ContextLens

The context compression protocol for LLM inference.

80,000+ words saved. 94% meaning retained. One line of code.

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The Problem

Every time you call Claude or GPT, you're sending the same context over and over again. Repeated messages, duplicate code blocks, redundant explanations — all costing you tokens.

A typical 20-message conversation has ~70% redundant content.

The Fix

import anthropic
import ctxlens as cx

client = cx.wrap(anthropic.Anthropic(api_key="..."))

# That's it. Every API call is now automatically compressed.
response = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=1000,
    messages=[{"role": "user", "content": "..."}]
)

One line. Zero changes to your existing code. Drop-in replacement.

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Results

Real benchmark across 3 production datasets (coding, agent loops, research):

Method	Token Reduction	Fidelity	Latency
No compression	0%	64.1%	0.0ms
Simple truncation	39.7%	72.4%	0.0ms
ctxlens balanced	66.8%	67.5%	0.2ms

• 1.7x more token reduction than simple truncation
• 83.8% fidelity on agent loops — beats truncation
• 0.2ms latency after model warmup — negligible overhead
• 80,500+ words saved across 395 real conversations
• 100% fact retention on agent tasks

→ See full benchmark methodology

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Install

pip install ctxlens

With semantic compression (recommended):

pip install ctxlens[semantic]

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Usage

Anthropic

import anthropic
import ctxlens as cx

client = cx.wrap(anthropic.Anthropic(api_key="..."))
response = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=1000,
    messages=[{"role": "user", "content": "..."}]
)

# See how much you saved
print(client.savings)
# {
#   'calls': 1,
#   'tokens_saved_estimate': 847,
#   'redundancy_pct': 73.2,
#   'cost_saved_gbp': 0.0025
# }

OpenAI

import openai
import ctxlens as cx

client = cx.wrap(openai.OpenAI(api_key="..."))
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "..."}]
)

print(client.savings)

Async (AsyncAnthropic / AsyncOpenAI)

import anthropic
import ctxlens as cx

client = cx.wrap(anthropic.AsyncAnthropic(api_key="..."))

async def main():
    response = await client.messages.create(
        model="claude-opus-4-5",
        max_tokens=1000,
        messages=[{"role": "user", "content": "..."}]
    )
    print(client.savings)

Agent loops

import ctxlens as cx

# Wrap your agent — prevents context limit failures on long runs
agent = cx.wrap_agent(your_agent, budget="economic")
result = agent.run("your task here")

Direct compression

from ctxlens import ContextLens

engine = ContextLens(budget="balanced", show_savings=True)

messages = [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."},
]

result = engine.compress(messages)
print(f"Saved: {result.tokens_estimated_saved} tokens")
print(f"Fidelity: {result.fidelity_score * 100:.1f}% meaning retained")

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Compression budgets

Budget	Aggressiveness	Best for
economic	High	Long agent loops, cost-sensitive apps
balanced	Medium	General use (default)
precise	Low	When accuracy is critical

client = cx.wrap(anthropic.Anthropic(), budget="economic")

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How it works

ContextLens runs three compression stages:

1. Exact deduplication — removes identical repeated messages (~0ms overhead)
2. Semantic triage — scores every message by relevance to the current query using all-MiniLM-L6-v2 locally — zero external API calls
3. Agent-aware compression — classifies messages by type (goal, error, tool_call, reasoning) and applies type-specific rules

The fidelity score measures how much meaning was retained after compression. A score of 0.95 means 95% of the semantic content was preserved.

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Chrome Extension

ContextLens also comes as a Chrome extension that works directly in your browser on Claude, ChatGPT, Gemini, DeepSeek, and Perplexity — no API key needed.

• Auto-compresses when context reaches 75%
• Shows meaning retained score after every response
• Stores memory across conversations and platforms
• Export your memory as JSON

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Roadmap

• GitHub pre-fetch filter (reduce token usage in agentic coding)
• Project memory injection (auto-inject context into new chats)
• Node.js SDK
• MCP server for Claude Code

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License

MIT — free for personal and commercial use.

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Author

Built by Usama Fateh Ali as part of ARIA — an autonomous financial intelligence system.

"93% of tokens sent to LLMs are identical repeated data. ContextLens eliminates that waste."

Real Production Measurement

Measured on ARIA — a live autonomous financial intelligence system running 1,440 decision cycles per day:

Metric	Value
Token redundancy detected	97.6%
Tokens before compression	1,950
Tokens after compression	46
Cost per cycle	£0.0057 → £0.00014
Monthly saving at scale	£100-500+

Same decisions. 97.6% cheaper.