Context Management

As conversations grow, your agent’s context window fills with messages, tool results, and system prompts. Without management, this leads to token limit errors, degraded performance, and loss of relevant information.

The SDK provides several layers of context management that you can use independently or together.

Automatic context management

For most agents with multi-turn conversations, you don’t need to configure conversation management and context offloading separately. Pass context_manager="auto" (Python) or contextManager: "auto" (TypeScript) and the SDK wires up both with tuned defaults:

Python
TypeScript

from strands import Agent

agent = Agent(context_manager="auto")

import { Agent } from '@strands-agents/sdk'

const agent = new Agent({
  contextManager: 'auto',
})

What it sets up

This composes two components whose defaults scored highest across ContextBench evaluations relative to other Strands Agent configurations:

SummarizingConversationManager (summary ratio of 0.3, compression threshold of 0.85): proactively summarizes older messages before the context window fills, preserving key information while freeing space for new turns.
ContextOffloader plugin (max result tokens of 1,500, preview tokens of 750): intercepts large tool results at execution time, stores them externally, and keeps a truncated preview in context. Registers a retrieve_offloaded_content tool so the agent can fetch full content on demand.

For full details on each component, see Conversation Management and Context Offloader.

Combining with explicit configuration

Your own settings take precedence when you need fine-grained control:

Custom conversation manager: If you also pass conversation_manager / conversationManager, your manager replaces the auto-composed SummarizingConversationManager. The SDK still adds the ContextOffloader plugin.
Existing ContextOffloader: If your plugins list already contains a ContextOffloader instance, no duplicate is added. Your configuration is preserved.

Python
TypeScript

from strands import Agent
from strands.agent.conversation_manager import (
    SlidingWindowConversationManager,
)

# Your conversation manager is used;
# ContextOffloader is still added automatically
agent = Agent(
    context_manager="auto",
    conversation_manager=SlidingWindowConversationManager(
        window_size=30,
    ),
)

import {
  Agent,
  SlidingWindowConversationManager,
} from '@strands-agents/sdk'

// Your conversation manager is used;
// ContextOffloader is still added automatically
const agent = new Agent({
  contextManager: 'auto',
  conversationManager: new SlidingWindowConversationManager({
    windowSize: 30,
  }),
})

Limitations

Stateful models: Stateful models manage conversation state server-side. Setting context_manager="auto" with a stateful model raises a ValueError (Python) or Error (TypeScript).

The auto-composed offloader uses in-memory storage that does not persist across process restarts. If your agent uses session management and needs durable offloaded content, configure an explicit ContextOffloader with FileStorage or S3Storage. See Storage Backends.