Awesome

📚 Table of Contents

• ✨ Features
• 🚀 Quick Start
• 🔗 Integrations
• 🌐 Getting Started
• ⚙️ Configuration
• 🔧 Advanced Features
• 📐 Examples
• 🏠 Self Hosting
• 🤝 Contributing
• 🔒 Security
• ❓ FAQ
• 📜 License

Langtrace is an open source observability software which lets you capture, debug and analyze traces and metrics from all your applications that leverages LLM APIs, Vector Databases and LLM based Frameworks.

✨ Features

• 📊 Open Telemetry Support: Built on OTEL standards for comprehensive tracing
• 🔄 Real-time Monitoring: Track LLM API calls, vector operations, and framework usage
• 🎯 Performance Insights: Analyze latency, costs, and usage patterns
• 🔍 Debug Tools: Trace and debug your LLM application workflows
• 📈 Analytics: Get detailed metrics and visualizations
• 🛠️ Framework Support: Extensive integration with popular LLM frameworks
• 🔌 Vector DB Integration: Support for major vector databases
• 🎨 Flexible Configuration: Customizable tracing and monitoring options

🚀 Quick Start

pip install langtrace-python-sdk

from langtrace_python_sdk import langtrace
langtrace.init(api_key='<your_api_key>') # Get your API key at langtrace.ai

🔗 Supported Integrations

Langtrace automatically captures traces from the following vendors:

LLM Providers

Frameworks

Vector Databases

🌐 Getting Started

Langtrace Cloud ☁️

1. Sign up by going to this link.
2. Create a new Project after signing up. Projects are containers for storing traces and metrics generated by your application. If you have only one application, creating 1 project will do.
3. Generate an API key by going inside the project.
4. In your application, install the Langtrace SDK and initialize it with the API key you generated in the step 3.
5. The code for installing and setting up the SDK is shown below

Framework Quick Starts

FastAPI

from fastapi import FastAPI
from langtrace_python_sdk import langtrace
from openai import OpenAI

langtrace.init()
app = FastAPI()
client = OpenAI()

@app.get("/")
def root():
    client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Say this is a test"}],
        stream=False,
    )
    return {"Hello": "World"}

Django

# settings.py
from langtrace_python_sdk import langtrace
langtrace.init()

# views.py
from django.http import JsonResponse
from openai import OpenAI

client = OpenAI()

def chat_view(request):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": request.GET.get('message', '')}]
    )
    return JsonResponse({"response": response.choices[0].message.content})

Flask

from flask import Flask
from langtrace_python_sdk import langtrace
from openai import OpenAI

app = Flask(__name__)
langtrace.init()
client = OpenAI()

@app.route('/')
def chat():
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    return {"response": response.choices[0].message.content}

LangChain

from langtrace_python_sdk import langtrace
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

langtrace.init()

# LangChain operations are automatically traced
chat = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}")
])
chain = prompt | chat
response = chain.invoke({"input": "Hello!"})

LlamaIndex

from langtrace_python_sdk import langtrace
from llama_index import VectorStoreIndex, SimpleDirectoryReader

langtrace.init()

# Document loading and indexing are automatically traced
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

# Queries are traced with metadata
query_engine = index.as_query_engine()
response = query_engine.query("What's in the documents?")

DSPy

from langtrace_python_sdk import langtrace
import dspy
from dspy.teleprompt import BootstrapFewShot

langtrace.init()

# DSPy operations are automatically traced
lm = dspy.OpenAI(model="gpt-4")
dspy.settings.configure(lm=lm)

class SimpleQA(dspy.Signature):
    """Answer questions with short responses."""
    question = dspy.InputField()
    answer = dspy.OutputField(desc="short answer")

compiler = BootstrapFewShot(metric=dspy.metrics.Answer())
program = compiler.compile(SimpleQA)

CrewAI

from langtrace_python_sdk import langtrace
from crewai import Agent, Task, Crew

langtrace.init()

# Agents and tasks are automatically traced
researcher = Agent(
    role="Researcher",
    goal="Research and analyze data",
    backstory="Expert data researcher",
    allow_delegation=False
)

task = Task(
    description="Analyze market trends",
    agent=researcher
)

crew = Crew(
    agents=[researcher],
    tasks=[task]
)

result = crew.kickoff()

For more detailed examples and framework-specific features, visit our documentation.

⚙️ Configuration

Initialize Options

The SDK can be initialized with various configuration options to customize its behavior:

langtrace.init(
    api_key: Optional[str] = None,          # API key for authentication
    batch: bool = True,                     # Enable/disable batch processing
    write_spans_to_console: bool = False,   # Console logging
    custom_remote_exporter: Optional[Any] = None,  # Custom exporter
    api_host: Optional[str] = None,         # Custom API host
    disable_instrumentations: Optional[Dict] = None,  # Disable specific integrations
    service_name: Optional[str] = None,     # Custom service name
    disable_logging: bool = False,          # Disable all logging
    headers: Dict[str, str] = {},           # Custom headers
)

Configuration Details

Environment Variables

Configure Langtrace behavior using these environment variables:

Performance Note: Setting TRACE_DSPY_CHECKPOINT=false is recommended in production environments as checkpoint tracing involves state serialization which can impact latency.

Security Note: When TRACE_PROMPT_COMPLETION_DATA=false, no prompt or completion data will be collected, ensuring sensitive information remains private.

🔧 Advanced Features

Root Span Decorator

Use the root span decorator to create custom trace hierarchies:

from langtrace_python_sdk import langtrace

@langtrace.with_langtrace_root_span(name="custom_operation")
def my_function():
    # Your code here
    pass

Additional Attributes

Inject custom attributes into your traces:

# Using decorator
@langtrace.with_additional_attributes({"custom_key": "custom_value"})
def my_function():
    pass

# Using context manager
with langtrace.inject_additional_attributes({"custom_key": "custom_value"}):
    # Your code here
    pass

Prompt Registry

Register and manage prompts for better traceability:

from langtrace_python_sdk import langtrace

# Register a prompt template
langtrace.register_prompt("greeting", "Hello, {name}!")

# Use registered prompt
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": langtrace.get_prompt("greeting", name="Alice")}]
)

User Feedback System

Collect and analyze user feedback:

from langtrace_python_sdk import langtrace

# Record user feedback for a trace
langtrace.record_feedback(
    trace_id="your_trace_id",
    rating=5,
    feedback_text="Great response!",
    metadata={"user_id": "123"}
)

DSPy Checkpointing

Manage DSPy checkpoints for workflow tracking:

from langtrace_python_sdk import langtrace

# Enable checkpoint tracing (disabled by default in production)
langtrace.init(
    api_key="your_api_key",
    dspy_checkpoint_tracing=True
)

Vector Database Operations

Track vector database operations:

from langtrace_python_sdk import langtrace

# Vector operations are automatically traced
with langtrace.inject_additional_attributes({"operation_type": "similarity_search"}):
    results = vector_db.similarity_search("query", k=5)

For more detailed examples and use cases, visit our documentation.

📐 Examples

🏠 Langtrace Self Hosted

Get started with self-hosted Langtrace:

from langtrace_python_sdk import langtrace
langtrace.init(write_spans_to_console=True)  # For console logging
# OR
langtrace.init(custom_remote_exporter=<your_exporter>, batch=<True or False>)  # For custom exporter

🤝 Contributing

We welcome contributions! To get started:

1. Fork this repository and start developing
2. Join our Discord workspace
3. Run examples:

   # In run_example.py, set ENABLED_EXAMPLES flag to True for desired example
   python src/run_example.py
   

4. Run tests:

   pip install '.[test]' && pip install '.[dev]'
   pytest -v
   

🔒 Security

To report security vulnerabilities, email us at security@scale3labs.com. You can read more on security here.

❓ Frequently Asked Questions

📜 License

Langtrace Python SDK is licensed under the Apache 2.0 License. You can read about this license here.