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You can use Redis as a vector database with Agno.

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Setup

For connecting to a remote Redis instance, pass your Redis connection string to the redis_url parameter and the index name to the index_name parameter of the RedisDB constructor. For a local docker setup, you can use the following command: 
docker run -d --name redis \
  -p 6379:6379 \
  -p 8001:8001 \
  redis/redis-stack:latest

docker start redis

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Example

agent_with_knowledge.py

import os

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.redis import RedisDB
from agno.vectordb.search import SearchType

# Configure Redis connection (from environment variables if available, otherwise use local defaults)
REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/0")
INDEX_NAME = os.getenv("REDIS_INDEX", "agno_cookbook_vectors")

# Initialize Redis Vector DB
vector_db = RedisDB(
    index_name=INDEX_NAME,
    redis_url=REDIS_URL,
    search_type=SearchType.vector,  # try SearchType.hybrid for hybrid search
)

# Build a Knowledge base backed by Redis
knowledge = Knowledge(
    name="My Redis Vector Knowledge Base",
    description="This knowledge base uses Redis + RedisVL as the vector store",
    vector_db=vector_db,
)

# Add content (ingestion + chunking + embedding handled by Knowledge)
knowledge.add_content(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
    skip_if_exists=True,
)

# Query with an Agent
agent = Agent(knowledge=knowledge)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)

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Redis Params

ParameterTypeDefaultDescription
index_namestrRequiredName of the Redis index to store vector data
redis_urlOptional[str]NoneRedis connection URL
redis_clientOptional[Redis]NoneRedis client instance
embedderOptional[Embedder]OpenAIEmbedder()Embedder instance to generate embeddings
search_typeSearchTypeSearchType.vectorType of search to perform (vector, keyword, hybrid)
distanceDistanceDistance.cosineDistance metric for vector comparisons
vector_score_weightfloat0.7Weight for vector similarity in hybrid search
**redis_kwargsAny-Additional Redis connection parameters

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