AgentOpsObservability and debugging platform for building reliable AI agents
Overview
Key features
- Agent session recording and replay
- LLM call and tool-use tracing
- Cost and token analytics
- Error and failure detection
- Framework SDKs for Python and JavaScript
- Dashboards for agent performance metrics
Pricing
- Model
- Free
- Category
- Observability
- Rating
- 4.5 / 5 (4)
Use cases
Debug multi-step agent workflows
Use session replay and LLM call tracing to pinpoint where an agent's reasoning or tool use breaks down across complex multi-step runs.
Monitor token usage and costs
Track per-run token consumption and spend across agents to control budgets and identify expensive prompts or inefficient tool calls.
Catch regressions before production
Detect errors and failures in agent behavior during development, helping teams ship agentic applications with measurable reliability.
Instrument LangChain, CrewAI, or AutoGen agents
Drop in Python or JavaScript SDKs to add tracing and performance dashboards to agents built on popular frameworks without custom logging.
Pros & Cons
Pros
- Detailed session replay and tracing
- Integrates with major agent frameworks
- Tracks token usage and cost per run
- Useful for debugging multi-step workflows
Cons
- Primarily targets developers, not non-technical users
- Value depends on framework compatibility
- Adds another tool to the LLM stack
Reviews
Average from 4 ratings.
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Compared a few options
Evaluated this against two competitors. Where it wins: cost and token analytics and detailed session replay and tracing. On balance the feature set — especially cost and token analytics — justifies the 5 stars for our use case.
Does the job
Pretty happy overall. Error and failure detection just works and integrates with major agent frameworks. Primarily targets developers, not non-technical users can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on cost and token analytics, and tracks token usage and cost per run caught me off guard. Adds another tool to the LLM stack is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Years in this space
I've evaluated a lot of these over the years. What stands out here is lLM call and tool-use tracing — handled better than most — and useful for debugging multi-step workflows. Worth the time if this is your use case.
Q&A
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