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  • License MIT

A local-first, token-efficient LLM chat interface

Package Exports

  • opengauge
  • opengauge/dist/server/index.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (opengauge) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

OpenGauge

A local-first, token-efficient LLM chat interface.

If you just want to use it, you only need one command:

npx opengauge

What this repo does

OpenGauge runs a local web chat app with:

  • Token optimization (compression + deduplication + checkpoints)
  • Context retrieval from conversation history (RAG-style)
  • Multiple providers: Anthropic, OpenAI, Gemini, Ollama
  • Local storage in SQLite at ~/.opengauge/opengauge.db
npx opengauge

You do not need to install OpenGauge globally first. npx will download the package (if not already cached) and run it. On first run, it may take a little longer while it fetches the package.

This starts a local server and opens the app in your browser.

Default URL:

http://localhost:3000

If port 3000 is busy:

npx opengauge --port 3001

First-time setup

When the app opens, configure your provider in the UI wizard, or create:

~/.opengauge/config.yml

Example:

providers:
  anthropic:
    api_key: YOUR_API_KEY
    default_model: claude-opus-4-6

defaults:
  provider: anthropic

Developer setup (from source)

git clone https://github.com/applytorque/opengauge.git
cd opengauge
npm install
npm run build
npm start

Useful commands

npm run build        # Compile TypeScript + copy UI assets
npm start            # Run CLI entry locally
npm pack --dry-run   # Preview npm package contents

Competitors and positioning

OpenGauge sits between chat UIs and full LLM observability suites.

PromptOps / Observability tools

Examples: PromptLayer, Helicone, Langfuse, Humanloop, Arize Phoenix

  • Great at: traces, eval pipelines, team dashboards, observability depth.
  • OpenGauge advantage: local-first workflow, in-chat prompt improvement, duplicate-risk + token-efficiency feedback in one loop.

Multi-model chat interfaces

Examples: LibreChat, Open WebUI, Chatbot UI, AnythingLLM

  • Great at: broad chat UX and plugin ecosystems.
  • OpenGauge advantage: prompt quality optimization is first-class (Improve + analytics), not only chat.

IDE coding assistants

Examples: Continue and other IDE-native AI assistants

  • Great at: deep coding workflows inside editors.
  • OpenGauge advantage: model-agnostic PromptOps for any user (product, ops, research, support), not just coding.

Cloud model platforms

Examples: Azure AI Studio, Vertex AI, Bedrock consoles

  • Great at: enterprise governance and managed cloud workflows.
  • OpenGauge advantage: fast setup, no cloud lock-in, supports local Ollama and cloud providers together.

Why choose OpenGauge

  • Improve prompts before send (optional Auto Improve mode)
  • Measure quality, duplicates, and token efficiency after send
  • Keep data local with SQLite and run quickly with npx opengauge

Positioning line:

OpenGauge is PromptOps in the loop: improve prompt quality before send, measure impact after send, and reduce token waste continuously.

License

MIT