| Name | noveum-trace JSON |
| Version |
0.3.7
JSON |
| download |
| home_page | None |
| Summary | Cloud-first, decorator-based tracing SDK for LLM applications and multi-agent systems |
| upload_time | 2025-08-28 17:21:20 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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| keywords |
ai
anthropic
langchain
llm
monitoring
multi-agent
observability
openai
tracing
|
| VCS |
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|
# Noveum Trace SDK
[](https://github.com/Noveum/noveum-trace/actions/workflows/ci.yml)
[](https://github.com/Noveum/noveum-trace/actions/workflows/release.yml)
[](https://codecov.io/gh/Noveum/noveum-trace)
[](https://badge.fury.io/py/noveum-trace)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/Apache-2.0)
**Simple, decorator-based tracing SDK for LLM applications and multi-agent systems.**
Noveum Trace provides an easy way to add observability to your LLM applications. With simple decorators, you can trace function calls, LLM interactions, agent workflows, and multi-agent coordination patterns.
## ✨ Key Features
- **🎯 Decorator-First API** - Add tracing with a single `@trace` decorator
- **🤖 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 Approaches** - Decorators, and context managers
## 🚀 Quick Start
### Installation
```bash
pip install noveum-trace
```
### Basic Usage
```python
import noveum_trace
# Initialize the SDK
noveum_trace.init(
api_key="your-api-key",
project="my-llm-app"
)
# Trace any function
@noveum_trace.trace
def process_document(document_id: str) -> dict:
# Your function logic here
return {"status": "processed", "id": document_id}
# Trace LLM calls with automatic metadata capture
@noveum_trace.trace_llm
def call_openai(prompt: str) -> str:
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
# Trace agent workflows
@noveum_trace.trace_agent(agent_id="researcher")
def research_task(query: str) -> dict:
# Agent logic here
return {"findings": "...", "confidence": 0.95}
```
### Multi-Agent Example
```python
import noveum_trace
noveum_trace.init(
api_key="your-api-key",
project="multi-agent-system"
)
@noveum_trace.trace_agent(agent_id="orchestrator")
def orchestrate_workflow(task: str) -> dict:
# Coordinate multiple agents
research_result = research_agent(task)
analysis_result = analysis_agent(research_result)
return synthesis_agent(research_result, analysis_result)
@noveum_trace.trace_agent(agent_id="researcher")
def research_agent(task: str) -> dict:
# Research implementation
return {"data": "...", "sources": [...]}
@noveum_trace.trace_agent(agent_id="analyst")
def analysis_agent(data: dict) -> dict:
# Analysis implementation
return {"insights": "...", "metrics": {...}}
```
## 🏗️ Architecture
```
noveum_trace/
├── core/ # Core tracing primitives (Trace, Span, Context)
├── decorators/ # Decorator-based API (@trace, @trace_llm, etc.)
├── context_managers/ # Context managers for inline tracing
├── transport/ # HTTP transport and batch processing
├── agents/ # Multi-agent system support
├── streaming/ # Streaming LLM support
├── threads/ # Conversation thread management
└── utils/ # Utilities (exceptions, serialization, etc.)
```
## 🔧 Configuration
### Environment Variables
```bash
export NOVEUM_API_KEY="your-api-key"
export NOVEUM_PROJECT="your-project-name"
export NOVEUM_ENVIRONMENT="production"
```
### Programmatic Configuration
```python
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
}
)
```
## 🎯 Available Decorators
### @trace - General Purpose Tracing
```python
@noveum_trace.trace
def my_function(arg1: str, arg2: int) -> dict:
return {"result": f"{arg1}_{arg2}"}
# With options
@noveum_trace.trace(capture_performance=True, capture_args=True)
def expensive_function(data: list) -> dict:
# Function implementation
return {"processed": len(data)}
```
### @trace_llm - LLM Call Tracing
```python
@noveum_trace.trace_llm
def call_llm(prompt: str) -> str:
# LLM call implementation
return response
# With provider specification
@noveum_trace.trace_llm(provider="openai", capture_tokens=True)
def call_openai(prompt: str) -> str:
# OpenAI specific implementation
return response
```
### @trace_agent - Agent Workflow Tracing
```python
# Required: agent_id parameter
@noveum_trace.trace_agent(agent_id="my_agent")
def agent_function(task: str) -> dict:
# Agent implementation
return result
# With full configuration
@noveum_trace.trace_agent(
agent_id="researcher",
role="information_gatherer",
capabilities=["web_search", "document_analysis"]
)
def research_agent(query: str) -> dict:
# Research implementation
return {"findings": "...", "sources": [...]}
```
### @trace_tool - Tool Usage Tracing
```python
@noveum_trace.trace_tool
def search_web(query: str) -> list:
# Tool implementation
return results
# With tool specification
@noveum_trace.trace_tool(tool_name="web_search", tool_type="api")
def search_api(query: str) -> list:
# API search implementation
return search_results
```
### @trace_retrieval - Retrieval Operation Tracing
```python
@noveum_trace.trace_retrieval
def retrieve_documents(query: str) -> list:
# Retrieval implementation
return documents
# With retrieval configuration
@noveum_trace.trace_retrieval(
retrieval_type="vector_search",
index_name="documents",
capture_scores=True
)
def vector_search(query: str, top_k: int = 5) -> list:
# Vector search implementation
return results
```
## 🔄 Context Managers - Inline Tracing
For scenarios where you need granular control or can't modify function signatures:
```python
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
```
## 🧵 Thread Management
Track conversation threads and multi-turn interactions:
```python
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:
```python
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:
```bash
# 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](CONTRIBUTING.md) for details.
### Development Setup
```bash
# 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](docs/examples/) directory for complete working examples:
- [Basic Usage](docs/examples/basic_usage.py) - Simple function tracing
- [Agent Workflow](docs/examples/agent_workflow_example.py) - Multi-agent coordination
- [Flexible Tracing](docs/examples/flexible_tracing_example.py) - Context managers and inline tracing
- [Streaming Example](docs/examples/streaming_example.py) - Real-time streaming support
- [Multimodal Examples](docs/examples/multimodal_examples.py) - Image, audio, and video tracing
## 🚀 Advanced Usage
### Manual Trace Creation
```python
# 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](LICENSE) file for details.
## 🙋♀️ Support
- [GitHub Issues](https://github.com/Noveum/noveum-trace/issues)
- [Documentation](https://github.com/Noveum/noveum-trace/tree/main/docs)
- [Examples](https://github.com/Noveum/noveum-trace/tree/main/examples)
---
**Built by the Noveum Team**
Raw data
{
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"name": "noveum-trace",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "Noveum Team <engineering@noveum.ai>",
"keywords": "ai, anthropic, langchain, llm, monitoring, multi-agent, observability, openai, tracing",
"author": null,
"author_email": "Noveum Team <engineering@noveum.ai>",
"download_url": "https://files.pythonhosted.org/packages/93/86/ef625bdc837070a77e62b34c4c005d209b503814195fecc865b0e79e2a88/noveum_trace-0.3.7.tar.gz",
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"description": "# Noveum Trace SDK\n\n[](https://github.com/Noveum/noveum-trace/actions/workflows/ci.yml)\n[](https://github.com/Noveum/noveum-trace/actions/workflows/release.yml)\n[](https://codecov.io/gh/Noveum/noveum-trace)\n[](https://badge.fury.io/py/noveum-trace)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/Apache-2.0)\n\n**Simple, decorator-based tracing SDK for LLM applications and multi-agent systems.**\n\nNoveum Trace provides an easy way to add observability to your LLM applications. With simple decorators, you can trace function calls, LLM interactions, agent workflows, and multi-agent coordination patterns.\n\n## \u2728 Key Features\n\n- **\ud83c\udfaf Decorator-First API** - Add tracing with a single `@trace` decorator\n- **\ud83e\udd16 Multi-Agent Support** - Built for multi-agent systems and workflows\n- **\u2601\ufe0f Cloud Integration** - Send traces to Noveum platform or custom endpoints\n- **\ud83d\udd0c Framework Agnostic** - Works with any Python LLM framework\n- **\ud83d\ude80 Zero Configuration** - Works out of the box with sensible defaults\n- **\ud83d\udcca Comprehensive Tracing** - Capture function calls, LLM interactions, and agent workflows\n- **\ud83d\udd04 Flexible Approaches** - Decorators, and context managers\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install noveum-trace\n```\n\n### Basic Usage\n\n```python\nimport noveum_trace\n\n# Initialize the SDK\nnoveum_trace.init(\n api_key=\"your-api-key\",\n project=\"my-llm-app\"\n)\n\n# Trace any function\n@noveum_trace.trace\ndef process_document(document_id: str) -> dict:\n # Your function logic here\n return {\"status\": \"processed\", \"id\": document_id}\n\n# Trace LLM calls with automatic metadata capture\n@noveum_trace.trace_llm\ndef call_openai(prompt: str) -> str:\n import openai\n client = openai.OpenAI()\n response = client.chat.completions.create(\n model=\"gpt-4\",\n messages=[{\"role\": \"user\", \"content\": prompt}]\n )\n return response.choices[0].message.content\n\n# Trace agent workflows\n@noveum_trace.trace_agent(agent_id=\"researcher\")\ndef research_task(query: str) -> dict:\n # Agent logic here\n return {\"findings\": \"...\", \"confidence\": 0.95}\n```\n\n### Multi-Agent Example\n\n```python\nimport noveum_trace\n\nnoveum_trace.init(\n api_key=\"your-api-key\",\n project=\"multi-agent-system\"\n)\n\n@noveum_trace.trace_agent(agent_id=\"orchestrator\")\ndef orchestrate_workflow(task: str) -> dict:\n # Coordinate multiple agents\n research_result = research_agent(task)\n analysis_result = analysis_agent(research_result)\n return synthesis_agent(research_result, analysis_result)\n\n@noveum_trace.trace_agent(agent_id=\"researcher\")\ndef research_agent(task: str) -> dict:\n # Research implementation\n return {\"data\": \"...\", \"sources\": [...]}\n\n@noveum_trace.trace_agent(agent_id=\"analyst\")\ndef analysis_agent(data: dict) -> dict:\n # Analysis implementation\n return {\"insights\": \"...\", \"metrics\": {...}}\n```\n\n## \ud83c\udfd7\ufe0f Architecture\n\n```\nnoveum_trace/\n\u251c\u2500\u2500 core/ # Core tracing primitives (Trace, Span, Context)\n\u251c\u2500\u2500 decorators/ # Decorator-based API (@trace, @trace_llm, etc.)\n\u251c\u2500\u2500 context_managers/ # Context managers for inline tracing\n\u251c\u2500\u2500 transport/ # HTTP transport and batch processing\n\u251c\u2500\u2500 agents/ # Multi-agent system support\n\u251c\u2500\u2500 streaming/ # Streaming LLM support\n\u251c\u2500\u2500 threads/ # Conversation thread management\n\u2514\u2500\u2500 utils/ # Utilities (exceptions, serialization, etc.)\n```\n\n## \ud83d\udd27 Configuration\n\n### Environment Variables\n\n```bash\nexport NOVEUM_API_KEY=\"your-api-key\"\nexport NOVEUM_PROJECT=\"your-project-name\"\nexport NOVEUM_ENVIRONMENT=\"production\"\n```\n\n### Programmatic Configuration\n\n```python\nimport noveum_trace\n\n# Basic configuration\nnoveum_trace.init(\n api_key=\"your-api-key\",\n project=\"my-project\",\n environment=\"production\"\n)\n\n# Advanced configuration with transport settings\nnoveum_trace.init(\n api_key=\"your-api-key\",\n project=\"my-project\",\n environment=\"production\",\n transport_config={\n \"batch_size\": 50,\n \"batch_timeout\": 2.0,\n \"retry_attempts\": 3,\n \"timeout\": 30\n },\n tracing_config={\n \"sample_rate\": 1.0,\n \"capture_errors\": True,\n \"capture_stack_traces\": False\n }\n)\n```\n\n## \ud83c\udfaf Available Decorators\n\n### @trace - General Purpose Tracing\n\n```python\n@noveum_trace.trace\ndef my_function(arg1: str, arg2: int) -> dict:\n return {\"result\": f\"{arg1}_{arg2}\"}\n\n# With options\n@noveum_trace.trace(capture_performance=True, capture_args=True)\ndef expensive_function(data: list) -> dict:\n # Function implementation\n return {\"processed\": len(data)}\n```\n\n### @trace_llm - LLM Call Tracing\n\n```python\n@noveum_trace.trace_llm\ndef call_llm(prompt: str) -> str:\n # LLM call implementation\n return response\n\n# With provider specification\n@noveum_trace.trace_llm(provider=\"openai\", capture_tokens=True)\ndef call_openai(prompt: str) -> str:\n # OpenAI specific implementation\n return response\n```\n\n### @trace_agent - Agent Workflow Tracing\n\n```python\n# Required: agent_id parameter\n@noveum_trace.trace_agent(agent_id=\"my_agent\")\ndef agent_function(task: str) -> dict:\n # Agent implementation\n return result\n\n# With full configuration\n@noveum_trace.trace_agent(\n agent_id=\"researcher\",\n role=\"information_gatherer\",\n capabilities=[\"web_search\", \"document_analysis\"]\n)\ndef research_agent(query: str) -> dict:\n # Research implementation\n return {\"findings\": \"...\", \"sources\": [...]}\n```\n\n### @trace_tool - Tool Usage Tracing\n\n```python\n@noveum_trace.trace_tool\ndef search_web(query: str) -> list:\n # Tool implementation\n return results\n\n# With tool specification\n@noveum_trace.trace_tool(tool_name=\"web_search\", tool_type=\"api\")\ndef search_api(query: str) -> list:\n # API search implementation\n return search_results\n```\n\n### @trace_retrieval - Retrieval Operation Tracing\n\n```python\n@noveum_trace.trace_retrieval\ndef retrieve_documents(query: str) -> list:\n # Retrieval implementation\n return documents\n\n# With retrieval configuration\n@noveum_trace.trace_retrieval(\n retrieval_type=\"vector_search\",\n index_name=\"documents\",\n capture_scores=True\n)\ndef vector_search(query: str, top_k: int = 5) -> list:\n # Vector search implementation\n return results\n```\n\n## \ud83d\udd04 Context Managers - Inline Tracing\n\nFor scenarios where you need granular control or can't modify function signatures:\n\n```python\nimport noveum_trace\n\ndef process_user_query(user_input: str) -> str:\n # Pre-processing (not traced)\n cleaned_input = user_input.strip().lower()\n\n # Trace just the LLM call\n with noveum_trace.trace_llm_call(model=\"gpt-4\", provider=\"openai\") as span:\n response = openai_client.chat.completions.create(\n model=\"gpt-4\",\n messages=[{\"role\": \"user\", \"content\": cleaned_input}]\n )\n\n # Add custom attributes\n span.set_attributes({\n \"llm.input_tokens\": response.usage.prompt_tokens,\n \"llm.output_tokens\": response.usage.completion_tokens\n })\n\n # Post-processing (not traced)\n return format_response(response.choices[0].message.content)\n\ndef multi_step_workflow(task: str) -> dict:\n results = {}\n\n # Trace agent operation\n with noveum_trace.trace_agent_operation(\n agent_type=\"planner\",\n operation=\"task_planning\"\n ) as span:\n plan = create_task_plan(task)\n span.set_attribute(\"plan.steps\", len(plan.steps))\n results[\"plan\"] = plan\n\n # Trace tool usage\n with noveum_trace.trace_operation(\"database_query\") as span:\n data = query_database(plan.query)\n span.set_attributes({\n \"query.results_count\": len(data),\n \"query.table\": \"tasks\"\n })\n results[\"data\"] = data\n\n return results\n```\n\n## \ud83e\uddf5 Thread Management\n\nTrack conversation threads and multi-turn interactions:\n\n```python\nfrom noveum_trace import ThreadContext\n\n# Create and manage conversation threads\nwith ThreadContext(name=\"customer_support\") as thread:\n thread.add_message(\"user\", \"Hello, I need help with my order\")\n\n # LLM response within thread context\n with noveum_trace.trace_llm_call(model=\"gpt-4\") as span:\n response = llm_client.chat.completions.create(...)\n thread.add_message(\"assistant\", response.choices[0].message.content)\n```\n\n## \ud83c\udf0a Streaming Support\n\nTrace streaming LLM responses with real-time metrics:\n\n```python\nfrom noveum_trace import trace_streaming\n\ndef stream_openai_response(prompt: str):\n with trace_streaming(model=\"gpt-4\", provider=\"openai\") as manager:\n stream = openai_client.chat.completions.create(\n model=\"gpt-4\",\n messages=[{\"role\": \"user\", \"content\": prompt}],\n stream=True\n )\n\n for chunk in stream:\n if chunk.choices[0].delta.content:\n content = chunk.choices[0].delta.content\n manager.add_token(content)\n yield content\n\n # Streaming metrics are automatically captured\n```\n\n## \ud83e\uddea Testing\n\nRun the test suite:\n\n```bash\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=noveum_trace --cov-report=html\n\n# Run specific test categories\npytest -m llm\npytest -m agent\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\n# Clone the repository\ngit clone https://github.com/Noveum/noveum-trace.git\ncd noveum-trace\n\n# Install in development mode\npip install -e \".[dev]\"\n\n# Run tests\npytest\n\n# Run examples\npython docs/examples/basic_usage.py\n```\n\n## \ud83d\udcd6 Examples\n\nCheck out the [examples](docs/examples/) directory for complete working examples:\n\n- [Basic Usage](docs/examples/basic_usage.py) - Simple function tracing\n- [Agent Workflow](docs/examples/agent_workflow_example.py) - Multi-agent coordination\n- [Flexible Tracing](docs/examples/flexible_tracing_example.py) - Context managers and inline tracing\n- [Streaming Example](docs/examples/streaming_example.py) - Real-time streaming support\n- [Multimodal Examples](docs/examples/multimodal_examples.py) - Image, audio, and video tracing\n\n## \ud83d\ude80 Advanced Usage\n\n### Manual Trace Creation\n\n```python\n# Create traces manually for full control\nclient = noveum_trace.get_client()\n\nwith client.create_contextual_trace(\"custom_workflow\") as trace:\n with client.create_contextual_span(\"step_1\") as span1:\n # Step 1 implementation\n span1.set_attributes({\"step\": 1, \"status\": \"completed\"})\n\n with client.create_contextual_span(\"step_2\") as span2:\n # Step 2 implementation\n span2.set_attributes({\"step\": 2, \"status\": \"completed\"})\n```\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4b\u200d\u2640\ufe0f Support\n\n- [GitHub Issues](https://github.com/Noveum/noveum-trace/issues)\n- [Documentation](https://github.com/Noveum/noveum-trace/tree/main/docs)\n- [Examples](https://github.com/Noveum/noveum-trace/tree/main/examples)\n\n---\n\n**Built by the Noveum Team**\n",
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