@localmode/langchain

LangChain.js adapters for LocalMode — drop-in local inference for existing LangChain applications. Swap 3 imports and go fully local.

See it live: the RAG Chat block at localmode.ai has a LangChain engine toggle that runs LocalModeEmbeddings, LocalModeVectorStore, and ChatLocalMode end-to-end in the browser — ingest, semantic search, and grounded answers through the real adapters, behind the same UI as the core pipeline.

Installation

pnpm install @localmode/langchain @localmode/core @localmode/transformers

Adapters

| LangChain Class | LocalMode Adapter | Wraps | |----------------|-------------------|-------| | Embeddings | LocalModeEmbeddings | EmbeddingModel | | BaseChatModel | ChatLocalMode | LanguageModel | | VectorStore | LocalModeVectorStore | VectorDB | | BaseDocumentCompressor | LocalModeReranker | RerankerModel |

Quick Start

Full RAG Chain

import { LocalModeEmbeddings, ChatLocalMode, LocalModeVectorStore } from '@localmode/langchain';
import { transformers } from '@localmode/transformers';
import { webllm } from '@localmode/webllm';
import { createVectorDB } from '@localmode/core';
import { RetrievalQAChain } from 'langchain/chains';

const embeddings = new LocalModeEmbeddings({
  model: transformers.embedding('Xenova/bge-small-en-v1.5'),
});
const llm = new ChatLocalMode({
  model: webllm.languageModel('Qwen3-1.7B-q4f16_1-MLC'),
});
const db = await createVectorDB({ name: 'docs', dimensions: 384 });
const store = new LocalModeVectorStore(embeddings, { db });

// Add documents
await store.addDocuments([
  { pageContent: 'LocalMode runs AI in the browser', metadata: { source: 'docs' } },
]);

// Query
const chain = RetrievalQAChain.fromLLM(llm, store.asRetriever());
const result = await chain.call({ query: 'What is LocalMode?' });

Reranker

import { LocalModeReranker } from '@localmode/langchain';
import { transformers } from '@localmode/transformers';

const reranker = new LocalModeReranker({
  model: transformers.reranker('Xenova/ms-marco-MiniLM-L-6-v2'),
  topK: 5,
});

const reranked = await reranker.compressDocuments(documents, 'search query');

Knowledge Base Engine

createLangChainKnowledgeBaseEngine() returns a kind: 'langchain' engine implementing the frozen KnowledgeBaseEngine contract from @localmode/core (chunk → embed → store, vector search, grounded ask) through the LocalModeEmbeddings / LocalModeVectorStore / ChatLocalMode adapters. It is result-equivalent to @localmode/core's createKnowledgeBaseEngine, so a knowledge base UI can toggle engines over one shared corpus. Because the models are injected, apps that never toggle the LangChain engine never pull this package.

import { createLangChainKnowledgeBaseEngine, ChatLocalMode } from '@localmode/langchain';
import { transformers } from '@localmode/transformers';

const engine = createLangChainKnowledgeBaseEngine({
  embeddingModel: transformers.embedding('Xenova/bge-small-en-v1.5'),
  getChatModel: () =>
    new ChatLocalMode({
      model: transformers.languageModel('onnx-community/granite-4.0-350m-ONNX-web'),
      maxTokens: 512,
    }),
});

await engine.ingest(docs, { chunking: 'recursive', chunkSize: 500 });
const hits = await engine.search('privacy and encryption', { topK: 10 });
const { answer, sources } = await engine.ask('How is data encrypted?');

Migration from Cloud

- import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
- import { PineconeStore } from '@langchain/pinecone';
+ import { ChatLocalMode, LocalModeEmbeddings, LocalModeVectorStore } from '@localmode/langchain';
+ import { transformers } from '@localmode/transformers';
+ import { webllm } from '@localmode/webllm';

- const llm = new ChatOpenAI({ modelName: 'gpt-4o-mini' });
- const embeddings = new OpenAIEmbeddings();
- const store = await PineconeStore.fromExistingIndex(embeddings, { pineconeIndex });
+ const llm = new ChatLocalMode({ model: webllm.languageModel('Qwen3-1.7B-q4f16_1-MLC') });
+ const embeddings = new LocalModeEmbeddings({ model: transformers.embedding('Xenova/bge-small-en-v1.5') });
+ const db = await createVectorDB({ name: 'docs', dimensions: 384 });
+ const store = new LocalModeVectorStore(embeddings, { db });

The chain code (RetrievalQAChain.fromLLM) is identical. Only provider instantiation changes.

Documentation

Full documentation at localmode.dev/docs/langchain.

Acknowledgments

This package is built on LangChain.js by LangChain — a framework for building applications powered by language models.

License

MIT