session-compression

AI Session Compression Techniques

Summary

Compress long AI conversations to fit context windows while preserving critical information.

Session compression enables production AI applications to manage multi-turn conversations efficiently by reducing token usage by 70-95% through summarization, embedding-based retrieval, and intelligent context management. Achieve 3-20x compression ratios with minimal performance degradation.

Key Benefits:

• Cost Reduction: 80-90% token cost savings through hierarchical memory
• Performance: 2x faster responses with compressed context
• Scalability: Handle conversations exceeding 1M tokens
• Quality: Preserve critical information with <2% accuracy loss

When to Use

Use session compression when:

• Multi-turn conversations approach context window limits (>50% capacity)
• Long-running chat sessions (customer support, tutoring, code assistants)
• Token costs become significant (high-volume applications)
• Response latency increases due to large context
• Managing conversation history across multiple sessions

Don't use when:

• Short conversations (<10 turns) fitting easily in context
• Every detail must be preserved verbatim (legal, compliance)
• Single-turn or stateless interactions
• Context window usage is <30%

Ideal scenarios:

• Chatbots with 50+ turn conversations
• AI code assistants tracking long development sessions
• Customer support with multi-session ticket history
• Educational tutors with student progress tracking
• Multi-day collaborative AI workflows

Quick Start

Basic Setup with LangChain

from langchain.memory import ConversationSummaryBufferMemory
from langchain_anthropic import ChatAnthropic
from anthropic import Anthropic

# Initialize Claude client
llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    api_key="your-api-key"
)

# Setup memory with automatic summarization
memory = ConversationSummaryBufferMemory(
    llm=llm,
    max_token_limit=2000,  # Summarize when exceeding this
    return_messages=True
)

# Add conversation turns
memory.save_context(
    {"input": "What's session compression?"},
    {"output": "Session compression reduces conversation token usage..."}
)

# Retrieve compressed context
context = memory.load_memory_variables({})