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

These details have not been verified by PyPI

Project links

Project description

Synthelion — Universal Token Compressor and Prompt Manager for AI Agents

Synthelion Logo 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).

Why Synthelion?

Every token sent to a model costs money and time. Synthelion removes the words that carry no meaning — articles, prepositions, conjunctions, auxiliary verbs — and reduces inflected words to their base form. The model receives exactly the same information, just without the grammatical packaging.

Before / After

English prose — 20 tokens → 7 tokens (−65%)

Before: I would like to know if it is possible to receive information about
        cheap restaurants in Rome.

After:  know possible receive information cheap restaurant Rome

Italian prose — 17 tokens → 8 tokens (−52%)

Before: Vorrei sapere se è possibile ricevere informazioni sui ristoranti
        economici a Roma, per favore.

After:  sapere possibile ricevere informazione ristorante economico Roma

JSON array — 256 tokens → 80 tokens (−69%)

// Before: full JSON with repeated keys on every object
[{"name":"Alice","age":30,"city":"Rome"},{"name":"Bob","age":25,"city":"Milan"},…]

// After: lossless markdown table
| name  | age | city  |
| ----- | --- | ----- |
| Alice | 30  | Rome  |
| Bob   | 25  | Milan |

HTML page — 192 tokens → 58 tokens (−70%)

// Before: full HTML with tags, attributes, scripts
<html><head>…</head><body><div class="…"><p>Visit Rome today…</p></div></body></html>

// After: clean extracted text, then NLP-compressed
Visit Rome today enjoy ancient history food culture

Benchmark — token savings by content type

Measured on GPT-4 token counts with real inputs.

NLP compression

Content	Original tokens	Light	Semantic	Aggressive
Prose EN	92	−35.9%	−34.8%	−34.8%
Prose IT	93	−23.7%	−28.0%	−51.6%
Prose DE	81	−25.9%	−28.4%	−35.8%
Prose FR	65	−33.8%	−32.3%	−38.5%
Prose ES	51	−27.5%	−19.6%	−27.5%
JSON array	256	−66.8%	−68.8%	−68.8%
Git diff	196	−51.0%	−58.2%	−58.2%
Build log	207	−32.4%	−62.3%	−62.3%
Markdown table	158	−60.8%	−64.6%	−64.6%
HTML page	192	−45.3%	−49.0%	−50.0%
Source code	249	−41.0%	−41.0%	−41.0%

Content router (Balanced profile — auto-selects the best strategy)

Content	Original	After	Saved	Strategy
Prose EN	92	60	−34.8%	NlpCompression
JSON array	256	134	−47.7%	JsonCrush:MarkdownTable
Git diff	196	137	−30.1%	DiffCompression
HTML page	192	58	−69.8%	HtmlExtract+NlpCompression
Source code	249	184	−26.1%	CodeCompression

What this means for your costs

Token pricing varies by model. As a rough example with GPT-4o ($2.50 / 1M input tokens):

Daily input volume	Without Synthelion	With Synthelion (40% avg savings)	Annual saving
500K tokens/day	$456/year	$274/year	$182/year
2M tokens/day	$1,825/year	$1,095/year	$730/year
10M tokens/day	$9,125/year	$5,475/year	$3,650/year

Savings scale with volume. For agent loops that send the same context on every call, real savings are often higher than the 40% average.

Energy & sustainability

Synthelion includes a built-in energy estimator. Every saved token avoids approximately 0.005 mWh of compute energy and 0.002 mg CO₂. At scale, that adds up.

result = svc.compress(long_prompt, CompressionLevel.SEMANTIC)
print(f"Energy saved: {result.estimated_energy_saved_mwh:.3f} mWh")
print(f"CO₂ avoided:  {result.estimated_co2_saved_mg:.3f} mg")

Quick install — one command

The fastest way: download one script and run it. It installs Synthelion, detects your Python path, configures Claude Code MCP, and sets up the auto-compression hook automatically.

Windows (PowerShell)

# Download and run
Invoke-WebRequest https://raw.githubusercontent.com/francescopaolopassaro/synthelion/main/install_claude.ps1 -OutFile install_claude.ps1
powershell -ExecutionPolicy Bypass -File install_claude.ps1

Or, if you already cloned the repo:

powershell -ExecutionPolicy Bypass -File install_claude.ps1

Linux / macOS (bash)

curl -fsSL https://raw.githubusercontent.com/francescopaolopassaro/synthelion/main/install_claude.sh | bash
# or, after cloning the repo:
chmod +x install_claude.sh && ./install_claude.sh

All platforms (Python — works everywhere)

python install_claude.py

Installer options

Flag	Description
`--upgrade`	Update Synthelion to the latest version
`--no-hook`	Skip the auto-compression hook
`--no-pip`	Skip pip install (Synthelion already installed)
`--uninstall`	Remove Synthelion and all Claude Code config

Examples:

python install_claude.py --upgrade          # update
python install_claude.py --uninstall        # remove everything
python install_claude.py --no-pip --no-hook # only update settings.json

Install (manual)

Requirements: Python 3.11+ — download from python.org and tick "Add to PATH" during setup.

# 1. Install Synthelion
pip install synthelion

# 2. Verify the CLI works
synthelion compress --text "Hello world, how are you today?" --json

# 3. Verify the MCP server starts (Ctrl+C to stop)
synthelion-mcp

If synthelion is not recognised after install, close and reopen the terminal (PATH refresh needed).

Linux

# 1. Install Synthelion
pip install synthelion
# or, in a virtualenv:
python3 -m venv ~/.venvs/synthelion
source ~/.venvs/synthelion/bin/activate
pip install synthelion

# 2. Verify
synthelion compress --text "Hello world, how are you today?" --json

# 3. If synthelion-mcp is not in PATH (virtualenv scenario), add it:
# Add the venv's bin directory to ~/.bashrc or use the absolute path in MCP config
echo 'export PATH="$HOME/.venvs/synthelion/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

macOS

# 1. Install with pip (system Python or Homebrew Python)
pip3 install synthelion
# or with uv (recommended — no PATH issues):
pip install uv
uvx synthelion-mcp   # runs the MCP server without a permanent install

# 2. Verify
synthelion compress --text "Hello world, how are you today?" --json

Zero-install with uvx (all platforms)

uv installs and runs Synthelion in an isolated environment — no pip install needed:

pip install uv       # one-time
uvx synthelion-mcp   # starts the MCP server directly

Update

Windows

pip install --upgrade synthelion

# Verify new version
synthelion --version

Linux / macOS

pip install --upgrade synthelion
# or, if installed in a virtualenv:
source ~/.venvs/synthelion/bin/activate
pip install --upgrade synthelion

With uv / uvx

uvx always fetches the latest version automatically — nothing to do.

Set up on Claude Code

Claude Code uses the MCP protocol to talk to Synthelion.

Step 1 — Install Synthelion (see above)

Step 2 — Add to `~/.claude/settings.json`

Open the file (%USERPROFILE%\.claude\settings.json on Windows, ~/.claude/settings.json on Linux/macOS) and add:

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

If synthelion-mcp is not in PATH (virtualenv, macOS Homebrew Python), use the absolute path:

{
  "mcpServers": {
    "synthelion": {
      "command": "/home/user/.venvs/synthelion/bin/synthelion-mcp"
    }
  }
}

Or use uvx — it always works without PATH issues:

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

Step 3 — Restart Claude Code

Close and reopen the Claude Code window (or run claude again in the terminal). Synthelion is now available as an MCP tool.

Step 4 — Verify

Type in Claude Code:

"Use Synthelion to compress this: I would like to know if it is possible to receive information about cheap restaurants in Rome."

Claude will call the MCP tool and return the compressed version.

Automatic prompt compression — Claude Code hook

How it works: every prompt longer than 200 characters is automatically compressed by Synthelion and the compressed version is injected as context for Claude. Claude receives both the original and the compressed form and focuses on the compressed one. This is the maximum automation currently possible within Claude Code's hook system (hooks cannot replace the original prompt text).

Windows (`~/.claude/settings.json`)

{
  "mcpServers": {
    "synthelion": { "command": "synthelion-mcp" }
  },
  "hooks": {
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "shell": "powershell",
            "command": "$j=[Console]::In.ReadToEnd()|ConvertFrom-Json;$p=$j.prompt;if($p -and $p.Length -gt 200){$r=($p|synthelion compress --json 2>$null)|ConvertFrom-Json;if($r -and $r.efficiency_pct -gt 15){@{hookSpecificOutput=@{hookEventName='UserPromptSubmit';additionalContext=\"[Synthelion - Prompt Compression - Compression Rate $([Math]::Round($r.efficiency_pct))% · $($r.energy_mwh) mWh · $($r.co2_mg) mg CO₂]\"}}|ConvertTo-Json -Compress}}",
            "statusMessage": "Compressing prompt...",
            "timeout": 15
          }
        ]
      }
    ]
  }
}

Linux / macOS (`~/.claude/settings.json`)

{
  "mcpServers": {
    "synthelion": { "command": "synthelion-mcp" }
  },
  "hooks": {
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "shell": "bash",
            "command": "prompt=$(cat | python3 -c \"import sys,json; print(json.load(sys.stdin).get('prompt',''))\"); if [ ${#prompt} -gt 200 ]; then r=$(printf '%s' \"$prompt\" | synthelion compress --json 2>/dev/null); if [ -n \"$r\" ]; then out=$(printf '%s' \"$r\" | python3 -c \"import sys,json; d=json.load(sys.stdin); eff=int(d.get('efficiency_pct',0)); label='[Synthelion - Prompt Compression - Compression Rate '+str(eff)+'% · '+str(d.get('energy_mwh',0))+' mWh · '+str(d.get('co2_mg',0))+' mg CO₂]'; print(json.dumps({'hookSpecificOutput':{'hookEventName':'UserPromptSubmit','additionalContext':label}})) if eff>15 else None\"); [ -n \"$out\" ] && printf '%s' \"$out\"; fi; fi",
            "statusMessage": "Compressing prompt...",
            "timeout": 15
          }
        ]
      }
    ]
  }
}

How to disable the hook

Remove the "hooks" block from ~/.claude/settings.json, or open /hooks in Claude Code to toggle it.

Using Synthelion with all agents — automatic compression

Synthelion can compress inputs automatically for any agent that supports the MCP protocol (Claude Code, Claude Desktop, OpenCode, Cursor, Windsurf, Continue…).

Configure all MCP-compatible agents

Add Synthelion to each agent's config file:

Agent	Config 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 UI
Continue	`.continue/config.json`

All use the same JSON block:

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

Instruct agents to compress automatically

Add this to your agent's system prompt or CLAUDE.md:

When processing long texts, files, or documents (>200 tokens), always use
the Synthelion MCP tool to compress the content before including it in your
reasoning. Call: mcp__synthelion__route_content or mcp__synthelion__compress.
Report the token reduction achieved.

Use the CLI in shell pipelines

# Compress a file before sending to any LLM API
cat long_context.txt | synthelion compress --level semantic > compressed.txt

# Pipe directly into any tool
synthelion route --file document.html | llm-cli --model gpt-4o

# Batch compress a directory
for f in docs/*.md; do
  synthelion compress --text "$(cat $f)" --json >> compressed_batch.jsonl
done

Integrations

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)

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, …).

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")

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

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.

Code examples

Text compression

from synthelion import CompressionService, CompressionLevel

svc = CompressionService()

# Semantic (default) — removes stop words and lemmatizes
r = svc.compress(
    "I would like to know if it is possible to receive information about cheap restaurants in Rome.",
    CompressionLevel.SEMANTIC,
)
print(r.compressed_text)      # know possible receive information cheap restaurant Rome
print(f"{r.efficiency_pct:.1f}% saved")   # 65.0% saved
print(f"{r.original_tokens} → {r.compressed_tokens} tokens")

# Aggressive — also removes generic verbs and adjectives
r = svc.compress("The important thing is to find a good and reliable solution.", CompressionLevel.AGGRESSIVE)
print(r.compressed_text)      # important find reliable solution

# Explicit language (skip auto-detection)
r = svc.apply_compression(
    "Ich hätte gerne einen Kaffee, bitte.",
    iso3="deu",
    level=CompressionLevel.SEMANTIC,
)
print(r.compressed_text)      # Kaffee

# Batch — compress many prompts at once
results = svc.compress_batch(
    ["Tell me about Rome.", "What is the capital of France?", "Explain neural networks."],
    CompressionLevel.SEMANTIC,
)
for r in results:
    print(r.compressed_text, f"({r.efficiency_pct:.0f}% saved)")

Language detection

from synthelion import LanguageDetector

det = LanguageDetector()

print(det.detect("Wo ist der nächste Bahnhof?"))        # deu
print(det.detect("Je voudrais une table pour deux."))   # fra
print(det.detect("Quiero información sobre Madrid."))   # spa

# Confidence scores for all matched languages
scores = det.detect_with_scores("Where is the nearest train station?")
# → {"eng": 0.42, "afr": 0.05, ...}
top = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:3]
print(top)   # [("eng", 0.42), ...]

Content router — auto-detects and picks the best algorithm

from synthelion import ContentRouter, CompressionProfile

router = ContentRouter.from_profile(CompressionProfile.BALANCED)

# JSON array → lossless markdown table or BM25 row-drop
json_data = '[{"name":"Alice","age":30,"city":"Rome"},{"name":"Bob","age":25,"city":"Milan"}]'
r = router.route(json_data)
print(r.strategy_used)   # JsonCrush:MarkdownTable
print(r.compressed)
# | name  | age | city  |
# | Alice | 30  | Rome  |
# | Bob   | 25  | Milan |
print(f"{r.savings_pct:.1f}% saved")

# HTML → extract text, then NLP-compress
html = "<html><body><h1>Visit Rome</h1><p>Rome is a beautiful city with ancient history.</p></body></html>"
r = router.route(html)
print(r.strategy_used)   # HtmlExtract+NlpCompression
print(r.compressed)      # Visit Rome Rome beautiful city ancient history

# Git diff → keeps +/- lines, trims context
diff = """--- a/main.py\n+++ b/main.py\n@@ -10,7 +10,7 @@\n def hello():\n-    print("Hello world")\n+    print("Hello Synthelion")\n     return True"""
r = router.route(diff)
print(r.strategy_used)   # DiffCompression

# Build log → deduplicates repeated lines
log = """ERROR: connection refused\nERROR: connection refused\nERROR: connection refused\nINFO: retrying..."""
r = router.route(log)
print(r.compressed)      # ERROR: connection refused  [×3]\nINFO: retrying...

# Source code → strips comments and blank lines
code = """
def greet(name):
    # This function greets the user
    # It prints a greeting message
    print(f"Hello, {name}!")  # say hello
"""
r = router.route(code)
print(r.compressed)      # def greet(name):\n    print(f"Hello, {name}!")

Summarization

from synthelion.nlp import TfIdfSummarizer, TextRankSummarizer

long_text = """
Rome is the capital of Italy and one of the most visited cities in the world.
It was founded in 753 BC and served as the center of the Roman Empire for centuries.
The city contains numerous ancient monuments including the Colosseum, the Pantheon,
and the Roman Forum. Vatican City, an independent state within Rome, is the seat of
the Catholic Church. Today Rome is a major European capital with a population of
nearly three million people. Its economy is driven by tourism, culture, and public
administration. Every year millions of tourists visit from every corner of the globe.
"""

# TF-IDF — best for factual/report text, picks sentences with rare distinctive words
tfidf = TfIdfSummarizer()
print(tfidf.summarize(long_text, sentence_count=3))

# TextRank — best for narrative text, picks sentences central to the storyline
tr = TextRankSummarizer()
print(tr.summarize(long_text, ratio=0.4))   # keep 40% of sentences

# Chain: summarize first, then compress — maximum token savings
summary = tr.summarize(long_text, sentence_count=3)
from synthelion import CompressionService, CompressionLevel
compressed = CompressionService().compress(summary, CompressionLevel.SEMANTIC)
print(compressed.compressed_text)
print(f"Final size: {compressed.compressed_tokens} tokens (was {len(long_text.split())})")

Agent memory & context window

from synthelion.agent import ContextWindow, MemoryStore, MemoryExtractor

# Rolling context window — auto-compacts when it exceeds the token budget
window = ContextWindow(max_tokens=2000, keep_last_turns=4)

for i in range(20):
    window.append("user", f"Message {i}: tell me about topic {i} in great detail...")
    window.append("assistant", f"Response {i}: here is a detailed explanation of topic {i}...")

print(f"Messages in window: {window.message_count}")   # stays bounded
print(window.to_messages_json(indent=2))               # ready for any LLM API

# Long-term memory across sessions
extractor = MemoryExtractor()
note = extractor.extract("The user lives in Rome and works in tech. They prefer Python over C#.", max_sentences=2)
# → {"summary": "User lives Rome works tech.", "keywords": ["Rome", "Python", "tech"]}

store = MemoryStore()
store.remember(note)
store.remember({"summary": "User prefers dark mode and short answers.", "keywords": ["dark mode", "concise"]})

# Save to disk, restore next session
json_blob = store.save()
store2 = MemoryStore()
store2.load(json_blob)

# Recall what's relevant for the current query
hits = store2.recall("What does the user prefer for coding?", top_k=2)
print(hits[0]["summary"])   # User lives Rome works tech.

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.

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.

Troubleshooting

synthelion-mcp: command not found

The CLI entry point is not in your PATH. Fixes (choose one):

// Option A — use the Python module form
{
  "mcpServers": {
    "synthelion": {
      "command": "python",
      "args": ["-m", "synthelion.plugins.mcp_server"]
    }
  }
}

// Option B — use uvx (always works, no PATH needed)
{
  "mcpServers": {
    "synthelion": {
      "command": "uvx",
      "args": ["synthelion-mcp"]
    }
  }
}

// Option C — absolute path to the installed binary
// Windows: find it with: where synthelion-mcp
// Linux/macOS: which synthelion-mcp
{
  "mcpServers": {
    "synthelion": {
      "command": "C:\\Users\\you\\AppData\\Local\\Programs\\Python\\Python312\\Scripts\\synthelion-mcp.exe"
    }
  }
}

Hook not firing (Windows)

Run where synthelion in PowerShell to verify the CLI is in PATH. If not, add the Scripts folder to PATH:

$env:PATH += ";$env:APPDATA\Python\Python312\Scripts"

Hook not firing (Linux/macOS)

Verify with which synthelion. If using a virtualenv, activate it before starting Claude Code or use the absolute path in the hook command.

Detection errors (wrong language detected)

Pass the language explicitly:

synthelion compress --text "..." --language ita

Or in Python:

result = svc.compress(text, iso3="ita")

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

1.0.8

Jun 27, 2026

This version

1.0.7

Jun 25, 2026

1.0.6

Jun 24, 2026

1.0.5

Jun 24, 2026

1.0.4

Jun 24, 2026

1.0.3

Jun 24, 2026

1.0.2

Jun 24, 2026

1.0.1

Jun 24, 2026

1.0.0

Jun 24, 2026

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

synthelion-1.0.7.tar.gz (12.8 MB view details)

Uploaded Jun 25, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

synthelion-1.0.7-py3-none-any.whl (12.8 MB view details)

Uploaded Jun 25, 2026 Python 3

File details

Details for the file synthelion-1.0.7.tar.gz.

File metadata

Download URL: synthelion-1.0.7.tar.gz
Upload date: Jun 25, 2026
Size: 12.8 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for synthelion-1.0.7.tar.gz
Algorithm	Hash digest
SHA256	`a20ba1306682d48667b05399822c6615e0c7abfdf90dd5f5b51027135d70522c`
MD5	`23fe2102f93a8af001e27cc109617c3c`
BLAKE2b-256	`399464941aa5b4782767c1f25b6e966c6a0e023e732187d82682f2b3fb5abb15`

See more details on using hashes here.

File details

Details for the file synthelion-1.0.7-py3-none-any.whl.

File metadata

Download URL: synthelion-1.0.7-py3-none-any.whl
Upload date: Jun 25, 2026
Size: 12.8 MB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for synthelion-1.0.7-py3-none-any.whl
Algorithm	Hash digest
SHA256	`71a4d09957c0a4618a2b0d4ccea47455d78fe4c29fd7ac878064df7e01292b5f`
MD5	`dd3021a19ce5695b97212df1acbca26d`
BLAKE2b-256	`982af05bfef0e454cd111884eb4bdadcf02f03ac671128f7ca2fd4819e9a94d4`

See more details on using hashes here.

synthelion 1.0.7

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Synthelion — Universal Token Compressor and Prompt Manager for AI Agents

Why Synthelion?

Before / After

Benchmark — token savings by content type

NLP compression

Content router (Balanced profile — auto-selects the best strategy)

What this means for your costs

Energy & sustainability

Quick install — one command

Windows (PowerShell)

Linux / macOS (bash)

All platforms (Python — works everywhere)

Installer options

Install (manual)

Linux

macOS

Zero-install with uvx (all platforms)

Update

Windows

Linux / macOS

With uv / uvx

Set up on Claude Code

Step 1 — Install Synthelion (see above)

Step 2 — Add to ~/.claude/settings.json

Step 3 — Restart Claude Code

Step 4 — Verify

Automatic prompt compression — Claude Code hook

Windows (~/.claude/settings.json)

Linux / macOS (~/.claude/settings.json)

How to disable the hook

Using Synthelion with all agents — automatic compression

Configure all MCP-compatible agents

Instruct agents to compress automatically

Use the CLI in shell pipelines

Integrations

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

LangChain — LangGraph, LCEL, ReAct agents

Python API — any custom agent or pipeline

CLI — shell scripts, pipelines, any language

Tools

Code examples

Text compression

Language detection

Content router — auto-detects and picks the best algorithm

Summarization

Agent memory & context window

Compression levels

Supported languages (50+)

Troubleshooting

Links

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distribution

File details

File metadata

File hashes

File details

File metadata

File hashes

Step 2 — Add to `~/.claude/settings.json`

Windows (`~/.claude/settings.json`)

Linux / macOS (`~/.claude/settings.json`)