feat: pruning conversation conversation manager docs

docs/examples/python/conversation_pruning.md

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+# Conversation Pruning
+
+This guide demonstrates how to use the `PruningConversationManager` to selectively manage conversation history through intelligent pruning strategies.
+
+## Overview
+
+The `PruningConversationManager` provides a flexible approach to conversation management that:
+
+- **Preserves Structure**: Unlike summarization, pruning maintains the conversation's message structure
+- **Uses Strategies**: Employs pluggable strategies to determine what and how to prune
+- **Selective Preservation**: Keeps important messages (initial and recent) while pruning middle content
+- **Proactive Management**: Can automatically prune when approaching token limits
+- **Context-Aware**: Makes intelligent decisions based on message content and context
+
+## Basic Usage
+
+### Simple Tool Result Pruning
+
+The most common use case is compressing large tool results that can consume significant context space:
+
+```python
+from strands import Agent
+from strands.agent.conversation_manager import PruningConversationManager
+from strands.agent.conversation_manager.strategies import LargeToolResultPruningStrategy
+
+# Create a strategy to compress large tool results
+tool_result_strategy = LargeToolResultPruningStrategy(
+    max_tool_result_tokens=10_000,  # Compress results larger than 10k tokens
+    compression_template="[Tool result compressed: {original_size} → {compressed_size} tokens. Status: {status}]"
+)
+
+# Create the pruning manager
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[tool_result_strategy],
+    preserve_recent_messages=3,      # Keep 3 most recent messages
+    preserve_initial_messages=1,     # Keep 1 initial message
+    enable_proactive_pruning=True,   # Enable automatic pruning
+    pruning_threshold=0.8,           # Prune when 80% of context is used
+    context_window_size=100_000      # 100k token context window
+)
+
+agent = Agent(
+    conversation_manager=conversation_manager
+)
+
+# The agent will now automatically compress large tool results
+# and proactively prune when the conversation grows too large
+```
+
+### Multiple Pruning Strategies
+
+You can combine multiple strategies for comprehensive pruning:
+
+```python
+from strands import Agent
+from strands.agent.conversation_manager import PruningConversationManager
+from strands.agent.conversation_manager.strategies import LargeToolResultPruningStrategy
+
+# Strategy 1: Compress large tool results
+tool_result_strategy = LargeToolResultPruningStrategy(
+    max_tool_result_tokens=5_000
+)
+
+# You can create custom strategies by implementing the PruningStrategy interface
+class OldMessagePruningStrategy:
+    """Custom strategy to remove very old messages."""
+
+    def should_prune_message(self, message, context):
+        # Prune messages that are more than 20 messages old
+        return context["message_index"] < context["total_messages"] - 20
+
+    def prune_message(self, message, agent):
+        # Remove the message entirely
+        return None
+
+    def get_strategy_name(self):
+        return "OldMessagePruningStrategy"
+
+old_message_strategy = OldMessagePruningStrategy()
+
+# Combine strategies
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[tool_result_strategy, old_message_strategy],
+    preserve_recent_messages=5,
+    preserve_initial_messages=2
+)
+
+agent = Agent(conversation_manager=conversation_manager)
+```
+
+## Advanced Configuration
+
+### Fine-Tuning Preservation Settings
+
+Control exactly which messages are preserved during pruning:
+
+```python
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[LargeToolResultPruningStrategy()],
+    preserve_initial_messages=3,     # Keep first 3 messages (system prompt, initial exchange)
+    preserve_recent_messages=5,      # Keep last 5 messages (recent context)
+    enable_proactive_pruning=True,
+    pruning_threshold=0.6,           # More aggressive - prune at 60% capacity
+    context_window_size=150_000
+)
+```
+
+### Reactive vs Proactive Pruning
+
+```python
+# Reactive only - prune only when context window is exceeded
+reactive_manager = PruningConversationManager(
+    pruning_strategies=[LargeToolResultPruningStrategy()],
+    enable_proactive_pruning=False   # Disable proactive pruning
+)
+
+# Proactive - prune before hitting limits
+proactive_manager = PruningConversationManager(
+    pruning_strategies=[LargeToolResultPruningStrategy()],
+    enable_proactive_pruning=True,
+    pruning_threshold=0.7,           # Prune when 70% full
+    context_window_size=200_000
+)
+```
+
+## Custom Pruning Strategies
+
+Create your own pruning strategies by implementing the `PruningStrategy` interface:
+
+```python
+from strands.agent.conversation_manager.pruning_conversation_manager import PruningStrategy
+from strands.types.content import Message
+from typing import Optional
+
+class TokenBasedPruningStrategy(PruningStrategy):
+    """Prune messages based on token count."""
+
+    def __init__(self, max_message_tokens: int = 1000):
+        self.max_message_tokens = max_message_tokens
+
+    def should_prune_message(self, message: Message, context) -> bool:
+        """Prune messages that exceed the token limit."""
+        return context["token_count"] > self.max_message_tokens
+
+    def prune_message(self, message: Message, agent) -> Optional[Message]:
+        """Truncate the message content."""
+        pruned_message = message.copy()
+
+        for content in pruned_message.get("content", []):
+            if "text" in content:
+                text = content["text"]
+                if len(text) > 500:  # Truncate long text
+                    content["text"] = text[:500] + "... [truncated]"
+
+        return pruned_message
+
+    def get_strategy_name(self) -> str:
+        return "TokenBasedPruningStrategy"
+
+# Use the custom strategy
+custom_strategy = TokenBasedPruningStrategy(max_message_tokens=2000)
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[custom_strategy]
+)
+```
+
+### Content-Aware Pruning Strategy
+
+Create strategies that understand message content:
+
+```python
+class DebugMessagePruningStrategy(PruningStrategy):
+    """Remove debug and logging messages to save context space."""
+
+    def should_prune_message(self, message: Message, context) -> bool:
+        """Identify debug messages by content patterns."""
+        for content in message.get("content", []):
+            if "text" in content:
+                text = content["text"].lower()
+                # Look for debug patterns
+                debug_patterns = ["debug:", "log:", "trace:", "verbose:"]
+                if any(pattern in text for pattern in debug_patterns):
+                    return True
+        return False
+
+    def prune_message(self, message: Message, agent) -> Optional[Message]:
+        """Remove debug messages entirely."""
+        return None  # Remove the message completely
+
+    def get_strategy_name(self) -> str:
+        return "DebugMessagePruningStrategy"
+```
+
+## Tool Result Compression
+
+The `LargeToolResultPruningStrategy` provides sophisticated compression for tool results:
+
+```python
+# Detailed configuration for tool result compression
+tool_strategy = LargeToolResultPruningStrategy(
+    max_tool_result_tokens=25_000,   # Compress results larger than 25k tokens
+    compression_template=(
+        "[COMPRESSED] Original: {original_size} tokens → Compressed: {compressed_size} tokens\n"
+        "Status: {status}\n"
+        "--- Compressed Content Below ---"
+    ),
+    enable_llm_compression=False     # Use simple compression (LLM compression not yet implemented)
+)
+
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[tool_strategy],
+    preserve_recent_messages=4,
+    preserve_initial_messages=2
+)
+```
+
+### Understanding Tool Result Compression
+
+The strategy compresses tool results by:
+
+1. **Text Truncation**: Long text content is truncated with indicators
+2. **JSON Summarization**: Large JSON objects are replaced with metadata and samples
+3. **Metadata Preservation**: Tool status and IDs are always preserved
+4. **Compression Notes**: Clear indicators show what was compressed
+
+Example of compressed output:
+```
+[Tool result compressed: 15000 tokens → 500 tokens. Status: success]
+{
+  "_compressed": true,
+  "_n_original_keys": 150,
+  "_size": 15000,
+  "_type": "dict",
+  "sample_key_1": "sample_value_1",
+  "sample_key_2": "sample_value_2"
+}
+```
+
+## Monitoring and Debugging
+
+### Tracking Pruning Activity
+
+```python
+# Access pruning statistics
+print(f"Messages removed: {conversation_manager.removed_message_count}")
+
+# Get current state for debugging
+state = conversation_manager.get_state()
+print(f"Manager state: {state}")
+```
+
+### Logging Pruning Decisions
+
+Enable logging to see pruning decisions:
+
+```python
+import logging
+
+# Enable debug logging for pruning
+logging.getLogger("strands.agent.conversation_manager.pruning_conversation_manager").setLevel(logging.DEBUG)
+logging.getLogger("strands.agent.conversation_manager.strategies.tool_result_pruning").setLevel(logging.DEBUG)
+
+# Now you'll see detailed logs about pruning decisions
+```
+
+## Best Practices
+
+### 1. Choose Appropriate Thresholds
+
+```python
+# For long-running conversations with large tool results
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[LargeToolResultPruningStrategy(max_tool_result_tokens=10_000)],
+    pruning_threshold=0.6,           # Prune early to avoid context overflow
+    preserve_recent_messages=5,      # Keep enough recent context
+    preserve_initial_messages=2      # Preserve system setup
+)
+```
+
+### 2. Preserve Critical Messages
+
+```python
+# For conversations where initial setup is crucial
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[LargeToolResultPruningStrategy()],
+    preserve_initial_messages=5,     # Keep more initial context
+    preserve_recent_messages=3,      # Standard recent context
+    pruning_threshold=0.8            # Less aggressive pruning
+)
+```
+
+### 3. Combine with Other Managers
+
+You can switch between different conversation managers based on use case:
+
+```python
+from strands.agent.conversation_manager import SummarizingConversationManager
+
+# Use pruning for tool-heavy conversations
+pruning_manager = PruningConversationManager(
+    pruning_strategies=[LargeToolResultPruningStrategy()]
+)
+
+# Use summarizing for text-heavy conversations  
+summarizing_manager = SummarizingConversationManager()
+
+# Switch based on conversation characteristics
+if has_many_tool_results:
+    agent.conversation_manager = pruning_manager
+else:
+    agent.conversation_manager = summarizing_manager
+```
+
+## Common Use Cases
+
+### 1. API Integration Agents
+
+For agents that make many API calls with large responses:
+
+```python
+api_strategy = LargeToolResultPruningStrategy(
+    max_tool_result_tokens=5_000,    # API responses can be large
+    compression_template="[API Response compressed: {original_size} → {compressed_size} tokens]"
+)
+
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[api_strategy],
+    preserve_recent_messages=4,      # Keep recent API context
+    pruning_threshold=0.7
+)
+```
+
+### 2. Data Analysis Agents
+
+For agents processing large datasets:
+
+```python
+data_strategy = LargeToolResultPruningStrategy(
+    max_tool_result_tokens=15_000,   # Data outputs can be very large
+)
+
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[data_strategy],
+    preserve_initial_messages=3,     # Keep data setup context
+    preserve_recent_messages=5,      # Keep recent analysis
+    pruning_threshold=0.6            # Aggressive pruning for large data
+)
+```
+
+### 3. Code Generation Agents
+
+For agents that generate and execute code:
+
+```python
+# Custom strategy for code execution results
+class CodeExecutionPruningStrategy(PruningStrategy):
+    def should_prune_message(self, message, context):
+        # Prune large code execution outputs
+        if context["has_tool_result"]:
+            for content in message.get("content", []):
+                if "toolResult" in content:
+                    # Check if it's a code execution result
+                    tool_result = content["toolResult"]
+                    if "code_execution" in str(tool_result).lower():
+                        return context["token_count"] > 2000
+        return False
+
+    def prune_message(self, message, agent):
+        # Compress code execution results
+        pruned_message = message.copy()
+        for content in pruned_message.get("content", []):
+            if "toolResult" in content:
+                result = content["toolResult"]
+                if result.get("content"):
+                    # Keep only first and last few lines of output
+                    for result_content in result["content"]:
+                        if "text" in result_content:
+                            lines = result_content["text"].split('\n')
+                            if len(lines) > 20:
+                                compressed = (
+                                    '\n'.join(lines[:5]) + 
+                                    f'\n... [{len(lines)-10} lines omitted] ...\n' +
+                                    '\n'.join(lines[-5:])
+                                )
+                                result_content["text"] = compressed
+        return pruned_message
+
+    def get_strategy_name(self):
+        return "CodeExecutionPruningStrategy"
+
+code_strategy = CodeExecutionPruningStrategy()
+conversation_manager = PruningConversationManager(
+    pruning_strategies=[code_strategy],
+    preserve_recent_messages=3
+)
+```
+
+This comprehensive guide shows how to effectively use conversation pruning to manage context while preserving important information and conversation structure.