Overview

Short-term memory holds session-level context so an agent remembers what was said across turns. It is essentially the conversation context sent to the model — the system prompt plus the message history — keyed by session_id: reuse the same session_id and the agent remembers earlier turns. When a user starts a conversation, VeADK automatically creates a Session object and tracks everything in that conversation.

Short-term memory builds on Google ADK’s Session mechanism. For background, see Google ADK Session.

Single entry point: ShortTermMemory

Whatever backend you use, you interact with one class — veadk.memory.short_term_memory.ShortTermMemory. It selects the storage mode based on backend (or db_url) and exposes a uniform session-management surface.

from veadk.memory.short_term_memory import ShortTermMemory

# Select the implementation via backend
stm = ShortTermMemory(backend="sqlite", local_database_path="./stm.db")

Parameters

Parameter	Type	Default	Description
`backend`	`"local" \| "sqlite" \| "mysql" \| "postgresql" \| "database"`	`"local"`	Selects the backend. `database` is deprecated and equivalent to `sqlite`.
`db_url`	`str`	`""`	A database connection string (e.g. `sqlite:///./test.db`). Once set, `backend` is ignored, and `db_url` is used with `db_kwargs`.
`backend_configs`	`dict`	`{}`	Backend-specific settings, such as overriding database connection configuration through `mysql_config` or `postgresql_config`.
`db_kwargs`	`dict`	`{}`	Additional database connection or connection-pool settings.
`local_database_path`	`str`	`/tmp/veadk_local_database.db`	Local DB file path, used only by `sqlite`.
`after_load_memory_callback`	`Callable \| None`	`None`	Callback invoked after a session is loaded; receives the loaded `Session`.

If a username or password in the connection string contains special characters such as @ or :, URL-encode it with urllib.parse.quote_plus first (e.g. p@ssword → p%40ssword), otherwise parsing fails.

Backend behavior

All backends provide the same session-management operations. They differ in persistence location and connection method:

local keeps sessions in the current process only;
sqlite writes sessions to a local file;
mysql and postgresql write sessions to an external database for multi-instance sharing.

Choosing a backend

Backend	Persistent	External service	Use case	Docs
`local`	No (memory only)	None	Local debugging, ephemeral sessions	In-memory
`sqlite`	Yes (local file)	None	Single-node persistence	SQLite
`mysql`	Yes	MySQL	Distributed persistence	MySQL
`postgresql`	Yes	PostgreSQL	Distributed persistence	PostgreSQL

Working with the Runner

Short-term memory is usually passed to the Runner, which then creates or restores sessions automatically. Reuse the same session_id at run time to continue the context.

import asyncio

from veadk import Agent, Runner
from veadk.memory.short_term_memory import ShortTermMemory

stm = ShortTermMemory(backend="sqlite", local_database_path="./stm.db")
agent = Agent(name="memory_agent", instruction="Remember what the user tells you.")
runner = Runner(agent=agent, short_term_memory=stm, app_name="memory_demo")

async def main():
    sid = "user-42-chat"
    print(await runner.run(messages="My name is Ming and my favorite color is blue.", session_id=sid))
    print(await runner.run(messages="What's my name? What's my favorite color?", session_id=sid))

asyncio.run(main())

If you pass neither short_term_memory nor session_service to the Runner, it falls back to creating a local (in-memory) short-term memory automatically.

Session management interface

You rarely create or manage a Session directly; instead you use session_service to manage the full lifecycle:

Start a session create_session(): create a new Session when the user starts interacting.
Resume a session get_session(): retrieve a Session by session_id to continue.
Save progress append_event(): append a new interaction (Event) to the history.
List sessions list_sessions(): query active sessions for a user and app.
Clean up delete_session(): delete a Session and its associated data.

Context compaction

As a conversation grows, the history keeps expanding, increasing what the model must process and slowing responses. Context compaction summarizes the history with a sliding window: when the history exceeds a threshold, older events are compacted automatically.

Configure compaction

Once configured, the Runner compacts the history each time the interval is reached.

from google.adk.apps.app import App, EventsCompactionConfig

from veadk import Agent

root_agent = Agent(
    name="my_agent",
    instruction="You are a helpful assistant who answers politely.",
)

app = App(
    name="my_agent",
    root_agent=root_agent,
    events_compaction_config=EventsCompactionConfig(
        compaction_interval=3,  # compact every 3 new calls
        overlap_size=1,         # overlap with the last event of the previous window
    ),
)

Custom compactor

Use LlmEventSummarizer to set the model and prompt template for compaction. Provide the model’s API key and base via environment variables (read from os.environ, never hard-code):

import os

from google.adk.apps.app import App, EventsCompactionConfig
from google.adk.apps.llm_event_summarizer import LlmEventSummarizer
from google.adk.models.lite_llm import LiteLlm

from veadk import Agent

root_agent = Agent(
    name="my_agent",
    instruction="You are a helpful assistant who answers politely.",
)

summarization_llm = LiteLlm(
    model="volcengine/doubao-seed-2-1-pro-260628",
    api_key=os.environ["MODEL_AGENT_API_KEY"],
    api_base=os.environ.get(
        "MODEL_AGENT_API_BASE", "https://ark.cn-beijing.volces.com/api/v3/"
    ),
)

my_compactor = LlmEventSummarizer(
    llm=summarization_llm,
    prompt_template="""Summarize this conversation:
1. Keep key entities, data points, and timeline;
2. Highlight the core problems discussed and their solutions;
3. Stay logically coherent and context-relevant;
4. Remove repetition and redundant details.""",
)

app = App(
    name="my_agent",
    root_agent=root_agent,
    events_compaction_config=EventsCompactionConfig(
        compactor=my_compactor,
        compaction_interval=5,
        overlap_size=1,
    ),
)

Get started

Core components

Observability

Extensions

Frontend

Security

Deployment

Overview

Single entry point: ShortTermMemory

Parameters

Backend behavior

Choosing a backend

Working with the Runner

Session management interface

Context compaction

Configure compaction

Custom compactor

​Single entry point: ShortTermMemory

​Parameters

​Backend behavior

​Choosing a backend

​Working with the Runner

​Session management interface

​Context compaction

​Configure compaction

​Custom compactor

Single entry point: ShortTermMemory

Parameters

Backend behavior

Choosing a backend

Working with the Runner

Session management interface

Context compaction

Configure compaction

Custom compactor