Concisr
Token compression for AI contexts, reducing token consumption by compressing conversation exchanges before they enter the LLM context window.
README
Concisr
Token compression for AI contexts. Reduce token consumption by compressing conversation exchanges before they enter the LLM context window.
Deterministic, embedding-free compression. No external APIs, no GPU required.
Why?
Every token costs money. Most conversation context is filler — greetings, hedges, repeated data, verbose JSON. Concisr strips that noise before it hits the model, so you fit more signal into fewer tokens.
Tools
| Tool | Purpose | Compression |
|---|---|---|
digest_input |
Strip incoming messages to essential signal | ~25-95% depending on mode |
compress_response |
Compress outgoing responses with sentence truncation | Preserves voice and meaning |
cache_reference |
Gzip-compressed key-value store with TTL expiry | Store large text, retrieve on demand |
session_stats |
Real-time token savings dashboard | Track ROI across sessions |
Compression Modes
| Mode | Level | Strategy |
|---|---|---|
checkin |
~25% | Extract structured metrics (pain, sleep, energy, food, weight, stress) |
task |
~50% | Strip filler words, greetings, hedges |
casual |
~75% | Light structural compression |
narrative |
~95% | Preserve detail with minimal trimming |
Content-type detection automatically applies JSON crushing, code comment stripping, or prose pass-through.
Quick Start
# Install via pip
pip install concisr
# Run locally
concisr
Or add to your MCP client config:
{
"mcpServers": {
"concisr": {
"url": "https://concisr.mcpize.run/mcp"
}
}
}
Deployed
Live on MCPize — Free tier (500 req/mo) and Pro ($50/mo unlimited).
Storage
- Cache DB:
~/.concisr/cache.db(SQLite, gzip-compressed blobs) - Stats:
~/.concisr/stats.json(persistent across sessions)
Author
Eric Ian Rodriguez
- Portfolio: tiny-bavarois-656e6c.netlify.app
- GitHub: github.com/NcrMancer
License
MIT
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