Chapter 8: Conversation Managers — Controlling Context Windows¶

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Conversation managers control how the conversation history is managed — truncation, summarization, or no management at all. This is different from session persistence (which stores history) — conversation managers decide what's in the context window when the LLM is called.

agents:
  assistant:
    model: default
    conversation_manager:
      type: strands.agent:SlidingWindowConversationManager
      params:
        window_size: 40
        should_truncate_results: false
    system_prompt: "You are a helpful assistant."

Built-in Conversation Managers¶

These come from strands-agents directly:

Class	What It Does
`strands.agent:SlidingWindowConversationManager`	Keeps the last N messages in context
`strands.agent:SummarizingConversationManager`	Summarizes older messages to save context
`strands.agent:NullConversationManager`	No management — keeps entire history

Using Them¶

# Fixed-window approach
conversation_manager:
  type: strands.agent:SlidingWindowConversationManager
  params:
    window_size: 20

# Summarization approach
conversation_manager:
  type: strands.agent:SummarizingConversationManager
  params:
    summary_ratio: 0.3
    preserve_recent_messages: 10

# No management (pass everything)
conversation_manager:
  type: strands.agent:NullConversationManager

Custom Conversation Managers¶

Write your own by subclassing strands.agent.conversation_manager.ConversationManager:

conversation_manager:
  type: ./managers.py:MySmartManager
  params:
    strategy: semantic_relevance

Tips & Tricks

Conversation managers are per-agent. Different agents in the same config can use different strategies.

For orchestration coordinators that do a lot of delegating, SlidingWindowConversationManager with a generous window_size prevents context overflow from long delegate tool results.

Unlike session managers, there's no "global" conversation manager — set it on each agent that needs one.

Next: Chapter 9 — MCP →