Session Compression

作者 bobmatnyc718070a7d622無授權條款77 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫2 個月前更新

AI session compression techniques for managing multi-turn conversations efficiently through summarization, embedding-based retrieval, and intelligent context management.

僅含說明AI & Agents
AI 產生的概覽

指導如何壓縮冗長的 AI 對話歷史以配合上下文視窗，方法包括摘要、檢索與快取。

功能
說明多輪 AI 對話的工作階段壓縮技術，涵蓋漸進式壓縮門檻、抽取式、生成式、階層式與滾動式摘要、以嵌入為基礎的向量檢索，以及提示快取。文中提供使用 Anthropic、LangChain、OpenAI 嵌入與 ChromaDB 的 Python 程式碼範例，並列出壓縮比、token 估算與各情境的節省效果。它屬於參考性指引，而非可直接執行的工具。
適用情境
適用於設計或調校多輪對話接近上下文上限的 AI 應用，或需要降低長歷史帶來的 token 成本與延遲時。也適合在聊天、客服、教學輔導或程式助理工作階段中權衡摘要、RAG 與快取方案。
執行需求
僅為說明文件，未附帶指令碼。範例假定使用 Python 及 Anthropic、OpenAI SDK、LangChain、scikit-learn、NumPy 與 ChromaDB 等向量資料庫，並需要 API 金鑰與網路存取以呼叫模型與嵌入介面。

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

python
from langchain.memory import ConversationSummaryBufferMemoryfrom langchain_anthropic import ChatAnthropicfrom anthropic import Anthropic
# Initialize Claude clientllm = ChatAnthropic(    model="claude-3-5-sonnet-20241022",    api_key="your-api-key")
# Setup memory with automatic summarizationmemory = ConversationSummaryBufferMemory(    llm=llm,    max_token_limit=2000,  # Summarize when exceeding this    return_messages=True)
# Add conversation turnsmemory.save_context(    {"input": "What's session compression?"},    {"output": "Session compression reduces conversation token usage..."})
# Retrieve compressed contextcontext = memory.load_memory_variables({})

Progressive Compression Pattern

python
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
# Usagecompressor = 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)

python
from anthropic import Anthropic
client = Anthropic(api_key="your-api-key")
# Build context with cache controlmessages = [    {        "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 verbatim70-85%          Light compression          Remove redundancy85-95%          Medium compression         Summarize old messages95-100%         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

python
from sklearn.feature_extraction.text import TfidfVectorizerimport 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

python
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 made2. Important context and facts3. Unresolved questions4. 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]
python
from anthropic import Anthropicfrom 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

python
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

python
from anthropic import Anthropicfrom openai import OpenAIimport 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

python
from sklearn.cluster import KMeansfrom openai import OpenAIimport 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

python
from openai import OpenAIimport numpy as npfrom 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

python
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

python
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.

python
from langchain.memory import ConversationSummaryMemoryfrom langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
memory = ConversationSummaryMemory(llm=llm)
# Add conversationmemory.save_context(    {"input": "Hi, I'm working on a Python project"},    {"output": "Great! How can I help with your Python project?"})
# Get summarysummary = 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.

python
from langchain.memory import ConversationSummaryBufferMemoryfrom 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 conversationfor i in range(50):    memory.save_context(        {"input": f"Question {i}"},        {"output": f"Answer {i}"}    )
# Automatically keeps recent messages + summary of oldcontext = 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.

python
from langchain.memory import ConversationTokenBufferMemoryfrom 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.

python
from langchain.memory import VectorStoreRetrieverMemoryfrom langchain_community.vectorstores import Chromafrom 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.

python
from anthropic import Anthropic
client = Anthropic(api_key="your-api-key")
# Long conversation contextconversation_history = [    {"role": "user", "content": "Message 1"},    {"role": "assistant", "content": "Response 1"},    # ... many more messages]
# Mark context for cachingmessages = []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.

python
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 processthinking_content = [    block for block in response.content    if block.type == "thinking"]
# Get compression recommendationrecommendation = response.content[-1].text

Production Patterns

Checkpointing and Persistence

Save compression state for recovery and resume.

python
import jsonimport picklefrom 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.

python
from anthropic import Anthropicimport 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)
# Usageclient = Anthropic(api_key="your-api-key")conversation = ResumableConversation(client, session_id="user123_session1")
# Continue across multiple sessionsresponse1 = conversation.add_turn("What's Python?")# ... later sessionresponse2 = conversation.add_turn("Show me an example")  # Remembers context

Hybrid Approaches (Best Practice)

Combine multiple techniques for optimal results.

python
from anthropic import Anthropicfrom openai import OpenAIimport 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
# Usageanthropic_client = Anthropic(api_key="your-anthropic-key")openai_client = OpenAI(api_key="your-openai-key")
memory = HybridMemorySystem(anthropic_client, openai_client)
# Add messages over timefor i in range(1000):    memory.add_message({        'role': 'user' if i % 2 == 0 else 'assistant',        'content': f"Message {i} with some content..."    })
# Retrieve optimized contextcurrent_query = "What did we discuss about pricing?"context = memory.get_context(current_query)
# Use with Clauderesponse = anthropic_client.messages.create(    model="claude-3-5-sonnet-20241022",    max_tokens=1024,    messages=context + [{        'role': 'user',        'content': current_query    }])

Performance Benchmarks

Compression Efficiency

TechniqueCompression RatioQuality LossLatencyCost Impact
Extractive2-3x<1%<10msNone
Abstractive5-10x2-5%1-2s+$0.001/turn
Hierarchical20x+5-8%2-5s+$0.003/turn
LLMLingua20x1.5%500msNone
RAGVariable<1%100-300ms+$0.0005/turn
Prompt CachingN/A0%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

python
from mem0 import MemoryClient
client = MemoryClient(api_key="your-mem0-key")
# Automatically handles compression, summarization, RAGmemory = 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 contextcontext = 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**

python
from zep_python import ZepClient
client = ZepClient(api_key="your-zep-key")
# Add to sessionclient.memory.add_memory(    session_id="session123",    messages=[        {"role": "user", "content": "Hello"},        {"role": "assistant", "content": "Hi there!"}    ])
# Auto-summarized retrievalmemory = 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**

python
import chromadb
client = chromadb.Client()collection = client.create_collection("conversations")
# Store embeddingscollection.add(    documents=["Message content"],    embeddings=[[0.1, 0.2, ...]],    ids=["msg1"])
# Retrieveresults = 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**

python
from langchain.memory import ConversationSummaryBufferMemoryfrom 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**

python
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:

python
from langchain.memory import ConversationSummaryBufferMemoryfrom 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 conversationfor turn in customer_conversation:    memory.save_context(        {"input": turn['customer_message']},        {"output": turn['agent_response']}    )
# Retrieve compressed contextcontext = 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:

python
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:

python
from langchain.memory import VectorStoreRetrieverMemoryfrom langchain_community.vectorstores import Chromafrom 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:

python
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:

python
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:

python
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:

python
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

Tools

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

來源與署名

來源：bobmatnyc/claude-mpm-skills位於toolchains/ai/techniques/session-compression提交718070a

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指導如何建立、組織並封裝模組化的代理技能，並運用漸進式揭露。

AI & Agents772 個月前更新

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Skill Development

anthropics

精選

指導建立 Claude Code 外掛技能，涵蓋結構、描述、漸進式揭露與驗證。

AI & Agents37K今天更新

Plugin Structure

anthropics

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說明 Claude Code 外掛的結構、資訊清單與元件配置方式。

AI & Agents37K今天更新

Command Development

anthropics

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指導建立 Claude Code 斜線命令，涵蓋結構、YAML frontmatter、參數與外掛功能。

AI & Agents37K今天更新

Claude Md Improver

anthropics

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稽核儲存庫中的 CLAUDE.md 檔案、評估其品質，並在取得核准後套用針對性的改進。

AI & Agents37K今天更新

Find Skills

vercel-labs

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指導透過 npx skills 命令列工具探索、評估並安裝代理技能。

AI & Agents33K今天更新

Plugin Creator

openai

精選

為 Codex 建立外掛目錄，產生 .codex-plugin/plugin.json 清單與可選的外掛市集項目。

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AI & Agents27K4 週前更新