langchain-redis
An integration package connecting Redis and LangChain for AI working memory
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and fills a clear need for Redis-backed memory in LangChain workflows. It is permissively licensed (MIT) and supports current Python versions (3.10–3.12). Install it if you are already running Redis and need vector storage, semantic caching, or chat history for a LangChain application.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Redis instance accessible at the configured REDIS_URL
- Low friction installation with a pure Python wheel.
- Active maintenance with recent commits and a stable release cycle since first release in 2024.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects alike.
last release 2025-11-25 (262 days) · last repo commit 2026-08-03 · 68 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 128,711 downloads/mo, #11,700 on PyPI
Alternatives
Verify before relying
pip install langchain-redis
from langchain_redis import RedisVectorStore, RedisConfig
from langchain_core.embeddings import Embeddings
config = RedisConfig(index_name="my_vectors", redis_url="redis://localhost:6379")
vector_store = RedisVectorStore(embeddings=Embeddings(), config=config)
vector_store.add_texts(["Document 1", "Document 2"])
docs = vector_store.similarity_search("query", k=2)- Whether the package handles connection pooling or retry logic automatically for production deployments
- Performance characteristics and scaling limits for vector search on large datasets
- Compatibility with Redis Cluster deployments beyond the Sentinel examples shown
What it is and what it does
langchain-redis bridges Redis and LangChain, enabling three core capabilities: vector storage with semantic search (via RedisVectorStore), LLM response caching with TTL and semantic matching (via RedisCache and RedisSemanticCache), and session-based chat history with full-text search (via RedisChatMessageHistory). It wraps redisvl for indexing and query operations, and depends on langchain-core for the LangChain integration layer.
The package is designed for developers building AI applications that need persistent, searchable memory layers. It supports multiple Redis deployment modes—standard Redis, Redis with SSL/TLS, and Redis Sentinel for high availability—via connection URL configuration. All three components (vector store, cache, history) accept a RedisConfig object for detailed tuning of index names, distance metrics, key prefixes, and metadata schemas.
Use it for
- Store and retrieve document embeddings for semantic search in RAG pipelines using RedisVectorStore
- Cache LLM responses by semantic similarity to reduce API calls and latency in production chatbots
- Maintain persistent, searchable chat histories across user sessions with automatic expiration via TTL
- Build high-availability AI systems using Redis Sentinel for failover and multi-node deployments
- Filter vector search results by metadata tags or numeric ranges to narrow retrieval in multi-tenant applications
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and fills a clear need for Redis-backed memory in LangChain workflows. It is permissively licensed (MIT) and supports current Python versions (3.10–3.12). Install it if you are already running Redis and need vector storage, semantic caching, or chat history for a LangChain application.
Install
langchain-redis on PyPI
Before you install
Low friction installation with a pure Python wheel. Active maintenance with recent commits and a stable release cycle since first release in 2024. Requires a running Redis instance, which is the primary operational dependency rather than an install-time concern.
Requires a running Redis instance accessible at the configured REDIS_URL
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects alike.
Quickstart
pip install langchain-redis
from langchain_redis import RedisVectorStore, RedisConfig
from langchain_core.embeddings import Embeddings
config = RedisConfig(index_name="my_vectors", redis_url="redis://localhost:6379")
vector_store = RedisVectorStore(embeddings=Embeddings(), config=config)
vector_store.add_texts(["Document 1", "Document 2"])
docs = vector_store.similarity_search("query", k=2)Verify before relying
- Whether the package handles connection pooling or retry logic automatically for production deployments
- Performance characteristics and scaling limits for vector search on large datasets
- Compatibility with Redis Cluster deployments beyond the Sentinel examples shown
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesanyiocertifihf-xethttpcorejinja2langchain-corepython-ulidredisvltyping-extensionsurllib3 |
| Maintenance | Actively maintained 262 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 128,711 / month, #11,700 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: langchain_redis-0.2.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “redis vector store langchain”
- langchain-redisIntegrates Redis with LangChain to provide vector storage, semantic…
- langgraph-checkpoint-redisProvides Redis-backed checkpoint storage and key-value stores for…
- llama-index-vector-stores-redisIntegrates Redis as a vector store backend for LlamaIndex, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also langchain-elasticsearch · langchain-cerebras · redisvl · langchain-milvus · langgraph-checkpoint-redis · langchain-qdrant · llama-index-vector-stores-redis · ramp-packer · langchain-mongodb · RLTest