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AgentOps

by AgentOps

Agent-focused LLM observability platform providing automatic session tracing, real-time cost tracking, and session replay for debugging multi-step APAC agent workflows built on LangChain, AutoGen, CrewAI, and custom frameworks.

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AIMenta verdict
Decent fit
4/5

"AI agent session tracking — APAC developers use AgentOps to monitor LLM agent sessions with automatic tracing, cost tracking, and replay for debugging multi-step APAC agent workflows across LangChain, AutoGen, and CrewAI."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Auto-instrumentation: one-line `agentops.init()` for APAC agent frameworks
  • Session replay: step-by-step replay of APAC agent task executions for debugging
  • Real-time cost tracking: per-session, per-task APAC LLM spend monitoring
  • Framework support: LangChain, AutoGen, CrewAI, smolagents APAC integration
  • Cost anomaly alerts: APAC runaway agent loop detection before large bills
  • Error tagging: mark failed APAC sessions for root-cause analysis
When to reach for it

Best for

  • APAC developers building multi-step AI agent workflows who need session-level observability and cost tracking — particularly teams debugging complex APAC agent failures where the root cause is buried in a sequence of LLM calls and tool invocations.
Don't get burned

Limitations to know

  • ! Agent-focused — less suited for simple APAC single-call LLM applications
  • ! Cloud-managed primary — APAC teams with strict data sovereignty should review data policies
  • ! Newer platform — APAC enterprise features (SSO, self-hosted) still maturing
Context

About AgentOps

AgentOps is an LLM observability platform focused specifically on AI agent workflows — providing automatic session-level tracing, real-time cost tracking, and session replay for debugging multi-step APAC agent tasks. Where general LLM tracing tools capture individual API calls, AgentOps groups agent actions into sessions that correspond to complete APAC agent task executions.

AgentOps integrates with APAC agent frameworks via one-line initialization — adding `agentops.init()` at the start of a LangChain, AutoGen, CrewAI, or custom APAC agent automatically instruments all LLM calls, tool invocations, and state transitions without manual span creation. APAC developers get full agent session visibility without restructuring existing APAC agent code.

AgentOps' session replay feature records the complete sequence of APAC agent actions — LLM calls, tool invocations, intermediate reasoning steps, errors, and final outputs — and allows APAC developers to replay any past session step-by-step in the AgentOps web UI. This makes APAC agent debugging significantly faster than log-based analysis for multi-step workflows where the failure cause may be 10+ steps before the final error.

AgentOps' real-time cost dashboard tracks LLM spend per agent session, per task type, and per APAC team — giving APAC engineering leads visibility into which agent workflows consume the most tokens and where APAC optimization opportunities exist. Cost anomaly alerts notify APAC teams when an agent session exceeds expected token budgets, catching runaway APAC agent loops before they generate large API bills.

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