llmobserve-sdk 0.3.2

Automatic cost tracking and observability for LLM applications

LLM Observe SDK

Automatic cost tracking and observability for LLM applications

Track costs, tokens, latency, and errors for OpenAI, Anthropic, and other LLM providers with just 2 lines of code.

Features

• ✅ Auto-instrumentation - No code changes needed beyond initialization
• 📊 Real-time tracking - Costs, tokens, latency, errors
• 🔍 Hierarchical tracing - Track agents, tools, and workflows
• 👥 Customer attribution - Track costs per end-user
• 🎯 Zero overhead - Async buffering, sub-millisecond impact
• 🔒 Privacy-first - Optional prompt/response capture
• 🚀 Production-ready - Battle-tested, scalable architecture

Supported Providers

• OpenAI (GPT-4, GPT-3.5, embeddings, images, audio, etc.)
• Pinecone (vector database operations)
• More coming soon! (Anthropic, Cohere, etc.)

Quick Start

1. Install

pip install llmobserve

2. Get Your API Key

Sign up at llmobserve.com to get your API key.

3. Add 2 Lines of Code

from llmobserve import observe
from openai import OpenAI

# Initialize observability
observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here"
)

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

That's it! 🎉 View your costs and metrics at app.llmobserve.com

Advanced Usage

Track Agents & Workflows

Automatic Tracking (Zero Config):

from llmobserve import observe
from langchain.agents import AgentExecutor

observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here"
)

# Agent tracking happens automatically - no manual labeling needed!
agent = AgentExecutor(...)
result = agent.run("query")  # Automatically tracked as agent

Manual Labeling (Optional, for Better UX):

from llmobserve import observe, section

observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here"
)

# Hierarchical tracking (optional - improves dashboard UX)
with section("agent:research_assistant"):
    with section("tool:web_search"):
        # Your search logic
        results = search_web(query)
    
    with section("tool:summarize"):
        # Your summarization logic
        summary = summarize(results)

Key Points:

• ✅ Costs are always tracked via HTTP interception (works everywhere)
• ✅ Framework agents auto-tracked (LangChain, CrewAI, AutoGen, LlamaIndex)
• ✅ Manual labeling is optional (improves dashboard UX but not required)
• ✅ Untracked costs still shown (labeled as "untracked" in dashboard)

Dashboard shows:

agent:research_assistant    $0.50  (10 calls)
├─ tool:web_search          $0.002  [ok]
└─ tool:summarize           $0.001  [ok]
untracked                   $0.10   (5 calls)  ← Costs without agent labels

Track Costs Per Customer

from llmobserve import observe, set_customer_id

observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here"
)

# Set customer ID for cost attribution
set_customer_id("customer_xyz")

# All subsequent API calls are attributed to this customer
client = OpenAI()
response = client.chat.completions.create(...)

Decorator Style

from llmobserve import trace

@trace(agent="customer_support")
async def handle_support_request(message: str):
    # All OpenAI calls inside are automatically tracked
    response = await client.chat.completions.create(...)
    return response

Framework Integration

FastAPI

from fastapi import FastAPI
from llmobserve import ObservabilityMiddleware, observe

app = FastAPI()
app.add_middleware(ObservabilityMiddleware)

observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here"
)

@app.post("/chat")
async def chat(request: Request):
    # Context is auto-reset per request
    # All OpenAI calls are tracked
    response = await client.chat.completions.create(...)
    return response

Flask

from flask import Flask
from llmobserve import flask_before_request, observe

app = Flask(__name__)
app.before_request(flask_before_request)

observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here"
)

@app.route("/chat")
def chat():
    # Context is auto-reset per request
    response = client.chat.completions.create(...)
    return response

What Gets Tracked

For every LLM API call:

• ✅ Costs - Calculated using latest pricing
• ✅ Tokens - Input, output, and cached tokens
• ✅ Latency - Request duration in milliseconds
• ✅ Status - Success, error, timeout, etc.
• ✅ Model - Which model was used
• ✅ Provider - OpenAI, Anthropic, etc.
• ✅ Section/Agent - Hierarchical context
• ✅ Customer ID - For cost attribution
• ✅ Timestamps - When the call was made
• ⚠️ Prompts/Responses - Optional (disabled by default)

Configuration

Environment Variables

# Disable observability
LLMOBSERVE_DISABLED=1

# Enable content capture (prompts/responses)
ALLOW_CONTENT_CAPTURE=true

Custom Flush Interval

observe(
    collector_url="https://api.llmobserve.com",
    api_key="llmo_sk_your_key_here",
    flush_interval_ms=1000  # Flush every 1 second (default: 500ms)
)

Dashboard Features

Visit app.llmobserve.com to view:

• 💰 Cost Overview - Total spend, trends, by provider/model
• 📊 Run Details - Individual request breakdowns
• 🎯 Agent Analytics - Which agents/workflows cost the most
• 👥 Customer Breakdown - Cost per end-user
• 🚨 Alerts - Cost spikes, errors, anomalies
• 📈 Insights - Auto-detected inefficiencies

Self-Hosting

Want to run your own instance?

git clone https://github.com/yourusername/llmobserve
cd llmobserve

# Start collector API
cd collector
pip install -r requirements.txt
uvicorn main:app --port 8000

# Start dashboard
cd ../web
npm install
npm run dev

See DEPLOYMENT.md for production deployment guide.

Architecture

┌─────────────────┐
│   Your App      │
│  + llmobserve   │  ← Auto-instruments OpenAI & Pinecone
│     SDK         │
└────────┬────────┘
         │ POST /events
         ↓
┌─────────────────┐
│  Collector API  │  ← FastAPI + PostgreSQL
│   (Backend)     │
└────────┬────────┘
         │ GET /runs, /stats
         ↓
┌─────────────────┐
│   Dashboard     │  ← Next.js + Clerk
│   (Frontend)    │
└─────────────────┘

FAQ

Q: Does this slow down my app? A: No. Events are buffered asynchronously with sub-millisecond overhead.

Q: Are my prompts/responses sent to your servers? A: No, by default only metadata is sent (tokens, cost, latency). Set ALLOW_CONTENT_CAPTURE=true to enable content logging.

Q: Can I use this in production? A: Yes! It's production-ready and tested at scale.

Q: What about other LLM providers? A: Anthropic, Cohere, and others coming soon! Request support via GitHub issues.

Q: How much does it cost? A: Free tier includes 10K tracked API calls/month. See pricing.

Contributing

We welcome contributions! See CONTRIBUTING.md.

License

MIT License - see LICENSE

Support

• 📖 Documentation
• 🐛 GitHub Issues
• 💬 Discord Community
• 📧 Email Support

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Made with ❤️ by the LLM Observe team