Zespan observability middleware for Flask
Project description
zespan-flask
Automatic LLM observability middleware for Flask. Traces every request, LLM call, and custom span with zero code changes for basic usage.
Installation
pip install zespan-flask
Quick Start
from flask import Flask
from zespan_flask import ZespanFlaskExtension, ZespanConfig
app = Flask(__name__)
config = ZespanConfig(api_key="zsp_your_api_key")
ext = ZespanFlaskExtension(app, config)
Every HTTP request is now automatically traced.
Application Factory Pattern
ext = ZespanFlaskExtension()
def create_app():
app = Flask(__name__)
ext.init_app(app)
return app
Trace LLM Calls
from zespan_flask import observe_llm
@observe_llm(model="gpt-4o", provider="openai")
def generate_response(prompt: str):
response = openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return response
Trace Custom Spans
from zespan_flask import observe_span
@observe_span("fetch_context", span_type="retrieval")
def fetch_context(query: str):
return vector_db.search(query)
Set Custom Attributes
from zespan_flask import set_attribute
@app.route("/chat")
def chat():
set_attribute("user.id", current_user.id)
set_attribute("session.id", session.get("id"))
Configuration
| Option | Default | Description |
|---|---|---|
api_key |
required | Zespan API key (zsp_...) |
base_url |
https://api.zespan.com |
API endpoint |
enabled |
True |
Toggle tracing on/off |
sample_rate |
1.0 |
Fraction of requests to trace (0.0–1.0) |
capture_request_body |
False |
Include request body in traces |
capture_response_body |
False |
Include response body in traces |
redact_fields |
["password","token","api_key"] |
Fields redacted automatically |
debug |
False |
Print flush errors to stdout |
Get your API key at zespan.com.
Project details
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