session-compression

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}")

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

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

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]

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}"
    }

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

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

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

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

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]

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]

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

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'])

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({})

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

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

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

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

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)

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

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
    }]
)

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?"
)

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")

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
)

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)

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

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({})

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

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
        })

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()

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]

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']

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)
    }