Your keys, your data — we never see them
You only need our API key for the compression part — your LLM API keys and usage are entirely managed by your own application. We don't have access to your SaaS, and we don't even know its name. We simply receive the text through our API, compress it, and return the response to your app. Your LLM — whether local, OpenAI, Claude, or any other — then processes it using your own API keys. Your data stays safe with you.
Add agentready.compress(), cut LLM token costs 40-60%.
Compression API. Compresses prompts before sending to your LLM (no secondary LLM call, ~5ms overhead). Two lines of code, works with any LLM.
2,847+ developers · No credit card · Free during beta
Try it live — paste your prompt
Calculate your savings
Estimated monthly savings
$420/mo
Based on 42% average token reduction across 2.4M+ API calls
From beta users
“Dropped our GPT-4 bill by 38% in one afternoon. Integration was 10 minutes.”
— Senior ML Engineer, Series B startup
“We process 50k prompts/day. AgentReady saves us ~$2,100/month.”
— CTO, AI automation company
“The monkey-patch mode is genius. Zero code changes, immediate savings.”
— Indie developer, Solo founder
How it works
Step 1: Compress Step 2: Call your LLM directly
───────────────────────────────── ─────────────────────────────────
messages = agentready.compress( response = openai.chat.completions
apiKey, messages .create(model, messages)
)
Your LLM key never touches
• filler words removed AgentReady. You call the LLM
• verbose → concise yourself, with fewer tokens.
• redundancy stripped
• ~5ms, no LLM call
10,000 tok ──▸ ≈ 4,200 tok ──▸ same quality responseWhat it removes
- • Filler words (basically, essentially, it should be noted…)
- • Verbose phrases ("in order to" → "to")
- • Redundant connectors (however, furthermore…)
- • Duplicate lines & boilerplate
- • Excess whitespace
What it preserves
- • All semantic meaning (not a summarizer)
- • Code blocks & inline code
- • URLs, numbers, dates
- • Sentence structure & reading order
- • Multi-language (EN, IT, FR, DE, ES)
Integration — 30 seconds
pip install agentready-sdk && agentready initnpx agentready-sdk init · or grab a key at /quick-keyimport agentready
from openai import OpenAI
import os
# Step 1 — compress via AgentReady
result = agentready.compress(
api_key=os.environ["AGENTREADY_API_KEY"],
messages=[{"role": "user", "content": "..."}]
)
# Step 2 — call OpenAI directly
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = client.chat.completions.create(
model="gpt-4o",
messages=result["messages"]
)Or monkey-patch existing code (zero changes):
# Zero changes to existing code
import os
from agentready import patch_openai
patch_openai(api_key=os.environ["AGENTREADY_API_KEY"])
# All existing OpenAI calls now go through AgentReady automaticallyFAQ
You should. This handles the mechanical noise that even good prompts contain — articles, filler words, verbose constructions. Think of it as gzip for natural language.
Tested across GPT-4, Claude, Gemini. Output quality delta is <2% on BLEU/ROUGE scores. The removed content is linguistically redundant.
No. You only need our API key for the compression step. Your LLM keys and usage stay entirely in your own app — we never see them. We receive text, compress it, and return it. Your LLM (local, OpenAI, Claude, or any other) then processes it with your own keys. We don't even know your app's name.
Free during beta. No credit card required. After beta: pay-per-token, still significantly less than the tokens you save.
Start saving on every API call
Free during open beta — no credit card required.
⚡ Early users keep free tier forever when we launch paid plans.