OpenTelemetry-native Auto instrumentation library for monitoring LLM Applications and GPUs, facilitating the integration of observability into your GenAI-driven projects
Project description
tmam
OpenTelemetry-native auto-instrumentation library for monitoring LLM applications, enabling cost tracking, tracing, and observability for your GenAI projects.
Installation
pip install tmam
Quick Start
import tmam
from openai import OpenAI
# Initialize tmam — auto-instruments all supported LLM providers
tmam.init(
environment="production",
application_name="my-llm-app",
)
# Use OpenAI as normal — calls are automatically traced
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello, world!"}],
)
print(response.choices[0].message.content)
# Traces with token counts, latency, and cost appear in your tmam dashboard
Features
- Auto-instrumentation for 40+ LLM providers (OpenAI, Anthropic, Cohere, Mistral, Bedrock, VertexAI, and more)
- Tracing with
@tmam.trace()decorator andtmam.start_trace()context manager - Metrics collection for token usage, cost, latency, and request volume
- Evaluations with built-in evaluators for hallucination, bias, and toxicity detection
- Experiments for running dataset-driven evaluations with concurrency control
- Vault integration for secure secret retrieval
- GPU monitoring for GPU utilization and memory tracking
Configuration
| Environment Variable | Description | Default |
|---|---|---|
OTEL_EXPORTER_OTLP_ENDPOINT |
tmam API endpoint | https://api.tmam.ai |
OTEL_EXPORTER_OTLP_HEADERS |
Headers including API key (x-api-key=pk_xxx.sk_xxx) |
— |
| Init Parameter | Description | Default |
|---|---|---|
environment |
Deployment environment name | 'default' |
application_name |
Application name for grouping traces | 'default' |
otlp_endpoint |
Override OTLP endpoint | env var |
otlp_headers |
Override OTLP headers dict | env var |
Cost Allocation Tags
Track spending by team, department, or cost center using API key tags:
- Go to Settings > API Keys in the platform
- Add tags to your API key (e.g.,
team: engineering,department: ML) - View cost breakdowns by tag in Cost Explorer
Guides
License
Apache 2.0 — see LICENSE for details.
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