LatentGate

LatentGate

*Give your AI the signal, not the noise.*

An MCP server that shrinks logs, JSON, docs and web pages before your AI reads them — offline, deterministic, in ~3ms, no API key. 66% fewer tokens across a mixed dev corpus; 90%+ on repetitive logs.

Use in Claude | Quick Start | Python API | REST API | Monitoring | Load Testing | Deployment | AI Tools | Benchmarks | Contributing

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See it work

[Try it in your browser →](https://kathanmodh259.github.io/latent-gate/) The real optimizer runs on your machine via WebAssembly; nothing you paste is uploaded.

A 400-line error log, read through LatentGate (balanced, measured with tiktoken):

BEFORE  13,641 tokens

2026-10-08 09:00:00 ERROR order 10000 failed: payment gateway timeout after 2982ms (retry 1/3) ... 398 more lines in the same shape ...

AFTER 145 tokens (98.9% saved) 2026-10-08 09:00:00 INFO app started, 3 workers 2026-10-08 09:00:00 ERROR order 10000 failed: payment gateway timeout after 2982ms (retry 1/3) [… 398 similar lines: order 10001…10398, 2900…3100 ms …] 2026-10-08 09:06:39 ERROR order 10399 failed: payment gateway timeout after 3030ms (retry 1/3) 2026-10-08 09:07:00 FATAL circuit breaker open for payments-api

Install it into Claude Code in one line, then ask *"read app.log and tell me why orders fail"*:

claude mcp add --scope user latent-gate -- uvx --from "latent-gate[mcp,tokens]" latent-gate-mcp

What you can rely on: the same input always gives the same output (so provider prompt caching keeps working); output is never larger than input; code blocks, URLs and quoted strings come back byte-for-byte; level="lossless" changes nothing but whitespace, JSON formatting and exact duplicates. What you trade at the default level: runs of near-identical lines are folded to first, last and value ranges, so individual IDs inside a folded run are not kept. The full corpus, including where it loses facts, is in Cost Benchmarks and reproducible with latent-gate --optimizer-benchmark.

Works in Claude Code, Claude Desktop, Cursor, Cline, Continue and Zed, free and offline.

Don't want to install anything? The official hosted LatentGate on mcprush also runs where local servers can't, such as claude.ai on the web and mobile. It has a free tier (300 calls a month) and paid plans from $9/month.

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The Problem

Every time you send an image or long prompt to GPT-4o / Claude / Gemini, you burn 1,000+ tokens on processing that could happen locally for free.

Traditional:  Image -> Cloud LLM (1,200 tokens) -> Answer

LatentGate: Image -> Local Ollama (FREE) -> Cloud LLM (200 tokens) -> Answer

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Features

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Use it in Claude

LatentGate plugs into Claude as an MCP server. Its optimizer tools work offline, with no Ollama and no API key — Claude reads big logs, JSON dumps and docs through it and spends a fraction of the context. A 400-line error log goes from 14,400 to ~100 tokens.

Requires uv (uvx fetches LatentGate from PyPI on first run). The first run downloads dependencies, which can exceed Claude's 30-second MCP startup limit on a slow connection; run this once beforehand (later starts take ~2s):

uvx --from "latent-gate[mcp,tokens]" latent-gate --optimizer-benchmark

Claude Code — plugin (MCP server + a skill that tells Claude when to use it):

/plugin marketplace add KathanModh259/latent-gate

/plugin install latent-gate@latent-gate

Claude Code — MCP server only:

claude mcp add latent-gate -- uvx --from "latent-gate[mcp,tokens]" latent-gate-mcp

Any agent — from the Agensi marketplace: LatentGate Context Saver is the official skill for Claude Code, Cursor, Codex CLI and 20+ other agents. It sets LatentGate up if it's missing, works with the local and hosted editions, and tells your agent when to use it.

Claude Desktop — add to claude_desktop_config.json and restart:

{

"mcpServers": { "latent-gate": { "command": "uvx", "args": ["--from", "latent-gate[mcp,tokens]", "latent-gate-mcp"] } } }

Then just ask: *"Read logs/app.log with latent-gate and tell me why orders fail."*

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Quick Start

Install

# Core install

pip install latent-gate

With MCP server (for Claude Desktop, Cursor, Cline, etc.)

pip install latent-gate[mcp]

With API server (for web applications)

pip install latent-gate[api]

With video processing

pip install latent-gate[video]

With embedding-based similarity (more accurate selective decoding)

pip install latent-gate[embeddings]

With LangChain integration

pip install latent-gate[langchain]

With AWS Bedrock support

pip install latent-gate[bedrock]

Exact token counts via tiktoken (otherwise a calibrated ~6%-error estimate)

pip install latent-gate[tokens]

With all features

pip install latent-gate[all]

Pull Ollama Models

ollama pull llava:7b      # Vision model (required for image queries)

ollama pull llama3:8b # Text model (required for text compression & prediction)

One-Command Quickstart

chmod +x scripts/quickstart.sh

./scripts/quickstart.sh

This starts everything: Ollama → pulls models → API server → website. See scripts/quickstart.sh for options like --no-pull, --no-website, --port 9000.

CLI Usage

# Image query

latent-gate photo.jpg "What is in this image?" --provider ollama -v

Text compression

latent-gate --text "Your long prompt here..." --provider ollama -v

Text from file

latent-gate --text-file prompt.txt --provider openai -v

Image + Text combined

latent-gate photo.jpg "Analyze" --text "Extra context..." -v

Full JSON output

latent-gate photo.jpg "Describe" --json -v

Compress only, deterministically (no LLM call, ~1ms, reproducible)

cat prompt.txt | latent-gate --text-file - --compress-only --deterministic --level balanced

Reproduce the token-savings benchmark (no Ollama needed)

latent-gate --optimizer-benchmark

Production benchmark

latent-gate --benchmark --benchmark-output reports/benchmark.json

Start API server (requires: pip install latent-gate[api])

latent-gate-api

Production Hardening

For API deployments, restrict direct image-path reads to trusted directories:

set LATENTGATE_ALLOWED_IMAGE_ROOTS=C:\safe-images;D:\datasets

latent-gate-api

Benchmark before releases so speed and savings are measured, not guessed:

latent-gate --benchmark --json

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Python API

Image Query

from latent_gate import LatentGatePipeline, PipelineConfig

config = PipelineConfig( vision_model="llava:7b", predictor_model="llama3:8b", remote_provider="openai", remote_model="gpt-4o-mini", )

with LatentGatePipeline(config) as pipeline: result = pipeline.query("photo.jpg", "What is in this image?")

print(result["answer"]) print(f"Tokens sent: ~{result['tokens_estimated']}") print(f"Timing: {result['timing']}")

Text Compression

# Long prompt compression

result = pipeline.query_text("Your 500-word prompt here...", mode="auto")

Conversation history compression

messages = [ {"role": "user", "content": "Help me with Kubernetes setup"}, {"role": "assistant", "content": "Sure! What's your target configuration?"}, {"role": "user", "content": "3 nodes, t3.large, us-east-1 with autoscaling"}, ] result = pipeline.query_conversation(messages, "Now give me the setup commands")

RAG document compression

documents = ["doc1 text...", "doc2 text...", "doc3 text..."] result = pipeline.query_documents(documents, "How do I implement JWT refresh?")

Universal (auto-detect input type)

result = pipeline.query_universal(text="Explain this code...", image="screenshot.png")

Batch Processing

# Sequential with selective decoding (skips redundant API calls)

results = pipeline.query_batch(image_paths, "Describe each scene