AgentOps
AI agent observability platform with session replay, LLM cost tracking, and error detection - instrument Python agents in one line of code.
AgentOps is an observability and testing platform designed specifically for AI agents and multi-step LLM pipelines. Developers add a single line of initialization code to their Python agent and AgentOps automatically captures every LLM call, tool invocation, and state transition across the entire agent session. The session replay feature visualizes the full agent run as a step-by-step timeline, making it possible to debug exactly where a complex agent went wrong without manual logging. AgentOps also tracks LLM API costs per session and per run, allowing teams to monitor spend against performance. It integrates natively with AutoGen, CrewAI, LangChain, and the OpenAI Agents SDK. AgentOps offers a free tier covering 1M events per month.
Key Features
- One-line Python initialization - agentops.init() captures all agent events automatically
- Session replay with step-by-step visualization of every LLM call and tool invocation in a run
- LLM cost tracking per session and per agent run across OpenAI, Anthropic, and other providers
- Error detection with alert webhooks for production agent failures requiring immediate attention
- Agent analytics dashboard with session success rates, token usage, and latency breakdowns
- Native integrations with AutoGen, CrewAI, LangChain, OpenAI Agents SDK, and custom agents
Use Cases
- AI engineers debugging multi-step agent pipelines by replaying exactly what happened at each step
- Product teams monitoring production AI agents for error rates and cost efficiency at scale
- ML teams benchmarking agent performance across multiple sessions to identify regression patterns
- Startup teams tracking LLM spend per feature or user to build accurate cost-per-query models
Pros
- One-line instrumentation works automatically with every major agent framework - no manual logging required
- Session replay for agent runs fills the observability gap that Langfuse and similar tools do not fully address
- Free tier at 1M events per month is large enough for most development and small production workloads
Cons
- Python-first SDK means TypeScript and other language agent developers have limited native support
- Newer platform with less community content and documentation than more established alternatives like Langfuse
- Session replay depth decreases for very long or complex agent runs with hundreds of nested tool calls
AgentOps Alternatives
Explore similar tools and alternatives
Looking for alternatives to AgentOps? Here are some similar tools you might like:
Langfuse
Open-source LLM observability platform for tracing, debugging, and evaluating AI application performance in production and development.
Braintrust
AI evaluation and testing platform for LLM applications with experiment tracking, human annotation, automated scoring, and production tracing.
Weights & Biases
ML experiment tracking, model monitoring, and dataset versioning platform - used by OpenAI, Toyota, and 1,000+ organizations to ship better models faster.
Ready to try AgentOps?
Visit the official website to explore all features and get started with AgentOps today.
Reviews
0 reviews for AgentOps
Based on 0 reviews
Share your experience
Log in to write a review for AgentOps
Ito
Only code review that runs your code. Provides runtime analysis with evidence (logs, video, screenshot) to show how code changes application actually work. Back-end, front-end, api, integration.
Cursor
AI-native code editor built on VS Code with built-in AI chat, autocomplete, and codebase understanding.
GitHub Copilot
AI pair programmer by GitHub/OpenAI that suggests code completions, functions, and entire files in your IDE.
Have an AI Tool?
List your AI tool for free, or go featured for top placement in your category - and reach thousands of potential users.
Submit Your Tool