contexere-ai 0.3.1

LLM Tracing and Context Engineering for Production AI Agents

Contexere

LLM Tracing and Context Engineering for Production AI Agents

Contexere is a lightweight Python library for tracing LLM applications and agent workflows in production.

Installation

pip install contexere

Quick Start

import contexere as conte
from openai import OpenAI

# Initialize Contexere
conte.init(
    api_key="ck_...",  # Your Contexere API key
    project_name="my-agent"
)

# Instrument your OpenAI client
client = conte.instrument_openai(OpenAI(api_key="sk_..."))

# Trace function executions
@conte.op()
def process_query(question):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

# All calls are now traced automatically
result = process_query("What is the capital of France?")

Features

• Function-level tracing: Use @conte.op() to trace any function
• LLM call tracing: Automatic instrumentation for OpenAI calls
• Captures everything: Inputs, outputs, exceptions, latency, token usage
• Project grouping: Organize traces by project name
• Non-blocking: Never breaks your code, even if tracing fails

API Reference

conte.init(api_key, project_name="default", endpoint=None, enabled=True)

Initialize the Contexere SDK.

• api_key: Your Contexere API key
• project_name: Logical project name for grouping traces
• endpoint: Custom backend endpoint (optional)
• enabled: Enable/disable tracing

@conte.op(name=None)

Decorator to trace function execution.

• name: Optional custom name for the span (defaults to function name)

conte.instrument_openai(client)

Wrap an OpenAI client to trace all LLM calls.

• client: An OpenAI client instance

Returns an instrumented client that traces all calls.

License

MIT