Use Redis storage

The redis backend uses Redis as the vector store. Memory text is embedded by an embedding model, written to Redis with a per-user index (the actual index name is veadk-ltm/{index}/{user_id}), and retrieved by cosine similarity.

When to use

You already run Redis with the RediSearch module enabled;
You want Redis as a low-latency vector-search backend, with persistence and multi-instance sharing.

Dependencies

pip install "veadk-python[extensions]"

Redis must support RediSearch (vector search).

Usage

Before running, provide Redis with RediSearch, an account allowed to create and access indexes, and the Redis and embedding variables below. Durability depends on Redis RDB/AOF and backup settings; VeADK does not enable them

from veadk import Agent
from veadk.memory.long_term_memory import LongTermMemory

ltm = LongTermMemory(backend="redis", index="ltm_demo")

agent = Agent(
    name="demo",
    instruction="Answer the user; when needed, use the `load_memory` tool to recall past conversations.",
    long_term_memory=ltm,
)

You can also pass connection config explicitly via backend_config:

import os

from veadk.memory.long_term_memory import LongTermMemory
from veadk.configs.database_configs import RedisConfig

ltm = LongTermMemory(
    backend="redis",
    index="ltm_demo",
    backend_config={
        "index": "ltm_demo",
        "redis_config": RedisConfig(
            host="localhost",
            port=6379,
            password=os.environ["DATABASE_REDIS_PASSWORD"],
            db=0,
        ),
    },
)

Parameters

LongTermMemory parameters

Parameter	Type	Default	Description
`backend`	`str \| BaseLongTermMemoryBackend`	`"opensearch"`	Set to `redis` for this page, or pass a configured backend instance
`backend_config`	`dict`	`{}`	Backend settings; a supplied backend instance takes precedence
`index`	`str`	`""`	Without backend_config, resolves from index, app_name, then default_app; supply a nonempty index with a configuration dictionary
`app_name`	`str`	`""`	Fallback for index; the actual user comes from the saved Session or search arguments
`top_k`	`int`	`5`	Number of retrieved chunks; use a positive integer
`user_id`	`str`	`""`	Deprecated; does not select the runtime user

Constructor parameters

backend_config supports the following settings:

Parameter	Type	Default	Description
`index`	`str`	No default; provided by `LongTermMemory`	Memory index name. The Redis backend imposes no extra naming constraints.
`redis_config`	`RedisConfig`	Read automatically from `DATABASE_REDIS_*` env vars	Redis connection config.
`embedding_config`	`EmbeddingModelConfig`	Read automatically from `MODEL_EMBEDDING_*` env vars	Embedding model config.

Redis connection config

redis_config is a RedisConfig with env prefix DATABASE_REDIS_:

Field	Env var	Type	Default	Description
`host`	`DATABASE_REDIS_HOST`	`str`	`""`	Redis host.
`port`	`DATABASE_REDIS_PORT`	`int`	`6379`	Port.
`username`	`DATABASE_REDIS_USERNAME`	`str \| None`	`None`	The field exists, but Redis long-term memory connections do not forward username; this field does not select an ACL user
`password`	`DATABASE_REDIS_PASSWORD`	`str`	`""`	Password.
`db`	`DATABASE_REDIS_DB`	`int`	`0`	Database number.
`secret_token`	`DATABASE_REDIS_SECRET_TOKEN`	`str`	`""`	STS temporary-credential token; not yet enabled.

Embedding config

embedding_config is an EmbeddingModelConfig with env prefix MODEL_EMBEDDING_:

Field	Env var	Type	Default	Description
`name`	`MODEL_EMBEDDING_NAME`	`str`	`doubao-embedding-vision-250615`	Embedding model name.
`dim`	`MODEL_EMBEDDING_DIM`	`int`	`2048`	Embedding vector dimension; used to build the vector field. Distance metric is cosine.
`api_base`	`MODEL_EMBEDDING_API_BASE`	`str`	`https://ark.cn-beijing.volces.com/api/v3/`	API base of the embedding service.
`api_key`	`MODEL_EMBEDDING_API_KEY`	`str`	Falls back to `MODEL_AGENT_API_KEY`, then an auto-fetched Ark token	Key for accessing the embedding service.

Environment variables

# Redis connection
export DATABASE_REDIS_HOST="localhost"
export DATABASE_REDIS_PORT=6379
export DATABASE_REDIS_PASSWORD="your-password"
export DATABASE_REDIS_DB=0

# Embedding model
export MODEL_EMBEDDING_NAME="doubao-embedding-vision-250615"
export MODEL_EMBEDDING_DIM=2048
export MODEL_EMBEDDING_API_KEY="your-ark-api-key"

The vector dimension comes from the embedding config (MODEL_EMBEDDING_DIM). The index uses the flat algorithm with a cosine distance metric, and an index is created per user (veadk-ltm/{index}/{user_id}).

Verify writes and retrieval

After configuring the dependencies and credentials on this page, run this standalone example. It saves user text and searches for that user directly without calling a conversation model

import asyncio

from google.adk.events import Event
from google.adk.sessions import Session
from google.genai import types
from veadk.memory.long_term_memory import LongTermMemory

async def main():
    memory = LongTermMemory(backend="redis", index="ltm_demo")
    session = Session(
        id="memory_check", app_name="ltm_demo", user_id="user_42",
        events=[Event(author="user", content=types.Content(
            role="user", parts=[types.Part(text="My preferred language is Chinese")]
        ))],
    )
    await memory.add_session_to_memory(session)
    result = await memory.search_memory(
        app_name="ltm_demo", user_id="user_42", query="preferred language"
    )
    for entry in result.memories:
        print(entry.content)

asyncio.run(main())

Results should contain the saved language preference. Managed services may extract memories asynchronously, so a completed write does not guarantee immediate retrieval. An empty result can also indicate permission, network, or service failure; check error logs and service records. The save method does not return a success Boolean

Overview

Get started

Core components

Observability

Extensions

Frontend

Security

Deployment

When to use

Dependencies

Usage

Parameters

LongTermMemory parameters

Constructor parameters

Redis connection config

Embedding config

Environment variables

Verify writes and retrieval

​When to use

​Dependencies

​Usage

​Parameters

​LongTermMemory parameters

​Constructor parameters

​Redis connection config

​Embedding config

​Environment variables

​Verify writes and retrieval

When to use

Dependencies

Usage

Parameters

LongTermMemory parameters

Constructor parameters

Redis connection config

Embedding config

Environment variables

Verify writes and retrieval