obiguard-trace-python-sdk 3.8.20

Python SDK for Obiguard Trace

Obiguard Trace Python SDK

Open Source & Open Telemetry(OTEL) Observability for LLM Applications

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📚 Table of Contents

• ✨ Features
• 🚀 Quick Start
• 🔗 Integrations
• 🌐 Getting Started
• ⚙️ Configuration
• 🔧 Advanced Features
• 🔒 Security
• 📜 License

Obiguard Trace 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 obiguard-trace-python-sdk

from obiguard_trace_python_sdk import langtrace
langtrace.init(api_key='<your Obiguard API or virtual key>') # Get your API key at obiguard.ai

🔗 Supported Integrations

Obiguard Trace automatically captures traces from the following vendors:

LLM Providers

Provider	TypeScript SDK	Python SDK
OpenAI	✅	✅
Anthropic	✅	✅
Azure OpenAI	✅	✅
Cohere	✅	✅
Groq	✅	✅
Perplexity	✅	✅
Gemini	❌	✅
Mistral	❌	✅
AWS Bedrock	✅	✅
Ollama	❌	✅
Cerebras	❌	✅

Frameworks

Framework	TypeScript SDK	Python SDK
Langchain	❌	✅
LlamaIndex	✅	✅
Langgraph	❌	✅
LiteLLM	❌	✅
DSPy	❌	✅
CrewAI	❌	✅
VertexAI	✅	✅
EmbedChain	❌	✅
Autogen	❌	✅
HiveAgent	❌	✅
Inspect AI	❌	✅
Graphlit	❌	✅
Phidata	❌	✅
Arch	❌	✅

Vector Databases

Database	TypeScript SDK	Python SDK
Pinecone	✅	✅
ChromaDB	✅	✅
QDrant	✅	✅
Weaviate	✅	✅
PGVector	✅	✅ (SQLAlchemy)
MongoDB	❌	✅
Milvus	❌	✅

🌐 Getting Started

Obiguard Security Control Panel for your AI ☁️

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 Obiguard 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

⚙️ 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

Parameter	Type	Default Value	Description
api_key	str	LANGTRACE_API_KEY or None	The API key for authentication. Can be set via environment variable
batch	bool	True	Whether to batch spans before sending them to reduce API calls
write_spans_to_console	bool	False	Enable console logging for debugging purposes
custom_remote_exporter	Optional[Exporter]	None	Custom exporter for sending traces to your own backend
api_host	Optional[str]	https://langtrace.ai/	Custom API endpoint for self-hosted deployments
disable_instrumentations	Optional[Dict]	None	Disable specific vendor instrumentations (e.g., {'only': ['openai']})
service_name	Optional[str]	None	Custom service name for trace identification
disable_logging	bool	False	Disable SDK logging completely
headers	Dict[str, str]	{}	Custom headers for API requests

Environment Variables

Configure Obiguard Trace behavior using these environment variables:

Variable	Description	Default	Impact
LANGTRACE_API_KEY	Primary authentication method	Required*	Required if not passed to init()
TRACE_PROMPT_COMPLETION_DATA	Control prompt/completion tracing	true	Set to 'false' to opt out of prompt/completion data collection
TRACE_DSPY_CHECKPOINT	Control DSPy checkpoint tracing	true	Set to 'false' to disable checkpoint tracing
LANGTRACE_ERROR_REPORTING	Control error reporting	true	Set to 'false' to disable Sentry error reporting
LANGTRACE_API_HOST	Custom API endpoint	https://langtrace.ai/	Override default API endpoint for self-hosted deployments

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 obiguard_trace_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 obiguard_trace_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 obiguard_trace_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 obiguard_trace_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 obiguard_trace_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.

🔒 Security

To report security vulnerabilities, email us at [e-mail].

📜 License

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