Never hit context limits again with smart conversation compression Got a conversation that’s too long for your AI model? Don’t panic. Message Transforms automatically compress your prompts to fit any context window while keeping the important stuff intact. Think of it as Marie Kondo for your AI conversations – we keep what sparks joy (the beginning and end) and thoughtfully compress the middle.

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The Magic Behind It

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Middle-Out Compression

Based on actual AI research, not guesswork Here’s the science: Large language models naturally pay less attention to the middle of long sequences. So when we need to squeeze a conversation down, we strategically remove content from the middle while preserving: ✨ The setup (system prompts, initial context)
✨ The latest context (recent messages, current question)
✨ The flow (conversation doesn’t feel choppy) Result: Your AI gets the essential context in a package that actually fits.

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Quick Start Guide

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Enable Transforms in One Line

It’s embarrassingly simple 
import requests

url = "https://api.anyapi.ai/api/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# 🎯 Your massive conversation that normally wouldn't fit
huge_conversation = [
    {"role": "system", "content": "You are a helpful AI assistant specialized in data analysis."},
    {"role": "user", "content": "Let's analyze this quarterly sales data..."},
    {"role": "assistant", "content": "I'd be happy to help analyze your sales data..."},
    # ... imagine 47 more messages of detailed back-and-forth ...
    {"role": "user", "content": "Based on everything we've discussed, what's your final recommendation?"}
]

payload = {
    "model": "openai/gpt-3.5-turbo",
    "messages": huge_conversation,
    "transforms": ["middle-out"]  # ✨ Magic happens here
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

# Your model gets a perfectly sized conversation that makes sense
print("✅ Got a response despite the massive context!")

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How the Compression Works

Smart algorithms, not random deletion 
# Before compression (hypothetical 15k tokens):
# [System prompt] - KEEP ✅
# [Initial user question] - KEEP ✅  
# [First AI response] - KEEP ✅
# [Message 4-47] - COMPRESS 📉 (intelligently truncated)
# [Recent user message] - KEEP ✅
# [Final user question] - KEEP ✅

# After compression (fits in 4k tokens):
# [System prompt] ✅
# [Initial context] ✅
# [Compressed middle] 📝 "...conversation continued..."
# [Recent context] ✅
# [Current question] ✅

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Real-World Examples

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Customer Support Hero

Keep your support bot working with marathon conversations 
class SupportBot:
    def __init__(self, api_key):
        self.api_key = api_key
        self.conversation_history = []
    
    def handle_customer_message(self, user_message):
        """Process customer messages with unlimited conversation length"""
        
        # Add the new message
        self.conversation_history.append({
            "role": "user", 
            "content": user_message
        })
        
        # 🚀 No need to worry about context limits anymore
        payload = {
            "model": "anthropic/claude-3-haiku-20240307",
            "messages": [
                {"role": "system", "content": "You are a helpful customer support agent. Be friendly and solution-focused."},
                *self.conversation_history
            ],
            "transforms": ["middle-out"],  # Handles long conversations automatically
            "temperature": 0.7
        }
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        response = requests.post(
            "https://api.anyapi.ai/api/v1/chat/completions",
            headers=headers,
            json=payload
        )
        
        result = response.json()
        assistant_message = result['choices'][0]['message']['content']
        
        # Add AI response to history
        self.conversation_history.append({
            "role": "assistant",
            "content": assistant_message
        })
        
        return assistant_message

# 💬 Usage - supports conversations of any length
support_bot = SupportBot(API_KEY)

# Even after 100+ messages, it still works perfectly
response = support_bot.handle_customer_message(
    "Can you remind me what we discussed about my refund request?"
)
print(f"Support bot: {response}")

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Document Analysis Powerhouse

Analyze huge documents without the headache 
async def analyze_massive_document(document_text, analysis_question):
    """Analyze documents of any size with follow-up questions"""
    
    # 📄 Break document into digestible chunks
    document_chunks = [document_text[i:i+3000] for i in range(0, len(document_text), 3000)]
    
    messages = [
        {
            "role": "system", 
            "content": "You are an expert document analyst. Provide thorough, accurate analysis based on the provided document."
        }
    ]
    
    # Add all document chunks
    for i, chunk in enumerate(document_chunks):
        messages.append({
            "role": "user",
            "content": f"Document part {i+1}: {chunk}"
        })
    
    # Add the analysis question
    messages.append({
        "role": "user",
        "content": f"Based on the entire document, please answer: {analysis_question}"
    })
    
    # 🧠 Let transforms handle the complexity
    payload = {
        "model": "openai/gpt-4",
        "messages": messages,
        "transforms": ["middle-out"],  # Automatically fits context
        "max_tokens": 1000
    }
    
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    async with aiohttp.ClientSession() as session:
        async with session.post(
            "https://api.anyapi.ai/api/v1/chat/completions",
            headers=headers,
            json=payload
        ) as response:
            result = await response.json()
            return result['choices'][0]['message']['content']

# 📊 Analyze a 50-page report without breaking a sweat
analysis = await analyze_massive_document(
    massive_quarterly_report,
    "What are the top 3 risks and opportunities identified?"
)
print(f"Analysis complete: {analysis}")

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Creative Writing Assistant

Keep the creative flow going indefinitely 
class CreativeWritingBot:
    def __init__(self, api_key, story_context):
        self.api_key = api_key
        self.story_messages = [
            {"role": "system", "content": f"You are a creative writing assistant. Story context: {story_context}"}
        ]
    
    def continue_story(self, user_input):
        """Continue a story regardless of how long it gets"""
        
        self.story_messages.append({
            "role": "user",
            "content": user_input
        })
        
        payload = {
            "model": "anthropic/claude-3-sonnet-20240229",
            "messages": self.story_messages,
            "transforms": ["middle-out"],  # Keep the story flowing
            "temperature": 0.8,  # More creative
            "max_tokens": 500
        }
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        response = requests.post(
            "https://api.anyapi.ai/api/v1/chat/completions",
            headers=headers,
            json=payload
        )
        
        result = response.json()
        story_continuation = result['choices'][0]['message']['content']
        
        self.story_messages.append({
            "role": "assistant",
            "content": story_continuation
        })
        
        return story_continuation
    
    def get_story_length(self):
        """See how epic your story has become"""
        return len(self.story_messages) - 1  # Subtract system message

# ✍️ Write epic novels without context anxiety
writer = CreativeWritingBot(API_KEY, "A cyberpunk detective story set in 2087")

# Even after 200+ exchanges, the story stays coherent
next_chapter = writer.continue_story(
    "The detective discovers a hidden message in the quantum data. What does it reveal?"
)
print(f"Story continues: {next_chapter}")
print(f"Story length: {writer.get_story_length()} exchanges")

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Advanced Configuration

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Smart Transform Settings

Fine-tune the compression for your use case 
def smart_conversation_handler(messages, priority="recent"):
    """Handle different conversation priorities intelligently"""
    
    if priority == "recent":
        # Prioritize the latest context
        config = {
            "model": "openai/gpt-4o-mini",
            "messages": messages,
            "transforms": ["middle-out"],
            "transform_config": {
                "preserve_recent": 10,  # Keep last 10 messages
                "preserve_initial": 3   # Keep first 3 messages
            }
        }
    
    elif priority == "system":
        # Prioritize system context and instructions
        config = {
            "model": "anthropic/claude-3-haiku-20240307",
            "messages": messages,
            "transforms": ["middle-out"],
            "transform_config": {
                "preserve_system": True,  # Always keep system messages
                "compression_ratio": 0.3  # More aggressive compression
            }
        }
    
    elif priority == "balanced":
        # Balanced approach
        config = {
            "model": "openai/gpt-3.5-turbo",
            "messages": messages,
            "transforms": ["middle-out"]  # Use defaults
        }
    
    return config

# 🎯 Usage for different scenarios
support_config = smart_conversation_handler(long_conversation, priority="recent")
analysis_config = smart_conversation_handler(document_messages, priority="system")
chat_config = smart_conversation_handler(casual_messages, priority="balanced")

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Disable When You Need Full Context

Sometimes you need everything 
def handle_critical_conversation(messages):
    """For conversations where every detail matters"""
    
    payload = {
        "model": "anthropic/claude-3-opus-20240229",  # High-context model
        "messages": messages,
        "transforms": [],  # 🚫 No compression - preserve everything
        "max_tokens": 2000
    }
    
    # This will fail if messages exceed context, but that's intentional
    # Better to fail than lose critical information
    
    return payload

# 💼 Use for legal analysis, medical consultations, technical debugging
critical_response = handle_critical_conversation(detailed_legal_messages)

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Monitoring and Debugging

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Track Transform Activity

See what’s happening under the hood 
class TransformTracker:
    def __init__(self, api_key):
        self.api_key = api_key
        self.transform_stats = {
            "requests_with_transforms": 0,
            "requests_without_transforms": 0,
            "average_compression_ratio": 0,
            "models_requiring_compression": set()
        }
    
    def make_tracked_request(self, messages, model, use_transforms=True):
        """Make requests while tracking transform usage"""
        
        original_token_count = self.estimate_tokens(messages)
        
        payload = {
            "model": model,
            "messages": messages,
            "transforms": ["middle-out"] if use_transforms else []
        }
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        response = requests.post(
            "https://api.anyapi.ai/api/v1/chat/completions",
            headers=headers,
            json=payload
        )
        
        result = response.json()
        
        # Track statistics
        if use_transforms:
            self.transform_stats["requests_with_transforms"] += 1
            if "transform_applied" in result:
                self.transform_stats["models_requiring_compression"].add(model)
        else:
            self.transform_stats["requests_without_transforms"] += 1
        
        return result
    
    def estimate_tokens(self, messages):
        """Rough token estimation"""
        return sum(len(msg["content"].split()) * 1.3 for msg in messages)
    
    def get_transform_report(self):
        """Generate transform usage report"""
        total_requests = (
            self.transform_stats["requests_with_transforms"] + 
            self.transform_stats["requests_without_transforms"]
        )
        
        if total_requests == 0:
            return "No requests tracked yet"
        
        transform_percentage = (
            self.transform_stats["requests_with_transforms"] / total_requests * 100
        )
        
        report = f"""
🔄 Transform Usage Report
========================
📊 Total Requests: {total_requests}
✨ With Transforms: {self.transform_stats["requests_with_transforms"]} ({transform_percentage:.1f}%)
🚫 Without Transforms: {self.transform_stats["requests_without_transforms"]}
🤖 Models Needing Compression: {len(self.transform_stats["models_requiring_compression"])}
        """
        
        return report

# 📈 Track your transform usage
tracker = TransformTracker(API_KEY)

# Make some requests
tracker.make_tracked_request(long_messages, "openai/gpt-3.5-turbo", use_transforms=True)
tracker.make_tracked_request(short_messages, "openai/gpt-4", use_transforms=False)

# See the impact
print(tracker.get_transform_report())

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Pro Tips & Best Practices

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🎯 When to Use Transforms

✅ Perfect for:
  • Customer support marathons (50+ message conversations)
  • Document analysis with follow-up questions
  • Creative writing sessions that go on forever
  • Educational tutoring with extensive back-and-forth
  • Brainstorming sessions that build over time
❌ Skip transforms for:
  • Legal document analysis (every word matters)
  • Code debugging (context is critical)
  • Medical consultations (details save lives)
  • Financial analysis (precision over convenience)

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🧠 Smart Context Management

def intelligent_context_manager(messages, task_type):
    """Choose the right approach based on your task"""
    
    strategies = {
        "customer_support": {
            "transforms": ["middle-out"],
            "model": "anthropic/claude-3-haiku-20240307",  # Fast and cheap
            "priority": "recent_context"
        },
        
        "document_analysis": {
            "transforms": ["middle-out"],
            "model": "openai/gpt-4",  # Better reasoning
            "priority": "system_and_recent"
        },
        
        "creative_writing": {
            "transforms": ["middle-out"],
            "model": "anthropic/claude-3-sonnet-20240229",  # More creative
            "priority": "narrative_flow"
        },
        
        "code_review": {
            "transforms": [],  # No compression for code
            "model": "openai/gpt-4",
            "priority": "full_context"
        }
    }
    
    return strategies.get(task_type, strategies["customer_support"])

# 🎨 Usage
config = intelligent_context_manager(my_messages, "creative_writing")

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📊 Performance Optimization

Memory Management: 
def optimize_message_history(messages, max_history=100):
    """Keep message history manageable"""
    
    if len(messages) > max_history:
        # Keep system message + recent messages
        system_messages = [msg for msg in messages if msg["role"] == "system"]
        recent_messages = messages[-(max_history-len(system_messages)):]
        messages = system_messages + recent_messages
    
    return messages
Model Selection: 
def choose_optimal_model(estimated_tokens):
    """Pick the right model for your context size"""
    
    if estimated_tokens < 4000:
        return "openai/gpt-3.5-turbo"  # Fast and cheap
    elif estimated_tokens < 8000:
        return "openai/gpt-4o-mini"    # Good balance
    elif estimated_tokens < 32000:
        return "openai/gpt-4"          # High context
    else:
        return "anthropic/claude-3-opus-20240229"  # Maximum context

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Troubleshooting Common Issues

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🔍 Debug Transform Behavior

def debug_transforms(messages, model):
    """Test with and without transforms to compare results"""
    
    print("🧪 Testing transform behavior...")
    
    # Test without transforms
    try:
        payload_no_transforms = {
            "model": model,
            "messages": messages,
            "transforms": []
        }
        
        response_no_transforms = requests.post(
            "https://api.anyapi.ai/api/v1/chat/completions",
            headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
            json=payload_no_transforms
        )
        
        print("✅ No transforms: Request succeeded")
        
    except Exception as e:
        print(f"❌ No transforms: Failed - {e}")
    
    # Test with transforms
    try:
        payload_with_transforms = {
            "model": model,
            "messages": messages,
            "transforms": ["middle-out"]
        }
        
        response_with_transforms = requests.post(
            "https://api.anyapi.ai/api/v1/chat/completions",
            headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
            json=payload_with_transforms
        )
        
        print("✅ With transforms: Request succeeded")
        
        # Compare response quality (manual inspection needed)
        return {
            "without_transforms": response_no_transforms.json() if 'response_no_transforms' in locals() else None,
            "with_transforms": response_with_transforms.json()
        }
        
    except Exception as e:
        print(f"❌ With transforms: Failed - {e}")
        return None

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🚨 Common Issues & Solutions

Problem: Responses seem disconnected or miss important context
Solution: Check if crucial information is in the middle of your conversation. Consider restructuring or using a higher-context model. Problem: Transform isn’t being applied when expected
Solution: Verify your conversation actually exceeds the model’s context limit. Transforms only activate when needed. Problem: Quality degradation with transforms
Solution: Test different models or consider manual conversation summarization for critical use cases.

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The Bottom Line

Message Transforms are your safety net for long conversations. They keep your AI applications running smoothly without forcing you to micromanage context windows or abandon complex interactions. Key benefits:
  • 🚀 Never hit context limits - Your conversations can go on forever
  • 🧠 Smart compression - Keeps the important stuff, compresses the rest
  • ⚡ Zero configuration - Works automatically when you need it
  • 💰 Cost effective - Use smaller, cheaper models for longer conversations
When to use them:
  • ✅ Long customer support sessions
  • ✅ Document analysis with follow-ups
  • ✅ Creative writing projects
  • ✅ Educational conversations
  • ❌ Critical analysis where every detail matters
Ready to break free from context limits? Add "transforms": ["middle-out"] to your next long conversation and watch the magic happen. Never lose a conversation to context limits again! 🎯