Key takeaways
- LangChain is a library that runs in your own process; whether data stays local depends on the model and stores you connect.
- Ollama is the easiest way to run open-source LLMs locally and plugs into LangChain and LlamaIndex.
- FAISS runs in process; Qdrant and Weaviate run locally in Docker; Pinecone offers only a local emulator for testing.
- Set LlamaIndex's LLM and embedding model explicitly to keep everything on your machine.
- Use ainvoke, astream and abatch with await for async LangChain calls; in notebooks, await directly.
Yes, LangChain runs locally: it is a Python or JavaScript library that runs wherever your code runs, and paired with a local model server such as Ollama and a local vector store such as FAISS or Qdrant, a whole RAG app can run offline on your PC. I build retrieval systems such as RAG.NextUpgrad, so this page covers running LangChain, LlamaIndex, open-source LLMs and vector databases locally, plus async calls and a common notebook crash.
Can LangChain run locally?
Yes. LangChain is an open-source library, so pip install langchain puts it on your machine, and it runs inside your own Python process. Whether the whole app is local depends on which model and vector store you connect it to.
For a fully local stack, use a local model through the langchain-ollama package, local embeddings such as a sentence-transformers model, and a local vector store such as FAISS, Chroma or Qdrant. Then documents, embeddings and prompts never leave your computer, which suits private data and offline work. Expect slower answers than large hosted models on modest hardware, and pick small models, around 3 to 8 billion parameters, that fit your memory. Turn off tracing integrations if you need nothing to leave the machine.
Where does LangChain run?
LangChain runs wherever your application code runs: a laptop, a Jupyter notebook, a server, a container or a serverless function. It is not a hosted service itself; it calls models and databases that may be local or in the cloud.
In a typical deployment, LangChain code sits inside a FastAPI or Next.js backend, calls a hosted model API or a self-hosted model server, and queries a vector database. LangGraph applications can also be deployed on LangChain's own platform, and LangSmith traces can go to the cloud or a self-hosted instance. If you see network calls you did not expect, check which integrations you configured, since each one decides where your data goes.
How do you run an open-source LLM locally?
The easiest way is Ollama: install it, run ollama pull llama3.2, then ollama run llama3.2 to chat in the terminal. It also serves an API on localhost port 11434 that LangChain and LlamaIndex can call.
Also asked as: how to run open source llm locally
Alternatives include LM Studio for a desktop interface, llama.cpp for lightweight, highly configurable inference with GGUF models, and vLLM for fast GPU serving. Choose model size by memory: quantised 7 or 8 billion parameter models run on many laptops with 16 GB of RAM, while larger models need a strong GPU. Check each model's licence before commercial use, and test answer quality on your own tasks, since small local models trade some accuracy for privacy and zero per-call cost.
How do you run AI models locally on your PC?
Check your hardware first: RAM, GPU and its VRAM decide what fits. Then install a runner such as Ollama or LM Studio, download a quantised model sized for your memory, and run it from the app or terminal.
Also asked as: how to run ai models locally on your pc
For other AI models, the pattern is similar. Speech recognition runs locally with Whisper variants, image generation with Stable Diffusion tools, and vision models with ONNX Runtime or PyTorch. Use a GPU if you have one, since CPUs are much slower for larger models. Keep models on a fast SSD, close heavy apps while running, and start small to confirm everything works before trying bigger models. In FaceVision, I run face models in the browser with ONNX Runtime Web, which shows how far small optimised models can go on ordinary devices.
Does FAISS run locally?
Yes. FAISS is a library from Meta for similarity search, not a server, so it runs entirely in your process: install faiss-cpu or faiss-gpu, build an index in memory, and save it to disk as a file.
In LangChain, FAISS.from_documents(docs, embeddings) builds an index, save_local writes it to a folder, and load_local reads it back. Recent versions require allow_dangerous_deserialization=True when loading, so only load index files you created. FAISS is excellent for prototypes, notebooks and read-heavy apps where one process owns the index. It lacks database features such as concurrent writes, replication and access control, so for multi-user production systems many teams move to Qdrant, Weaviate, Milvus or PostgreSQL with pgvector.
Can I run Pinecone locally?
Pinecone is a managed cloud service, so production indexes run on Pinecone's infrastructure. For development and testing, Pinecone offers Pinecone Local, an in-memory emulator that runs as a Docker container on your machine.
Pinecone Local is meant for tests and CI pipelines, not production: data does not persist after the container stops, and it does not match every feature or the performance of the real service. If you need a vector database that runs fully on your own hardware in production, choose a self-hostable option such as Qdrant, Weaviate, Milvus, Chroma or pgvector. Keep your code behind a small retrieval interface so switching stores later means changing one module rather than the whole app.
How do you run Qdrant locally?
Run the official Docker image with docker run -p 6333:6333 qdrant/qdrant, then open the dashboard at localhost:6333/dashboard. Add a volume mount, such as -v ./qdrant_storage:/qdrant/storage, so your data survives restarts.
Also asked as: how to run qdrant locally
For quick experiments without Docker, the Python client has a local mode: QdrantClient(":memory:") keeps data in memory, and QdrantClient(path="./qdrant_db") stores it on disk. LangChain connects through the langchain-qdrant package. Qdrant supports metadata filtering, hybrid dense and sparse search, and snapshots, so the same local setup scales to a server or Qdrant Cloud later. Expose port 6334 too if you want the faster gRPC interface, and pin the image version in Docker Compose for reproducible environments.
How do you run Weaviate locally?
Use Docker Compose with the official Weaviate image: Weaviate's documentation provides a configurator that generates a docker-compose.yml with the modules you need. Run docker compose up -d, and Weaviate listens on localhost port 8080.
Also asked as: how to run weaviate locally
Then connect with the Python client using weaviate.connect_to_local(). The v4 client also uses gRPC on port 50051, so expose that port as well. You can bring your own vectors, computed by local embedding models, or enable vectoriser modules that call a model for you, including local ones served through Ollama. The client also offers an embedded mode for quick experiments. Mount a volume for persistence, and give Docker enough memory, since Weaviate needs more than lightweight stores like Chroma.
Can I run LlamaIndex locally?
Yes. LlamaIndex is an open-source Python library that runs in your own process. Point it at a local LLM through its Ollama integration and a local embedding model, and indexing and querying happen entirely on your machine.
Set Settings.llm to an Ollama model and Settings.embed_model to a HuggingFace embedding model, then build a VectorStoreIndex from your documents. By default it stores the index in memory; persist it with storage_context.persist() or connect a local vector store such as Qdrant or Chroma. Without these settings, LlamaIndex defaults to OpenAI, so set them explicitly to avoid accidental API calls. Small local models may follow instructions less reliably, so test retrieval and answer quality on your own documents.
How do you run async methods in LangChain?
Every LangChain runnable has async versions of its methods: ainvoke, astream, abatch and astream_events. Call them with await inside an async function, and run that function with asyncio.run(main()) in a script.
Also asked as: how to run async methods in langchain
In Jupyter, an event loop is already running, so call await chain.ainvoke(...) directly in a cell instead of asyncio.run, which raises an error there. Use asyncio.gather to run several calls concurrently, which speeds up batch work against hosted APIs. Async helps most in web backends such as FastAPI, where the server handles other requests while waiting for the model. If a notebook kernel dies while running LangChain, the cause is usually memory, such as loading a large local model or index, so try a smaller model or restart with more RAM.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
