
AgentNeo
Python SDK for 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
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AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework designed to provide deep insights into AI agents, Large Language Model (LLM) calls, and tool interactions. It offers robust logging, visualization, and evaluation capabilities to help debug and optimize AI applications with ease. With features like tracing LLM calls, monitoring agents and tools, tracking interactions, detailed metrics collection, flexible data storage, simple instrumentation, interactive dashboard, project management, execution graph visualization, and evaluation tools, AgentNeo empowers users to build efficient, cost-effective, and high-quality AI-driven solutions.
README:
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.
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.
- 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.
- Python: Version 3.9 or higher
Install AgentNeo effortlessly using pip:
pip install agentneo
Get up and running with AgentNeo in just a few steps!
from agentneo import AgentNeo, Tracer, Evaluation, launch_dashboard
neo_session = AgentNeo(session_name="my_session")
neo_session.create_project(project_name="my_project")
tracer = Tracer(session=neo_session)
tracer.start()
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
exe = Evaluation(session=neo_session, trace_id=tracer.trace_id)
# run a single metric
exe.evaluate(metric_list=['metric_name'])
# get your evaluated metrics results
metric_results = exe.get_results()
print(metric_results)
tracer.stop()
launch_dashboard(port=3000)
Access the interactive dashboard by visiting http://localhost:3000
in your web browser.
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")
- Goal Decomposition Efficiency (goal_decomposition_efficiency)
- Goal Fulfillment Rate (goal_fulfillment_rate)
- Tool Call Correctness Rate (tool_call_correctness_rate)
- Tool Call Success Rate (tool_call_success_rate)
- Run multiple metrics together
exe.evaluate(metric_list=['metric_name1', 'metric_name2', ..])
- Use your own config and metadata related to the metric
exe.evaluate(metric_list=['metric_name'], config={}, metadata={})
## sample config and metadata
# config = {"model": "gpt-4o-mini"}
# metadata = {
# "tools": [
# {
# "name": "flight_price_estimator_tool",
# "description": "flight_price_estimator_tool"
# },
# {
# "name": "currency_converter_tool",
# "description": "currency_converter_tool"
# },
# ]
# }
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.
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
neo_session.launch_dashboard(port=3000)
We are committed to continuously improving AgentNeo. Here's a glimpse of what's on the horizon:
Feature | Status |
---|---|
Local Data Storage Improvements | β Completed |
Support for Additional LLMs | β Completed |
Integration with AutoGen | β Completed |
Integration with CrewAI | β Completed |
Integration with Langraph | β Completed |
Tracing User Interactions | β Completed |
Tracing Network Calls | β Completed |
Comprehensive Logging Enhancements | β Completed |
Custom Agent Orchestration Support | β Completed |
Advanced Error Detection Tools | π In Progress |
Multi-Agent Framework Visualization | β Completed |
Performance Bottleneck Identification | β Completed |
Evaluation Metrics for Agentic Application | β Completed |
Code Execution Sandbox | π Coming Soon |
Prompt Caching for Latency Reduction | π Planned |
Real-Time Guardrails Implementation | π Planned |
Open-Source Agentic Apps Integration | π Planned |
Security Checks and Jailbreak Detection | π Planned |
Regression Testing Capabilities | π Planned |
Agent Battleground for A/B Testing | π Planned |
IDE Plugins Development | π Planned |
VLM(Vision Language Model) Evaluation | π Planned |
Voice Agents Evaluation | π Planned |
- β Completed
- π In Progress
- π Coming Soon
- π Planned
For more details, explore the full AgentNeo Documentation
For reference, Watch a demo video AgentNeo Demo Video
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!
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