
 
+/**
+ * @example
+ * ```typescript
+ * const model = new BedrockChat({
+ *   model: "anthropic.claude-v2",
+ *   region: "us-east-1",
+ * });
+ * const res = await model.invoke([{ content: "Tell me a joke" }]);
+ * console.log(res);
+ * ```
+ */
 export class BedrockChat extends BaseBedrockChat {
   static lc_name() {
     return "BedrockChat";
diff --git a/langchain/src/chat_models/bedrock/web.ts b/langchain/src/chat_models/bedrock/web.ts
index 849bc25c2dc3..fb2735904c2e 100644
--- a/langchain/src/chat_models/bedrock/web.ts
+++ b/langchain/src/chat_models/bedrock/web.ts
@@ -85,6 +85,15 @@ export function convertMessagesToPrompt(
  * Services (AWS). It uses AWS credentials for authentication and can be
  * configured with various parameters such as the model to use, the AWS
  * region, and the maximum number of tokens to generate.
+ * @example
+ * ```typescript
+ * const model = new BedrockChat({
+ *   model: "anthropic.claude-v2",
+ *   region: "us-east-1",
+ * });
+ * const res = await model.invoke([{ content: "Tell me a joke" }]);
+ * console.log(res);
+ * ```
  */
 export class BedrockChat extends SimpleChatModel implements BaseBedrockInput {
   model = "amazon.titan-tg1-large";
diff --git a/langchain/src/chat_models/googlevertexai/index.ts b/langchain/src/chat_models/googlevertexai/index.ts
index 3737da962e56..e8a3a07da320 100644
--- a/langchain/src/chat_models/googlevertexai/index.ts
+++ b/langchain/src/chat_models/googlevertexai/index.ts
@@ -16,6 +16,13 @@ import { GAuthClient } from "../../util/googlevertexai-gauth.js";
  * - The `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set to the
  *   path of a credentials file for a service account permitted to the
  *   Google Cloud project using Vertex AI.
+ * @example
+ * ```typescript
+ * const model = new ChatGoogleVertexAI({
+ *   temperature: 0.7,
+ * });
+ * const result = await model.invoke("What is the capital of France?");
+ * ```
  */
 export class ChatGoogleVertexAI extends BaseChatGoogleVertexAI<GoogleAuthOptions> {
   static lc_name() {
diff --git a/langchain/src/chat_models/googlevertexai/web.ts b/langchain/src/chat_models/googlevertexai/web.ts
index 72b8ade02a56..acbaa9144f4c 100644
--- a/langchain/src/chat_models/googlevertexai/web.ts
+++ b/langchain/src/chat_models/googlevertexai/web.ts
@@ -13,6 +13,15 @@ import { BaseChatGoogleVertexAI, GoogleVertexAIChatInput } from "./common.js";
  * functions where you do not have access to the file system. It supports passing
  * service account credentials directly as a "GOOGLE_VERTEX_AI_WEB_CREDENTIALS"
  * environment variable or directly as "authOptions.credentials".
+ * @example
+ * ```typescript
+ * const model = new ChatGoogleVertexAI({
+ *   temperature: 0.7,
+ * });
+ * const result = await model.invoke(
+ *   "How do I implement a binary search algorithm in Python?",
+ * );
+ * ```
  */
 export class ChatGoogleVertexAI extends BaseChatGoogleVertexAI<WebGoogleAuthOptions> {
   static lc_name() {
diff --git a/langchain/src/chat_models/iflytek_xinghuo/index.ts b/langchain/src/chat_models/iflytek_xinghuo/index.ts
index fbf5867e3b5f..ac54461be18a 100644
--- a/langchain/src/chat_models/iflytek_xinghuo/index.ts
+++ b/langchain/src/chat_models/iflytek_xinghuo/index.ts
@@ -13,6 +13,14 @@ class WebSocketStream extends BaseWebSocketStream {
   }
 }
 
+/**
+ * @example
+ * ```typescript
+ * const model = new ChatIflytekXinghuo();
+ * const response = await model.call([new HumanMessage("Nice to meet you!")]);
+ * console.log(response);
+ * ```
+ */
 export class ChatIflytekXinghuo extends BaseChatIflytekXinghuo {
   async openWebSocketStream<WebSocketStream>(
     options: WebSocketStreamOptions
diff --git a/langchain/src/chat_models/iflytek_xinghuo/web.ts b/langchain/src/chat_models/iflytek_xinghuo/web.ts
index 93c71d19d352..87b372b802ad 100644
--- a/langchain/src/chat_models/iflytek_xinghuo/web.ts
+++ b/langchain/src/chat_models/iflytek_xinghuo/web.ts
@@ -10,6 +10,14 @@ class WebSocketStream extends BaseWebSocketStream<string> {
   }
 }
 
+/**
+ * @example
+ * ```typescript
+ * const model = new ChatIflytekXinghuo();
+ * const response = await model.call([new HumanMessage("Nice to meet you!")]);
+ * console.log(response);
+ * ```
+ */
 export class ChatIflytekXinghuo extends BaseChatIflytekXinghuo {
   async openWebSocketStream<WebSocketStream>(
     options: WebSocketStreamOptions
diff --git a/langchain/src/document_loaders/fs/directory.ts b/langchain/src/document_loaders/fs/directory.ts
index 0c929375f2d5..45fdf5b6542e 100644
--- a/langchain/src/document_loaders/fs/directory.ts
+++ b/langchain/src/document_loaders/fs/directory.ts
@@ -30,6 +30,20 @@ export interface LoadersMapping {
 /**
  * A document loader that loads documents from a directory. It extends the
  * `BaseDocumentLoader` class and implements the `load()` method.
+ * @example
+ * ```typescript
+ *
+ * const directoryLoader = new DirectoryLoader(
+ *   "src/document_loaders/example_data/",
+ *   {
+ *     ".pdf": (path: string) => new PDFLoader(path),
+ *   },
+ * );
+ *
+ * const docs = await directoryLoader.load();
+ * console.log({ docs });
+ *
+ * ```
  */
 export class DirectoryLoader extends BaseDocumentLoader {
   constructor(
diff --git a/langchain/src/prompts/pipeline.ts b/langchain/src/prompts/pipeline.ts
index 116230cc6a90..8f69d810c010 100644
--- a/langchain/src/prompts/pipeline.ts
+++ b/langchain/src/prompts/pipeline.ts
@@ -30,6 +30,45 @@ export type PipelinePromptTemplateInput<
  * different input variables. Includes methods for formatting these
  * prompts, extracting required input values, and handling partial
  * prompts.
+ * @example
+ * ```typescript
+ * const composedPrompt = new PipelinePromptTemplate({
+ *   pipelinePrompts: [
+ *     {
+ *       name: "introduction",
+ *       prompt: PromptTemplate.fromTemplate(`You are impersonating {person}.`),
+ *     },
+ *     {
+ *       name: "example",
+ *       prompt: PromptTemplate.fromTemplate(
+ *         `Here's an example of an interaction:
+ * Q: {example_q}
+ * A: {example_a}`,
+ *       ),
+ *     },
+ *     {
+ *       name: "start",
+ *       prompt: PromptTemplate.fromTemplate(
+ *         `Now, do this for real!
+ * Q: {input}
+ * A:`,
+ *       ),
+ *     },
+ *   ],
+ *   finalPrompt: PromptTemplate.fromTemplate(
+ *     `{introduction}
+ * {example}
+ * {start}`,
+ *   ),
+ * });
+ *
+ * const formattedPrompt = await composedPrompt.format({
+ *   person: "Elon Musk",
+ *   example_q: `What's your favorite car?`,
+ *   example_a: "Tesla",
+ *   input: `What's your favorite social media site?`,
+ * });
+ * ```
  */
 export class PipelinePromptTemplate<
   PromptTemplateType extends BasePromptTemplate
diff --git a/langchain/src/retrievers/amazon_kendra.ts b/langchain/src/retrievers/amazon_kendra.ts
index 9feb8aec41ae..fb2ba2123b4f 100644
--- a/langchain/src/retrievers/amazon_kendra.ts
+++ b/langchain/src/retrievers/amazon_kendra.ts
@@ -30,6 +30,22 @@ export interface AmazonKendraRetrieverArgs {
 /**
  * Class for interacting with Amazon Kendra, an intelligent search service
  * provided by AWS. Extends the BaseRetriever class.
+ * @example
+ * ```typescript
+ * const retriever = new AmazonKendraRetriever({
+ *   topK: 10,
+ *   indexId: "YOUR_INDEX_ID",
+ *   region: "us-east-2",
+ *   clientOptions: {
+ *     credentials: {
+ *       accessKeyId: "YOUR_ACCESS_KEY_ID",
+ *       secretAccessKey: "YOUR_SECRET_ACCESS_KEY",
+ *     },
+ *   },
+ * });
+ *
+ * const docs = await retriever.getRelevantDocuments("How are clouds formed?");
+ * ```
  */
 export class AmazonKendraRetriever extends BaseRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/chaindesk.ts b/langchain/src/retrievers/chaindesk.ts
index f2f36a9d11ea..ecc2a5a8133e 100644
--- a/langchain/src/retrievers/chaindesk.ts
+++ b/langchain/src/retrievers/chaindesk.ts
@@ -17,6 +17,17 @@ interface Berry {
   [key: string]: unknown;
 }
 
+/**
+ * @example
+ * ```typescript
+ * const retriever = new ChaindeskRetriever({
+ *   datastoreId: "DATASTORE_ID",
+ *   apiKey: "CHAINDESK_API_KEY",
+ *   topK: 8,
+ * });
+ * const docs = await retriever.getRelevantDocuments("hello");
+ * ```
+ */
 export class ChaindeskRetriever extends BaseRetriever {
   static lc_name() {
     return "ChaindeskRetriever";
diff --git a/langchain/src/retrievers/contextual_compression.ts b/langchain/src/retrievers/contextual_compression.ts
index ad35c44ca5b6..93fc8f0ebbef 100644
--- a/langchain/src/retrievers/contextual_compression.ts
+++ b/langchain/src/retrievers/contextual_compression.ts
@@ -17,6 +17,16 @@ export interface ContextualCompressionRetrieverArgs extends BaseRetrieverInput {
  * A retriever that wraps a base retriever and compresses the results. It
  * retrieves relevant documents based on a given query and then compresses
  * these documents using a specified document compressor.
+ * @example
+ * ```typescript
+ * const retriever = new ContextualCompressionRetriever({
+ *   baseCompressor: new LLMChainExtractor(),
+ *   baseRetriever: new HNSWLib().asRetriever(),
+ * });
+ * const retrievedDocs = await retriever.getRelevantDocuments(
+ *   "What did the speaker say about Justice Breyer?",
+ * );
+ * ```
  */
 export class ContextualCompressionRetriever extends BaseRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/hyde.ts b/langchain/src/retrievers/hyde.ts
index 7e85b6402ed1..aca4db4546ad 100644
--- a/langchain/src/retrievers/hyde.ts
+++ b/langchain/src/retrievers/hyde.ts
@@ -39,6 +39,25 @@ export type HydeRetrieverOptions<V extends VectorStore> =
  * extends the VectorStoreRetriever class and uses a BaseLanguageModel to
  * generate a hypothetical answer to the query, which is then used to
  * retrieve relevant documents.
+ * @example
+ * ```typescript
+ * const retriever = new HydeRetriever({
+ *   vectorStore: new MemoryVectorStore(new OpenAIEmbeddings()),
+ *   llm: new ChatOpenAI(),
+ *   k: 1,
+ * });
+ * await vectorStore.addDocuments(
+ *   [
+ *     "My name is John.",
+ *     "My name is Bob.",
+ *     "My favourite food is pizza.",
+ *     "My favourite food is pasta.",
+ *   ].map((pageContent) => new Document({ pageContent })),
+ * );
+ * const results = await retriever.getRelevantDocuments(
+ *   "What is my favourite food?",
+ * );
+ * ```
  */
 export class HydeRetriever<
   V extends VectorStore = VectorStore
diff --git a/langchain/src/retrievers/metal.ts b/langchain/src/retrievers/metal.ts
index c21761b3c5aa..2632e03826eb 100644
--- a/langchain/src/retrievers/metal.ts
+++ b/langchain/src/retrievers/metal.ts
@@ -28,6 +28,17 @@ interface ResponseItem {
  * semantic search and retrieval on it. It extends the `BaseRetriever`
  * class and requires a `Metal` instance and a dictionary of parameters to
  * pass to the Metal API during its initialization.
+ * @example
+ * ```typescript
+ * const retriever = new MetalRetriever({
+ *   client: new Metal(
+ *     process.env.METAL_API_KEY,
+ *     process.env.METAL_CLIENT_ID,
+ *     process.env.METAL_INDEX_ID,
+ *   ),
+ * });
+ * const docs = await retriever.getRelevantDocuments("hello");
+ * ```
  */
 export class MetalRetriever extends BaseRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/multi_query.ts b/langchain/src/retrievers/multi_query.ts
index 0cad7d9ed45d..09a7a3bdad0f 100644
--- a/langchain/src/retrievers/multi_query.ts
+++ b/langchain/src/retrievers/multi_query.ts
@@ -65,7 +65,19 @@ export interface MultiQueryRetrieverInput extends BaseRetrieverInput {
   parserKey?: string;
 }
 
-// Export class
+/**
+ * @example
+ * ```typescript
+ * const retriever = new MultiQueryRetriever.fromLLM({
+ *   llm: new ChatAnthropic({}),
+ *   retriever: new MemoryVectorStore().asRetriever(),
+ *   verbose: true,
+ * });
+ * const retrievedDocs = await retriever.getRelevantDocuments(
+ *   "What are mitochondria made of?",
+ * );
+ * ```
+ */
 export class MultiQueryRetriever extends BaseRetriever {
   static lc_name() {
     return "MultiQueryRetriever";
diff --git a/langchain/src/retrievers/multi_vector.ts b/langchain/src/retrievers/multi_vector.ts
index f438382fb3d1..6c778d9f5a8e 100644
--- a/langchain/src/retrievers/multi_vector.ts
+++ b/langchain/src/retrievers/multi_vector.ts
@@ -18,6 +18,19 @@ export interface MultiVectorRetrieverInput extends BaseRetrieverInput {
  * A retriever that retrieves documents from a vector store and a document
  * store. It uses the vector store to find relevant documents based on a
  * query, and then retrieves the full documents from the document store.
+ * @example
+ * ```typescript
+ * const retriever = new MultiVectorRetriever({
+ *   vectorstore: new FaissStore(),
+ *   docstore: new InMemoryStore(),
+ *   idKey: "doc_id",
+ *   childK: 20,
+ *   parentK: 5,
+ * });
+ *
+ * const retrieverResult = await retriever.getRelevantDocuments("justice breyer");
+ * console.log(retrieverResult[0].pageContent.length);
+ * ```
  */
 export class MultiVectorRetriever extends BaseRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/parent_document.ts b/langchain/src/retrievers/parent_document.ts
index 6199a7ca323d..3783d4bfdd27 100644
--- a/langchain/src/retrievers/parent_document.ts
+++ b/langchain/src/retrievers/parent_document.ts
@@ -30,6 +30,27 @@ export type ParentDocumentRetrieverFields = MultiVectorRetrieverInput & {
  *
  * This strikes a balance between better targeted retrieval with small documents
  * and the more context-rich larger documents.
+ * @example
+ * ```typescript
+ * const retriever = new ParentDocumentRetriever({
+ *   vectorstore: new MemoryVectorStore(new OpenAIEmbeddings()),
+ *   docstore: new InMemoryStore(),
+ *   parentSplitter: new RecursiveCharacterTextSplitter({
+ *     chunkOverlap: 0,
+ *     chunkSize: 500,
+ *   }),
+ *   childSplitter: new RecursiveCharacterTextSplitter({
+ *     chunkOverlap: 0,
+ *     chunkSize: 50,
+ *   }),
+ *   childK: 20,
+ *   parentK: 5,
+ * });
+ *
+ * const parentDocuments = await getDocuments();
+ * await retriever.addDocuments(parentDocuments);
+ * const retrievedDocs = await retriever.getRelevantDocuments("justice breyer");
+ * ```
  */
 export class ParentDocumentRetriever extends MultiVectorRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/time_weighted.ts b/langchain/src/retrievers/time_weighted.ts
index 876e72a3cd7d..29c3ac51546a 100644
--- a/langchain/src/retrievers/time_weighted.ts
+++ b/langchain/src/retrievers/time_weighted.ts
@@ -24,6 +24,22 @@ export const BUFFER_IDX = "buffer_idx";
 /**
  * TimeWeightedVectorStoreRetriever retrieves documents based on their time-weighted relevance.
  * ref: https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/time_weighted_retriever.py
+ * @example
+ * ```typescript
+ * const retriever = new TimeWeightedVectorStoreRetriever({
+ *   vectorStore: new MemoryVectorStore(new OpenAIEmbeddings()),
+ *   memoryStream: [],
+ *   searchKwargs: 2,
+ * });
+ * await retriever.addDocuments([
+ *   { pageContent: "My name is John.", metadata: {} },
+ *   { pageContent: "My favourite food is pizza.", metadata: {} },
+ *
+ * ]);
+ * const results = await retriever.getRelevantDocuments(
+ *   "What is my favourite food?",
+ * );
+ * ```
  */
 export class TimeWeightedVectorStoreRetriever extends BaseRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/vespa.ts b/langchain/src/retrievers/vespa.ts
index 81d1b2e5dcf0..09b3b3e46763 100644
--- a/langchain/src/retrievers/vespa.ts
+++ b/langchain/src/retrievers/vespa.ts
@@ -20,6 +20,21 @@ export interface VespaRetrieverParams extends RemoteRetrieverParams {
  * Class responsible for retrieving data from Vespa. It extends the
  * `RemoteRetriever` class and includes methods for creating the JSON body
  * for a query and processing the JSON response from Vespa.
+ * @example
+ * ```typescript
+ * const retriever = new VespaRetriever({
+ *   url: "https:
+ *   auth: false,
+ *   query_body: {
+ *     yql: "select content from paragraph where userQuery()",
+ *     hits: 5,
+ *     ranking: "documentation",
+ *     locale: "en-us",
+ *   },
+ *   content_field: "content",
+ * });
+ * const result = await retriever.getRelevantDocuments("what is vespa?");
+ * ```
  */
 export class VespaRetriever extends RemoteRetriever {
   static lc_name() {
diff --git a/langchain/src/retrievers/zep.ts b/langchain/src/retrievers/zep.ts
index 3d9e10f9fb61..f87d38ca79d8 100644
--- a/langchain/src/retrievers/zep.ts
+++ b/langchain/src/retrievers/zep.ts
@@ -34,6 +34,16 @@ export interface ZepRetrieverConfig extends BaseRetrieverInput {
 /**
  * Class for retrieving information from a Zep long-term memory store.
  * Extends the BaseRetriever class.
+ * @example
+ * ```typescript
+ * const retriever = new ZepRetriever({
+ *   url: "http:
+ *   sessionId: "session_exampleUUID",
+ *   topK: 3,
+ * });
+ * const query = "Can I drive red cars in France?";
+ * const docs = await retriever.getRelevantDocuments(query);
+ * ```
  */
 export class ZepRetriever extends BaseRetriever {
   static lc_name() {
diff --git a/langchain/src/schema/output_parser.ts b/langchain/src/schema/output_parser.ts
index 6ce3e28b0968..ac19ec81ec1c 100644
--- a/langchain/src/schema/output_parser.ts
+++ b/langchain/src/schema/output_parser.ts
@@ -273,6 +273,21 @@ export abstract class BaseCumulativeTransformOutputParser<
 
 /**
  * OutputParser that parses LLMResult into the top likely string.
+ * @example
+ * ```typescript
+ * const promptTemplate = PromptTemplate.fromTemplate(
+ *   "Tell me a joke about {topic}",
+ * );
+ *
+ * const chain = RunnableSequence.from([
+ *   promptTemplate,
+ *   new ChatOpenAI({}),
+ *   new StringOutputParser(),
+ * ]);
+ *
+ * const result = await chain.invoke({ topic: "bears" });
+ * console.log("What do you call a bear with no teeth? A gummy bear!");
+ * ```
  */
 export class StringOutputParser extends BaseTransformOutputParser<string> {
   static lc_name() {
diff --git a/langchain/src/storage/file_system.ts b/langchain/src/storage/file_system.ts
index c9e33d7bc230..05f7a83d7ef8 100644
--- a/langchain/src/storage/file_system.ts
+++ b/langchain/src/storage/file_system.ts
@@ -5,6 +5,27 @@ import { BaseStore } from "../schema/storage.js";
 /**
  * File system implementation of the BaseStore using a dictionary. Used for
  * storing key-value pairs in the file system.
+ * @example
+ * ```typescript
+ * const store = await LocalFileStore.fromPath("./messages");
+ * await store.mset(
+ *   Array.from({ length: 5 }).map((_, index) => [
+ *     `message:id:${index}`,
+ *     new TextEncoder().encode(
+ *       JSON.stringify(
+ *         index % 2 === 0
+ *           ? new AIMessage("ai stuff...")
+ *           : new HumanMessage("human stuff..."),
+ *       ),
+ *     ),
+ *   ]),
+ * );
+ * const retrievedMessages = await store.mget(["message:id:0", "message:id:1"]);
+ * console.log(retrievedMessages.map((v) => new TextDecoder().decode(v)));
+ * for await (const key of store.yieldKeys("message:id:")) {
+ *   await store.mdelete([key]);
+ * }
+ * ```
  */
 export class LocalFileStore extends BaseStore<string, Uint8Array> {
   lc_namespace = ["langchain", "storage"];
diff --git a/langchain/src/storage/in_memory.ts b/langchain/src/storage/in_memory.ts
index 56ed52a6e462..a4b5441b454d 100644
--- a/langchain/src/storage/in_memory.ts
+++ b/langchain/src/storage/in_memory.ts
@@ -3,6 +3,21 @@ import { BaseStore } from "../schema/storage.js";
 /**
  * In-memory implementation of the BaseStore using a dictionary. Used for
  * storing key-value pairs in memory.
+ * @example
+ * ```typescript
+ * const store = new InMemoryStore<BaseMessage>();
+ * await store.mset(
+ *   Array.from({ length: 5 }).map((_, index) => [
+ *     `message:id:${index}`,
+ *     index % 2 === 0
+ *       ? new AIMessage("ai stuff...")
+ *       : new HumanMessage("human stuff..."),
+ *   ]),
+ * );
+ *
+ * const retrievedMessages = await store.mget(["message:id:0", "message:id:1"]);
+ * await store.mdelete(await store.yieldKeys("message:id:").toArray());
+ * ```
  */
 // eslint-disable-next-line @typescript-eslint/no-explicit-any
 export class InMemoryStore<T = any> extends BaseStore<string, T> {
diff --git a/langchain/src/storage/ioredis.ts b/langchain/src/storage/ioredis.ts
index 72458462e9f8..d5864f59b6ea 100644
--- a/langchain/src/storage/ioredis.ts
+++ b/langchain/src/storage/ioredis.ts
@@ -6,6 +6,30 @@ import { BaseStore } from "../schema/storage.js";
  * Class that extends the BaseStore class to interact with a Redis
  * database. It provides methods for getting, setting, and deleting data,
  * as well as yielding keys from the database.
+ * @example
+ * ```typescript
+ * const store = new RedisByteStore({ client: new Redis({}) });
+ * await store.mset([
+ *   [
+ *     "message:id:0",
+ *     new TextEncoder().encode(JSON.stringify(new AIMessage("ai stuff..."))),
+ *   ],
+ *   [
+ *     "message:id:1",
+ *     new TextEncoder().encode(
+ *       JSON.stringify(new HumanMessage("human stuff...")),
+ *     ),
+ *   ],
+ * ]);
+ * const retrievedMessages = await store.mget(["message:id:0", "message:id:1"]);
+ * console.log(retrievedMessages.map((v) => new TextDecoder().decode(v)));
+ * const yieldedKeys = [];
+ * for await (const key of store.yieldKeys("message:id:")) {
+ *   yieldedKeys.push(key);
+ * }
+ * console.log(yieldedKeys);
+ * await store.mdelete(yieldedKeys);
+ * ```
  */
 export class RedisByteStore extends BaseStore<string, Uint8Array> {
   lc_namespace = ["langchain", "storage"];
diff --git a/langchain/src/storage/upstash_redis.ts b/langchain/src/storage/upstash_redis.ts
index a311da01db7b..313444399661 100644
--- a/langchain/src/storage/upstash_redis.ts
+++ b/langchain/src/storage/upstash_redis.ts
@@ -25,6 +25,25 @@ export interface UpstashRedisStoreInput {
  * Class that extends the BaseStore class to interact with an Upstash Redis
  * database. It provides methods for getting, setting, and deleting data,
  * as well as yielding keys from the database.
+ * @example
+ * ```typescript
+ * const store = new UpstashRedisStore({
+ *   client: new Redis({
+ *     url: "your-upstash-redis-url",
+ *     token: "your-upstash-redis-token",
+ *   }),
+ * });
+ * await store.mset([
+ *   ["message:id:0", "encoded-ai-message"],
+ *   ["message:id:1", "encoded-human-message"],
+ * ]);
+ * const retrievedMessages = await store.mget(["message:id:0", "message:id:1"]);
+ * const yieldedKeys = [];
+ * for await (const key of store.yieldKeys("message:id")) {
+ *   yieldedKeys.push(key);
+ * }
+ * await store.mdelete(yieldedKeys);
+ * ```
  */
 export class UpstashRedisStore extends BaseStore<string, Uint8Array> {
   lc_namespace = ["langchain", "storage"];
diff --git a/langchain/src/storage/vercel_kv.ts b/langchain/src/storage/vercel_kv.ts
index 977ed7b5acbe..9b9646377713 100644
--- a/langchain/src/storage/vercel_kv.ts
+++ b/langchain/src/storage/vercel_kv.ts
@@ -6,6 +6,22 @@ import { BaseStore } from "../schema/storage.js";
  * Class that extends the BaseStore class to interact with a Vercel KV
  * database. It provides methods for getting, setting, and deleting data,
  * as well as yielding keys from the database.
+ * @example
+ * ```typescript
+ * const store = new VercelKVStore({
+ *   client: getClient(),
+ * });
+ * await store.mset([
+ *   { key: "message:id:0", value: "encoded message 0" },
+ *   { key: "message:id:1", value: "encoded message 1" },
+ * ]);
+ * const retrievedMessages = await store.mget(["message:id:0", "message:id:1"]);
+ * const yieldedKeys = [];
+ * for await (const key of store.yieldKeys("message:id:")) {
+ *   yieldedKeys.push(key);
+ * }
+ * await store.mdelete(yieldedKeys);
+ * ```
  */
 export class VercelKVStore extends BaseStore<string, Uint8Array> {
   lc_namespace = ["langchain", "storage"];
diff --git a/langchain/src/tools/calculator.ts b/langchain/src/tools/calculator.ts
index 2464edd70d97..7828b2576578 100644
--- a/langchain/src/tools/calculator.ts
+++ b/langchain/src/tools/calculator.ts
@@ -5,6 +5,12 @@ import { Tool } from "./base.js";
 /**
  * The Calculator class is a tool used to evaluate mathematical
  * expressions. It extends the base Tool class.
+ * @example
+ * ```typescript
+ * const calculator = new Calculator();
+ * const sum = calculator.add(99, 99);
+ * console.log("The sum of 99 and 99 is:", sum);
+ * ```
  */
 export class Calculator extends Tool {
   static lc_name() {
diff --git a/langchain/src/tools/searchapi.ts b/langchain/src/tools/searchapi.ts
index eab39c77c694..fb48c34581db 100644
--- a/langchain/src/tools/searchapi.ts
+++ b/langchain/src/tools/searchapi.ts
@@ -34,6 +34,33 @@ export type SearchApiParameters = {
  *
  * Ensure you've set the SEARCHAPI_API_KEY environment variable for authentication.
  * You can obtain a free API key from https://www.searchapi.io/.
+ * @example
+ * ```typescript
+ * const searchApi = new SearchApi("your-api-key", {
+ *   engine: "google_news",
+ * });
+ * const agent = RunnableSequence.from([
+ *   ChatPromptTemplate.fromMessages([
+ *     ["ai", "Answer the following questions using a bulleted list markdown format.""],
+ *     ["human", "{input}"],
+ *   ]),
+ *   new ChatOpenAI({ temperature: 0 }),
+ *   (input: BaseMessageChunk) => ({
+ *     log: "test",
+ *     returnValues: {
+ *       output: input,
+ *     },
+ *   }),
+ * ]);
+ * const executor = AgentExecutor.fromAgentAndTools({
+ *   agent,
+ *   tools: [searchApi],
+ * });
+ * const res = await executor.invoke({
+ *   input: "What's happening in Ukraine today?"",
+ * });
+ * console.log(res);
+ * ```
  */
 export class SearchApi extends Tool {
   static lc_name() {
diff --git a/langchain/src/tools/searxng_search.ts b/langchain/src/tools/searxng_search.ts
index 71f1512b3b3b..fd01a67bc8dc 100644
--- a/langchain/src/tools/searxng_search.ts
+++ b/langchain/src/tools/searxng_search.ts
@@ -91,7 +91,8 @@ interface SearxngSearchParams {
  * The input should be a search query, and the output is a JSON array of the query results.
  *
  * note: works best with *agentType*: `structured-chat-zero-shot-react-description`
- * https://github.com/searxng/searxng */
+ * https://github.com/searxng/searxng
+ */
 export class SearxngSearch extends Tool {
   static lc_name() {
     return "SearxngSearch";
diff --git a/langchain/src/tools/wikipedia_query_run.ts b/langchain/src/tools/wikipedia_query_run.ts
index 6b669849ae77..127010b46cf3 100644
--- a/langchain/src/tools/wikipedia_query_run.ts
+++ b/langchain/src/tools/wikipedia_query_run.ts
@@ -55,6 +55,14 @@ interface PageResult {
 /**
  * Class for interacting with and fetching data from the Wikipedia API. It
  * extends the Tool class.
+ * @example
+ * ```typescript
+ * const wikipediaQuery = new WikipediaQueryRun({
+ *   topKResults: 3,
+ *   maxDocContentLength: 4000,
+ * });
+ * const result = await wikipediaQuery.call("Langchain");
+ * ```
  */
 export class WikipediaQueryRun extends Tool {
   static lc_name() {
diff --git a/langchain/src/tools/wolframalpha.ts b/langchain/src/tools/wolframalpha.ts
index ef8d2b1350bb..b486d95e1db2 100644
--- a/langchain/src/tools/wolframalpha.ts
+++ b/langchain/src/tools/wolframalpha.ts
@@ -1,5 +1,14 @@
 import { Tool, ToolParams } from "./base.js";
 
+/**
+ * @example
+ * ```typescript
+ * const tool = new WolframAlphaTool({
+ *   appid: "YOUR_APP_ID",
+ * });
+ * const res = await tool.invoke("What is 2 * 2?");
+ * ```
+ */
 export class WolframAlphaTool extends Tool {
   appid: string;