feat(summarizing_conversation_manager): introduce docs

Author: stefanoamorelli

docs/user-guide/concepts/agents/context-management.md

@@ -64,3 +64,100 @@ Key features of the `SlidingWindowConversationManager`:
 - **Maintains Window Size**: Automatically removes messages from the window if the number of messages exceeds the limit.
 - **Dangling Message Cleanup**: Removes incomplete message sequences to maintain valid conversation state.
 - **Overflow Trimming**: In the case of a context window overflow, it will trim the oldest messages from history until the request fits in the models context window.
+
+#### SummarizingConversationManager
+
+The [`SummarizingConversationManager`](../../../api-reference/agent.md#strands.agent.conversation_manager.summarizing_conversation_manager.SummarizingConversationManager) implements intelligent conversation context management by summarizing older messages instead of simply discarding them. This approach preserves important information while staying within context limits.
+
+**Basic Usage:**
+
+```python
+from strands import Agent
+from strands.agent.conversation_manager import SummarizingConversationManager
+
+# Create the summarizing conversation manager with default settings
+conversation_manager = SummarizingConversationManager(
+    summary_ratio=0.3,  # Summarize 30% of messages when context reduction is needed
+    preserve_recent_messages=10,  # Always keep 10 most recent messages
+)
+
+agent = Agent(
+    conversation_manager=conversation_manager
+)
+```
+
+**Advanced Configuration with Custom Summarization Agent:**
+
+```python
+from strands import Agent
+from strands.agent.conversation_manager import SummarizingConversationManager
+from strands.models import AnthropicModel
+
+# Create a cheaper, faster model for summarization tasks
+summarization_model = AnthropicModel(
+    model_id="claude-haiku-4-20250514",  # More cost-effective for summarization
+    max_tokens=1000,
+    params={"temperature": 0.1}  # Low temperature for consistent summaries
+)
+custom_summarization_agent = Agent(model=summarization_model)
+
+conversation_manager = SummarizingConversationManager(
+    summary_ratio=0.4,
+    preserve_recent_messages=8,
+    summarization_agent=custom_summarization_agent
+)
+
+agent = Agent(
+    conversation_manager=conversation_manager
+)
+```
+
+**Custom System Prompt for Domain-Specific Summarization:**
+
+```python
+from strands import Agent
+from strands.agent.conversation_manager import SummarizingConversationManager
+
+# Custom system prompt for technical conversations
+custom_system_prompt = """
+You are summarizing a technical conversation. Create a concise bullet-point summary that:
+- Focuses on code changes, architectural decisions, and technical solutions
+- Preserves specific function names, file paths, and configuration details
+- Omits conversational elements and focuses on actionable information
+- Uses technical terminology appropriate for software development
+
+Format as bullet points without conversational language.
+"""
+
+conversation_manager = SummarizingConversationManager(
+    summary_ratio=0.4,
+    preserve_recent_messages=8,
+    summarization_system_prompt=custom_system_prompt
+)
+
+agent = Agent(
+    conversation_manager=conversation_manager
+)
+```
+
+Key features of the `SummarizingConversationManager`:
+
+- **Context Window Management**: Automatically reduces context when token limits are exceeded
+- **Intelligent Summarization**: Uses structured bullet-point summaries to capture key information
+- **Tool Pair Preservation**: Ensures tool use and result message pairs aren't broken during summarization
+- **Flexible Configuration**: Customize summarization behavior through various parameters
+- **Fallback Safety**: Handles summarization failures gracefully
+
+Configuration parameters:
+
+- **`summary_ratio`** (float, default: 0.3): Percentage of messages to summarize when reducing context (0.0 to 1.0)
+- **`preserve_recent_messages`** (int, default: 10): Minimum number of recent messages to always keep
+- **`summarization_agent`** (Agent, optional): Custom agent for generating summaries. If not provided, uses the main agent instance.
+- **`summarization_system_prompt`** (str, optional): Custom system prompt for summarization. If not provided, uses a default prompt that creates structured bullet-point summaries focusing on key topics, tools used, and technical information in third-person format.
+
+How it works:
+
+1. **Proactive Management**: The `apply_management()` method can be extended for proactive context management
+2. **Context Reduction**: When context limits are exceeded, `reduce_context()` generates summaries of older messages
+3. **Smart Splitting**: Automatically adjusts split points to preserve tool use/result message pairs
+4. **Summary Generation**: Creates structured summaries using bullet points, focusing on key topics and technical information