synthelion 1.0.2

Universal token compressor for AI agents — MCP, OpenAI, LangChain, CLI. 50+ languages, zero ML models.

Synthelion — Universal Token Compressor for AI Agents

Synthelion compresses prompts before they reach any AI model — cutting token usage by up to 70%, reducing API costs, and speeding up responses. It works with any agent or framework: Claude Code, OpenAI, LangChain, OpenCode, Cursor, and more.

Supports 50+ languages out of the box. No AI model required. No configuration.

"Why use many tokens when few tokens do trick?" — A caveman (and your wallet).

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Install

pip install synthelion

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Integrations

MCP — Claude Code, Claude Desktop, OpenCode, Cursor, Windsurf, Continue…

Any agent that supports the Model Context Protocol can use Synthelion as a tool server.

1. Open your agent's MCP settings file:

Agent	Settings file
Claude Code	~/.claude/settings.json
Claude Desktop (macOS)	~/Library/Application Support/Claude/claude_desktop_config.json
Claude Desktop (Windows)	%APPDATA%\Claude\claude_desktop_config.json
OpenCode	~/.config/opencode/config.json
Cursor / Windsurf	MCP settings in the app

2. Add this block:

{
  "mcpServers": {
    "synthelion": {
      "command": "synthelion-mcp"
    }
  }
}

3. Restart the agent. Done.

Zero-install with uvx (no pip install needed if you have uv):

{
  "mcpServers": {
    "synthelion": {
      "command": "uvx",
      "args": ["synthelion-mcp"]
    }
  }
}

Once connected, just ask naturally:

"Compress this text to save tokens"
"Summarize this article in 3 sentences"
"Detect the language of this message"
"Compress this JSON / HTML / diff / log"

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OpenAI — GPT-4, GPT-4o, Codex, and any OpenAI-compatible API

from openai import OpenAI
from synthelion.plugins.openai_tools import get_tool_definitions, execute_tool

client = OpenAI()
tools = get_tool_definitions()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Compress this text: I would like to know if it is possible..."}],
    tools=tools,
    tool_choice="auto",
)

# Handle tool calls returned by the model
for tool_call in response.choices[0].message.tool_calls or []:
    result = execute_tool(tool_call.function.name, tool_call.function.arguments)
    print(result)

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LangChain — LangGraph, LCEL, ReAct agents

pip install "synthelion[langchain]"

from synthelion.plugins.langchain_tools import get_tools
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

llm = ChatOpenAI(model="gpt-4o")
tools = get_tools()

agent = create_react_agent(llm, tools)
result = agent.invoke({"messages": [{"role": "user", "content": "Compress this prompt: ..."}]})

Works with any LangChain-compatible LLM (OpenAI, Anthropic, Groq, Ollama, …).

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Python API — any custom agent or pipeline

from synthelion import CompressionService, CompressionLevel, ContentRouter, CompressionProfile

# Compress text
svc = CompressionService()
result = svc.compress(
    "I would like to know if it is possible to receive information about cheap restaurants in Rome.",
    CompressionLevel.SEMANTIC,
)
print(result.compressed_text)   # "know possible receive information cheap restaurant Rome"
print(f"{result.efficiency_pct:.1f}% saved")

# Auto-route any content type (JSON, HTML, diff, log, code, prose)
router = ContentRouter.from_profile(CompressionProfile.BALANCED)
routed = router.route(my_content)
print(routed.strategy_used, f"{routed.savings_pct:.1f}% saved")

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CLI — shell scripts, pipelines, any language

# Compress text
synthelion compress --text "I would like to know if it is possible..." --level semantic

# Detect language
synthelion detect --text "Guten Morgen, wie geht es Ihnen?"

# Auto-route a file
synthelion route --file context.json

# Summarize
synthelion summarize --text "..." --sentences 3

# Start MCP server manually
synthelion serve-mcp

Pipe-friendly — reads from stdin if no --text or --file is given:

cat big_prompt.txt | synthelion compress --level aggressive

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Tools

Tool	What it does
compress	Removes stop words, lemmatizes content words. Up to 70% token reduction.
detect_language	Identifies language of any text. Returns ISO 639-3 code.
route_content	Auto-detects JSON, HTML, diff, log, code or prose and applies the best algorithm.
summarize	Extractive summarization — keeps the most important sentences (TF-IDF or TextRank).
compress_batch	Compresses a list of texts in one call.

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Compression levels

Level	What it removes	Typical savings
light	Stop words (articles, prepositions, conjunctions…)	25–35%
semantic	Stop words + lemmatization to base form	30–69%
aggressive	Everything above + generic verbs and descriptive adjectives	35–70%

Default: semantic.

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Supported languages (50+)

Afrikaans · Arabic · Armenian · Basque · Belarusian · Bengali · Bulgarian · Catalan · Chinese · Croatian · Czech · Danish · Dutch · English · Estonian · Finnish · French · Galician · German · Greek · Hebrew · Hindi · Hungarian · Icelandic · Indonesian · Irish · Italian · Japanese · Kannada · Kazakh · Korean · Latin · Latvian · Lithuanian · Macedonian · Malay · Marathi · Norwegian · Persian · Polish · Portuguese · Romanian · Russian · Serbian · Slovak · Slovenian · Spanish · Swedish · Tamil · Telugu · Thai · Turkish · Ukrainian · Urdu · Vietnamese

Language is detected automatically from the text. Pass an explicit ISO 639-3 code to override.

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Troubleshooting

synthelion-mcp: command not found

Use the module form instead:

{
  "mcpServers": {
    "synthelion": {
      "command": "python",
      "args": ["-m", "synthelion.plugins.mcp_server"]
    }
  }
}

Or use uvx — it always works without PATH issues.

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Links

• PyPI: https://pypi.org/project/synthelion/
• Source: https://github.com/francescopaolopassaro/synthelion
• Original C# project (Caveman): https://github.com/francescopaolopassaro/caveman

© 2026 Passaro Francesco Paolo — Digitalsolutions.it