noveum-trace 0.3.9

Cloud-first, decorator-based tracing SDK for LLM applications and multi-agent systems

Noveum Trace SDK

Simple, intuitive tracing SDK for LLM applications and multi-agent systems.

Noveum Trace provides an easy way to add observability to your LLM applications. With intuitive context managers, you can trace function calls, LLM interactions, agent workflows, and multi-agent coordination patterns.

✨ Key Features

• 🎯 Simple Context Manager API - Add tracing with intuitive with statements
• 🤖 Multi-Agent Support - Built for multi-agent systems and workflows
• ☁️ Cloud Integration - Send traces to Noveum platform or custom endpoints
• 🔌 Framework Agnostic - Works with any Python LLM framework
• 🚀 Zero Configuration - Works out of the box with sensible defaults
• 📊 Comprehensive Tracing - Capture function calls, LLM interactions, and agent workflows
• 🔄 Flexible Integration - Context managers for granular control

🚀 Quick Start

Installation

pip install noveum-trace

Basic Usage

import noveum_trace

# Initialize the SDK
noveum_trace.init(
    api_key="your-api-key",
    project="my-llm-app"
)

# Trace any operation using context managers
def process_document(document_id: str) -> dict:
    with noveum_trace.trace_operation("process_document") as span:
        # Your function logic here
        span.set_attribute("document_id", document_id)
        return {"status": "processed", "id": document_id}

# Trace LLM calls with automatic metadata capture
def call_openai(prompt: str) -> str:
    import openai
    client = openai.OpenAI()
    
    with noveum_trace.trace_llm_call(model="gpt-4", provider="openai") as span:
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        span.set_attributes({
            "llm.input_tokens": response.usage.prompt_tokens,
            "llm.output_tokens": response.usage.completion_tokens
        })
        return response.choices[0].message.content

🏗️ Architecture

noveum_trace/
├── core/              # Core tracing primitives (Trace, Span, Context)
├── context_managers/  # Context managers for inline tracing
├── transport/         # HTTP transport and batch processing
├── integrations/      # Framework integrations (LangChain, etc.)
├── streaming/         # Streaming LLM support
├── threads/           # Conversation thread management
└── utils/             # Utilities (exceptions, serialization, etc.)

🔧 Configuration

Environment Variables

export NOVEUM_API_KEY="your-api-key"
export NOVEUM_PROJECT="your-project-name"
export NOVEUM_ENVIRONMENT="production"

Programmatic Configuration

import noveum_trace

# Basic configuration
noveum_trace.init(
    api_key="your-api-key",
    project="my-project",
    environment="production"
)

# Advanced configuration with transport settings
noveum_trace.init(
    api_key="your-api-key",
    project="my-project",
    environment="production",
    transport_config={
        "batch_size": 50,
        "batch_timeout": 2.0,
        "retry_attempts": 3,
        "timeout": 30
    },
    tracing_config={
        "sample_rate": 1.0,
        "capture_errors": True,
        "capture_stack_traces": False
    }
)

🔄 Context Manager Usage

For scenarios with granular control:

import noveum_trace

def process_user_query(user_input: str) -> str:
    # Pre-processing (not traced)
    cleaned_input = user_input.strip().lower()

    # Trace just the LLM call
    with noveum_trace.trace_llm_call(model="gpt-4", provider="openai") as span:
        response = openai_client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": cleaned_input}]
        )

        # Add custom attributes
        span.set_attributes({
            "llm.input_tokens": response.usage.prompt_tokens,
            "llm.output_tokens": response.usage.completion_tokens
        })

    # Post-processing (not traced)
    return format_response(response.choices[0].message.content)

def multi_step_workflow(task: str) -> dict:
    results = {}

    # Trace agent operation
    with noveum_trace.trace_agent_operation(
        agent_type="planner",
        operation="task_planning"
    ) as span:
        plan = create_task_plan(task)
        span.set_attribute("plan.steps", len(plan.steps))
        results["plan"] = plan

    # Trace tool usage
    with noveum_trace.trace_operation("database_query") as span:
        data = query_database(plan.query)
        span.set_attributes({
            "query.results_count": len(data),
            "query.table": "tasks"
        })
        results["data"] = data

    return results

🔗 LangChain Integration

Noveum Trace provides seamless integration with LangChain and LangGraph applications through a simple callback handler.

from noveum_trace.integrations import NoveumTraceCallbackHandler
from langchain_openai import ChatOpenAI

# Initialize Noveum Trace
import noveum_trace
noveum_trace.init(project="my-langchain-app", api_key="your-api-key")

# Create callback handler
handler = NoveumTraceCallbackHandler()

# Add to your LangChain components
llm = ChatOpenAI(callbacks=[handler])
response = llm.invoke("What is the capital of France?")

What Gets Traced

• LLM Calls: Model, prompts, responses, token usage
• Chains: Input/output flow, execution steps
• Agents: Decision-making, tool usage, reasoning
• Tools: Function calls, inputs, outputs
• LangGraph Nodes: Graph execution, node transitions
• Routing Decisions: Conditional routing logic and decisions

Advanced Features

The integration also supports:

• Manual Trace Control for complex workflows
• Custom Parent Relationships for explicit span hierarchies
• LangGraph Routing Tracking for routing decisions

For complete details and examples, see the LangChain Integration Guide.

🧵 Thread Management

Track conversation threads and multi-turn interactions:

from noveum_trace import ThreadContext

# Create and manage conversation threads
with ThreadContext(name="customer_support") as thread:
    thread.add_message("user", "Hello, I need help with my order")

    # LLM response within thread context
    with noveum_trace.trace_llm_call(model="gpt-4") as span:
        response = llm_client.chat.completions.create(...)
        thread.add_message("assistant", response.choices[0].message.content)

🌊 Streaming Support

Trace streaming LLM responses with real-time metrics:

from noveum_trace import trace_streaming

def stream_openai_response(prompt: str):
    with trace_streaming(model="gpt-4", provider="openai") as manager:
        stream = openai_client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            stream=True
        )

        for chunk in stream:
            if chunk.choices[0].delta.content:
                content = chunk.choices[0].delta.content
                manager.add_token(content)
                yield content

        # Streaming metrics are automatically captured

🧪 Testing

Run the test suite:

# Install development dependencies
pip install -e ".[dev]"

# Run all tests
pytest

# Run with coverage
pytest --cov=noveum_trace --cov-report=html

# Run specific test categories
pytest -m llm
pytest -m agent

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/Noveum/noveum-trace.git
cd noveum-trace

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run examples
python docs/examples/basic_usage.py

📖 Examples

Check out the examples directory for complete working examples:

• Basic Usage - Simple function tracing
• Agent Workflow - Multi-agent coordination
• Flexible Tracing - Context managers and inline tracing
• Streaming Example - Real-time streaming support
• Multimodal Examples - Image, audio, and video tracing
• LangGraph Routing - LangGraph routing decision tracking

🚀 Advanced Usage

Manual Trace Creation

# Create traces manually for full control
client = noveum_trace.get_client()

with client.create_contextual_trace("custom_workflow") as trace:
    with client.create_contextual_span("step_1") as span1:
        # Step 1 implementation
        span1.set_attributes({"step": 1, "status": "completed"})

    with client.create_contextual_span("step_2") as span2:
        # Step 2 implementation
        span2.set_attributes({"step": 2, "status": "completed"})

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙋‍♀️ Support

• GitHub Issues
• Documentation
• Examples

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Built by the Noveum Team