Synthelion — Universal Token Compressor and Prompt Manager for AI Agents

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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 → 9 tokens (−55%)

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

After:  like know possible receive information about cheap restaurant Rome

Italian prose — 16 tokens → 9 tokens (−44%)

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

After:  sapere è possibile ricevere informazione ristorante economico Roma favore

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 with synthelion's own token counter against real inputs — reproduce with synthelion bench --json (content router) or CompressionService.compress directly (NLP-only, per-level).

NLP compression (prose, per level)

Content	Original tokens	Light	Semantic	Aggressive
Prose EN	20	−55.0%	−55.0%	−75.0%
Prose IT	16	−43.8%	−43.8%	−62.5%
Prose DE	19	−47.4%	−47.4%	−63.2%
Prose FR	18	−38.9%	−38.9%	−55.6%
Prose ES	17	−47.1%	−47.1%	−52.9%

Content router (`synthelion bench --json`, auto-selects the best strategy)

Content	Original	After	Saved
Plain text EN	58	36	−37.9%
Plain text IT	51	36	−29.4%
JSON array	46	32	−30.4%
Git diff	51	31	−39.2%
Python code	56	31	−44.6%
Log / stacktrace	101	45	−55.4%
HTML page	51	16	−68.6%

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

Windows PowerShell (install_claude.ps1)

Flag	Description
`-Upgrade`	Update Synthelion to the latest version
`-NoHook`	Skip the auto-compression hook
`-NoPip`	Skip pip install (Synthelion already installed)
`-Uninstall`	Remove Synthelion and all Claude Code config

powershell -ExecutionPolicy Bypass -File install_claude.ps1 -Upgrade     # update
powershell -ExecutionPolicy Bypass -File install_claude.ps1 -Uninstall   # remove everything
powershell -ExecutionPolicy Bypass -File install_claude.ps1 -NoPip -NoHook  # only update settings.json

Linux / macOS (install_claude.sh) and Python (install_claude.py)

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

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 — Register with one command (new in 1.0.7)

synthelion install           # writes to ~/.claude.json (global)
synthelion install --local   # writes to .claude/settings.json (project-only)

Or manually:

Step 2 (manual) — 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. The compressed text is injected as additionalContext for Claude (invisible in the terminal — that's the part that actually does the token-saving work), while a short systemMessage — [Synthelion N% saved - X mWh - Y mg CO2 saved] — is shown visibly so you can confirm it fired.

Automatic setup (recommended)

synthelion install          # writes hook + MCP to ~/.claude.json
synthelion install --local  # project-local .claude/settings.json

Manual — 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 $r.efficiency_pct -gt 15){$pct=[Math]::Round($r.efficiency_pct);$label='[Synthelion '+$pct+'% saved - '+$r.energy_mwh+' mWh - '+$r.co2_mg+' mg CO2 saved]';@{systemMessage=$label;hookSpecificOutput=@{hookEventName='UserPromptSubmit';additionalContext=$r.compressed}}|ConvertTo-Json -Compress}}",
            "statusMessage": "Compressing prompt...",
            "timeout": 15
          }
        ]
      }
    ]
  }
}

Manual — 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 '+str(eff)+'% saved - '+str(d.get('energy_mwh',0))+' mWh - '+str(d.get('co2_mg',0))+' mg CO2 saved]'; print(json.dumps({'systemMessage':label,'hookSpecificOutput':{'hookEventName':'UserPromptSubmit','additionalContext':d.get('compressed','')}})) 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/opencode.json` (global) or `opencode.json` (project)
Cursor	`~/.cursor/mcp.json`
Windsurf	`~/.codeium/windsurf/mcp_config.json`
Continue	`.continue/config.json`

Claude / Claude Desktop / Cursor / Windsurf / Continue all use the same JSON block:

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

OpenCode uses its own MCP schema (mcp key, explicit type, command as an array):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "synthelion": {
      "type": "local",
      "command": ["synthelion-mcp"],
      "enabled": true
    }
  }
}

Or register it automatically:

synthelion install --agent opencode           # global: ~/.config/opencode/opencode.json
synthelion install --agent opencode --local    # project: ./opencode.json

Once registered, all 13 Synthelion tools (compress, route_content, compress_for_context, deduplicate, …) show up as callable tools in OpenCode — ask it to "use the synthelion tool to compress this text" or let it call them automatically per your agent instructions (see below).

Cursor and Windsurf read the same mcpServers shape as Claude, just at their own config path — register with:

synthelion install --agent cursor
synthelion install --agent windsurf

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), use Synthelion:
- mcp__synthelion__compress_for_context  — fit any content in your token budget
- mcp__synthelion__compress_conversation — compress older turns before sending history
- mcp__synthelion__deduplicate           — remove overlapping retrieved chunks
- mcp__synthelion__route_content         — auto-detect content type and compress
- mcp__synthelion__session_record        — save decisions for cross-session recall
- mcp__synthelion__session_recall        — retrieve past decisions by query
Report the token reduction achieved (synthelion_metrics field).

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()   # 11 StructuredTools, including all new context 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, …).

SynthelionMemory — drop-in compressing memory

from langchain.chains import ConversationChain
from synthelion.plugins.langchain_tools import SynthelionMemory

# Compresses history turns and injects relevant past decisions via RAG
memory = SynthelionMemory(max_context_tokens=4000, recall_limit=5)
chain = ConversationChain(llm=llm, memory=memory)

chain.predict(input="Tell me about Rome.")
chain.predict(input="What are the best restaurants there?")
# Older turns are automatically compressed; RAG recalls relevant notes from past sessions

Claude & OpenAI Adapters — auto-compression with one import

pip install "synthelion[claude]"    # for ClaudeAdapter
pip install "synthelion[openai]"    # for OpenAIAdapter

from synthelion.integrations.claude_adapter import ClaudeAdapter

# Replaces anthropic.Anthropic — same interface, auto-compresses every message
client = ClaudeAdapter()
reply = client.chat("claude-sonnet-4-6", [
    {"role": "user", "content": "Explain how the Renaissance shaped modern science in detail..."}
])
print(reply)                  # model answer
print(client.total_saved)     # tokens saved so far

from synthelion.integrations.openai_adapter import OpenAIAdapter

client = OpenAIAdapter()
reply = client.chat("gpt-4o", [
    {"role": "user", "content": "Explain how the Renaissance shaped modern science in detail..."}
])

RagAgent — stateful agent with memory, RAG, and cost tracking

from synthelion.agent.rag_agent import RagAgent

agent = RagAgent(max_context_tokens=8000, recall_limit=5)

# Each add_turn compresses the message, recalls past decisions, and updates the rolling window
agent.add_turn("user", "I decided to use PostgreSQL for the user database.")
agent.add_turn("assistant", "Good choice. PostgreSQL handles JSONB fields well for config data.")

agent.add_turn("user", "What did we decide about the database?")
# The agent automatically recalls past decisions about PostgreSQL
recalled = agent.recall("database")
for d in recalled:
    print(d["text"])    # "I decided to use PostgreSQL for the user database."

# Get compressed message list ready for any LLM API
messages = agent.get_context_messages()
print(agent.total_saved, "tokens saved")

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

# Start the local read-only web dashboard
synthelion serve-dashboard

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

cat big_prompt.txt | synthelion compress --level aggressive

Diagnostics & setup

# Health check — verifies MCP package, ledger, session DB, PATH, Claude config
synthelion doctor
synthelion doctor --json      # machine-readable output

# Register the MCP server automatically (global Claude Code config)
synthelion install
synthelion install --agent gemini            # Gemini CLI
synthelion install --agent opencode          # OpenCode (global: ~/.config/opencode/opencode.json)
synthelion install --agent opencode --local  # OpenCode (project: ./opencode.json)
synthelion install --agent claude --local    # project-local .claude/settings.json
synthelion install --agent cursor            # Cursor (~/.cursor/mcp.json)
synthelion install --agent windsurf          # Windsurf (~/.codeium/windsurf/mcp_config.json)

Analytics & savings tracking

# Show total tokens saved, cost estimate, and tool breakdown
synthelion status

# Show savings history (last 7 days)
synthelion gain --days 7
synthelion gain --all --json   # full history, machine-readable

# Benchmark on a built-in corpus (prose, JSON, diff, code, logs, HTML)
synthelion bench
synthelion bench --json

# Export ledger to CSV or JSONL for analysis in Excel / Grafana / pandas
synthelion export                          # CSV to stdout
synthelion export --format jsonl -o savings.jsonl
synthelion export --days 30 -o last_month.csv

Self-upgrade

synthelion upgrade            # pip install --upgrade synthelion
synthelion upgrade --dry-run  # show what would run, don't run it

Web dashboard

A local, read-only web dashboard over everything Synthelion has compressed — no external calls, no CDN, works offline. Built for a multi-session setup: every synthelion-mcp process (one per agent session) and every CLI/hook invocation writes to the same lock-free ledger, and the dashboard aggregates them live.

synthelion serve-dashboard                    # http://127.0.0.1:8787
synthelion serve-dashboard --port 9000        # custom port
synthelion serve-dashboard --host 0.0.0.0     # explicit opt-in to expose it on the network

Synthelion dashboard — overview

KPIs at a glance: calls, tokens saved, avg efficiency, CO₂ saved, active sessions, avg calls per session, tools used, best single call, and latency (avg / p95 / max) — plus a version badge next to the title showing exactly which Synthelion build is running. Charts for tokens saved over time and by tool.

Synthelion dashboard — sessions and requests

Scroll down for a breakdown by content type, recent session-memory decisions, a Sessions table (one row per synthelion-mcp/CLI process — PID, calls, tools used, first/last activity), and a Recent requests feed (every individual call with before/after tokens, efficiency, and latency).

Auto-start with Claude Code — add a SessionStart hook so the dashboard is already running whenever you open a session:

{
  "hooks": {
    "SessionStart": [
      {
        "hooks": [
          {
            "type": "command",
            "shell": "powershell",
            "command": "try{$c=Get-NetTCPConnection -LocalPort 8787 -State Listen -ErrorAction SilentlyContinue;if(-not $c){Start-Process -FilePath \"synthelion\" -ArgumentList 'serve-dashboard' -WindowStyle Hidden}}catch{}",
            "timeout": 10
          }
        ]
      }
    ]
  }
}

The check is non-blocking and idempotent — if the dashboard is already listening on the port, the hook does nothing.

Cluster deployment

For an AI-provider-scale deployment (many nodes, thousands of concurrent agent sessions), point every node at a shared session/vector store instead of each one keeping its own local ledger — then any dashboard replica shows the whole cluster's activity, not just its own.

1. Configure

synthelion configure --session-store redis --redis-url redis://redis-host:6379/0 \
                      --vector-store qdrant --qdrant-url http://qdrant-host:6333 \
                      --dashboard-host 0.0.0.0
synthelion configure --show   # print the effective config without writing

Writes ~/.synthelion/config.json (or --output <path> / SYNTHELION_CONFIG env var for a per-node/ConfigMap-mounted file). See synthelion.config.example.json in the repo root for the full key reference — every key has a built-in default, so a partial file only needs to override what changes.

Backends:

	Options
Session store (active sessions, savings ledger)	`local` (single-node file), `redis`, `postgres`
Vector store (cross-session RAG memory)	`chromadb` (bundled embedding), `qdrant` (deterministic hashed vectors — no ML model, see below), `lexical` (no external service)
Dashboard realtime	`websocket` (push updates), `polling`

Qdrant support keeps Synthelion's "zero ML models" design: rather than pulling in an embedding model just for Qdrant, it indexes a deterministic FNV-1a hashed bag-of-words vector — the same lexical scoring the fallback path already does, just queryable through Qdrant's ANN index across a cluster.

2. Docker

docker build -t synthelion:latest .
docker compose up -d                       # single node, local-file storage
docker compose --profile cluster up -d     # + redis + postgres + qdrant containers

See the Dockerfile and docker-compose.yml at the repo root — the image runs synthelion serve-dashboard --host 0.0.0.0 by default and exposes 8787 (dashboard) and 8788 (WebSocket realtime updates).

For Docker Swarm, the same compose file works with docker stack deploy -c docker-compose.yml synthelion; scale with docker service scale synthelion_dashboard=3 (Swarm's routing mesh load-balances the published port across replicas — unlike plain docker compose up, where each replica binds the host port directly, so scale there via multiple named services or a reverse proxy instead).

3. Kubernetes

Manifests in k8s/: namespace.yaml, configmap.yaml (holds synthelion.config.json — edit the Redis/Qdrant URLs to match your cluster), deployment.yaml (3 replicas + HorizontalPodAutoscaler, readiness/liveness probes on /api/summary, non-root), service.yaml (ClusterIP — front it with your own Ingress/auth layer), and backing-services.yaml (minimal in-cluster Redis + Qdrant StatefulSets for evaluation; swap for managed services in production).

kubectl apply -f k8s/namespace.yaml
kubectl apply -f k8s/backing-services.yaml   # or point configmap.yaml at managed Redis/Qdrant instead
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

4. Load balancer (no orchestrator)

Run synthelion serve-dashboard --host 0.0.0.0 on N plain nodes (systemd service, synthelion install-style), all pointed at the same SYNTHELION_CONFIG, and put any standard load balancer (nginx, HAProxy, a cloud LB) in front on port 8787. Every node reads/writes the same shared Redis/Postgres/Qdrant backend, so it doesn't matter which node a request lands on.

Tools

13 MCP tools — all marked readOnlyHint: true so Claude Code and other MCP clients can call them safely in parallel.

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.
compress_for_context	Compresses content to fit a token budget. Chains routing → NLP → TextRank until budget met.
compress_conversation	Compresses a messages list. Keeps last N verbatim, summarizes/collapses older turns.
deduplicate	Removes near-duplicate texts using cosine bag-of-words similarity. Configurable threshold.
session_record	Persists a decision or context note across sessions (ChromaDB or lexical fallback).
session_recall	Retrieves past decisions by semantic or keyword similarity.
session_start / session_end	Track session boundaries and emit summaries.
compress_file	Read a file by path and return only the compressed content. Avoids loading raw files into context.
synthelion_status	Returns aggregate token savings and estimated cost as structured JSON.

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, please.",
    CompressionLevel.SEMANTIC,
)
print(r.compressed_text)      # like know possible receive information about cheap restaurant Rome
print(f"{r.efficiency_pct:.1f}% saved")   # 55.0% 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)      # solution

# Statistical — TF-IDF word scoring instead of curated dictionaries
r = svc.compress(
    "I would like to know if it is possible to receive information about cheap "
    "restaurants in Rome, please. The city has many wonderful places to eat.",
    CompressionLevel.STATISTICAL,
)
print(r.compressed_text)      # like possible receive information about cheap restaurant Rome city wonderful eat

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

# 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.

AI-agent context tools

compress_file — read and compress a file by path

# Instead of: content = open("big_log.txt").read()  → 8000 tokens sent to LLM
# Do this:
r = execute_tool("compress_file", {"path": "big_log.txt", "profile": "agent"})
print(r["compressed"])          # deduplicated log, ~1200 tokens
print(r["synthelion_metrics"])  # "before=8000 after=1200 saved=6800 (85.0%) ~$0.02040"
print(r["detected_type"])       # "log"

# With a token budget
r2 = execute_tool("compress_file", {
    "path": "src/big_module.py",
    "max_tokens": 500,
    "profile": "agent",
})
print(r2["fits_budget"])        # True if ≤ 500 tokens

compress_for_context — fit content into a token budget

from synthelion.plugins.openai_tools import execute_tool

long_article = """Artificial intelligence is a branch of computer science that aims to create
intelligent machines... [1000+ token document]"""

# Compress without a budget — route + NLP, agent profile
r = execute_tool("compress_for_context", {"content": long_article, "profile": "agent"})
print(r["compressed"])           # compressed text
print(r["synthelion_metrics"])   # "before=213 after=82 saved=131 (61.5%) ~$0.00039"
print(r["detected_type"])        # "prose"
print(r["strategy"])             # "NlpCompression"

# Compress to fit in a 200-token context window
r2 = execute_tool("compress_for_context", {
    "content": long_article,
    "max_tokens": 200,
    "prefer": "auto",    # "compress" | "summarize" | "auto"
})
print(r2["fits_budget"])         # True or False
print(r2["budget_exceeded_by"])  # 0 if fits, else delta

compress_conversation — shrink a message history

conversation = [
    {"role": "user",      "content": "Tell me about machine learning in detail."},
    {"role": "assistant", "content": "Machine learning is a subset of AI that enables..."},
    {"role": "user",      "content": "Can you explain supervised vs unsupervised learning?"},
    {"role": "assistant", "content": "Supervised learning uses labeled data, like spam detection..."},
    {"role": "user",      "content": "What Python libraries should I use?"},   # ← kept verbatim
]

r = execute_tool("compress_conversation", {
    "messages": conversation,
    "keep_last_n": 2,    # last 2 messages verbatim
    "max_tokens": 150,   # collapse older turns if still over budget
})
print(r["messages_before"], "→", r["messages_after"])
print(r["strategy"])     # "nlp_compress" or "summarize_collapse"
for m in r["messages"]:
    print(f"[{m['role']}] {m['content'][:80]}")

deduplicate — remove overlapping retrieved chunks

# Classic RAG problem: multiple retrieval sources return similar chunks
chunks = [
    "Python is a high-level programming language used for web development and data science.",
    "Python programming language high-level web development data science applications.",  # near-dup
    "Rome is the capital city of Italy and a center of civilization for thousands of years.",
    "JavaScript is primarily used for web front-end development in browsers.",
    "Rome, Italy capital, civilization center, history monuments.",   # near-dup of Rome
]

r = execute_tool("deduplicate", {"texts": chunks, "threshold": 0.75})
print(f"Kept {r['deduplicated_count']}/{r['original_count']} chunks")
# Kept 3/5 chunks
for t in r["texts"]:
    print("-", t[:70])

Analytics — track cumulative savings

from synthelion.analytics.ledger import get_ledger

ledger = get_ledger()
summary = ledger.summary()
print(f"Total calls:  {summary['total_calls']}")
print(f"Tokens saved: {summary['tokens_saved']:,}")
print(f"Cost saved:   ${summary['cost_usd_saved']:.4f}")
print(f"Note:         {summary['pricing_note']}")
# Cost saved:   $0.0234
# Note:         Estimated at Sonnet 4.6 input price ($3.00/MTok)

Compression levels

Level	What it removes	Typical savings
`light`	Stop words (articles, prepositions, conjunctions…)	25–55%
`semantic`	Stop words + lemmatization to base form	30–55%
`aggressive`	Everything above + generic verbs and descriptive adjectives	35–75%
`statistical`	TF-IDF word scoring instead of curated dictionaries — keeps words that score above the prompt's own median relevance	40–65%
`syntactic`	Rule-based pruning: keeps grammatical glue only where it touches a surviving word, plus (when POS data is available) elides a leading hedging/matrix clause in favour of the sentence's last verb	45–70%

Negation particles ("non"/"not"/"ne...pas"/"no"/"nicht"/"não"/"不") are always protected and never dropped, at every level, in every supported language they apply to — dropping a negation doesn't just cost fluency, it inverts the sentence's meaning.

Default: semantic. All levels are additive — earlier levels are never removed or replaced when a new one ships.

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")

Something not working? Run the health check first:

synthelion doctor

Output:

[✓] mcp package installed (mcp 1.9.4)
[✓] synthelion 1.1.0
[✓] savings ledger: ~/.synthelion/savings.jsonl (42 entries)
[!] session DB: chromadb not installed — lexical fallback active
[✓] synthelion-mcp in PATH
[✓] Claude MCP config: ~/.claude.json → synthelion registered

Install chromadb for semantic (vector) session recall:

pip install "synthelion[chromadb]"

Optional extras

Extra	Installs	Enables
`synthelion[langchain]`	`langchain-core`	`get_tools()`, `SynthelionMemory`
`synthelion[openai]`	`openai`	`OpenAIAdapter`
`synthelion[claude]`	`anthropic`	`ClaudeAdapter`
`synthelion[chromadb]`	`chromadb`	Vector session recall in `session_record` / `session_recall`
`synthelion[all]`	everything above	Full stack

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synthelion 1.2.0

Synthelion — Universal Token Compressor and Prompt Manager for AI Agents

Technology Partnership

Why Synthelion?

Before / After

Benchmark — token savings by content type

NLP compression (prose, per level)

Content router (synthelion bench --json, 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 — Register with one command (new in 1.0.7)

Step 2 (manual) — Add to ~/.claude/settings.json

Step 3 — Restart Claude Code

Step 4 — Verify

Automatic prompt compression — Claude Code hook

Automatic setup (recommended)

Manual — Windows (~/.claude/settings.json)

Manual — 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

SynthelionMemory — drop-in compressing memory

Claude & OpenAI Adapters — auto-compression with one import

RagAgent — stateful agent with memory, RAG, and cost tracking

Python API — any custom agent or pipeline

CLI — shell scripts, pipelines, any language

Diagnostics & setup

Analytics & savings tracking

Self-upgrade

Web dashboard

Cluster deployment

1. Configure

2. Docker

3. Kubernetes

4. Load balancer (no orchestrator)

Tools

Code examples

Text compression

Language detection

Content router — auto-detects and picks the best algorithm

Summarization

Agent memory & context window

AI-agent context tools

compress_file — read and compress a file by path

compress_for_context — fit content into a token budget

compress_conversation — shrink a message history

deduplicate — remove overlapping retrieved chunks

Analytics — track cumulative savings

Compression levels

Supported languages (50+)

Troubleshooting

Optional extras

Links

Project links

Key dates

1 maintainer

Credits

License expression

Requires

Provides Extra

Content router (`synthelion bench --json`, auto-selects the best strategy)

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

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

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