{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"RedisVL is a Python client for building AI applications with Redis vector search, enabling semantic search, RAG pipelines, and vector-backed retrieval with metadata filtering and hybrid search capabilities.","skillfed_tags":["vector-search","rag-pipeline","redis-client"],"use_cases":["Build RAG systems that retrieve relevant documents from Redis based on semantic similarity to user queries.","Implement AI agent memory by storing and retrieving conversation context as embeddings with semantic routing.","Create recommendation engines that search for similar items using vector similarity with metadata filtering.","Cache embeddings to reduce LLM API costs while maintaining fast semantic search performance.","Perform hybrid search combining vector similarity with full-text keyword matching on the same dataset.","Manage multiple vector indexes from a CLI without writing Python, using schema files and batch operations."],"what_it_does":"RedisVL is a production-ready Python client that turns Redis into a vector database for AI applications. It provides schema-driven index management, vector search with metadata filtering, and hybrid search combining semantic and full-text signals. The package handles the boilerplate of defining indexes, loading embeddings, and executing queries\u2014all backed by Redis's native vector search engine.\n\nTypical workflows include building RAG pipelines with real-time retrieval, implementing AI agent memory systems, and creating recommendation engines. It ships with async support, CLI tools for index management, and integrations with multiple embedding providers. The eight runtime dependencies (numpy, pydantic, redis, tenacity, jsonpath-ng, ml-dtypes, python-ulid, pyyaml) are all well-maintained libraries, keeping installation friction low.","worth_installing":"Yes. RedisVL is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and solves a real problem\u2014bridging Python and Redis for vector-backed AI workloads. Install friction is low and it supports modern Python versions (3.10\u20133.14). Best for teams already using or planning to adopt Redis, or those seeking a lightweight vector search layer without a separate database."},"id":"redisvl","links":{"html":"https://skillfed.io/packages/redisvl","md":"https://skillfed.io/packages/redisvl.md","pypi":"https://pypi.org/project/redisvl/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"MIT","license_treatment":"permissive","name":"redisvl","python_support":"supports_current","summary":"Python client library and CLI for using Redis as a vector database"},"popularity":{"monthly_downloads":2796777,"position":2885,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.25.1"}