LLM Optimization Tools - Awesome LLM Apps

Overview

LLM API costs are directly tied to token count. These optimization tools help you reduce token usage while maintaining accuracy, enabling cost-effective AI applications at scale.

TOON Format

63.9% average token reduction for structured data

Headroom

47-92% token savings through intelligent compression

Why Optimize?

Cost Savings
Performance
Scale

Impact on API Costs

Based on GPT-4 pricing ($0.03/1K input tokens):

Usage Volume	Standard Cost	With Optimization (60% reduction)	Savings
1,000 calls	$2.55	$1.02	$1.53
100,000 calls	$255.00	$102.00	$153.00
1M calls	$2,550.00	$1,020.00	$1,530.00
10M calls	$25,500.00	$10,200.00	$15,300.00

Toonify Token Optimization

Reduce token usage by 30-73% using TOON (Token-Oriented Object Notation) format

What is TOON?

TOON is a compact serialization format designed specifically for LLM token efficiency. It achieves CSV-like compression while maintaining structure and readability.

Key Benefits

63.9% Average Reduction

Verified across 50 real-world datasets

73.4% for Tabular Data

Optimal for structured, uniform data

Human Readable

Still easy to understand and debug

<1ms Overhead

Negligible conversion time

Format Comparison

{
  "products": [
    {"id": 101, "name": "Laptop Pro", "price": 1299},
    {"id": 102, "name": "Magic Mouse", "price": 79},
    {"id": 103, "name": "USB-C Cable", "price": 19}
  ]
}

Token Savings: 85 → 39 tokens (54.1% reduction)Cost Impact:

2.55/1K requests →

1.17/1K requests

Implementation

Install Toonify

pip install toonify

Convert Data to TOON

from toon import encode, decode
import json

# Your structured data
data = {
    "products": [
        {"id": 1, "name": "Laptop", "price": 1299, "stock": 45},
        {"id": 2, "name": "Mouse", "price": 79, "stock": 120},
    ]
}

# Convert to TOON format
toon_str = encode(data)
print(f"JSON: {len(json.dumps(data))} bytes")
print(f"TOON: {len(toon_str)} bytes")

Send to LLM

from openai import OpenAI

client = OpenAI()

# Use TOON format in prompt
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": f"Analyze this product data:\n\n{toon_str}"
    }]
)

print(response.choices[0].message.content)

Decode if Needed

# Convert back to Python objects
original_data = decode(toon_str)
assert original_data == data  # Roundtrip verification

Real-World Example

E-commerce Product Analysis

from toon import encode
from openai import OpenAI
import json

client = OpenAI()

# Sample product catalog (could be 100s of products)
products = [
    {"id": 1, "name": "Laptop Pro", "price": 1299, "stock": 45, "category": "Electronics"},
    {"id": 2, "name": "Magic Mouse", "price": 79, "stock": 120, "category": "Accessories"},
    {"id": 3, "name": "USB-C Hub", "price": 49, "stock": 200, "category": "Accessories"},
    {"id": 4, "name": "4K Monitor", "price": 599, "stock": 30, "category": "Electronics"},
    {"id": 5, "name": "Keyboard", "price": 129, "stock": 85, "category": "Accessories"},
    # ... potentially hundreds more
]

# Measure token reduction
json_str = json.dumps(products)
toon_str = encode(products)

print(f"JSON size: {len(json_str)} bytes")
print(f"TOON size: {len(toon_str)} bytes")
print(f"Reduction: {((len(json_str) - len(toon_str)) / len(json_str) * 100):.1f}%")

# Send optimized data to LLM
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": f"""
        Analyze this product catalog and provide:
        1. Total inventory value
        2. Low stock items (< 50 units)
        3. Average price by category
        
        Data:
        {toon_str}
        """
    }]
)

print(response.choices[0].message.content)

Results:

JSON: 487 bytes, ~165 tokens
TOON: 186 bytes, ~68 tokens
Reduction: 58.8% tokens, 61.8% bytes

Best Use Cases

Tabular Data

Optimal for:

Product catalogs
CSV exports
Database query results
API response data
Survey results
Analytics data

Token savings: 60-73%

Structured JSON

Good for:

Configuration files
Uniform object arrays
API payloads
Log data

Token savings: 50-65%

When NOT to Use

Avoid TOON for:

Highly nested data (greater than 3 levels)
Irregular/heterogeneous structures
Small payloads (less than 100 bytes)
Binary data
When JSON compatibility is critical

Interactive Demo

Streamlit App Code

import streamlit as st
import json
from toon import encode, decode
import tiktoken

st.title("🎯 Toonify Token Optimizer")

# Token counter
enc = tiktoken.encoding_for_model("gpt-4")

def count_tokens(text):
    return len(enc.encode(text))

# Input data
data_input = st.text_area(
    "Paste your JSON data",
    value=json.dumps({
        "products": [
            {"id": 1, "name": "Laptop", "price": 1299},
            {"id": 2, "name": "Mouse", "price": 79},
        ]
    }, indent=2),
    height=200
)

if data_input:
    try:
        # Parse JSON
        data = json.loads(data_input)
        
        # Convert to TOON
        toon_str = encode(data)
        
        # Calculate metrics
        json_tokens = count_tokens(data_input)
        toon_tokens = count_tokens(toon_str)
        reduction = ((json_tokens - toon_tokens) / json_tokens) * 100
        
        # Display results
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("JSON Format")
            st.code(data_input, language="json")
            st.metric("Tokens", json_tokens)
            st.metric("Bytes", len(data_input))
        
        with col2:
            st.subheader("TOON Format")
            st.code(toon_str, language="text")
            st.metric("Tokens", toon_tokens)
            st.metric("Bytes", len(toon_str))
        
        # Savings
        st.success(f"Token Reduction: {reduction:.1f}%")
        
        # Cost calculator
        st.subheader("Cost Savings Calculator")
        requests = st.number_input("Number of API requests", value=1000, step=1000)
        
        gpt4_cost_per_1k = 0.03
        json_cost = (json_tokens / 1000) * gpt4_cost_per_1k * requests
        toon_cost = (toon_tokens / 1000) * gpt4_cost_per_1k * requests
        savings = json_cost - toon_cost
        
        st.write(f"**JSON cost**: ${json_cost:.2f}")
        st.write(f"**TOON cost**: ${toon_cost:.2f}")
        st.write(f"**💰 Savings**: ${savings:.2f}")
        
    except Exception as e:
        st.error(f"Error: {e}")

Run with: streamlit run toonify_app.py

Performance Benchmarks

Token Reduction
Conversion Speed
Scale Test

Dataset Type	Avg Reduction	Best Case	Worst Case
Tabular	68.5%	73.4%	62.1%
Structured JSON	61.2%	67.8%	54.3%
Nested JSON	48.7%	56.2%	41.5%
Mixed	55.4%	63.9%	47.8%

Operation	Time (avg)	Time (p99)
Encode	0.42ms	1.2ms
Decode	0.38ms	1.0ms
Roundtrip	0.85ms	2.1ms

Headroom Context Optimization

Reduce token usage by 47-92% through intelligent context compression for AI agents

What is Headroom?

Headroom is a context optimization layer that compresses tool outputs and conversation history while preserving accuracy. Unlike simple truncation, it uses statistical analysis to keep what matters.

Key Benefits

47-92% Token Reduction

Verified across production workloads

Zero Code Changes

Transparent proxy integration

Reversible Compression

LLM can retrieve original data via CCR

Provider Caching

Optimizes for OpenAI/Anthropic caching

Core Features

SmartCrusher
CacheAligner
CCR System

Statistical Compression

Keeps:

First N items (context)
Last N items (recency)
Anomalies (statistical outliers)
Query-relevant matches

Removes:

Repetitive boilerplate
Redundant middle sections
Low-information content

from headroom import SmartCrusher

crusher = SmartCrusher(
    keep_first=2,
    keep_last=2,
    keep_anomalies=True,
    compression_ratio=0.3
)

# Compress tool output
compressed = crusher.compress(tool_output)

Prefix Optimization

Stabilizes message prefixes for better provider-side caching:

from headroom import CacheAligner

aligner = CacheAligner(provider="anthropic")

# Optimize for cache hits
optimized_messages = aligner.align(messages)

Cache hit rate improvement:

OpenAI: 35% → 78%
Anthropic: 42% → 85%

Compress-Cache-Retrieve

Reversible compression:

Compress: Reduce tokens
Cache: Store original
Retrieve: LLM requests if needed

from headroom import CCR

ccr = CCR()

# Compress with retrieval capability
compressed, cache_id = ccr.compress(large_content)

# LLM can request original
if llm_needs_more_detail:
    original = ccr.retrieve(cache_id)

Installation & Setup

Install Headroom

pip install headroom-ai

Choose Integration Method

Proxy (Zero Code)
LangChain
Agno

# Start proxy server
headroom proxy --port 8787

# Point existing tools at proxy
export OPENAI_BASE_URL=http://localhost:8787/v1
export ANTHROPIC_BASE_URL=http://localhost:8787

# Use tools normally - compression is automatic

from langchain_openai import ChatOpenAI
from headroom.integrations import HeadroomChatModel

# Wrap your model
llm = HeadroomChatModel(ChatOpenAI(model="gpt-4o"))

# Use normally
response = llm.invoke("Analyze these logs")

# Check savings
print(f"Tokens saved: {llm.total_tokens_saved}")

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from headroom.integrations.agno import HeadroomAgnoModel

# Wrap model
model = HeadroomAgnoModel(OpenAIChat(id="gpt-4o"))

# Create agent
agent = Agent(
    model=model,
    tools=[search_github, search_code, query_db]
)

# Run with automatic compression
response = agent.run("Find memory leaks")
print(f"Saved: {model.total_tokens_saved} tokens")

Real-World Performance

These are actual results from production API calls, not estimates.

Code Search
SRE Debugging
Agent Workflow

GitHub Code Search (100 results)

Scenario: Search 100 code files for error handling patterns

Metric	Before	After	Savings
Tokens	17,765	1,408	92%
Cost (GPT-4)	$0.53	$0.04	$0.49
Response Time	8.2s	2.1s	74%

Compression strategy:

Keep first 2 and last 2 results
Extract only relevant code sections
Remove boilerplate imports/comments
Preserve error handling patterns

Incident Log Analysis

Scenario: Debug production outage from 65K token log file

Metric	Before	After	Savings
Tokens	65,694	5,118	92%
Cost (GPT-4)	$1.97	$0.15	$1.82
Time to insight	12.4s	3.2s	74%

What was preserved:

All ERROR and FATAL entries
Anomalous log patterns
First/last entries for timeline
Stack traces

What was compressed:

Repetitive INFO logs
Standard health checks
Redundant timestamps

Multi-Tool Agent

Scenario: Agent using 5 tools (search, database, API, logs, docs)

Tool Call	Tokens Before	Tokens After	Reduction
GitHub search	15,200	1,850	88%
DB query	8,400	1,200	86%
API response	12,600	2,100	83%
Log analysis	18,900	2,400	87%
Docs search	9,800	1,550	84%
Total	64,900	9,100	86%

Monthly savings at 1K agent runs: $1,674

Needle in Haystack Test

Complete Test Results

Setup:

100 production log entries
1 critical FATAL error at position 67
Question: “What caused the outage? Error code? Fix?”

Baseline (no compression):

Tokens: 10,144
Cost: $0.30
Response time: 4.8s
Answer: ✅ Correct (payment-gateway, PG-5523, increase max_connections)

With Headroom:

Tokens: 1,260 (87.6% reduction)
Cost: $0.04 (86.7% savings)
Response time: 1.2s (75% faster)
Answer: ✅ Correct (same details)

What Headroom kept:

Position 67: FATAL error (the needle)
Position 1-2: Context (timeline start)
Position 99-100: Most recent state
Position 45: Anomaly (connection spike)

What Headroom removed:

96 INFO/DEBUG entries
Repetitive health checks
Standard operational logs

Result: Same accuracy, 87.6% fewer tokens

Configuration

HeadroomChatModel

class

LangChain integration with compression

Show Parameters

model

ChatModel

required

Underlying LangChain chat model

compression_ratio

float

default:"0.3"

Target compression (0.3 = keep 30% of tokens)

enable_ccr

bool

default:"true"

Enable Compress-Cache-Retrieve for reversibility

cache_alignment

bool

default:"true"

Optimize for provider-side caching

Best Use Cases

AI Agents with Tools

Optimal for:

Multi-tool workflows
Code search agents
Database query agents
API integration agents
Log analysis agents

Average savings: 75-90%

Large Tool Outputs

Ideal for:

Code search results (100+ files)
Database query results (1000+ rows)
API responses (large JSON)
Log files (10K+ lines)
Documentation searches

Average savings: 80-92%

Conversation History

Useful for:

Long chat sessions
Multi-turn debugging
Context-heavy conversations
Memory-intensive agents

Average savings: 50-70%

Safety Guarantees

Never Removes Human Content

User and assistant messages are always preserved in full

Never Breaks Tool Pairing

Tool calls and responses stay together

Parse Failures = No-op

Malformed content passes through unchanged

Reversible Compression

LLM can retrieve original data via CCR

Best Practices

Choose the Right Tool

Use Case	Tool	Expected Savings
Structured data (JSON, CSV)	TOON	60-73%
AI agent tool outputs	Headroom	75-92%
Large API responses	Both	80-95%
Conversation history	Headroom	50-70%
Mixed/nested JSON	TOON	45-60%

Measure Impact

import tiktoken

enc = tiktoken.encoding_for_model("gpt-4")

def measure_optimization(original, optimized):
    original_tokens = len(enc.encode(original))
    optimized_tokens = len(enc.encode(optimized))
    
    reduction = ((original_tokens - optimized_tokens) / original_tokens) * 100
    
    # Calculate cost savings (GPT-4 pricing)
    cost_per_1k = 0.03
    original_cost = (original_tokens / 1000) * cost_per_1k
    optimized_cost = (optimized_tokens / 1000) * cost_per_1k
    
    return {
        "original_tokens": original_tokens,
        "optimized_tokens": optimized_tokens,
        "reduction_percent": reduction,
        "cost_savings": original_cost - optimized_cost
    }

Monitor in Production

from headroom.integrations import HeadroomChatModel
import logging

# Set up monitoring
llm = HeadroomChatModel(base_model)

# Log metrics
def log_metrics():
    logging.info(f"Total tokens saved: {llm.total_tokens_saved}")
    logging.info(f"Total cost saved: ${llm.total_cost_saved:.2f}")
    logging.info(f"Compression ratio: {llm.avg_compression_ratio:.1%}")

# Call after batch of requests
log_metrics()

Combine Techniques

from toon import encode
from headroom.integrations import HeadroomChatModel

# Use both TOON and Headroom
def optimized_agent_call(structured_data, tools):
    # 1. Convert structured data to TOON
    toon_data = encode(structured_data)
    
    # 2. Use Headroom for tool outputs
    llm = HeadroomChatModel(base_model)
    
    # 3. Combine for maximum savings
    response = llm.invoke(
        f"Analyze this data and search for patterns:\n{toon_data}"
    )
    
    return response

# Result: 80-95% total token reduction

Cost Calculator

Interactive Cost Calculator

import streamlit as st

st.title("LLM Optimization Cost Calculator")

# Inputs
col1, col2 = st.columns(2)

with col1:
    avg_tokens = st.number_input("Average tokens per request", value=5000, step=100)
    requests_per_day = st.number_input("Requests per day", value=1000, step=100)
    
with col2:
    model = st.selectbox("Model", ["GPT-4", "GPT-4o", "Claude 3.5 Sonnet"])
    optimization = st.slider("Token reduction %", 0, 95, 60)

# Pricing
pricing = {
    "GPT-4": 0.03,
    "GPT-4o": 0.0025,
    "Claude 3.5 Sonnet": 0.003
}

cost_per_1k = pricing[model]

# Calculate
monthly_requests = requests_per_day * 30
yearly_requests = requests_per_day * 365

# Baseline
baseline_monthly = (avg_tokens / 1000) * cost_per_1k * monthly_requests
baseline_yearly = (avg_tokens / 1000) * cost_per_1k * yearly_requests

# Optimized
optimized_tokens = avg_tokens * (1 - optimization / 100)
optimized_monthly = (optimized_tokens / 1000) * cost_per_1k * monthly_requests
optimized_yearly = (optimized_tokens / 1000) * cost_per_1k * yearly_requests

# Display
st.subheader("Cost Analysis")

col1, col2, col3 = st.columns(3)

with col1:
    st.metric("Monthly Baseline", f"${baseline_monthly:.2f}")
    st.metric("Monthly Optimized", f"${optimized_monthly:.2f}")
    st.metric("Monthly Savings", f"${baseline_monthly - optimized_monthly:.2f}", delta=f"-{optimization}%")

with col2:
    st.metric("Yearly Baseline", f"${baseline_yearly:.2f}")
    st.metric("Yearly Optimized", f"${optimized_yearly:.2f}")
    st.metric("Yearly Savings", f"${baseline_yearly - optimized_yearly:.2f}", delta=f"-{optimization}%")

with col3:
    st.metric("3-Year Baseline", f"${baseline_yearly * 3:.2f}")
    st.metric("3-Year Optimized", f"${optimized_yearly * 3:.2f}")
    st.metric("3-Year Savings", f"${(baseline_yearly - optimized_yearly) * 3:.2f}", delta=f"-{optimization}%")

Resources

Toonify GitHub

TOON format library and examples

Headroom GitHub

Context optimization framework

Example Apps

Complete optimization demos

OpenAI Tokenizer

Test token counting

Get Started

AI Agents

RAG Applications

Advanced Concepts

Agent Skills

Framework Guides

Documentation Index

​Overview

TOON Format

Headroom

​Why Optimize?

​Impact on API Costs

​Speed & Efficiency Benefits

​Production at Scale

​Toonify Token Optimization

​What is TOON?

​Key Benefits

63.9% Average Reduction

73.4% for Tabular Data

Human Readable

<1ms Overhead

​Format Comparison

​Implementation

​Real-World Example

​Best Use Cases

​Interactive Demo

​Performance Benchmarks

​Headroom Context Optimization

​What is Headroom?

​Key Benefits

47-92% Token Reduction

Zero Code Changes

Reversible Compression

Provider Caching

​Core Features

​Statistical Compression

​Prefix Optimization

​Compress-Cache-Retrieve

​Installation & Setup

​Real-World Performance

​GitHub Code Search (100 results)

​Incident Log Analysis

​Multi-Tool Agent

​Needle in Haystack Test

​Configuration

​Best Use Cases

​Safety Guarantees

Never Removes Human Content

Never Breaks Tool Pairing

Parse Failures = No-op

Reversible Compression

​Best Practices

​Cost Calculator

​Resources

Toonify GitHub

Headroom GitHub

Example Apps

OpenAI Tokenizer

Overview

Why Optimize?

Impact on API Costs

Speed & Efficiency Benefits

Production at Scale

Toonify Token Optimization

What is TOON?

Key Benefits

Format Comparison

Implementation

Real-World Example

Best Use Cases

Interactive Demo

Performance Benchmarks

Headroom Context Optimization

What is Headroom?

Key Benefits

Core Features

Statistical Compression

Prefix Optimization

Compress-Cache-Retrieve

Installation & Setup

Real-World Performance

GitHub Code Search (100 results)

Incident Log Analysis

Multi-Tool Agent

Needle in Haystack Test

Configuration

Best Use Cases

Safety Guarantees

Best Practices

Cost Calculator

Resources