tokensense 0.1.2

AI orchestration engine — semantic retrieval, context compression, and intelligent model routing

TokenSense

AI orchestration engine — Reduce LLM token usage by up to 72% with semantic retrieval, context compression, and intelligent model routing.

TokenSense sits between you and any LLM backend, transparently optimizing every request. Send only relevant context, pay less, get better answers.

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Features

• Semantic Retrieval — Vector search powered by Actian VectorAI DB
• Context Compression — Deduplicates and trims context to fit your token budget
• Intelligent Routing — Auto-selects the best model based on task complexity
• Multi-Backend — Works with OpenRouter, Gemini, or any LLM API
• Full Telemetry — Tracks tokens, cost, and latency for every query
• Three Interfaces — CLI, Web UI, and REST API

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Installation

Option A — Install CLI from PyPI (recommended)

pip install tokensense

Option B — Install from source

git clone https://github.com/yourusername/TokenSense.git
cd TokenSense
pip install -e .

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Quick Start

1. Start the backend and vector database

TokenSense requires a FastAPI backend and Actian VectorAI DB. Clone the repo and run:

# Start Actian VectorAI DB (Docker)
docker run -d -p 50051:50051 actian/vectorai-db

# Set environment variables
cp .env.example .env
# Edit .env with your API keys

# Start the backend
cd backend
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

2. Configure the CLI

tokensense init
# API URL: http://localhost:8000
# API key: <your-tokensense-api-key>

3. Index your codebase

tokensense index ./my-project

4. Ask questions

tokensense ask "how does the authentication flow work?"

Output:

┌─────────────────── Answer ───────────────────┐
│ The authentication flow uses verify_api_key   │
│ middleware that checks the X-API-Key header…  │
└───────────────────────────────────────────────┘

┌──────────────┬──────────────┐
│ Model        │ claude-haiku │
│ Input tokens │ 2,100        │
│ Reduction    │ 74%          │
│ Cost         │ $0.001200    │
└──────────────┴──────────────┘

5. View your savings

tokensense stats

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CLI Commands

Command	Description
tokensense init	Configure API URL and key
tokensense index <path>	Index a directory into the vector DB
tokensense ask "<query>"	Send a query through the optimization pipeline
tokensense stats	View usage analytics and cost savings

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API Endpoints

Once the backend is running on http://localhost:8000:

POST /index

curl -X POST http://localhost:8000/index \
  -H "X-API-Key: your-key" \
  -d '{"path": "./my-app", "file_extensions": [".py", ".ts"]}'

POST /ask

curl -X POST http://localhost:8000/ask \
  -H "X-API-Key: your-key" \
  -d '{"query": "explain the auth flow", "token_budget": 8000}'

POST /optimize

Context optimization only (no LLM call):

curl -X POST http://localhost:8000/optimize \
  -H "X-API-Key: your-key" \
  -d '{"query": "describe the routing agent", "token_budget": 8000}'

GET /stats

curl http://localhost:8000/stats?limit=20 \
  -H "X-API-Key: your-key"

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Architecture

User Input (CLI / Web)
  │
  ├─> Query Agent        (generates embeddings, classifies task)
  ├─> Retrieval Agent    (fetches relevant chunks from Actian VectorAI)
  ├─> Context Optimizer  (deduplicates, compresses, fits token budget)
  ├─> Routing Agent      (selects best model based on complexity)
  ├─> LLM Call           (OpenRouter or Gemini)
  └─> Telemetry Agent    (logs tokens, cost, latency to SQLite)

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Tech Stack

Layer	Technology
CLI	Typer + httpx + rich
Backend	FastAPI + Python 3.11+
Vector DB	Actian VectorAI DB (Docker)
Model Routing	OpenRouter API
Fallback LLM	Gemini API
Frontend	Next.js 14 + React 18 + Tailwind CSS (planned)

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Environment Variables

Create a .env file in the backend/ directory:

TOKENSENSE_API_KEY=your-secret-api-key
OPENROUTER_API_KEY=sk-or-...
GEMINI_API_KEY=AIza...
ACTIAN_HOST=localhost
ACTIAN_PORT=50051

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Development

Run tests

# Backend + Actian integration tests
cd tests
python test_actian_via_api.py
python test_actian_direct.py

Build the package locally

pip install build
python -m build
pip install dist/tokensense-0.1.0-py3-none-any.whl

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License

MIT

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Contributing

See CLAUDE.md for the full architecture and build plan.