nanomon 0.2.2

Track LLM usage across DSPy program runs

Nanomon

LLM observability for DSPy applications.

What is Nanomon?

Nanomon is an observability platform for LLM applications built with DSPy. It automatically tracks token usage, costs, and tool calls across your DSPy program runs, giving you full visibility into your AI workflows.

Features

• Automatic LLM token usage tracking
• Cost calculation and analytics
• Tool call tracking with the @nanomon decorator
• DSPy integration (Predict, ReAct, ChainOfThought)
• Run context management for grouping related calls
• Multiple storage backends (API, SQLite)

Quick Start

import dspy
from nanomon import NanomonRunContext, configure_nanomon

# 1. Create context and configure tool tracking
context = NanomonRunContext(default_tags=["production"])
configure_nanomon(context._sink)

# 2. Instrument your LM
lm = dspy.LM(model="openai/gpt-4o-mini")
lm = context.instrument_lm(lm)
dspy.configure(lm=lm)

# 3. Track runs - DSPy callbacks are auto-configured
with context.run(tags=["my-task"], metadata={"version": "1.0"}):
    predictor = dspy.Predict(MySignature)
    result = await predictor.acall(input="Hello")
    # Each DSPy module call is automatically tracked as a separate run

Note: Use .acall() for async DSPy module calls to ensure proper callback integration.

Links

• Website: https://nanomon.ai
• Dashboard: https://app.nanomon.ai
• Documentation: https://docs.nanomon.ai

License

MIT