Model Guide

import { initChatModel } from "langchain";

// Initialize with provider:model format
const model = initChatModel("openai:gpt-4o");

import { initChatModel } from "langchain";

const model = initChatModel("openai:gpt-4o", {
  temperature: 0.7,
  maxTokens: 1000,
  timeout: 30000,
});

import { createAgent, initChatModel } from "langchain";

// Option 1: Pass string directly
const agent1 = createAgent({
  model: "openai:gpt-4o",
  tools: [],
});

// Option 2: Initialize model first
const model = initChatModel("anthropic:claude-3-5-sonnet-20241022", {
  temperature: 0.5,
});

const agent2 = createAgent({
  model: model,
  tools: [],
});

const gpt4o = initChatModel("openai:gpt-4o");
const gpt4oMini = initChatModel("openai:gpt-4o-mini");
const gpt4Turbo = initChatModel("openai:gpt-4-turbo");
const gpt35 = initChatModel("openai:gpt-3.5-turbo");

const sonnet = initChatModel("anthropic:claude-3-5-sonnet-20241022");
const haiku = initChatModel("anthropic:claude-3-5-haiku-20241022");
const opus = initChatModel("anthropic:claude-3-opus-20240229");

// Vertex AI
const vertexPro = initChatModel("google-vertexai:gemini-1.5-pro");
const vertexFlash = initChatModel("google-vertexai:gemini-1.5-flash");

// Vertex AI (web)
const vertexWeb = initChatModel("google-vertexai-web:gemini-1.5-pro");

// Generative AI
const genaiPro = initChatModel("google-genai:gemini-1.5-pro");
const genaiFlash = initChatModel("google-genai:gemini-1.5-flash");

// Cohere
const commandR = initChatModel("cohere:command-r-plus");

// Mistral AI
const mistral = initChatModel("mistralai:mistral-large-latest");

// AWS Bedrock
const bedrock = initChatModel("bedrock:anthropic.claude-3-5-sonnet-20241022-v2:0");

// Ollama (local)
const llama = initChatModel("ollama:llama3.1");

// Groq
const groq = initChatModel("groq:llama-3.3-70b-versatile");

// Cerebras
const cerebras = initChatModel("cerebras:llama3.1-70b");

// DeepSeek
const deepseek = initChatModel("deepseek:deepseek-chat");

// X.AI
const grok = initChatModel("xai:grok-2-latest");

// Fireworks
const fireworks = initChatModel("fireworks:accounts/fireworks/models/llama-v3p1-70b-instruct");

// Together AI
const together = initChatModel("together:meta-llama/Llama-3-70b-chat-hf");

// Perplexity
const perplexity = initChatModel("perplexity:llama-3.1-sonar-large-128k-online");

const model = initChatModel("openai:gpt-4o", {
  // Temperature (0.0 to 2.0)
  temperature: 0.7,

  // Maximum tokens to generate
  maxTokens: 1000,

  // Request timeout (milliseconds)
  timeout: 30000,

  // Enable streaming
  streaming: true,

  // Top-p sampling
  topP: 0.9,

  // Frequency penalty
  frequencyPenalty: 0.5,

  // Presence penalty
  presencePenalty: 0.5,

  // Stop sequences
  stop: ["\n\n", "END"],
});

// OpenAI-specific
const openai = initChatModel("openai:gpt-4o", {
  temperature: 0.7,
  logitBias: { "50256": -100 },
  user: "user-123",
});

// Anthropic-specific
const anthropic = initChatModel("anthropic:claude-3-5-sonnet-20241022", {
  temperature: 0.5,
  topK: 40,
});

// Ollama-specific
const ollama = initChatModel("ollama:llama3.1", {
  temperature: 0.8,
  baseUrl: "http://localhost:11434",
});

import { createAgent } from "langchain";

// Start with OpenAI
let agent = createAgent({
  model: "openai:gpt-4o",
  tools: [],
});

// Switch to Anthropic (same interface)
agent = createAgent({
  model: "anthropic:claude-3-5-sonnet-20241022",
  tools: [],
});

// Switch to local Ollama (same interface)
agent = createAgent({
  model: "ollama:llama3.1",
  tools: [],
});

// All agents work the same way
const result = await agent.invoke({
  messages: [{ role: "user", content: "Hello!" }],
});

import { createAgent, initChatModel } from "langchain";

const agent = createAgent({
  model: (state) => {
    // Select model based on task complexity
    if (state.messages.length <= 2) {
      return initChatModel("openai:gpt-4o-mini"); // Fast model
    }
    return initChatModel("openai:gpt-4o"); // Powerful model
  },
  tools: [],
});

import { initChatModel } from "langchain";

async function getModel() {
  try {
    // Try primary model
    return initChatModel("openai:gpt-4o");
  } catch (error) {
    console.log("Primary model unavailable, using fallback");
    // Fallback to alternative
    return initChatModel("anthropic:claude-3-5-sonnet-20241022");
  }
}

const model = await getModel();

# OpenAI
export OPENAI_API_KEY="sk-..."

# Anthropic
export ANTHROPIC_API_KEY="sk-ant-..."

# Google
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"

# Cohere
export COHERE_API_KEY="..."

# Groq
export GROQ_API_KEY="gsk_..."

# Mistral
export MISTRAL_API_KEY="..."

# DeepSeek
export DEEPSEEK_API_KEY="..."

# X.AI
export XAI_API_KEY="..."

export AZURE_OPENAI_API_KEY="..."
export AZURE_OPENAI_API_INSTANCE_NAME="..."
export AZURE_OPENAI_API_DEPLOYMENT_NAME="..."
export AZURE_OPENAI_API_VERSION="..."

export AWS_ACCESS_KEY_ID="..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_REGION="us-east-1"

import { ChatOpenAI } from "@langchain/openai";
import { createAgent } from "langchain";

// Create fully customized model instance
const customModel = new ChatOpenAI({
  modelName: "gpt-4o",
  temperature: 0.9,
  apiKey: process.env.CUSTOM_OPENAI_KEY,
  organization: "org-123",
  maxRetries: 5,
});

// Use with agent
const agent = createAgent({
  model: customModel,
  tools: [],
});

const model = initChatModel("openai:gpt-4o");

// Override configuration for specific call
const response = await model.invoke(
  [{ role: "user", content: "Hello" }],
  {
    temperature: 0.9, // Override default
    maxTokens: 500,
  }
);

const model = initChatModel("openai:gpt-4o", {
  streaming: true,
});

const stream = await model.stream([
  { role: "user", content: "Tell me a story" },
]);

for await (const chunk of stream) {
  process.stdout.write(chunk.content);
}

const model = initChatModel("openai:gpt-4o");

const results = await model.batch([
  [{ role: "user", content: "What is 2+2?" }],
  [{ role: "user", content: "What is the capital of France?" }],
  [{ role: "user", content: "Name a color" }],
]);

results.forEach((result, i) => {
  console.log(`Result ${i}:`, result.content);
});