Use in-memory storage

The local backend keeps long-term memory in the current process’s memory. Memory text is embedded by an embedding model and written to a llama-index in-memory vector index; retrieval recalls by semantic similarity. Because the data lives only in process memory, it is cleared when the process exits and cannot be shared across processes. The backend is therefore intended for local development, debugging, and quickly validating the end-to-end long-term memory flow — not for production.

When to use

Quickly try the full long-term memory flow locally, from write to cross-session retrieval;
Develop and debug agent logic without introducing external storage;
Write demos or unit tests where data does not need to persist.

Dependencies

The local backend relies on vector search, which requires the extensions:

pip install "veadk-python[extensions]"

Usage

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

# The local backend imposes no naming constraints on index
ltm = LongTermMemory(backend="local", app_name="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,
)

For a full cross-session example, see Long-term memory · Cross-session example.

Parameters

Usually you only pass index or app_name to LongTermMemory. To customize embedding, pass embedding_config through backend_config.

Constructor parameters

backend_config supports the following settings:

Parameter	Type	Default	Description
`index`	`str`	No default; provided by `LongTermMemory`	Memory index name. The `local` backend imposes no naming constraints.
`embedding_config`	`EmbeddingModelConfig`	Read automatically from `MODEL_EMBEDDING_*` env vars	Embedding model config used to vectorize memory text.

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; must match the model.
`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

# Embedding model; if unset, defaults are used and the agent model's key is reused when no key is set
export MODEL_EMBEDDING_NAME="doubao-embedding-vision-250615"
export MODEL_EMBEDDING_DIM=2048
export MODEL_EMBEDDING_API_BASE="https://ark.cn-beijing.volces.com/api/v3/"
export MODEL_EMBEDDING_API_KEY="your-ark-api-key"

If MODEL_EMBEDDING_API_KEY is not set separately, VeADK reuses MODEL_AGENT_API_KEY and then falls back to an auto-fetched Ark token.

The local backend keeps memory only in process memory; data is lost when the process exits and cannot be shared across processes. For persistence or multi-instance sharing, use VikingDB, Mem0, OpenSearch, or Redis.

Overview

Get started

Core components

Observability

Extensions

Frontend

Security

Deployment

When to use

Dependencies

Usage

Parameters

Constructor parameters

Embedding config

Environment variables

​When to use

​Dependencies

​Usage

​Parameters

​Constructor parameters

​Embedding config

​Environment variables

When to use

Dependencies

Usage

Parameters

Constructor parameters

Embedding config

Environment variables