AgentOps
Observability, tracing, and debugging for AI agents
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What this product helps you do
AgentOps is an observability platform and open-source SDK for instrumenting agent applications. It captures traces across supported frameworks and model providers, then presents agent runs, spans, tool calls, latency, token usage, cost estimates, errors, and replay data in a dashboard for development and production review.
Problems it solves
- Agent behavior is difficult to reconstruct from application logs alone
- Multi-step model and tool activity needs a shared trace across frameworks
- Token usage, latency, errors, and cost need run-level visibility
- Teams need an audit trail for debugging and production review
AgentOps is an observability and debugging platform for AI agents and LLM applications. Its open-source Python and TypeScript SDKs can instrument supported agent frameworks and model providers, capture traces and spans, and send run data to the AgentOps dashboard.
Developers can inspect timelines, model and tool calls, token use, estimated cost, latency, errors, and replay data while adding custom traces when automatic instrumentation is not enough. AgentOps offers a limited free tier, usage-based Pro access, and custom enterprise packaging.
Information
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AgentOps is an observability and debugging platform for AI agents and LLM applications. Its open-source Python and TypeScript SDKs can instrument supported agent frameworks and model providers, capture traces and spans, and send run data to the AgentOps dashboard.
Developers can inspect timelines, model and tool calls, token use, estimated cost, latency, errors, and replay data while adding custom traces when automatic instrumentation is not enough. AgentOps offers a limited free tier, usage-based Pro access, and custom enterprise packaging.
What this product helps you do
AgentOps is an observability platform and open-source SDK for instrumenting agent applications. It captures traces across supported frameworks and model providers, then presents agent runs, spans, tool calls, latency, token usage, cost estimates, errors, and replay data in a dashboard for development and production review.
Problems it solves
- Agent behavior is difficult to reconstruct from application logs alone
- Multi-step model and tool activity needs a shared trace across frameworks
- Token usage, latency, errors, and cost need run-level visibility
- Teams need an audit trail for debugging and production review
Information
You are leaving botconomy for the provider’s website. Link verified 9/22/2026.
Related AI products
Owlity
Owlity combines a desktop recorder for reusable no-code web tests, local and parallel cloud execution, CI/CD triggers, self-healing maintenance, rich run artifacts, collaboration, and managed QA coverage options.
DeepFace
DeepFace is an MIT-licensed Python framework that wraps multiple face-recognition models and detectors behind functions for verification, search, embeddings, face extraction, attribute analysis, anti-spoofing, streaming, and API service.
Face Search AI
Face Search AI is an AI-powered platform that enables users to perform reverse image searches to find matching faces across the internet. By uploading a photo, the tool scans and analyzes facial features to locate similar images online, providing corresponding website links and, in some cases, additional information about the individual.
