metrxbot 0.2.0

Metrx Python SDK — LLM cost tracking and attribution

metrxbot

Python SDK for Metrx — LLM cost tracking and attribution for AI agents.

Track every LLM call your agents make, attribute costs to individual agents, and log business outcomes for ROI analysis.

Installation

The PyPI package is metrxbot; the import name is metrx:

pip install metrxbot

With provider extras:

pip install metrxbot[openai]       # OpenAI support
pip install metrxbot[anthropic]    # Anthropic support
pip install metrxbot[all]          # All providers

Quick Start

from metrx import Metrx

m = Metrx(api_key="sk_metrx_...", agent_key="my-agent")
m.instrument()  # Auto-detects and patches OpenAI + Anthropic

# Use your LLM clients as normal — calls are tracked automatically
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

m.shutdown()

Framework Support

instrument() automatically detects your AI framework and enriches every event with framework metadata. Supported frameworks:

• LangChain / LangGraph
• CrewAI
• AutoGen
• LlamaIndex
• Haystack
• Semantic Kernel

No extra configuration needed — since these frameworks use OpenAI or Anthropic under the hood, monkey-patching captures all LLM calls automatically.

Manual Instrumentation

If you prefer explicit control, patch individual clients:

from metrx import Metrx
import openai
import anthropic

m = Metrx(api_key="sk_metrx_...")

# Patch specific clients
oai = m.instrument_openai(openai.OpenAI())
ant = m.instrument_anthropic(anthropic.Anthropic())

# All calls through these clients are now tracked
response = oai.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
)

Sessions

Group related LLM calls under a session for attribution:

with m.session(session_id="user-123-conv-1"):
    # All LLM calls in this block share the same session_id
    response = client.chat.completions.create(...)

Outcome Logging

Log business outcomes to measure agent ROI:

m.log_outcome(
    outcome_type="sale",
    value_cents=5000,
    customer_id="cust-456",
    reference_id="order-789",
    metadata={"product": "pro-plan"},
)

Configuration

Parameter	Default	Description
api_key	required	Your Metrx API key
base_url	https://gateway.metrxbot.com	API endpoint
agent_key	None	Default agent identifier for all events
flush_interval	5.0	Seconds between background flushes
max_batch_size	50	Events per batch
debug	False	Enable debug logging

Context Manager

with Metrx(api_key="sk_metrx_...") as m:
    m.instrument()
    # ... your code ...
# Transport shuts down automatically

License

MIT