agentneo 1.1.2

A powerful tracing library for monitoring and analyzing AI agents, LLM calls, and tool interactions.

AgentNeo  

Empower Your AI Applications with Unparalleled Observability and Optimization

AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework. Designed to elevate your AI development experience, AgentNeo provides deep insights into your AI agents, Large Language Model (LLM) calls, and tool interactions. By leveraging AgentNeo, you can build more efficient, cost-effective, and high-quality AI-driven solutions.

⚡ Why AgentNeo?

Whether you're a seasoned AI developer or just starting out, AgentNeo offers robust logging, visualization, and evaluation capabilities to help you debug and optimize your applications with ease.

🚀 Key Features

• Trace LLM Calls: Monitor and analyze LLM calls from various providers like OpenAI and LiteLLM.
• Trace Agents and Tools: Instrument and monitor your agents and tools to gain deeper insights into their behavior.
• Monitor Interactions: Keep track of tool and agent interactions to understand system behavior.
• Detailed Metrics: Collect comprehensive metrics on token usage, costs, and execution time.
• Flexible Data Storage: Store trace data in SQLite databases and JSON log files for easy access and analysis.
• Simple Instrumentation: Utilize easy-to-use decorators to instrument your code without hassle.
• Interactive Dashboard: Visualize trace data and execution graphs in a user-friendly dashboard.
• Project Management: Manage multiple projects seamlessly within the framework.
• Execution Graph Visualization: Gain insights into your application's flow with detailed execution graphs.
• Evaluation Tools: Assess and improve your AI agent's performance with built-in evaluation tools.

🛠 Requirements

• Python: Version 3.8 or higher

📦 Installation

Install AgentNeo effortlessly using pip:

pip install agentneo

🌟 Quick Start Guide

Get up and running with AgentNeo in just a few steps!

1. Import the Necessary Components

from agentneo import AgentNeo, Tracer, Evaluation, launch_dashboard, Execution

2. Create a Session and Project

neo_session = AgentNeo(session_name="my_session")
neo_session.create_project(project_name="my_project")

3. Initialize the Tracer

tracer = Tracer(session=neo_session)
tracer.start()

4. Instrument Your Code

Wrap your functions with AgentNeo's decorators to start tracing:

@tracer.trace_llm("my_llm_call")
async def my_llm_function():
    # Your LLM call here
    pass

@tracer.trace_tool("my_tool")
def my_tool_function():
    # Your tool logic here
    pass

@tracer.trace_agent("my_agent")
def my_agent_function():
    # Your agent logic here
    pass

5. Evaluate your AI Agent's performance

exe = Execution(session=neo_session, trace_id=1)

# run a single metric
exe.execute(metric_list=['metric_name'])

# get your evaluated metrics results
metric_results = exe.get_results()
print(metric_results)

6. Stop Tracing and Launch the Dashboard

tracer.stop()

launch_dashboard(port=3000)

Access the interactive dashboard by visiting http://localhost:3000 in your web browser.

🔧 Advanced Usage

Project Management

Manage multiple projects with ease.

• List All Projects

  projects = neo_session.list_projects()
  

• Connect to an Existing Project

  neo_session.connect_project(project_name="existing_project")
  

Metrics Evaluation

Supported Metrics

1. Goal Decomposition Efficiency (goal_decomposition_efficiency)
2. Goal Fulfillment Rate (goal_fulfillment_rate)
3. Tool Correctness Metric (tool_correctness_metric)
4. Tool Call Success Rate Metric (tool_call_success_rate_metric)

• Run multiple metrics together

exe.execute(metric_list=['metric_name1', 'metric_name2', ..])

• Use your own config and metadata related to the metric

exe.execute(metric_list=['metric_name'], config={}, metadata={})

Execution Graph Visualization

AgentNeo generates an execution graph that visualizes the flow of your AI application, including LLM calls, tool usage, and agent interactions. Explore this graph in the interactive dashboard to gain deeper insights.

📊 Dashboard Overview

The AgentNeo dashboard offers a comprehensive view of your AI application's performance:

• Project Overview
• System Information
• LLM Call Statistics
• Tool and Agent Interaction Metrics
• Execution Graph Visualization
• Timeline of Events

Launching the Dashboard

neo_session.launch_dashboard(port=3000)

🛣️ Roadmap

We are committed to continuously improving AgentNeo. Here's a glimpse of what's on the horizon:

Feature	Status
Local Data Storage Improvements	✅ Completed
Support for Additional LLMs	✅ Completed
Integration with AutoGen	✅ Completed
Integration with CrewAI	✅ Completed
Integration with Langraph	✅ Completed
Tracing User Interactions	✅ Completed
Tracing Network Calls	✅ Completed
Comprehensive Logging Enhancements	✅ Completed
Custom Agent Orchestration Support	✅ Completed
Advanced Error Detection Tools	🔄 In Progress
Multi-Agent Framework Visualization	✅ Completed
Performance Bottleneck Identification	✅ Completed
Evaluation Metrics for Agentic Application	✅ Completed
Code Execution Sandbox	🔜 Coming Soon
Prompt Caching for Latency Reduction	📝 Planned
Real-Time Guardrails Implementation	📝 Planned
Open-Source Agentic Apps Integration	📝 Planned
Security Checks and Jailbreak Detection	📝 Planned
Regression Testing Capabilities	📝 Planned
Agent Battleground for A/B Testing	📝 Planned
IDE Plugins Development	📝 Planned

Legend

• ✅ Completed
• 🔄 In Progress
• 🔜 Coming Soon
• 📝 Planned

📚 Documentation

For more details, explore the full AgentNeo Documentation

🤝 Contributing

We warmly welcome contributions from the community! Whether it's reporting bugs, suggesting new features, or improving documentation, your input is invaluable.

• GitHub Repository: raga-ai-hub/agentneo
• Contribution Guidelines: Check out our Contribution Guidelines on GitHub to get started.

Join us in making AgentNeo even better!