raga-ai-hub / AgentNeo

Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
https://www.raga.ai/agentneo
GNU General Public License v3.0
741 stars 23 forks source link
agentic-ai-development agentneo agents ai-agent-monitoring ai-application-debugging ai-evaluation-tools ai-performance-optimization ai-tool-interaction-monitoring llm-testing llm-tracing llmops

AgentNeo   GitHub release (latest by date) GitHub stars Issues GitHub license PyPI - Python Version

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.

AgentNeo Overview

āš” 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.

šŸš€ Key Features

šŸ›  Requirements

šŸ“¦ Installation

Install AgentNeo effortlessly using pip:

pip install agentneo

šŸŒŸ 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.

AgentNeo Evaluation

šŸ”§ Advanced Usage

Project Management

Manage multiple projects with ease.

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)

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.

šŸ“Š Dashboard Overview

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

AgentNeo Analysis

Launching the Dashboard

neo_session.launch_dashboard(port=3000)

šŸ›£ļø Roadmap

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

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

Legend

šŸ“š Documentation

For more details, explore the full AgentNeo Documentation

šŸ¤ Contributing

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

Join us in making AgentNeo even better!