tracellm 0.2.0

Local-first observability for LLM applications

tracellm

Lightweight tracing for LLM applications. One decorator — every API interaction logged locally, queryable from your terminal.

No backend. No signup. Nothing leaves your machine.

Install

pip install tracellm

Usage

from tracellm import trace import groq

client = groq.Groq(api_key="your-key")

@trace def llm_call(model="llama-3.1-8b-instant", messages=[{"role": "user", "content": "hello"}]): return client.chat.completions.create(model=model, messages=messages)

llm_call()

That's it. Every call is traced automatically.

Query traces from terminal

python -m tracellm.cli --Status failed python -m tracellm.cli --Latency 2.0 python -m tracellm.cli --Model llama-3.1-8b-instant python -m tracellm.cli --Status failed --Latency 1.5

What gets captured

• Model, prompt, response
• Tokens used, latency, finish reason
• Error type and message on failures
• Timestamp for every call

Limitations

Storage is append-only JSON lines. Latency query supports >= for latency, exact match for everything else. Early days.

Roadmap

• Binary storage for faster querying at scale
• Cost calculation per model
• Terminal dashboard