tokenmesh 0.2.1

A semantic token optimizer for LLM prompts — reduce tokens, preserve meaning

🧵 TokenMesh

Why send 8000 tokens when 3000 do the same job?

GitHub · Live Demo

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What is this?

TokenMesh is a Python library that optimizes LLM prompts before sending them to APIs.

It removes:

• duplicate instructions
• repeated content
• filler text

while preserving meaning, constraints, and rules.

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Why use it?

LLM prompts are often verbose and redundant, which:

• increases token cost
• slows responses
• hits context limits

TokenMesh helps you:

• reduce 40–75% tokens
• lower API costs
• keep prompts clean and efficient

Same meaning → fewer tokens → better performance.

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Install

pip install tokenmesh

• Python 3.9+
• Works on Windows, Mac, Linux

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Quick Start

from tokenmesh import TokenMesh

tm = TokenMesh()

result = tm.optimize(
    text=your_prompt,
    query="optional"
)

print(result.optimized_text)
print(result.reduction_percent)

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Claude Integration

from tokenmesh.integrations.claude import TokenMeshClaude

client = TokenMeshClaude()

response = client.chat(
    system=long_prompt,
    user="your question",
    model="claude-sonnet-4"
)

print(response.content)

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Modes

Lite (safe)

• 20–40% reduction
• preserves strict rules

from tokenmesh import TokenMeshLite
result = TokenMeshLite().optimize(text)

Aggressive (max compression)

• 55–75% reduction
• best for long content

from tokenmesh import TokenMeshAggressive
result = TokenMeshAggressive().optimize(text, query="...")

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Example

Before: ~684 tokens After: ~290 tokens

Same instructions. ~57% fewer tokens.

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License

MIT