langchain-qdrant
An integration package connecting Qdrant and LangChain
Decision gist · record as of 2026-08-14
Yes, if you are building a LangChain application that needs vector search. Low install friction, active maintenance, no security issues, and permissive MIT license make it a straightforward choice. The only prerequisite is Python 3.10+ and access to a Qdrant instance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to <4.0.0).
- Qdrant server or in-memory instance must be available.
- Low friction: pure Python wheel with three runtime dependencies (langchain-core, pydantic, qdrant-client).
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
last release 2025-10-22 (296 days) · last repo commit 2026-08-14 · 144,267 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 848,403 downloads/mo, #4,911 on PyPI
Alternatives
Verify before relying
pip install langchain-qdrant
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
vector_store = QdrantVectorStore.from_documents(
documents, embeddings, client=client, collection_name="my_collection"
)- Whether this package handles vector dimension mismatches or type validation automatically
- Performance characteristics with large document collections (millions of embeddings)
- Supported Qdrant server versions and backward compatibility guarantees
What it is and what it does
langchain-qdrant is a bridge between LangChain and Qdrant, a vector database designed for semantic search. It lets you store document embeddings in Qdrant and retrieve them through LangChain's standard vector store interface, enabling retrieval-augmented generation (RAG) and semantic search workflows. The package wraps Qdrant's Python client and integrates with LangChain's embedding and document abstractions.
Typically used to build applications that combine large language models with external knowledge: you embed documents, store them in Qdrant via this package, then query them semantically to augment LLM prompts. It depends on langchain-core for the vector store contract, pydantic for data validation, and qdrant-client to communicate with the Qdrant service.
Use it for
- Build RAG systems that retrieve relevant documents from Qdrant to augment LLM responses
- Implement semantic search over a corpus of documents stored in Qdrant
- Create chatbots with long-term memory by storing conversation embeddings in Qdrant
- Filter and rerank search results using Qdrant's metadata and payload capabilities within LangChain workflows
- Prototype vector search applications without managing Qdrant client code directly
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a LangChain application that needs vector search.
Low install friction, active maintenance, no security issues, and permissive MIT license make it a straightforward choice. The only prerequisite is Python 3.10+ and access to a Qdrant instance.
Install
langchain-qdrant on PyPI
Before you install
Low friction: pure Python wheel with three runtime dependencies (langchain-core, pydantic, qdrant-client). Actively maintained as of 2026-08-14 with no known vulnerabilities.
Requires Python 3.10 or later (supports up to <4.0.0). Qdrant server or in-memory instance must be available.
License in practice
MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install langchain-qdrant
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
vector_store = QdrantVectorStore.from_documents(
documents, embeddings, client=client, collection_name="my_collection"
)Verify before relying
- Whether this package handles vector dimension mismatches or type validation automatically
- Performance characteristics with large document collections (millions of embeddings)
- Supported Qdrant server versions and backward compatibility guarantees
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0.0,>=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packageslangchain-corepydanticqdrant-client |
| Maintenance | Actively maintained 296 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 848,403 / month, #4,911 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: langchain_qdrant-1.1.0-py3-none-any.whl
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