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Why use LangChain with Alchemyst AI?

LangChain is a popular open-source framework for building context-aware AI applications in JavaScript and TypeScript. It lets you chain together LLMs, retrievers, memory, and tools to create powerful, production-ready AI agents. However, LLMs alone are “forgetful”—they don’t remember previous conversations, business rules, or your proprietary data. This is where context becomes critical. Without context, AI agents give generic, disconnected answers. With context, they become knowledgeable partners that can reason, personalize, and act based on your data and workflows. (See What is AI Context? and Why you need AI Context?) Alchemyst AI’s LangChain integration solves this by providing a plug-and-play retriever that connects your LangChain agents to the context stored in Alchemyst’s memory architecture. This means your agents can:
  • Instantly access relevant documents, files, and knowledge bases.
  • Maintain both short-term and long-term memory across sessions.
  • Personalize responses and follow complex workflows using your proprietary data.
  • Avoid repetitive questions and deliver context-aware outputs.
With Alchemyst, you simply upload your data, and the retriever handles context injection for you—no need to build your own memory system. (See How Alchemyst Works)

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Installation

To get started with the Alchemyst Langchain Integration, install the @alchemystai/langchain-js package from npm. 
npm i @alchemystai/langchain-js

See Source
NOTE: In accordance with the release cycle of LangChain 1.0 release, we have decided to maintain the project separately, as discussed with the maintainers of Langchain Community as well.

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Usage

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As Retriever

The AlchemystRetriever class can be used like any other Retriever class in LangChain. See the example below:
retriever.ts
import { AlchemystRetriever } from "@alchemystai/langchain-js";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { RunnableSequence } from "@langchain/core/runnables";
import dotenv from "dotenv";
// filepath: /Users/anuran/Alchemyst/integrations/playground/example.ts

dotenv.config();

// Instantiate the retriever with your API key and optional config
const retriever = new AlchemystRetriever({
  apiKey: process.env.ALCHEMYST_AI_API_KEY!,
  similarityThreshold: 0.8,
  minimumSimilarityThreshold: 0.5,
  scope: "internal"
});

// Example: Use the retriever in a LangChain pipeline
async function main() {
  // Create a simple pipeline that retrieves documents and outputs their content
  const pipeline = RunnableSequence.from([
    async (input: string) => {
      const docs = await retriever.getRelevantDocuments(input);
      return docs.map(doc => doc.pageContent).join("\n---\n");
    },
    new StringOutputParser()
  ]);

  const query = "Tell me about Quantum Entanglement"; // Put your query here
  const result = await pipeline.invoke(query);

  console.log("Retrieved Documents:\n", result);
}

main().catch(console.error);

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As Memory

The AlchemystMemory class can be used like any other Memory class in LangChain as well. See the example below:
memory.ts
import { AlchemystMemory } from "@alchemystai/langchain-js";
import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { ChatOpenAI } from "@langchain/openai";
import { randomUUID } from "crypto";

async function main() {
  console.log("Boot: starting test with env:", {
    OPENAI_API_KEY: process.env.OPENAI_API_KEY ? "set" : "missing",
    ALCHEMYST_AI_API_KEY: process.env.ALCHEMYST_AI_API_KEY ? "set" : "missing",
  });
  const sessionId = randomUUID();
  console.log("Session:", sessionId);

  const memory = new AlchemystMemory({
    apiKey: process.env.ALCHEMYST_AI_API_KEY || "YOUR_ALCHEMYST_API_KEY",
    sessionId,
  });

  const model = new ChatOpenAI({
    model: "gpt-5-nano",
  });

  // Create a prompt template with message history
  const prompt = ChatPromptTemplate.fromMessages([
    ["system", "You are a helpful AI assistant. Have a conversation with the human, using the chat history for context."],
    new MessagesPlaceholder("history"),
    ["human", "{input}"],
  ]);

  // Create the chain using LCEL (LangChain Expression Language)
  const chain = RunnableSequence.from([
    RunnablePassthrough.assign({
      history: async () => {
        const memoryVars = await memory.loadMemoryVariables({});
        return memoryVars.history || [];
      },
    }),
    prompt,
    model,
  ]);

  console.log("Invoke #1 ->");
  const first = await chain.invoke({ input: "Hi, my name is Anuran. Anuran is from Bangalore." });

  // Save to memory
  await memory.saveContext(
    { input: "Hi, my name is Anuran. Anuran is from Bangalore." },
    { output: first.content }
  );

  console.log("First reply:", first.content);

  console.log("Invoke #2 ->");
  const second = await chain.invoke({ input: "Who is Anuran? Where is Anuran from?" });

  // Save to memory
  await memory.saveContext(
    { input: "Who is Anuran? Where is Anuran from?" },
    { output: second.content }
  );

  console.log("Second reply:", second.content);
}

main().catch((err) => {
  console.error(err);
  process.exit(1);
});