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({})

Progressive Compression Pattern

from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")

class ProgressiveCompressor: def init(self, thresholds=[0.70, 0.85, 0.95]): self.thresholds = thresholds self.messages = [] self.max_tokens = 200000 # Claude context window

def add_message(self, role: str, content: str):
    self.messages.append({"role": role, "content": content})

    # Check if compression needed
    current_usage = self._estimate_tokens()
    usage_ratio = current_usage / self.max_tokens

    if usage_ratio >= self.thresholds[0]:
        self._compress(level=self._get_compression_level(usage_ratio))

def _estimate_tokens(self):
    return sum(len(m["content"]) // 4 for m in self.messages)

def _get_compression_level(self, ratio):
    for i, threshold in enumerate(self.thresholds):
        if ratio < threshold:
            return i
    return len(self.thresholds)

def _compress(self, level: int):
    """Apply compression based on severity level."""
    if level == 1:  # 70% threshold: Light compression
        self._remove_redundant_messages()
    elif level == 2:  # 85% threshold: Medium compression
        self._summarize_old_messages(keep_recent=10)
    else:  # 95% threshold: Aggressive compression
        self._summarize_old_messages(keep_recent=5)

def _remove_redundant_messages(self):
    """Remove duplicate or low-value messages."""
    # Implementation: Use semantic deduplication
    pass

def _summarize_old_messages(self, keep_recent: int):
    """Summarize older messages, keep recent ones verbatim."""
    if len(self.messages) <= keep_recent:
        return

    # Messages to summarize
    to_summarize = self.messages[:-keep_recent]
    recent = self.messages[-keep_recent:]

    # Generate summary
    conversation_text = "\n\n".join([
        f"{m['role'].upper()}: {m['content']}"
        for m in to_summarize
    ])

    response = client.messages.create(
        model="claude-3-5-haiku-20241022",
        max_tokens=500,
        messages=[{
            "role": "user",
            "content": f"Summarize this conversation:\n\n{conversation_text}"
        }]
    )

    # Replace old messages with summary
    summary = {
        "role": "system",
        "content": f"[Summary]\n{response.content[0].text}"
    }
    self.messages = [summary] + recent

Usage

compressor = ProgressiveCompressor()

for i in range(100): compressor.add_message("user", f"Message {i}") compressor.add_message("assistant", f"Response {i}")

Using Anthropic Prompt Caching (90% Cost Reduction)

from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")

Build context with cache control

messages = [ { "role": "user", "content": [ { "type": "text", "text": "Long conversation context here...", "cache_control": {"type": "ephemeral"} # Cache this } ] }, { "role": "assistant", "content": "Previous response..." }, { "role": "user", "content": "New question" # Not cached, changes frequently } ]

response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, messages=messages )

Cache hit reduces costs by 90% for cached content

Core Concepts

Context Windows and Token Limits

Context window: Maximum tokens an LLM can process in a single request (input + output).

Current limits (2025):

• Claude 3.5 Sonnet: 200K tokens (~150K words, ~600 pages)

• GPT-4 Turbo: 128K tokens (~96K words, ~384 pages)

• Gemini 1.5 Pro: 2M tokens (~1.5M words, ~6000 pages)

Token estimation:

• English: ~4 characters per token

• Code: ~3 characters per token

• Rule of thumb: 1 token ≈ 0.75 words

Why compression matters:

• Cost: Claude Sonnet costs $3/$15 per 1M input/output tokens

• Latency: Larger contexts increase processing time

• Quality: Excessive context can dilute attention on relevant information

Compression Ratios

Compression ratio = Original tokens / Compressed tokens

Industry benchmarks:

• Extractive summarization: 2-3x

• Abstractive summarization: 5-10x

• Hierarchical summarization: 20x+

• LLMLingua (prompt compression): 20x with 1.5% accuracy loss

• KVzip (KV cache compression): 3-4x with 2x speed improvement

Target ratios by use case:

• Customer support: 5-7x (preserve details)

• General chat: 8-12x (balance quality/efficiency)

• Code assistants: 3-5x (preserve technical accuracy)

• Long documents: 15-20x (extract key insights)

Progressive Compression Thresholds

Industry standard pattern:

Context Usage Action Technique ───────────────────────────────────────────────────────── 0-70% No compression Store verbatim 70-85% Light compression Remove redundancy 85-95% Medium compression Summarize old messages 95-100% Aggressive compression Hierarchical + RAG

Implementation guidelines:

• 70% threshold: Remove duplicate/redundant messages, semantic deduplication

• 85% threshold: Summarize messages older than 20 turns, keep recent 10-15

• 95% threshold: Multi-level hierarchical summarization + vector store archival

• Emergency (100%): Drop least important messages, aggressive summarization

Compression Techniques

1. Summarization Techniques

1.1 Extractive Summarization

Selects key sentences/phrases without modification.

Pros: No hallucination, fast, deterministic Cons: Limited compression (2-3x), may feel disjointed Best for: Legal/compliance, short-term compression

from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np

def extractive_compress(messages: list, compression_ratio: float = 0.3): """Extract most important messages using TF-IDF scoring.""" texts = [msg['content'] for msg in messages]

# Calculate TF-IDF scores
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(texts)
scores = np.array(tfidf_matrix.sum(axis=1)).flatten()

# Select top messages
n_keep = max(1, int(len(messages) * compression_ratio))
top_indices = sorted(np.argsort(scores)[-n_keep:])

return [messages[i] for i in top_indices]

1.2 Abstractive Summarization

Uses LLMs to semantically condense conversation history.

Pros: Higher compression (5-10x), coherent, synthesizes information Cons: Risk of hallucination, higher cost, less deterministic Best for: General chat, customer support, multi-session continuity

from anthropic import Anthropic

def abstractive_compress(messages: list, client: Anthropic): """Generate semantic summary using Claude.""" conversation_text = "\n\n".join([ f"{msg['role'].upper()}: {msg['content']}" for msg in messages ])

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=500,
    messages=[{
        "role": "user",
        "content": f"""Summarize this conversation, preserving:

1. Key decisions made
2. Important context and facts
3. Unresolved questions
4. Action items

Conversation: {conversation_text}

Summary (aim for 1/5 the original length):""" }] )

return {
    "role": "assistant",
    "content": f"[Summary]\n{response.content[0].text}"
}

1.3 Hierarchical Summarization (Multi-Level)

Creates summaries of summaries in a tree structure.

Pros: Extreme compression (20x+), handles 1M+ token conversations Cons: Complex implementation, multiple LLM calls, information loss accumulates Best for: Long-running conversations, multi-session applications

Architecture:

Level 0 (Raw): [Msg1][Msg2][Msg3][Msg4][Msg5][Msg6][Msg7][Msg8] Level 1 (Chunk): [Summary1-2] [Summary3-4] [Summary5-6] [Summary7-8] Level 2 (Group): [Summary1-4] [Summary5-8] Level 3 (Session): [Overall Session Summary]

from anthropic import Anthropic from typing import List, Dict

class HierarchicalMemory: def init(self, client: Anthropic, chunk_size: int = 10): self.client = client self.chunk_size = chunk_size self.levels: List[List[Dict]] = [[]] # Level 0 = raw messages

def add_message(self, message: Dict):
    """Add message and trigger summarization if needed."""
    self.levels[0].append(message)

    if len(self.levels[0]) >= self.chunk_size * 2:
        self._summarize_level(0)

def _summarize_level(self, level: int):
    """Summarize a level into the next higher level."""
    messages = self.levels[level]

    # Ensure next level exists
    while len(self.levels) <= level + 1:
        self.levels.append([])

    # Summarize first chunk
    chunk = messages[:self.chunk_size]
    summary = self._generate_summary(chunk, level)

    # Move to next level
    self.levels[level + 1].append(summary)
    self.levels[level] = messages[self.chunk_size:]

    # Recursively check if next level needs summarization
    if len(self.levels[level + 1]) >= self.chunk_size * 2:
        self._summarize_level(level + 1)

def _generate_summary(self, messages: List[Dict], level: int) -> Dict:
    """Generate summary for a chunk."""
    conversation_text = "\n\n".join([
        f"{msg['role'].upper()}: {msg['content']}"
        for msg in messages
    ])

    response = self.client.messages.create(
        model="claude-3-5-haiku-20241022",
        max_tokens=300,
        messages=[{
            "role": "user",
            "content": f"Summarize this Level {level} conversation chunk:\n\n{conversation_text}"
        }]
    )

    return {
        "role": "system",
        "content": f"[L{level+1} Summary] {response.content[0].text}",
        "level": level + 1
    }

def get_context(self, max_tokens: int = 4000) -> List[Dict]:
    """Retrieve context within token budget."""
    context = []
    token_count = 0

    # Prioritize recent raw messages
    for msg in reversed(self.levels[0]):
        msg_tokens = len(msg['content']) // 4
        if token_count + msg_tokens > max_tokens * 0.6:
            break
        context.insert(0, msg)
        token_count += msg_tokens

    # Add summaries from higher levels
    for level in range(1, len(self.levels)):
        for summary in self.levels[level]:
            summary_tokens = len(summary['content']) // 4
            if token_count + summary_tokens > max_tokens:
                break
            context.insert(0, summary)
            token_count += summary_tokens

    return context

Academic reference: "Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models" (arXiv:2308.15022)

1.4 Rolling Summarization (Continuous)

Continuously compresses conversation with sliding window.

Pros: Low latency, predictable token usage, simple Cons: Early details over-compressed, no information recovery Best for: Real-time chat, streaming conversations

from anthropic import Anthropic

class RollingMemory: def init(self, client: Anthropic, window_size: int = 10, compress_threshold: int = 15): self.client = client self.window_size = window_size self.compress_threshold = compress_threshold self.rolling_summary = None self.recent_messages = []

def add_message(self, message: dict):
    self.recent_messages.append(message)

    if len(self.recent_messages) >= self.compress_threshold:
        self._compress()

def _compress(self):
    """Compress older messages into rolling summary."""
    messages_to_compress = self.recent_messages[:-self.window_size]

    parts = []
    if self.rolling_summary:
        parts.append(f"Existing summary:\n{self.rolling_summary}")

    parts.append("\nNew messages:\n" + "\n\n".join([
        f"{msg['role']}: {msg['content']}"
        for msg in messages_to_compress
    ]))

    response = self.client.messages.create(
        model="claude-3-5-haiku-20241022",
        max_tokens=400,
        messages=[{
            "role": "user",
            "content": "\n".join(parts) + "\n\nUpdate the summary:"
        }]
    )

    self.rolling_summary = response.content[0].text
    self.recent_messages = self.recent_messages[-self.window_size:]

def get_context(self):
    context = []
    if self.rolling_summary:
        context.append({
            "role": "system",
            "content": f"[Summary]\n{self.rolling_summary}"
        })
    context.extend(self.recent_messages)
    return context

2. Embedding-Based Approaches

2.1 RAG (Retrieval-Augmented Generation)

Store full conversation in vector database, retrieve only relevant chunks.

Pros: Extremely scalable, no information loss, high relevance Cons: Requires vector DB infrastructure, retrieval latency Best for: Knowledge bases, customer support with large history

from anthropic import Anthropic from openai import OpenAI import chromadb

class RAGMemory: def init(self, anthropic_client: Anthropic, openai_client: OpenAI): self.anthropic = anthropic_client self.openai = openai_client

    # Initialize vector store
    self.chroma = chromadb.Client()
    self.collection = self.chroma.create_collection(
        name="conversation",
        metadata={"hnsw:space": "cosine"}
    )

    self.recent_messages = []
    self.recent_window = 5
    self.message_counter = 0

def add_message(self, message: dict):
    """Add to recent memory and vector store."""
    self.recent_messages.append(message)

    if len(self.recent_messages) > self.recent_window:
        old_msg = self.recent_messages.pop(0)
        self._store_in_vectordb(old_msg)

def _store_in_vectordb(self, message: dict):
    """Archive to vector database."""
    # Generate embedding
    response = self.openai.embeddings.create(
        model="text-embedding-3-small",
        input=message['content']
    )

    self.collection.add(
        embeddings=[response.data[0].embedding],
        documents=[message['content']],
        metadatas=[{"role": message['role']}],
        ids=[f"msg_{self.message_counter}"]
    )
    self.message_counter += 1

def retrieve_context(self, query: str, max_tokens: int = 4000):
    """Retrieve relevant context using RAG."""
    context = []
    token_count = 0

    # 1. Recent messages (short-term memory)
    for msg in self.recent_messages:
        context.append(msg)
        token_count += len(msg['content']) // 4

    # 2. Retrieve relevant historical context
    if token_count < max_tokens:
        query_embedding = self.openai.embeddings.create(
            model="text-embedding-3-small",
            input=query
        )

        n_results = min(10, (max_tokens - token_count) // 100)
        results = self.collection.query(
            query_embeddings=[query_embedding.data[0].embedding],
            n_results=n_results
        )

        for i, doc in enumerate(results['documents'][0]):
            if token_count + len(doc) // 4 > max_tokens:
                break

            metadata = results['metadatas'][0][i]
            context.insert(0, {
                "role": metadata['role'],
                "content": f"[Retrieved] {doc}"
            })
            token_count += len(doc) // 4

    return context

Vector database options:

• ChromaDB: Embedded, easy local development

• Pinecone: Managed, 50ms p95 latency

• Weaviate: Open-source, hybrid search

• Qdrant: High performance, payload filtering

2.2 Vector Search and Clustering

Group similar messages into clusters, represent with centroids.

Pros: Reduces redundancy, identifies themes, multi-topic handling Cons: Requires sufficient data, may lose nuances Best for: Multi-topic conversations, meeting summaries

from sklearn.cluster import KMeans from openai import OpenAI import numpy as np

class ClusteredMemory: def init(self, openai_client: OpenAI, n_clusters: int = 5): self.client = openai_client self.n_clusters = n_clusters self.messages = [] self.embeddings = []

def add_messages(self, messages: list):
    for msg in messages:
        self.messages.append(msg)

        response = self.client.embeddings.create(
            model="text-embedding-3-small",
            input=msg['content']
        )
        self.embeddings.append(response.data[0].embedding)

def compress_by_clustering(self):
    """Cluster messages and return representatives."""
    if len(self.messages) < self.n_clusters:
        return self.messages

    embeddings_array = np.array(self.embeddings)
    kmeans = KMeans(n_clusters=self.n_clusters, random_state=42)
    labels = kmeans.fit_predict(embeddings_array)

    # Select message closest to each centroid
    compressed = []
    for cluster_id in range(self.n_clusters):
        cluster_indices = np.where(labels == cluster_id)[0]
        centroid = kmeans.cluster_centers_[cluster_id]
        cluster_embeddings = embeddings_array[cluster_indices]
        distances = np.linalg.norm(cluster_embeddings - centroid, axis=1)
        closest_idx = cluster_indices[np.argmin(distances)]

        compressed.append({
            **self.messages[closest_idx],
            "cluster_id": int(cluster_id),
            "cluster_size": len(cluster_indices)
        })

    return compressed

2.3 Semantic Deduplication

Remove semantically similar messages that convey redundant information.

Pros: Reduces redundancy without losing unique content Cons: Requires threshold tuning, O(n²) complexity Best for: FAQ systems, repetitive conversations

from openai import OpenAI import numpy as np from sklearn.metrics.pairwise import cosine_similarity

class SemanticDeduplicator: def init(self, openai_client: OpenAI, similarity_threshold: float = 0.85): self.client = openai_client self.threshold = similarity_threshold

def deduplicate(self, messages: list):
    """Remove semantically similar messages."""
    if len(messages) <= 1:
        return messages

    # Generate embeddings
    embeddings = []
    for msg in messages:
        response = self.client.embeddings.create(
            model="text-embedding-3-small",
            input=msg['content']
        )
        embeddings.append(response.data[0].embedding)

    embeddings_array = np.array(embeddings)
    similarity_matrix = cosine_similarity(embeddings_array)

    # Mark unique messages
    keep_indices = []
    for i in range(len(messages)):
        is_unique = True
        for j in keep_indices:
            if similarity_matrix[i][j] > self.threshold:
                is_unique = False
                break

        if is_unique:
            keep_indices.append(i)

    return [messages[i] for i in keep_indices]

3. Token-Efficient Strategies

3.1 Message Prioritization

Assign importance scores and retain only high-priority content.

Pros: Retains most important information, flexible criteria Cons: Scoring is heuristic-based, may break flow Best for: Mixed-importance conversations, filtering noise

import re

class MessagePrioritizer: def score_message(self, msg: dict, index: int, total: int) -> float: """Calculate composite importance score.""" scores = []

    # Length score (longer = more info)
    scores.append(min(len(msg['content']) / 500, 1.0))

    # Question score
    if msg['role'] == 'user':
        scores.append(min(msg['content'].count('?') * 0.5, 1.0))

    # Entity score (capitalized words)
    entities = len(re.findall(r'\b[A-Z][a-z]+', msg['content']))
    scores.append(min(entities / 10, 1.0))

    # Recency score (linear decay)
    scores.append(index / max(total - 1, 1))

    # Role score
    scores.append(0.6 if msg['role'] == 'user' else 0.4)

    return sum(scores) / len(scores)

def prioritize(self, messages: list, target_count: int):
    """Select top N messages by priority."""
    scored = [
        (msg, self.score_message(msg, i, len(messages)), i)
        for i, msg in enumerate(messages)
    ]

    scored.sort(key=lambda x: x[1], reverse=True)
    top_messages = scored[:target_count]
    top_messages.sort(key=lambda x: x[2])  # Restore chronological order

    return [msg for msg, score, idx in top_messages]

3.2 Delta Compression

Store only changes between consecutive messages.

Pros: Highly efficient for incremental changes Cons: Reconstruction overhead, not suitable for all content Best for: Code assistants with incremental edits

import difflib

class DeltaCompressor: def init(self): self.base_messages = [] self.deltas = []

def add_message(self, message: dict):
    if not self.base_messages:
        self.base_messages.append(message)
        return

    # Find most similar previous message
    last_msg = self.base_messages[-1]

    if last_msg['role'] == message['role']:
        # Calculate delta
        diff = list(difflib.unified_diff(
            last_msg['content'].splitlines(),
            message['content'].splitlines(),
            lineterm=''
        ))

        if len('\n'.join(diff)) < len(message['content']) * 0.7:
            # Store as delta if compression achieved
            self.deltas.append({
                'base_index': len(self.base_messages) - 1,
                'delta': diff,
                'role': message['role']
            })
            return

    # Store as new base message
    self.base_messages.append(message)

def reconstruct(self):
    """Reconstruct full conversation from bases + deltas."""
    messages = self.base_messages.copy()

    for delta_info in self.deltas:
        base_content = messages[delta_info['base_index']]['content']
        # Apply diff to reconstruct (simplified)
        reconstructed = base_content  # Full implementation would apply diff
        messages.append({
            'role': delta_info['role'],
            'content': reconstructed
        })

    return messages

4. LangChain Memory Types

4.1 ConversationSummaryMemory

Automatically summarizes conversation as it progresses.

from langchain.memory import ConversationSummaryMemory from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")

memory = ConversationSummaryMemory(llm=llm)

Add conversation

memory.save_context( {"input": "Hi, I'm working on a Python project"}, {"output": "Great! How can I help with your Python project?"} )

Get summary

summary = memory.load_memory_variables({}) print(summary['history'])

Pros: Automatic summarization, simple API Cons: Every turn triggers LLM call Best for: Medium conversations (20-50 turns)

4.2 ConversationSummaryBufferMemory

Hybrid: Recent messages verbatim, older summarized.

from langchain.memory import ConversationSummaryBufferMemory from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-haiku-20241022")

memory = ConversationSummaryBufferMemory( llm=llm, max_token_limit=2000, # Summarize when exceeding return_messages=True )

Add conversation

for i in range(50): memory.save_context( {"input": f"Question {i}"}, {"output": f"Answer {i}"} )

Automatically keeps recent messages + summary of old

context = memory.load_memory_variables({})

Pros: Best balance of detail and compression Cons: Requires token limit tuning Best for: Most production applications

4.3 ConversationTokenBufferMemory

Maintains fixed token budget, drops oldest when exceeded.

from langchain.memory import ConversationTokenBufferMemory from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")

memory = ConversationTokenBufferMemory( llm=llm, max_token_limit=2000 )

Simple FIFO when token limit exceeded

Pros: Predictable token usage, simple Cons: Loses old information completely Best for: Real-time chat with strict limits

4.4 VectorStoreRetrieverMemory

Stores all messages in vector database, retrieves relevant ones.

from langchain.memory import VectorStoreRetrieverMemory from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings() vectorstore = Chroma(embedding_function=embeddings)

memory = VectorStoreRetrieverMemory( retriever=vectorstore.as_retriever(search_kwargs={"k": 5}) )

Automatically retrieves most relevant context

Pros: Infinite conversation length, semantic retrieval Cons: Requires vector DB, retrieval overhead Best for: Long-running conversations, knowledge bases

5. Anthropic-Specific Patterns

5.1 Prompt Caching (90% Cost Reduction)

Cache static context to reduce token costs.

from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")

Long conversation context

conversation_history = [ {"role": "user", "content": "Message 1"}, {"role": "assistant", "content": "Response 1"}, # ... many more messages ]

Mark context for caching

messages = [] for i, msg in enumerate(conversation_history[:-1]): content = msg['content']

# Add cache control to last context message
if i == len(conversation_history) - 2:
    messages.append({
        "role": msg['role'],
        "content": [
            {
                "type": "text",
                "text": content,
                "cache_control": {"type": "ephemeral"}
            }
        ]
    })
else:
    messages.append(msg)

Add new user message (not cached)

messages.append(conversation_history[-1])

response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, messages=messages )

Subsequent calls with same cached context cost 90% less

Cache TTL: 5 minutes Savings: 90% cost reduction for cached tokens Limits: Max 4 cache breakpoints per request Best practices:

• Cache conversation history, not current query

• Update cache when context changes significantly

• Combine with summarization for maximum efficiency

5.2 Extended Thinking for Compression Planning

Use extended thinking to plan optimal compression strategy.

from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")

response = client.messages.create( model="claude-3-7-sonnet-20250219", max_tokens=16000, thinking={ "type": "enabled", "budget_tokens": 10000 }, messages=[{ "role": "user", "content": f"""Analyze this conversation and recommend compression:

{conversation_text}

Current token count: {current_tokens} Target: {target_tokens} Required compression: {compression_ratio}x

Recommend optimal strategy.""" }] )

Access thinking process

thinking_content = [ block for block in response.content if block.type == "thinking" ]

Get compression recommendation

recommendation = response.content[-1].text

Production Patterns

Checkpointing and Persistence

Save compression state for recovery and resume.

import json import pickle from pathlib import Path

class PersistentMemory: def init(self, checkpoint_dir: str = "./checkpoints"): self.checkpoint_dir = Path(checkpoint_dir) self.checkpoint_dir.mkdir(exist_ok=True) self.memory = [] self.summary = None

def save_checkpoint(self, session_id: str):
    """Save current memory state."""
    checkpoint = {
        'messages': self.memory,
        'summary': self.summary,
        'timestamp': time.time()
    }

    checkpoint_file = self.checkpoint_dir / f"{session_id}.json"
    with open(checkpoint_file, 'w') as f:
        json.dump(checkpoint, f, indent=2)

def load_checkpoint(self, session_id: str):
    """Load memory state from checkpoint."""
    checkpoint_file = self.checkpoint_dir / f"{session_id}.json"

    if checkpoint_file.exists():
        with open(checkpoint_file, 'r') as f:
            checkpoint = json.load(f)

        self.memory = checkpoint['messages']
        self.summary = checkpoint.get('summary')
        return True

    return False

def auto_checkpoint(self, session_id: str, interval: int = 10):
    """Automatically save every N messages."""
    if len(self.memory) % interval == 0:
        self.save_checkpoint(session_id)

Resume Workflows

Continue conversations across sessions.

from anthropic import Anthropic import json

class ResumableConversation: def init(self, client: Anthropic, session_id: str): self.client = client self.session_id = session_id self.memory = self._load_or_create()

def _load_or_create(self):
    """Load existing session or create new."""
    try:
        with open(f'sessions/{self.session_id}.json', 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        return {
            'messages': [],
            'summary': None,
            'created_at': time.time()
        }

def add_turn(self, user_message: str):
    """Add user message and get response."""
    # Add user message
    self.memory['messages'].append({
        'role': 'user',
        'content': user_message
    })

    # Build context (with compression)
    context = self._build_context()

    # Get response
    response = self.client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=context + [{
            'role': 'user',
            'content': user_message
        }]
    )

    # Save response
    assistant_message = response.content[0].text
    self.memory['messages'].append({
        'role': 'assistant',
        'content': assistant_message
    })

    # Compress if needed
    if len(self.memory['messages']) > 20:
        self._compress()

    # Save state
    self._save()

    return assistant_message

def _build_context(self):
    """Build context with compression."""
    context = []

    # Add summary if exists
    if self.memory['summary']:
        context.append({
            'role': 'system',
            'content': f"[Previous conversation summary]\n{self.memory['summary']}"
        })

    # Add recent messages
    context.extend(self.memory['messages'][-10:])

    return context

def _compress(self):
    """Compress older messages."""
    if len(self.memory['messages']) < 15:
        return

    # Messages to summarize
    to_summarize = self.memory['messages'][:-10]

    # Generate summary
    conversation_text = "\n\n".join([
        f"{msg['role']}: {msg['content']}"
        for msg in to_summarize
    ])

    response = self.client.messages.create(
        model="claude-3-5-haiku-20241022",
        max_tokens=500,
        messages=[{
            'role': 'user',
            'content': f"Summarize this conversation:\n\n{conversation_text}"
        }]
    )

    # Update memory
    self.memory['summary'] = response.content[0].text
    self.memory['messages'] = self.memory['messages'][-10:]

def _save(self):
    """Save session to disk."""
    with open(f'sessions/{self.session_id}.json', 'w') as f:
        json.dump(self.memory, f, indent=2)

Usage

client = Anthropic(api_key="your-api-key") conversation = ResumableConversation(client, session_id="user123_session1")

Continue across multiple sessions

response1 = conversation.add_turn("What's Python?")

... later session

response2 = conversation.add_turn("Show me an example") # Remembers context

Hybrid Approaches (Best Practice)

Combine multiple techniques for optimal results.

from anthropic import Anthropic from openai import OpenAI import chromadb

class HybridMemorySystem: """ Combines: - Rolling summarization (short-term compression) - RAG retrieval (long-term memory) - Prompt caching (cost optimization) - Progressive compression (adaptive behavior) """

def __init__(self, anthropic_client: Anthropic, openai_client: OpenAI):
    self.anthropic = anthropic_client
    self.openai = openai_client

    # Recent messages (verbatim)
    self.recent_messages = []
    self.recent_window = 10

    # Rolling summary
    self.rolling_summary = None

    # Vector store (long-term)
    self.chroma = chromadb.Client()
    self.collection = self.chroma.create_collection(name="memory")
    self.message_counter = 0

    # Compression thresholds
    self.thresholds = {
        'light': 0.70,    # Start basic compression
        'medium': 0.85,   # Aggressive summarization
        'heavy': 0.95     # Emergency measures
    }

def add_message(self, message: dict):
    """Add message with intelligent compression."""
    self.recent_messages.append(message)

    # Check compression needs
    usage_ratio = self._estimate_usage()

    if usage_ratio >= self.thresholds['heavy']:
        self._emergency_compress()
    elif usage_ratio >= self.thresholds['medium']:
        self._medium_compress()
    elif usage_ratio >= self.thresholds['light']:
        self._light_compress()

def _light_compress(self):
    """Remove redundancy, archive to vector store."""
    if len(self.recent_messages) > self.recent_window * 1.5:
        # Archive oldest to vector store
        to_archive = self.recent_messages[:5]
        for msg in to_archive:
            self._archive_to_vectorstore(msg)

        self.recent_messages = self.recent_messages[5:]

def _medium_compress(self):
    """Generate rolling summary, aggressive archival."""
    if len(self.recent_messages) > self.recent_window:
        # Summarize older messages
        to_summarize = self.recent_messages[:-self.recent_window]

        summary_text = "\n\n".join([
            f"{msg['role']}: {msg['content']}"
            for msg in to_summarize
        ])

        if self.rolling_summary:
            summary_text = f"Existing: {self.rolling_summary}\n\nNew: {summary_text}"

        response = self.anthropic.messages.create(
            model="claude-3-5-haiku-20241022",
            max_tokens=400,
            messages=[{
                'role': 'user',
                'content': f"Update summary:\n{summary_text}"
            }]
        )

        self.rolling_summary = response.content[0].text

        # Archive all summarized messages
        for msg in to_summarize:
            self._archive_to_vectorstore(msg)

        self.recent_messages = self.recent_messages[-self.recent_window:]

def _emergency_compress(self):
    """Extreme compression for near-limit situations."""
    # Keep only 5 most recent messages
    to_archive = self.recent_messages[:-5]
    for msg in to_archive:
        self._archive_to_vectorstore(msg)

    self.recent_messages = self.recent_messages[-5:]

    # Compress summary further if needed
    if self.rolling_summary and len(self.rolling_summary) > 1000:
        response = self.anthropic.messages.create(
            model="claude-3-5-haiku-20241022",
            max_tokens=200,
            messages=[{
                'role': 'user',
                'content': f"Create ultra-concise summary:\n{self.rolling_summary}"
            }]
        )
        self.rolling_summary = response.content[0].text

def _archive_to_vectorstore(self, message: dict):
    """Store in vector database for retrieval."""
    embedding_response = self.openai.embeddings.create(
        model="text-embedding-3-small",
        input=message['content']
    )

    self.collection.add(
        embeddings=[embedding_response.data[0].embedding],
        documents=[message['content']],
        metadatas=[{'role': message['role']}],
        ids=[f"msg_{self.message_counter}"]
    )
    self.message_counter += 1

def get_context(self, current_query: str, max_tokens: int = 8000):
    """Build optimal context for current query."""
    context = []
    token_count = 0

    # 1. Add rolling summary (if exists)
    if self.rolling_summary:
        summary_msg = {
            'role': 'system',
            'content': [
                {
                    'type': 'text',
                    'text': f"[Conversation Summary]\n{self.rolling_summary}",
                    'cache_control': {'type': 'ephemeral'}  # Cache it
                }
            ]
        }
        context.append(summary_msg)
        token_count += len(self.rolling_summary) // 4

    # 2. Retrieve relevant historical context (RAG)
    if token_count < max_tokens * 0.3:
        query_embedding = self.openai.embeddings.create(
            model="text-embedding-3-small",
            input=current_query
        )

        results = self.collection.query(
            query_embeddings=[query_embedding.data[0].embedding],
            n_results=5
        )

        for i, doc in enumerate(results['documents'][0]):
            if token_count + len(doc) // 4 > max_tokens * 0.3:
                break

            metadata = results['metadatas'][0][i]
            context.append({
                'role': metadata['role'],
                'content': f"[Retrieved] {doc}"
            })
            token_count += len(doc) // 4

    # 3. Add recent messages verbatim
    for msg in self.recent_messages:
        if token_count + len(msg['content']) // 4 > max_tokens * 0.8:
            break
        context.append(msg)
        token_count += len(msg['content']) // 4

    return context

def _estimate_usage(self):
    """Estimate current context window usage."""
    total_tokens = 0

    if self.rolling_summary:
        total_tokens += len(self.rolling_summary) // 4

    for msg in self.recent_messages:
        total_tokens += len(msg['content']) // 4

    return total_tokens / 200000  # Claude Sonnet context window

Usage

anthropic_client = Anthropic(api_key="your-anthropic-key") openai_client = OpenAI(api_key="your-openai-key")

memory = HybridMemorySystem(anthropic_client, openai_client)

Add messages over time

for i in range(1000): memory.add_message({ 'role': 'user' if i % 2 == 0 else 'assistant', 'content': f"Message {i} with some content..." })

Retrieve optimized context

current_query = "What did we discuss about pricing?" context = memory.get_context(current_query)

Use with Claude

response = anthropic_client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1024, messages=context + [{ 'role': 'user', 'content': current_query }] )

Performance Benchmarks

Compression Efficiency

Technique Compression Ratio Quality Loss Latency Cost Impact

Extractive 2-3x <1% <10ms None

Abstractive 5-10x 2-5% 1-2s +$0.001/turn

Hierarchical 20x+ 5-8% 2-5s +$0.003/turn

LLMLingua 20x 1.5% 500ms None

RAG Variable <1% 100-300ms +$0.0005/turn

Prompt Caching N/A 0% 0ms -90%

Token Savings by Use Case

Customer Support (50-turn conversation):

• No compression: ~8,000 tokens/request

• Rolling summary: ~2,000 tokens/request (75% reduction)

• Hybrid (RAG + summary): ~1,500 tokens/request (81% reduction)

Code Assistant (100-turn session):

• No compression: ~25,000 tokens/request

• Hierarchical: ~5,000 tokens/request (80% reduction)

• Hybrid + caching: ~1,000 tokens/request effective (96% cost reduction)

Educational Tutor (multi-session):

• No compression: Would exceed context window

• RAG + summarization: ~3,000 tokens/request

• Infinite session length enabled

Cost Analysis

Example: Claude Sonnet pricing ($3 input, $15 output per 1M tokens)

1,000 conversations, 50 turns each:

No compression:

• Avg 8K tokens/request × 50K requests = 400M tokens

• Cost: $1,200

With rolling summarization:

• Avg 2K tokens/request × 50K requests = 100M tokens

• Summarization overhead: +10M tokens

• Cost: $330 (72% savings)

With hybrid system + caching:

• First turn: 2K tokens (no cache)

• Subsequent: 200 tokens effective (90% cache hit)

• Total: ~15M tokens effective

• Cost: $45 (96% savings)

Tool Recommendations

Memory Management Tools

Mem0 (Recommended for Production)

Best for: Hybrid memory systems with minimal code

from mem0 import MemoryClient

client = MemoryClient(api_key="your-mem0-key")

Automatically handles compression, summarization, RAG

memory = client.create_memory( user_id="user123", messages=[ {"role": "user", "content": "I'm working on a Python project"}, {"role": "assistant", "content": "Great! What kind of project?"} ] )

Retrieve relevant context

context = client.get_memory( user_id="user123", query="What programming language am I using?" )

Features:

• Automatic hierarchical summarization

• Built-in RAG retrieval

• Multi-user session management

• Analytics dashboard

Pricing: $0.40/1K memory operations

Zep

Best for: Low-latency production deployments**

from zep_python import ZepClient

client = ZepClient(api_key="your-zep-key")

Add to session

client.memory.add_memory( session_id="session123", messages=[ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"} ] )

Auto-summarized retrieval

memory = client.memory.get_memory(session_id="session123")

Features:

• <100ms retrieval latency

• Automatic fact extraction

• Entity recognition

• Session management

Pricing: Open-source (self-hosted) or $0.50/1K operations (cloud)

ChromaDB

Best for: Self-hosted vector storage**

import chromadb

client = chromadb.Client() collection = client.create_collection("conversations")

Store embeddings

collection.add( documents=["Message content"], embeddings=[[0.1, 0.2, ...]], ids=["msg1"] )

Retrieve

results = collection.query( query_embeddings=[[0.1, 0.2, ...]], n_results=5 )

Features:

• Fully open-source

• Embedded or client-server

• Fast local development

Pricing: Free (self-hosted)

LangChain

Best for: Rapid prototyping and experimentation**

from langchain.memory import ConversationSummaryBufferMemory from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20241022") memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=2000)

Features:

• Multiple memory types

• Framework integration

• Extensive documentation

Pricing: Free (uses your LLM API costs)

Compression Libraries

LLMLingua

Best for: Extreme compression with minimal quality loss**

from llmlingua import PromptCompressor

compressor = PromptCompressor()

compressed = compressor.compress_prompt( context="Long conversation history...", instruction="Current user query", target_token=500 )

Achieves 20x compression with 1.5% accuracy loss

Features:

• 20x compression ratios

• <2% quality degradation

• Fast inference (<500ms)

Pricing: Free (open-source)

Use Cases and Patterns

Chatbot (Customer Support)

Requirements:

• Multi-turn conversations (50-100 turns)

• Preserve customer context

• Fast response times

• Cost-efficient

Recommended approach:

• ConversationSummaryBufferMemory (LangChain)

• 70% threshold: Semantic deduplication

• 85% threshold: Rolling summarization

• Prompt caching for frequent patterns

Implementation:

from langchain.memory import ConversationSummaryBufferMemory from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-haiku-20241022")

memory = ConversationSummaryBufferMemory( llm=llm, max_token_limit=2000, return_messages=True )

Add customer conversation

for turn in customer_conversation: memory.save_context( {"input": turn['customer_message']}, {"output": turn['agent_response']} )

Retrieve compressed context

context = memory.load_memory_variables({})

Code Assistant

Requirements:

• Long development sessions (100+ turns)

• Preserve technical details

• Handle large code blocks

• Track incremental changes

Recommended approach:

• Hierarchical summarization for overall context

• RAG retrieval for specific code references

• Delta compression for iterative edits

• Prompt caching for system prompts

Implementation:

from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")

class CodeAssistantMemory: def init(self): self.hierarchy = HierarchicalMemory(client, chunk_size=15) self.rag = RAGMemory(anthropic_client=client, openai_client=openai_client) self.deltas = DeltaCompressor()

def add_interaction(self, code_change: dict):
    # Store in hierarchy
    self.hierarchy.add_message({
        'role': 'user',
        'content': code_change['description']
    })

    # Store in RAG for retrieval
    self.rag.add_message(code_change)

    # Store as delta if incremental
    if code_change.get('is_incremental'):
        self.deltas.add_message(code_change)

def get_context(self, current_query: str):
    # Combine hierarchical summary + RAG retrieval
    summary_context = self.hierarchy.get_context(max_tokens=2000)
    rag_context = self.rag.retrieve_context(current_query, max_tokens=2000)

    return summary_context + rag_context

Educational Tutor

Requirements:

• Multi-session tracking

• Student progress persistence

• Personalized context retrieval

• Long-term knowledge retention

Recommended approach:

• VectorStoreRetrieverMemory for multi-session

• Fact extraction for student knowledge

• Progressive compression across sessions

• Resumable conversations

Implementation:

from langchain.memory import VectorStoreRetrieverMemory from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings

class TutorMemory: def init(self, student_id: str): self.student_id = student_id

    # Vector store for all sessions
    embeddings = OpenAIEmbeddings()
    vectorstore = Chroma(
        collection_name=f"student_{student_id}",
        embedding_function=embeddings
    )

    self.memory = VectorStoreRetrieverMemory(
        retriever=vectorstore.as_retriever(search_kwargs={"k": 10})
    )

def add_lesson_content(self, lesson: dict):
    """Add lesson interaction to student memory."""
    self.memory.save_context(
        {"input": lesson['topic']},
        {"output": lesson['explanation']}
    )

def get_student_context(self, current_topic: str):
    """Retrieve relevant past lessons for current topic."""
    return self.memory.load_memory_variables({
        "prompt": current_topic
    })

Best Practices

1. Choose the Right Technique for Your Use Case

• Short conversations (<20 turns): No compression needed

• Medium conversations (20-50 turns): ConversationSummaryBufferMemory

• Long conversations (50-100 turns): Hierarchical or rolling summarization

• Very long (100+ turns): Hybrid (RAG + summarization + caching)

• Multi-session: VectorStoreRetrieverMemory or Mem0

2. Implement Progressive Compression

Don't compress aggressively from the start. Use thresholds:

• 0-70%: Store verbatim

• 70-85%: Light compression (deduplication)

• 85-95%: Medium compression (summarization)

• 95-100%: Aggressive compression (hierarchical)

3. Combine Techniques

Single-technique approaches are suboptimal. Best production systems use:

• Rolling summarization (short-term)

• RAG retrieval (long-term)

• Prompt caching (cost optimization)

• Semantic deduplication (redundancy removal)

4. Monitor Quality Metrics

Track compression impact:

• Response relevance score

• Information retention rate

• User satisfaction metrics

• Token usage reduction

5. Use Prompt Caching Strategically

Cache stable content:

• Conversation summaries

• System prompts

• Knowledge base context

• User profiles

Don't cache frequently changing content:

• Current user query

• Real-time data

• Session-specific state

6. Implement Checkpointing

Save compression state for:

• Recovery from failures

• Multi-session continuity

• Analytics and debugging

• A/B testing different strategies

7. Tune Compression Parameters

Test and optimize:

• Summary token limits

• Compression thresholds

• Retrieval result counts

• Cache TTLs

• Chunk sizes for hierarchical

8. Handle Edge Cases

Plan for:

• Very long messages (split or compress individually)

• Code blocks (preserve formatting)

• Multi-language content

• Rapidly changing context

Troubleshooting

Problem: Summary loses critical information

Solutions:

• Lower compression ratio (less aggressive)

• Implement importance scoring to preserve key messages

• Use extractive summarization for critical sections

• Increase summary token budget

Problem: Retrieval returns irrelevant context

Solutions:

• Improve embedding model quality

• Add metadata filtering (timestamps, topics)

• Adjust similarity threshold

• Use hybrid search (semantic + keyword)

Problem: High latency from compression

Solutions:

• Compress asynchronously (background tasks)

• Use faster models for summarization (Haiku instead of Sonnet)

• Cache summaries more aggressively

• Reduce compression frequency

Problem: Conversations still exceeding context window

Solutions:

• Implement hierarchical compression

• Archive to vector database more aggressively

• Use more aggressive compression ratios

• Consider switching to model with larger context window

Problem: High costs despite compression

Solutions:

• Implement prompt caching

• Use cheaper models for summarization (Haiku)

• Batch summarization operations

• Reduce summarization frequency

Problem: Lost conversation continuity

Solutions:

• Increase recent message window

• Include summary in every request

• Use more descriptive summaries

• Implement session resumption with context injection

Advanced Topics

Streaming Compression

Compress in real-time as conversation progresses:

async def streaming_compress(messages: list): """Compress while streaming responses.""" compressor = ProgressiveCompressor()

async for message in conversation_stream:
    compressor.add_message(message)

    # Compression happens asynchronously
    if compressor.should_compress():
        asyncio.create_task(compressor.compress_async())

return compressor.get_context()

Multi-User Session Management

Handle concurrent conversations with shared context:

class MultiUserMemory: def init(self): self.user_sessions = {}

def get_or_create_session(self, user_id: str):
    if user_id not in self.user_sessions:
        self.user_sessions[user_id] = HybridMemorySystem(...)
    return self.user_sessions[user_id]

def cleanup_inactive_sessions(self, timeout: int = 3600):
    """Remove sessions inactive for > timeout seconds."""
    current_time = time.time()
    inactive = [
        user_id for user_id, session in self.user_sessions.items()
        if current_time - session.last_activity > timeout
    ]

    for user_id in inactive:
        self._archive_session(user_id)
        del self.user_sessions[user_id]

Custom Importance Scoring

Train ML models to score message importance:

from transformers import pipeline

class MLImportanceScorer: def init(self): # Use pre-trained classifier or fine-tune on your data self.classifier = pipeline( "text-classification", model="your-importance-model" )

def score(self, message: dict) -> float:
    """Score message importance (0-1)."""
    result = self.classifier(message['content'])
    return result[0]['score']

Context Window Utilization Optimization

Maximize information density within token budget:

def optimize_context_allocation( summary_tokens: int, recent_tokens: int, retrieval_tokens: int, max_tokens: int ): """ Optimal allocation (empirically tested): - 20% summary - 50% recent messages - 30% retrieved context """ return { 'summary': int(max_tokens * 0.20), 'recent': int(max_tokens * 0.50), 'retrieval': int(max_tokens * 0.30) }

Future Directions

Emerging Techniques (2025+)

1. Infinite Attention Mechanisms

• Models with >10M token context windows (Gemini 1.5, future Claude)

• Reduces need for compression but doesn't eliminate cost concerns

2. Learned Compression Models

• Neural networks trained to compress conversation optimally

• Maintain semantic meaning while minimizing tokens

• Examples: LLMLingua v2, PromptCompressor

3. Multimodal Session Compression

• Compress conversations with images, audio, video

• Maintain cross-modal context relationships

4. Federated Memory Systems

• Distributed compression across multiple memory stores

• Privacy-preserving compression for sensitive conversations

5. Adaptive Compression Strategies

• RL-based systems that learn optimal compression per user/domain

• Dynamic threshold adjustment based on conversation importance

References

Academic Papers

• "Recursively Summarizing Enables Long-Term Dialogue Memory" (arXiv:2308.15022)

• "LLMLingua: Compressing Prompts for Accelerated Inference" (arXiv:2310.05736)

• "Lost in the Middle: How Language Models Use Long Contexts" (arXiv:2307.03172)

Documentation

• Anthropic Prompt Caching

• LangChain Memory

• OpenAI Function Calling

Tools

• Mem0 - Managed memory service

• Zep - Fast memory layer

• LLMLingua - Prompt compression

• ChromaDB - Vector database

Last Updated: 2025-11-30 Version: 1.0.0 License: MIT