Intelligent Conversation Compaction

Intelligent Conversation Compaction extracts the conversation’s topic, current goal, key decisions, and action items into a structured summary that preserves narrative continuity.

Quick Start

Enable on Agent via ManagerConfig

from praisonaiagents import Agent, ManagerConfig

agent = Agent(
    name="LongChat",
    instructions="Help the user across many turns.",
    context=ManagerConfig(
        auto_compact=True,
        strategy="conversation",
        conversation_compaction=True,
        conversation_analyzer_strategy="hybrid",
        conversation_min_compaction_ratio=0.3,
    ),
)

agent.start("Let's design a new product over the next few hours...")

Direct API for full control

from praisonaiagents import (
    get_conversation_analyzer,
    get_conversation_compactor,
)

analyzer = get_conversation_analyzer(strategy="hybrid")
compactor = get_conversation_compactor(analyzer, min_compaction_ratio=0.3)

compacted, context = compactor.compact_conversation(
    messages,
    target_tokens=4000,
    preserve_recent=5,
)
print(context.main_topic, context.current_goal, context.key_decisions)

How It Works

Analyzer Strategy Decision:

Strategy	Uses LLM?	When to use
`hybrid` (default)	LLM with rule-based fallback	Best balance — recommended
`rule_based`	No	Predictable / no extra LLM cost
`llm_only`	Yes (required)	Highest fidelity, requires `llm_analyze_fn`

ConversationContext Shape

Field	Type	Description
`main_topic`	`str`	Primary conversation topic
`current_goal`	`str`	User’s current objective
`progress_summary`	`str`	Progress made towards goal
`key_decisions`	`List[str]`	Important decisions made
`important_facts`	`List[str]`	Critical factual information
`action_items`	`List[str]`	Next steps or todo items
`user_preferences`	`List[str]`	Expressed user preferences
`tool_results_summary`	`List[str]`	Summaries of tool outputs
`conversation_tone`	`str`	Tone: casual, professional, technical
`original_message_count`	`int`	Messages before compaction
`compacted_message_count`	`int`	Messages after compaction
`compaction_timestamp`	`float`	When compaction occurred

.to_summary_message() example output:

[💬 **Conversation Summary** - 47 messages compacted]

📋 **Topic**: Product design for developer tools
🎯 **Current Goal**: Create wireframes for the main dashboard
📈 **Progress**: Defined user personas, sketched initial layouts
🔑 **Key Decisions**:
• Using React with TypeScript
• Focus on mobile-first design
• Integrate with GitHub API
💡 **Important Context**:
• Users prefer dark mode interface
• Performance is critical for large repos
✅ **Action Items**:
• Finalize color scheme
• Create interactive prototype
⚙️ **User Preferences**: minimal UI, keyboard shortcuts, offline support

ManagerConfig Fields

Field	Type	Default	Description
`conversation_compaction`	`bool`	`False`	Enable intelligent conversation compaction
`conversation_analyzer_strategy`	`str`	`"hybrid"`	`"hybrid"`, `"rule_based"`, or `"llm_only"`
`conversation_min_compaction_ratio`	`float`	`0.3`	Skip compaction if savings below this ratio

OptimizerStrategy.CONVERSATION

Use strategy="conversation" for:

Long conversations with topic evolution — preserves narrative flow better than basic summarization
Multi-hour planning sessions — tracks decisions and action items across topic changes
Iterative development — maintains context of completed work and next steps

When to use different strategies:

strategy="conversation" → Structured summaries with topic/goal tracking
strategy="smart" → Adaptive optimization (pruning, sliding window, etc.)
strategy="summarize" → Simple LLM summarization without structure

Common Patterns

Multi-hour planning sessions:

agent = Agent(
    name="ProductPlanner",
    instructions="Help design products over extended sessions.",
    context=ManagerConfig(
        auto_compact=True,
        strategy="conversation",
        conversation_compaction=True,
        conversation_min_compaction_ratio=0.3,
    ),
)

Agent handoff with context preservation:

# First agent analyzes conversation
analyzer = get_conversation_analyzer(strategy="hybrid")
context = analyzer.analyze_conversation(messages)

# Pass structured context to new agent
new_agent.start(f"""
Continue this conversation about {context.main_topic}.
Current goal: {context.current_goal}
Progress: {context.progress_summary}
Recent decisions: {'; '.join(context.key_decisions[-3:])}
""")

Recover from ineffective basic summarization:

# If basic summarization lost important context
compactor = get_conversation_compactor(
    analyzer=get_conversation_analyzer("hybrid"),
    min_compaction_ratio=0.3
)
recovered_messages, rich_context = compactor.compact_conversation(
    original_messages,
    target_tokens=4000,
    preserve_recent=5
)

Best Practices

Keep min_compaction_ratio >= 0.3

Setting the minimum compaction ratio to at least 30% ensures summaries provide meaningful token savings. Lower ratios may waste computational resources on minimal gains.

Use hybrid unless you have strong reasons

The hybrid strategy provides the best balance of quality and reliability. Use rule_based only when LLM costs are prohibitive, and llm_only only when maximum fidelity is critical.

preserve_recent=5 is a good default

Preserving the last 5 messages maintains immediate context while allowing effective compaction. Increase this for tool-heavy conversations where recent outputs are critical.

Inspect key_decisions after compaction

Always verify that critical decisions were captured correctly by checking context.key_decisions. This helps catch cases where important information might be lost.

LLM Context Compression

LLM-driven compression with session lineage and head/tail protection

Context Optimizer

Overview of all optimization strategies including conversation compaction

Context Strategies

Choosing the right optimization approach for your use case

Context Compaction

Basic compaction strategies and when to use intelligent compaction

​Quick Start

​How It Works

​ConversationContext Shape

​ManagerConfig Fields

​OptimizerStrategy.CONVERSATION

​Common Patterns

​Best Practices

​Related