Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI.
One-Minute Overview#
AgentOps is an observability and DevTool platform for AI agents that helps developers build, evaluate, and monitor AI agents. From prototype to production, AgentOps provides session replays, LLM cost management, framework integrations, and self-hosting capabilities. It offers developers a comprehensive debugging and analysis toolkit for AI systems.
Core Value: Add complete observability and monitoring to your AI agents with just two lines of code, significantly improving development and debugging efficiency.
Quick Start#
Installation Difficulty: Low - Simple pip installation, only requires two lines of code to integrate into existing projects
# Install AgentOps
pip install agentops
# Initialize at the beginning of your program
import agentops
agentops.init("YOUR_API_KEY")
# End the session at the end of your program
agentops.end_session('Success')
Is this suitable for my scenario?
- ✅ Building multi-agent systems: Need to monitor interactions and decision flows between multiple agents
- ✅ LLM cost control: Need to track and optimize costs when interacting with large language models
- ✅ AI application debugging: Need detailed analysis of agent execution processes and decision chains
- ❌ Simple script applications: May be overly complex for one-time execution scripts that don't require complex monitoring
Core Capabilities#
1. Agent Execution Replay and Debugging - Solving AI behavior black box problem#
- Provides detailed replay of agent execution steps, including input/output and decision processes
- Visualizes interactions and decision paths between agents Actual Value: Developers can intuitively understand AI agent behavior like debugging traditional code, quickly locating issues
2. LLM Cost Management - Solving uncontrollable AI application costs problem#
- Automatically tracks interaction costs with major LLM providers (OpenAI, Anthropic, etc.)
- Provides cost analysis and visualization charts Actual Value: Real-time understanding of AI application running costs, optimizing prompts and model selection, effectively controlling development and deployment costs
3. Framework Native Integration - Solving fragmented cross-framework monitoring problem#
- Seamless integration with mainstream AI frameworks like CrewAI, AG2, Langchain, OpenAI Agents
- Supports both Python and TypeScript Actual Value: Consistent and comprehensive monitoring experience regardless of which AI framework is used, eliminating the need to learn different debugging tools
4. Smart Decorator System - Solving complex fine-grained monitoring implementation problem#
- Provides decorators like @session, @agent, @operation, @task, @workflow
- Supports nested decorators to create monitoring hierarchy
- Automatically records input/output, exception handling, async functions, etc. Actual Value: Add detailed monitoring functionality with minimal code, allowing developers to focus on business logic rather than monitoring implementation
5. Session Analysis and Summary - Solving difficult AI performance evaluation problem#
- Provides statistical analysis and trend charts for agent performance
- Supports custom tags and metadata categorization Actual Value: Quantitatively evaluate AI agent performance, continuously optimize system performance, and provide data support for decision-making
Tech Stack and Integration#
Development Language: Primarily Python, with TypeScript support Main Dependencies: Lightweight design, mainly depending on official SDKs of various AI frameworks Integration Method: API / SDK / Decorators
Maintenance Status#
- Development Activity: Actively maintained, with continuous updates to adapt to new AI frameworks and features
- Recent Updates: Recent new framework integrations and functional improvements
- Community Response: Has an active community with Discord support, quickly responding to user feedback
Commercial and Licensing#
License: MIT
- ✅ Commercial Use: Allowed for commercial applications
- ✅ Modification: Allowed to modify and distribute
- ⚠️ Restrictions: Must include original copyright and license notice
Documentation and Learning Resources#
- Documentation Quality: Comprehensive, including detailed guides, example code, and best practices
- Official Documentation: Complete documentation with Chat with Docs functionality
- Example Code: Rich example code covering various framework integration scenarios